diff --git a/.gitattributes b/.gitattributes index 89853b26b6a01baa2d3da17182f0348b43ea291b..074443e28bd48a1f79b201d71e79601e66a342a6 100644 --- a/.gitattributes +++ b/.gitattributes @@ -39,3 +39,4 @@ MedNeXt_Base/fold_1/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTra nnUNet_Large/fold_1/nnUNet_results/Dataset001_CT/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_1/progress.png filter=lfs diff=lfs merge=lfs -text nnUNet_Large/fold_0_MRA/nnUNet_results/Dataset001_MRA/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/progress.png filter=lfs diff=lfs merge=lfs -text MedNeXt_Base/fold_0_MRA/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/progress.png filter=lfs diff=lfs merge=lfs -text +MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/progress.png filter=lfs diff=lfs merge=lfs -text diff --git a/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json new file mode 100644 index 0000000000000000000000000000000000000000..d7335f0e99253986b046024554661180e4680bf2 --- /dev/null +++ b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json @@ -0,0 +1,88 @@ +{ + "all_tr_losses": "[]", + "all_val_eval_metrics": "[]", + "all_val_losses": "[]", + "all_val_losses_tr_mode": "[]", + "also_val_in_tr_mode": "False", + "amp_grad_scaler": "None", + "base_num_features": "30", + "basic_generator_patch_size": "[205 205 205]", + "batch_dice": "False", + "batch_size": "2", + "best_MA_tr_loss_for_patience": "None", + "best_epoch_based_on_MA_tr_loss": "None", + "best_val_eval_criterion_MA": "None", + "classes": "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36]", + "conv_per_stage": "2", + "data_aug_params": "{'selected_data_channels': None, 'selected_seg_channels': [0], 'do_elastic': False, 'elastic_deform_alpha': (0.0, 900.0), 'elastic_deform_sigma': (9.0, 13.0), 'p_eldef': 0.2, 'do_scaling': True, 'scale_range': (0.7, 1.4), 'independent_scale_factor_for_each_axis': False, 'p_independent_scale_per_axis': 1, 'p_scale': 0.2, 'do_rotation': True, 'rotation_x': (-0.5235987755982988, 0.5235987755982988), 'rotation_y': (-0.5235987755982988, 0.5235987755982988), 'rotation_z': (-0.5235987755982988, 0.5235987755982988), 'rotation_p_per_axis': 1, 'p_rot': 0.2, 'random_crop': False, 'random_crop_dist_to_border': None, 'do_gamma': True, 'gamma_retain_stats': True, 'gamma_range': (0.7, 1.5), 'p_gamma': 0.3, 'do_mirror': True, 'mirror_axes': (0, 1, 2), 'dummy_2D': False, 'mask_was_used_for_normalization': OrderedDict({0: False}), 'border_mode_data': 'constant', 'all_segmentation_labels': None, 'move_last_seg_chanel_to_data': False, 'cascade_do_cascade_augmentations': False, 'cascade_random_binary_transform_p': 0.4, 'cascade_random_binary_transform_p_per_label': 1, 'cascade_random_binary_transform_size': (1, 8), 'cascade_remove_conn_comp_p': 0.2, 'cascade_remove_conn_comp_max_size_percent_threshold': 0.15, 'cascade_remove_conn_comp_fill_with_other_class_p': 0.0, 'do_additive_brightness': False, 'additive_brightness_p_per_sample': 0.15, 'additive_brightness_p_per_channel': 0.5, 'additive_brightness_mu': 0.0, 'additive_brightness_sigma': 0.1, 'num_threads': 8, 'num_cached_per_thread': 2, 'patch_size_for_spatialtransform': array([128, 128, 128])}", + "dataset_directory": "/marimo/TopBrain2026/nnUNet_preprocessed/Task001_Brain2026", + "deep_supervision_scales": "[[1, 1, 1], [np.float64(0.5), np.float64(0.5), np.float64(0.5)], [np.float64(0.25), 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sha256:c26703f1241db000cad13c59ca4d752fceb74387c195a234be2d7fb028987709 +size 602181 diff --git a/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_11_7_14_01_19.txt b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_11_7_14_01_19.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ac2679ea73845ac4ee7546815e0548baa291a53 --- /dev/null +++ b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_11_7_14_01_19.txt @@ -0,0 +1,2096 @@ +Starting... +2026-11-07 14:01:19.305997: Creating new 5-fold cross-validation split... +2026-11-07 14:01:19.306852: Desired fold for training: 0 +2026-11-07 14:01:19.306916: This split has 20 training and 5 validation cases. +2026-11-07 14:01:19.988350: TRAINING KEYS: + odict_keys([np.str_('topcow_ct_001'), np.str_('topcow_ct_002'), np.str_('topcow_ct_003'), np.str_('topcow_ct_004'), np.str_('topcow_ct_005'), np.str_('topcow_ct_006'), np.str_('topcow_ct_007'), np.str_('topcow_ct_008'), np.str_('topcow_ct_011'), np.str_('topcow_ct_012'), np.str_('topcow_ct_013'), np.str_('topcow_ct_014'), np.str_('topcow_ct_016'), np.str_('topcow_ct_017'), np.str_('topcow_ct_020'), np.str_('topcow_ct_021'), np.str_('topcow_ct_023'), np.str_('topcow_ct_025'), np.str_('topcow_ct_026'), np.str_('topcow_ct_027')]) +2026-11-07 14:01:19.988708: VALIDATION KEYS: + odict_keys([np.str_('topcow_ct_010'), np.str_('topcow_ct_015'), np.str_('topcow_ct_018'), np.str_('topcow_ct_022'), np.str_('topcow_ct_024')]) +2026-11-07 14:01:21.755934: lr: 0.001 +2026-11-07 14:01:33.008792: Unable to plot network architecture: +2026-11-07 14:01:33.013592: No module named 'hiddenlayer' +2026-11-07 14:01:33.014073: +printing the network instead: + +2026-11-07 14:01:33.014357: MedNeXt( + (stem): Conv3d(1, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (enc_block_0): Sequential( + (0): MedNeXtBlock( + (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32) + (norm): GroupNorm(32, 32, eps=1e-05, affine=True) + (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(96, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (1): MedNeXtBlock( + (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32) + (norm): GroupNorm(32, 32, eps=1e-05, affine=True) + (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(96, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (2): MedNeXtBlock( + (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32) + (norm): GroupNorm(32, 32, eps=1e-05, affine=True) + (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(96, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + ) + (down_0): MedNeXtDownBlock( + (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=32) + (norm): GroupNorm(32, 32, eps=1e-05, affine=True) + (conv2): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(128, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (res_conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(2, 2, 2)) + ) + (enc_block_1): Sequential( + (0): MedNeXtBlock( + (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64) + (norm): GroupNorm(64, 64, eps=1e-05, affine=True) + (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (1): MedNeXtBlock( + (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64) + (norm): GroupNorm(64, 64, eps=1e-05, affine=True) + (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (2): MedNeXtBlock( + (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64) + (norm): GroupNorm(64, 64, eps=1e-05, affine=True) + (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (3): MedNeXtBlock( + (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64) + (norm): GroupNorm(64, 64, eps=1e-05, affine=True) + (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + ) + (down_1): MedNeXtDownBlock( + (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=64) + (norm): GroupNorm(64, 64, eps=1e-05, affine=True) + (conv2): Conv3d(64, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (res_conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(2, 2, 2)) + ) + (enc_block_2): Sequential( + (0): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (1): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (2): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (3): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (4): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (5): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (6): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (7): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + ) + (down_2): MedNeXtDownBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (res_conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2)) + ) + (enc_block_3): Sequential( + (0): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (1): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (2): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (3): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (4): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (5): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (6): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (7): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + ) + (down_3): MedNeXtDownBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (res_conv): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2)) + ) + (bottleneck): Sequential( + (0): MedNeXtBlock( + (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512) + (norm): GroupNorm(512, 512, eps=1e-05, affine=True) + (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (1): MedNeXtBlock( + (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512) + (norm): GroupNorm(512, 512, eps=1e-05, affine=True) + (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (2): MedNeXtBlock( + (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512) + (norm): GroupNorm(512, 512, eps=1e-05, affine=True) + (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (3): MedNeXtBlock( + (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512) + (norm): GroupNorm(512, 512, eps=1e-05, affine=True) + (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (4): MedNeXtBlock( + (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512) + (norm): GroupNorm(512, 512, eps=1e-05, affine=True) + (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (5): MedNeXtBlock( + (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512) + (norm): GroupNorm(512, 512, eps=1e-05, affine=True) + (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (6): MedNeXtBlock( + (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512) + (norm): GroupNorm(512, 512, eps=1e-05, affine=True) + (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (7): MedNeXtBlock( + (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512) + (norm): GroupNorm(512, 512, eps=1e-05, affine=True) + (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + ) + (up_3): MedNeXtUpBlock( + (conv1): ConvTranspose3d(512, 512, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=512) + (norm): GroupNorm(512, 512, eps=1e-05, affine=True) + (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(4096, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (res_conv): ConvTranspose3d(512, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2)) + ) + (dec_block_3): Sequential( + (0): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (1): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (2): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (3): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (4): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (5): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (6): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (7): MedNeXtBlock( + (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + ) + (up_2): MedNeXtUpBlock( + (conv1): ConvTranspose3d(256, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=256) + (norm): GroupNorm(256, 256, eps=1e-05, affine=True) + (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(2048, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (res_conv): ConvTranspose3d(256, 128, kernel_size=(1, 1, 1), stride=(2, 2, 2)) + ) + (dec_block_2): Sequential( + (0): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (1): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (2): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (3): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (4): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (5): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (6): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (7): MedNeXtBlock( + (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + ) + (up_1): MedNeXtUpBlock( + (conv1): ConvTranspose3d(128, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=128) + (norm): GroupNorm(128, 128, eps=1e-05, affine=True) + (conv2): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(512, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (res_conv): ConvTranspose3d(128, 64, kernel_size=(1, 1, 1), stride=(2, 2, 2)) + ) + (dec_block_1): Sequential( + (0): MedNeXtBlock( + (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64) + (norm): GroupNorm(64, 64, eps=1e-05, affine=True) + (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (1): MedNeXtBlock( + (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64) + (norm): GroupNorm(64, 64, eps=1e-05, affine=True) + (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (2): MedNeXtBlock( + (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64) + (norm): GroupNorm(64, 64, eps=1e-05, affine=True) + (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (3): MedNeXtBlock( + (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64) + (norm): GroupNorm(64, 64, eps=1e-05, affine=True) + (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + ) + (up_0): MedNeXtUpBlock( + (conv1): ConvTranspose3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=64) + (norm): GroupNorm(64, 64, eps=1e-05, affine=True) + (conv2): Conv3d(64, 192, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(192, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (res_conv): ConvTranspose3d(64, 32, kernel_size=(1, 1, 1), stride=(2, 2, 2)) + ) + (dec_block_0): Sequential( + (0): MedNeXtBlock( + (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32) + (norm): GroupNorm(32, 32, eps=1e-05, affine=True) + (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(96, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (1): MedNeXtBlock( + (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32) + (norm): GroupNorm(32, 32, eps=1e-05, affine=True) + (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(96, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (2): MedNeXtBlock( + (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32) + (norm): GroupNorm(32, 32, eps=1e-05, affine=True) + (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + (act): GELU(approximate='none') + (conv3): Conv3d(96, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + ) + (out_0): OutBlock( + (conv_out): ConvTranspose3d(32, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (out_1): OutBlock( + (conv_out): ConvTranspose3d(64, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (out_2): OutBlock( + (conv_out): ConvTranspose3d(128, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (out_3): OutBlock( + (conv_out): ConvTranspose3d(256, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) + (out_4): OutBlock( + (conv_out): ConvTranspose3d(512, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1)) + ) +) +2026-11-07 14:01:33.057024: + +2026-11-07 14:01:33.062272: +epoch: 0 +2026-11-07 14:05:55.888730: train loss : -0.0062 +2026-11-07 14:06:04.062181: validation loss: -0.0747 +2026-11-07 14:06:04.063290: Average global foreground Dice: [np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0)] +2026-11-07 14:06:04.063700: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:06:04.347000: lr: 0.000999 +2026-11-07 14:06:04.347270: This epoch took 271.283888 s + +2026-11-07 14:06:04.347366: +epoch: 1 +2026-11-07 14:10:13.402230: train loss : -0.1034 +2026-11-07 14:10:21.514684: validation loss: -0.1156 +2026-11-07 14:10:21.515628: Average global foreground Dice: [np.float32(0.1755), np.float32(0.0), np.float32(0.0439), np.float32(0.0009), np.float32(0.0002), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0022), np.float32(0.0054), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0074), np.float32(0.0), np.float32(0.0072), np.float32(0.0022), np.float32(0.0), np.float32(0.0), np.float32(0.003), np.float32(1e-04), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3014), np.float32(0.1311)] +2026-11-07 14:10:21.515782: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:10:21.804230: lr: 0.000998 +2026-11-07 14:10:22.158040: saving checkpoint... +2026-11-07 14:10:22.704887: done, saving took 0.90 seconds +2026-11-07 14:10:22.707081: This epoch took 258.359642 s + +2026-11-07 14:10:22.707181: +epoch: 2 +2026-11-07 14:14:24.657978: train loss : -0.1403 +2026-11-07 14:14:32.744125: validation loss: -0.1933 +2026-11-07 14:14:32.745112: Average global foreground Dice: [np.float32(0.4532), np.float32(0.0599), np.float32(0.1158), np.float32(0.2975), np.float32(0.213), np.float32(0.3245), np.float32(0.1625), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0323), np.float32(0.0486), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1116), np.float32(0.0279), np.float32(0.1073), np.float32(0.0098), np.float32(0.0042), np.float32(0.0), np.float32(0.0005), np.float32(0.2836), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2669), np.float32(0.3554)] +2026-11-07 14:14:32.745316: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:14:33.046400: lr: 0.000997 +2026-11-07 14:14:33.095586: saving checkpoint... +2026-11-07 14:14:33.658229: done, saving took 0.61 seconds +2026-11-07 14:14:33.660770: This epoch took 250.953522 s + +2026-11-07 14:14:33.660933: +epoch: 3 +2026-11-07 14:18:22.385767: train loss : -0.2330 +2026-11-07 14:18:30.464390: validation loss: -0.2781 +2026-11-07 14:18:30.465650: Average global foreground Dice: [np.float32(0.7539), np.float32(0.1513), np.float32(0.1553), np.float32(0.4226), np.float32(0.1989), np.float32(0.3807), np.float32(0.3486), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1477), np.float32(0.3686), np.float32(0.1393), np.float32(0.1391), np.float32(0.0), np.float32(0.0), np.float32(0.194), np.float32(0.1505), np.float32(0.1768), np.float32(0.0242), np.float32(0.0219), np.float32(0.0354), np.float32(0.0683), np.float32(0.4609), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4823), np.float32(0.1044)] +2026-11-07 14:18:30.466011: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:18:30.745034: lr: 0.000996 +2026-11-07 14:18:30.769867: saving checkpoint... +2026-11-07 14:18:31.304415: done, saving took 0.56 seconds +2026-11-07 14:18:31.306812: This epoch took 237.645721 s + +2026-11-07 14:18:31.307055: +epoch: 4 +2026-11-07 14:22:51.651868: train loss : -0.3604 +2026-11-07 14:22:59.757548: validation loss: -0.3705 +2026-11-07 14:22:59.758464: Average global foreground Dice: [np.float32(0.8004), np.float32(0.2833), np.float32(0.3674), np.float32(0.3966), np.float32(0.2796), np.float32(0.4291), np.float32(0.326), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1457), np.float32(0.3915), np.float32(0.2315), np.float32(0.1786), np.float32(0.0), np.float32(0.0), np.float32(0.3838), np.float32(0.1378), np.float32(0.1491), np.float32(0.222), np.float32(0.0898), np.float32(0.107), np.float32(0.3573), np.float32(0.399), np.float32(0.1405), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4803), np.float32(0.1326)] +2026-11-07 14:22:59.758618: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:23:00.050644: lr: 0.000995 +2026-11-07 14:23:00.074280: saving checkpoint... +2026-11-07 14:23:00.596847: done, saving took 0.55 seconds +2026-11-07 14:23:00.599271: This epoch took 269.292037 s + +2026-11-07 14:23:00.599658: +epoch: 5 +2026-11-07 14:27:20.450311: train loss : -0.3922 +2026-11-07 14:27:28.613293: validation loss: -0.3565 +2026-11-07 14:27:28.614278: Average global foreground Dice: [np.float32(0.8082), np.float32(0.4069), np.float32(0.3475), np.float32(0.2106), np.float32(0.2186), np.float32(0.187), np.float32(0.1822), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2079), np.float32(0.3392), np.float32(0.4251), np.float32(0.1784), np.float32(0.0), np.float32(0.0), np.float32(0.3288), np.float32(0.1596), np.float32(0.1017), np.float32(0.105), np.float32(0.0947), np.float32(0.2039), np.float32(0.362), np.float32(0.3383), np.float32(0.255), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0465), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2033), np.float32(0.3132)] +2026-11-07 14:27:28.614457: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:27:28.907166: lr: 0.000995 +2026-11-07 14:27:28.931661: saving checkpoint... +2026-11-07 14:27:29.491727: done, saving took 0.58 seconds +2026-11-07 14:27:29.494304: This epoch took 268.894471 s + +2026-11-07 14:27:29.494484: +epoch: 6 +2026-11-07 14:31:44.667922: train loss : -0.4160 +2026-11-07 14:31:52.804435: validation loss: -0.3832 +2026-11-07 14:31:52.805446: Average global foreground Dice: [np.float32(0.8517), np.float32(0.2618), np.float32(0.3754), np.float32(0.3318), np.float32(0.2507), np.float32(0.5655), np.float32(0.2158), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1335), np.float32(0.5343), np.float32(0.2644), np.float32(0.3253), np.float32(0.0), np.float32(0.0), np.float32(0.3347), np.float32(0.1763), np.float32(0.1724), np.float32(0.1954), np.float32(0.3006), np.float32(0.114), np.float32(0.2905), np.float32(0.3199), np.float32(0.2624), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2225), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3191), np.float32(0.3443)] +2026-11-07 14:31:52.805603: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:31:53.103033: lr: 0.000994 +2026-11-07 14:31:53.127188: saving checkpoint... +2026-11-07 14:31:53.669641: done, saving took 0.57 seconds +2026-11-07 14:31:53.672827: This epoch took 264.178165 s + +2026-11-07 14:31:53.672996: +epoch: 7 +2026-11-07 14:36:11.357423: train loss : -0.4380 +2026-11-07 14:36:19.477538: validation loss: -0.4019 +2026-11-07 14:36:19.478556: Average global foreground Dice: [np.float32(0.7864), np.float32(0.488), np.float32(0.5021), np.float32(0.3543), np.float32(0.2403), np.float32(0.503), np.float32(0.137), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3549), np.float32(0.3487), np.float32(0.3748), np.float32(0.2619), np.float32(0.0), np.float32(0.0), np.float32(0.4079), np.float32(0.2651), np.float32(0.3636), np.float32(0.4124), np.float32(0.0886), np.float32(0.3417), np.float32(0.5352), np.float32(0.2792), np.float32(0.282), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2824), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4623), np.float32(0.3741)] +2026-11-07 14:36:19.478716: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:36:19.742244: lr: 0.000993 +2026-11-07 14:36:19.770436: saving checkpoint... +2026-11-07 14:36:20.314662: done, saving took 0.57 seconds +2026-11-07 14:36:20.317068: This epoch took 266.643891 s + +2026-11-07 14:36:20.317284: +epoch: 8 +2026-11-07 14:40:29.613507: train loss : -0.4492 +2026-11-07 14:40:37.736645: validation loss: -0.3834 +2026-11-07 14:40:37.737570: Average global foreground Dice: [np.float32(0.8252), np.float32(0.3851), np.float32(0.3965), np.float32(0.2359), np.float32(0.1884), np.float32(0.3739), np.float32(0.1767), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.388), np.float32(0.3608), np.float32(0.2267), np.float32(0.3267), np.float32(0.0), np.float32(0.0), np.float32(0.2849), np.float32(0.2793), np.float32(0.1688), np.float32(0.2257), np.float32(0.144), np.float32(0.371), np.float32(0.5112), np.float32(0.4442), np.float32(0.249), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.283), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4733), np.float32(0.2691)] +2026-11-07 14:40:37.737853: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:40:38.004430: lr: 0.000992 +2026-11-07 14:40:38.029267: saving checkpoint... +2026-11-07 14:40:39.234137: done, saving took 1.23 seconds +2026-11-07 14:40:39.236490: This epoch took 258.919118 s + +2026-11-07 14:40:39.236646: +epoch: 9 +2026-11-07 14:44:35.522252: train loss : -0.4740 +2026-11-07 14:44:43.632459: validation loss: -0.3654 +2026-11-07 14:44:43.633479: Average global foreground Dice: [np.float32(0.8825), np.float32(0.3564), np.float32(0.1964), np.float32(0.3155), np.float32(0.2249), np.float32(0.2281), np.float32(0.3651), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3796), np.float32(0.3306), np.float32(0.3592), np.float32(0.2894), np.float32(0.0), np.float32(0.0), np.float32(0.2015), np.float32(0.2783), np.float32(0.2138), np.float32(0.157), np.float32(0.1726), np.float32(0.2918), np.float32(0.5398), np.float32(0.3642), np.float32(0.2477), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1594), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1841), np.float32(0.1457)] +2026-11-07 14:44:43.633801: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:44:43.898326: lr: 0.000991 +2026-11-07 14:44:43.927064: saving checkpoint... +2026-11-07 14:44:46.650406: done, saving took 2.75 seconds +2026-11-07 14:44:46.652507: This epoch took 247.415785 s + +2026-11-07 14:44:46.652735: +epoch: 10 +2026-11-07 14:48:46.739721: train loss : -0.4851 +2026-11-07 14:48:54.836893: validation loss: -0.4544 +2026-11-07 14:48:54.837839: Average global foreground Dice: [np.float32(0.8784), np.float32(0.4702), np.float32(0.4737), np.float32(0.6803), np.float32(0.3894), np.float32(0.6785), np.float32(0.5923), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4256), np.float32(0.3348), np.float32(0.3772), np.float32(0.3502), np.float32(0.0), np.float32(0.0), np.float32(0.312), np.float32(0.3772), np.float32(0.3735), np.float32(0.3659), np.float32(0.3524), np.float32(0.3844), np.float32(0.6562), np.float32(0.6), np.float32(0.3887), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3307), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6162), np.float32(0.645)] +2026-11-07 14:48:54.837994: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:48:55.105460: lr: 0.00099 +2026-11-07 14:48:55.163797: saving checkpoint... +2026-11-07 14:48:55.720622: done, saving took 0.61 seconds +2026-11-07 14:48:55.723146: This epoch took 249.070277 s + +2026-11-07 14:48:55.723323: +epoch: 11 +2026-11-07 14:53:09.358904: train loss : -0.5037 +2026-11-07 14:53:17.458750: validation loss: -0.3774 +2026-11-07 14:53:17.459746: Average global foreground Dice: [np.float32(0.8697), np.float32(0.3123), np.float32(0.3735), np.float32(0.3311), np.float32(0.3629), np.float32(0.3588), np.float32(0.3268), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3085), np.float32(0.3314), np.float32(0.3372), np.float32(0.3155), np.float32(0.0), np.float32(0.0), np.float32(0.3257), np.float32(0.3456), np.float32(0.3018), np.float32(0.3023), np.float32(0.2338), np.float32(0.1436), np.float32(0.2522), np.float32(0.5395), np.float32(0.251), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3439), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.374), np.float32(0.3999)] +2026-11-07 14:53:17.460006: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:53:17.730912: lr: 0.000989 +2026-11-07 14:53:17.765011: saving checkpoint... +2026-11-07 14:53:18.338620: done, saving took 0.61 seconds +2026-11-07 14:53:18.340785: This epoch took 262.617381 s + +2026-11-07 14:53:18.340988: +epoch: 12 +2026-11-07 14:57:17.219390: train loss : -0.5164 +2026-11-07 14:57:25.335155: validation loss: -0.3529 +2026-11-07 14:57:25.336402: Average global foreground Dice: [np.float32(0.8963), np.float32(0.2952), np.float32(0.3785), np.float32(0.0224), np.float32(0.0696), np.float32(0.4871), np.float32(0.2988), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0393), np.float32(0.455), np.float32(0.2045), np.float32(0.3353), np.float32(0.0), np.float32(0.0), np.float32(0.1552), np.float32(0.184), np.float32(0.148), np.float32(0.1494), np.float32(0.3843), np.float32(0.2449), np.float32(0.4602), np.float32(0.5084), np.float32(0.1411), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2825), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3629), np.float32(0.4663)] +2026-11-07 14:57:25.336590: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 14:57:25.617036: lr: 0.000988 +2026-11-07 14:57:25.648967: saving checkpoint... +2026-11-07 14:57:26.193867: done, saving took 0.58 seconds +2026-11-07 14:57:26.196568: This epoch took 247.855486 s + +2026-11-07 14:57:26.196683: +epoch: 13 +2026-11-07 15:01:47.303136: train loss : -0.5277 +2026-11-07 15:01:55.438213: validation loss: -0.2657 +2026-11-07 15:01:55.439203: Average global foreground Dice: [np.float32(0.8826), np.float32(0.1367), np.float32(0.1134), np.float32(0.0914), np.float32(0.0172), np.float32(0.1179), np.float32(0.0743), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1304), np.float32(0.2476), np.float32(0.4101), np.float32(0.137), np.float32(0.0), np.float32(0.0), np.float32(0.0545), np.float32(0.0426), np.float32(0.0923), np.float32(0.088), np.float32(0.2131), np.float32(0.0231), np.float32(0.2675), np.float32(0.1366), np.float32(0.0688), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1014), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1253), np.float32(0.1102)] +2026-11-07 15:01:55.439598: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:01:55.709606: lr: 0.000987 +2026-11-07 15:01:55.709818: This epoch took 269.513081 s + +2026-11-07 15:01:55.709895: +epoch: 14 +2026-11-07 15:05:44.730468: train loss : -0.5461 +2026-11-07 15:05:52.811684: validation loss: -0.3709 +2026-11-07 15:05:52.812800: Average global foreground Dice: [np.float32(0.8553), np.float32(0.4011), np.float32(0.3702), np.float32(0.2684), np.float32(0.3923), np.float32(0.4316), np.float32(0.361), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2459), np.float32(0.4166), np.float32(0.2384), np.float32(0.2668), np.float32(0.0), np.float32(0.0), np.float32(0.2378), np.float32(0.1759), np.float32(0.2392), np.float32(0.1708), np.float32(0.2175), np.float32(0.2184), np.float32(0.427), np.float32(0.4878), np.float32(0.2054), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3593), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4131), np.float32(0.4491)] +2026-11-07 15:05:52.813242: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:05:53.095269: lr: 0.000986 +2026-11-07 15:05:53.128048: saving checkpoint... +2026-11-07 15:05:53.653757: done, saving took 0.56 seconds +2026-11-07 15:05:53.656110: This epoch took 237.946159 s + +2026-11-07 15:05:53.656322: +epoch: 15 +2026-11-07 15:10:08.555506: train loss : -0.5337 +2026-11-07 15:10:16.664843: validation loss: -0.4634 +2026-11-07 15:10:16.665768: Average global foreground Dice: [np.float32(0.8001), np.float32(0.5952), np.float32(0.6709), np.float32(0.6946), np.float32(0.5825), np.float32(0.7724), np.float32(0.4779), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5086), np.float32(0.6371), np.float32(0.4381), np.float32(0.4155), np.float32(0.0), np.float32(0.0), np.float32(0.3575), np.float32(0.1263), np.float32(0.2731), np.float32(0.1036), np.float32(0.4655), np.float32(0.4867), np.float32(0.3512), np.float32(0.4613), np.float32(0.358), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3261), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6755), np.float32(0.6959)] +2026-11-07 15:10:16.665897: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:10:16.950059: lr: 0.000986 +2026-11-07 15:10:16.975946: saving checkpoint... +2026-11-07 15:10:17.524513: done, saving took 0.57 seconds +2026-11-07 15:10:17.526458: This epoch took 263.870048 s + +2026-11-07 15:10:17.526551: +epoch: 16 +2026-11-07 15:14:26.088677: train loss : -0.5288 +2026-11-07 15:14:34.174691: validation loss: -0.3758 +2026-11-07 15:14:34.175687: Average global foreground Dice: [np.float32(0.9064), np.float32(0.3825), np.float32(0.2925), np.float32(0.4395), np.float32(0.401), np.float32(0.3979), np.float32(0.3193), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3612), np.float32(0.3798), np.float32(0.4745), np.float32(0.4395), np.float32(0.0), np.float32(0.0), np.float32(0.2695), np.float32(0.2253), np.float32(0.1434), np.float32(0.1409), np.float32(0.3708), np.float32(0.4092), np.float32(0.4608), np.float32(0.6108), np.float32(0.3293), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3471), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.344), np.float32(0.3717)] +2026-11-07 15:14:34.175939: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:14:34.456220: lr: 0.000985 +2026-11-07 15:14:34.480771: saving checkpoint... +2026-11-07 15:14:35.008525: done, saving took 0.55 seconds +2026-11-07 15:14:35.010476: This epoch took 257.483869 s + +2026-11-07 15:14:35.010624: +epoch: 17 +2026-11-07 15:18:54.164434: train loss : -0.5543 +2026-11-07 15:19:02.267172: validation loss: -0.5162 +2026-11-07 15:19:02.268014: Average global foreground Dice: [np.float32(0.9095), np.float32(0.5873), np.float32(0.6845), np.float32(0.6806), np.float32(0.6649), np.float32(0.7307), np.float32(0.6698), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5333), np.float32(0.6139), np.float32(0.4882), np.float32(0.3545), np.float32(0.0), np.float32(0.0), np.float32(0.7184), np.float32(0.5006), np.float32(0.692), np.float32(0.6022), np.float32(0.5017), np.float32(0.5371), np.float32(0.7145), np.float32(0.712), np.float32(0.4414), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4079), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7603), np.float32(0.7843)] +2026-11-07 15:19:02.268217: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:19:02.554697: lr: 0.000984 +2026-11-07 15:19:02.582857: saving checkpoint... +2026-11-07 15:19:05.197006: done, saving took 2.64 seconds +2026-11-07 15:19:05.199543: This epoch took 270.188795 s + +2026-11-07 15:19:05.199636: +epoch: 18 +2026-11-07 15:22:55.759707: train loss : -0.5640 +2026-11-07 15:23:03.874565: validation loss: -0.5216 +2026-11-07 15:23:03.875572: Average global foreground Dice: [np.float32(0.9032), np.float32(0.5845), np.float32(0.6656), np.float32(0.6214), np.float32(0.6864), np.float32(0.7315), np.float32(0.7086), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.557), np.float32(0.5748), np.float32(0.4934), np.float32(0.437), np.float32(0.0), np.float32(0.0), np.float32(0.7155), np.float32(0.5189), np.float32(0.6928), np.float32(0.5708), np.float32(0.4929), np.float32(0.3672), np.float32(0.7046), np.float32(0.6846), np.float32(0.3659), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5184), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7687), np.float32(0.7898)] +2026-11-07 15:23:03.875919: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:23:04.199480: lr: 0.000983 +2026-11-07 15:23:04.232164: saving checkpoint... +2026-11-07 15:23:06.835780: done, saving took 2.64 seconds +2026-11-07 15:23:06.838751: This epoch took 241.639039 s + +2026-11-07 15:23:06.838898: +epoch: 19 +2026-11-07 15:27:08.212113: train loss : -0.5438 +2026-11-07 15:27:16.304001: validation loss: -0.4925 +2026-11-07 15:27:16.305054: Average global foreground Dice: [np.float32(0.9), np.float32(0.5587), np.float32(0.7138), np.float32(0.6658), np.float32(0.6188), np.float32(0.7405), np.float32(0.6364), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5741), np.float32(0.5946), np.float32(0.5), np.float32(0.394), np.float32(0.0), np.float32(0.0), np.float32(0.6447), np.float32(0.5311), np.float32(0.6636), np.float32(0.5331), np.float32(0.4268), np.float32(0.4871), np.float32(0.671), np.float32(0.6955), np.float32(0.4521), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4988), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6971), np.float32(0.705)] +2026-11-07 15:27:16.305500: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:27:16.596486: lr: 0.000982 +2026-11-07 15:27:16.620529: saving checkpoint... +2026-11-07 15:27:17.138268: done, saving took 0.54 seconds +2026-11-07 15:27:17.140445: This epoch took 250.301443 s + +2026-11-07 15:27:17.140556: +epoch: 20 +2026-11-07 15:31:14.276651: train loss : -0.5562 +2026-11-07 15:31:22.365485: validation loss: -0.4635 +2026-11-07 15:31:22.366458: Average global foreground Dice: [np.float32(0.8445), np.float32(0.5585), np.float32(0.5525), np.float32(0.5338), np.float32(0.4282), np.float32(0.6041), np.float32(0.4921), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4782), np.float32(0.5322), np.float32(0.4706), np.float32(0.3257), np.float32(0.0), np.float32(0.0), np.float32(0.3847), np.float32(0.3011), np.float32(0.3584), np.float32(0.35), np.float32(0.5454), np.float32(0.4079), np.float32(0.6519), np.float32(0.5446), np.float32(0.2501), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4546), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5583), np.float32(0.6048)] +2026-11-07 15:31:22.366724: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:31:22.668084: lr: 0.000981 +2026-11-07 15:31:22.749144: saving checkpoint... +2026-11-07 15:31:23.293451: done, saving took 0.62 seconds +2026-11-07 15:31:23.295506: This epoch took 246.154781 s + +2026-11-07 15:31:23.295685: +epoch: 21 +2026-11-07 15:35:07.171805: train loss : -0.5559 +2026-11-07 15:35:15.252508: validation loss: -0.4443 +2026-11-07 15:35:15.253473: Average global foreground Dice: [np.float32(0.9021), np.float32(0.5315), np.float32(0.4561), np.float32(0.4833), np.float32(0.4389), np.float32(0.5293), np.float32(0.4402), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4288), np.float32(0.4831), np.float32(0.5064), np.float32(0.5139), np.float32(0.0), np.float32(0.0), np.float32(0.3307), np.float32(0.2507), np.float32(0.3466), np.float32(0.3339), np.float32(0.4968), np.float32(0.2782), np.float32(0.5998), np.float32(0.4975), np.float32(0.2368), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.426), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.513), np.float32(0.527)] +2026-11-07 15:35:15.253770: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:35:15.571560: lr: 0.00098 +2026-11-07 15:35:15.608503: saving checkpoint... +2026-11-07 15:35:16.174143: done, saving took 0.60 seconds +2026-11-07 15:35:16.177181: This epoch took 232.881414 s + +2026-11-07 15:35:16.177323: +epoch: 22 +2026-11-07 15:39:09.684137: train loss : -0.5598 +2026-11-07 15:39:17.775173: validation loss: -0.3875 +2026-11-07 15:39:17.776151: Average global foreground Dice: [np.float32(0.8924), np.float32(0.4408), np.float32(0.3716), np.float32(0.3987), np.float32(0.3659), np.float32(0.4161), np.float32(0.3216), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3202), np.float32(0.4164), np.float32(0.4365), np.float32(0.4253), np.float32(0.0), np.float32(0.0), np.float32(0.2799), np.float32(0.2241), np.float32(0.263), np.float32(0.247), np.float32(0.4068), np.float32(0.2455), np.float32(0.5192), np.float32(0.3996), np.float32(0.2217), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3862), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3921), np.float32(0.4117)] +2026-11-07 15:39:17.776503: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:39:18.066972: lr: 0.000979 +2026-11-07 15:39:18.098645: saving checkpoint... +2026-11-07 15:39:18.640399: done, saving took 0.57 seconds +2026-11-07 15:39:18.643136: This epoch took 242.465756 s + +2026-11-07 15:39:18.643291: +epoch: 23 +2026-11-07 15:43:32.385173: train loss : -0.5603 +2026-11-07 15:43:40.472449: validation loss: -0.2965 +2026-11-07 15:43:40.473643: Average global foreground Dice: [np.float32(0.8707), np.float32(0.1886), np.float32(0.304), np.float32(0.1344), np.float32(0.0075), np.float32(0.2525), np.float32(0.1801), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.19), np.float32(0.1629), np.float32(0.3079), np.float32(0.1647), np.float32(0.0), np.float32(0.0), np.float32(0.0103), np.float32(0.0177), np.float32(0.256), np.float32(0.2263), np.float32(0.1493), np.float32(0.1709), np.float32(0.395), np.float32(0.3058), np.float32(0.2474), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1984), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1523), np.float32(0.388)] +2026-11-07 15:43:40.473926: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:43:40.755464: lr: 0.000978 +2026-11-07 15:43:40.755872: This epoch took 262.112499 s + +2026-11-07 15:43:40.756058: +epoch: 24 +2026-11-07 15:47:46.539372: train loss : -0.5620 +2026-11-07 15:47:54.641296: validation loss: -0.3746 +2026-11-07 15:47:54.642213: Average global foreground Dice: [np.float32(0.8869), np.float32(0.3612), np.float32(0.3252), np.float32(0.5202), np.float32(0.2119), np.float32(0.4038), np.float32(0.2697), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3449), np.float32(0.3846), np.float32(0.4853), np.float32(0.3009), np.float32(0.0), np.float32(0.0), np.float32(0.0987), np.float32(0.0716), np.float32(0.2275), np.float32(0.1906), np.float32(0.3042), np.float32(0.2308), np.float32(0.4698), np.float32(0.3977), np.float32(0.2672), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4125), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3414), np.float32(0.3681)] +2026-11-07 15:47:54.642577: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:47:54.924091: lr: 0.000977 +2026-11-07 15:47:54.924475: This epoch took 254.168324 s + +2026-11-07 15:47:54.924768: +epoch: 25 +2026-11-07 15:51:59.687250: train loss : -0.5995 +2026-11-07 15:52:07.805508: validation loss: -0.4417 +2026-11-07 15:52:07.806520: Average global foreground Dice: [np.float32(0.9001), np.float32(0.4863), np.float32(0.4669), np.float32(0.4602), np.float32(0.4179), np.float32(0.5371), np.float32(0.4353), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3929), np.float32(0.4471), np.float32(0.4644), np.float32(0.4524), np.float32(0.0), np.float32(0.0), np.float32(0.3397), np.float32(0.258), np.float32(0.3712), np.float32(0.3079), np.float32(0.4431), np.float32(0.2848), np.float32(0.6478), np.float32(0.5532), np.float32(0.2187), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4911), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5331), np.float32(0.5665)] +2026-11-07 15:52:07.806707: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:52:08.095435: lr: 0.000977 +2026-11-07 15:52:08.095842: This epoch took 253.170465 s + +2026-11-07 15:52:08.096016: +epoch: 26 +2026-11-07 15:56:28.386183: train loss : -0.5956 +2026-11-07 15:56:36.484506: validation loss: -0.3020 +2026-11-07 15:56:36.485437: Average global foreground Dice: [np.float32(0.8867), np.float32(0.2031), np.float32(0.1912), np.float32(0.1447), np.float32(0.0413), np.float32(0.213), np.float32(0.1784), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2048), np.float32(0.2335), np.float32(0.2419), np.float32(0.2306), np.float32(0.0), np.float32(0.0), np.float32(0.0736), np.float32(0.0684), np.float32(0.1256), np.float32(0.1573), np.float32(0.2251), np.float32(0.1167), np.float32(0.3134), np.float32(0.2046), np.float32(0.0348), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2344), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1756), np.float32(0.2075)] +2026-11-07 15:56:36.485609: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 15:56:36.773022: lr: 0.000976 +2026-11-07 15:56:36.773458: This epoch took 268.677358 s + +2026-11-07 15:56:36.773725: +epoch: 27 +2026-11-07 16:01:04.238358: train loss : -0.5857 +2026-11-07 16:01:12.384900: validation loss: -0.3250 +2026-11-07 16:01:12.385949: Average global foreground Dice: [np.float32(0.896), np.float32(0.203), np.float32(0.2952), np.float32(0.1673), np.float32(0.0592), np.float32(0.4179), np.float32(0.209), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2484), np.float32(0.2857), np.float32(0.4924), np.float32(0.3325), np.float32(0.0), np.float32(0.0), np.float32(0.0615), np.float32(0.0572), np.float32(0.1079), np.float32(0.1534), np.float32(0.2288), np.float32(0.2375), np.float32(0.387), np.float32(0.2873), np.float32(0.1225), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2402), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.187), np.float32(0.2856)] +2026-11-07 16:01:12.386108: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:01:12.670559: lr: 0.000975 +2026-11-07 16:01:12.670878: This epoch took 275.896924 s + +2026-11-07 16:01:12.671123: +epoch: 28 +2026-11-07 16:05:24.748623: train loss : -0.5856 +2026-11-07 16:05:32.892735: validation loss: -0.3304 +2026-11-07 16:05:32.893901: Average global foreground Dice: [np.float32(0.8978), np.float32(0.2417), np.float32(0.2318), np.float32(0.2315), np.float32(0.261), np.float32(0.2907), np.float32(0.3208), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1663), np.float32(0.2561), np.float32(0.3218), np.float32(0.2008), np.float32(0.0), np.float32(0.0), np.float32(0.2441), np.float32(0.1851), np.float32(0.2244), np.float32(0.1873), np.float32(0.2885), np.float32(0.1793), np.float32(0.3885), np.float32(0.2485), np.float32(0.1576), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2959), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2595), np.float32(0.2703)] +2026-11-07 16:05:32.894397: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:05:33.181397: lr: 0.000974 +2026-11-07 16:05:33.181823: This epoch took 260.510528 s + +2026-11-07 16:05:33.181966: +epoch: 29 +2026-11-07 16:09:40.922513: train loss : -0.5764 +2026-11-07 16:09:49.055134: validation loss: -0.4895 +2026-11-07 16:09:49.056457: Average global foreground Dice: [np.float32(0.8736), np.float32(0.5311), np.float32(0.6511), np.float32(0.5939), np.float32(0.6874), np.float32(0.6794), np.float32(0.6634), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.46), np.float32(0.5425), np.float32(0.4643), np.float32(0.278), np.float32(0.0), np.float32(0.0), np.float32(0.6963), np.float32(0.5082), np.float32(0.6659), np.float32(0.4398), np.float32(0.5235), np.float32(0.5064), np.float32(0.7703), np.float32(0.7558), np.float32(0.4375), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5122), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6665), np.float32(0.7021)] +2026-11-07 16:09:49.056684: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:09:49.349333: lr: 0.000973 +2026-11-07 16:09:49.349564: This epoch took 256.167519 s + +2026-11-07 16:09:49.349642: +epoch: 30 +2026-11-07 16:13:50.888363: train loss : -0.5768 +2026-11-07 16:13:59.002617: validation loss: -0.4137 +2026-11-07 16:13:59.004101: Average global foreground Dice: [np.float32(0.8899), np.float32(0.4502), np.float32(0.4384), np.float32(0.3905), np.float32(0.3412), np.float32(0.4634), np.float32(0.3613), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3304), np.float32(0.4104), np.float32(0.4383), np.float32(0.3856), np.float32(0.0), np.float32(0.0), np.float32(0.3082), np.float32(0.293), np.float32(0.3181), np.float32(0.3134), np.float32(0.3252), np.float32(0.1906), np.float32(0.6196), np.float32(0.5558), np.float32(0.2119), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4167), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4619), np.float32(0.5041)] +2026-11-07 16:13:59.004427: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:13:59.337785: lr: 0.000972 +2026-11-07 16:13:59.338213: This epoch took 249.988517 s + +2026-11-07 16:13:59.338492: +epoch: 31 +2026-11-07 16:18:13.843298: train loss : -0.6019 +2026-11-07 16:18:21.961611: validation loss: -0.4339 +2026-11-07 16:18:21.962522: Average global foreground Dice: [np.float32(0.8902), np.float32(0.444), np.float32(0.5391), np.float32(0.4161), np.float32(0.3992), np.float32(0.4906), np.float32(0.4308), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2803), np.float32(0.397), np.float32(0.2782), np.float32(0.3234), np.float32(0.0), np.float32(0.0), np.float32(0.3694), np.float32(0.3892), np.float32(0.3362), np.float32(0.3287), np.float32(0.2746), np.float32(0.3088), np.float32(0.6947), np.float32(0.7273), np.float32(0.2753), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4528), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5072), np.float32(0.5744)] +2026-11-07 16:18:21.962714: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:18:22.251741: lr: 0.000971 +2026-11-07 16:18:22.251994: This epoch took 262.913254 s + +2026-11-07 16:18:22.252089: +epoch: 32 +2026-11-07 16:22:18.025982: train loss : -0.5865 +2026-11-07 16:22:26.155305: validation loss: -0.4659 +2026-11-07 16:22:26.156263: Average global foreground Dice: [np.float32(0.8999), np.float32(0.5275), np.float32(0.5259), np.float32(0.4852), np.float32(0.4191), np.float32(0.5407), np.float32(0.4815), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3964), np.float32(0.4731), np.float32(0.4866), np.float32(0.4168), np.float32(0.0), np.float32(0.0), np.float32(0.3496), np.float32(0.2662), np.float32(0.406), np.float32(0.3382), np.float32(0.4267), np.float32(0.3143), np.float32(0.6792), np.float32(0.6379), np.float32(0.2252), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5064), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.584), np.float32(0.625)] +2026-11-07 16:22:26.156420: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:22:26.443513: lr: 0.00097 +2026-11-07 16:22:26.471777: saving checkpoint... +2026-11-07 16:22:27.035205: done, saving took 0.59 seconds +2026-11-07 16:22:27.038144: This epoch took 244.785956 s + +2026-11-07 16:22:27.038502: +epoch: 33 +2026-11-07 16:26:33.512494: train loss : -0.6089 +2026-11-07 16:26:41.646576: validation loss: -0.5379 +2026-11-07 16:26:41.647542: Average global foreground Dice: [np.float32(0.9046), np.float32(0.633), np.float32(0.7118), np.float32(0.7487), np.float32(0.6706), np.float32(0.7799), np.float32(0.683), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5867), np.float32(0.6337), np.float32(0.523), np.float32(0.4724), np.float32(0.0), np.float32(0.0), np.float32(0.7154), np.float32(0.5832), np.float32(0.6881), np.float32(0.5278), np.float32(0.5549), np.float32(0.547), np.float32(0.7892), np.float32(0.7613), np.float32(0.3715), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.483), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7829), np.float32(0.7928)] +2026-11-07 16:26:41.647859: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:26:41.972549: lr: 0.000969 +2026-11-07 16:26:42.004459: saving checkpoint... +2026-11-07 16:26:42.573207: done, saving took 0.60 seconds +2026-11-07 16:26:42.575527: This epoch took 255.536872 s + +2026-11-07 16:26:42.575679: +epoch: 34 +2026-11-07 16:31:28.844649: train loss : -0.6026 +2026-11-07 16:31:36.986846: validation loss: -0.4375 +2026-11-07 16:31:36.987972: Average global foreground Dice: [np.float32(0.8861), np.float32(0.4696), np.float32(0.4789), np.float32(0.4812), np.float32(0.3946), np.float32(0.5185), np.float32(0.4438), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3757), np.float32(0.4252), np.float32(0.5127), np.float32(0.4643), np.float32(0.0), np.float32(0.0), np.float32(0.3132), np.float32(0.3028), np.float32(0.3284), np.float32(0.2922), np.float32(0.4234), np.float32(0.2501), np.float32(0.6729), np.float32(0.6136), np.float32(0.2307), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4538), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5265), np.float32(0.5608)] +2026-11-07 16:31:36.988385: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:31:37.274328: lr: 0.000968 +2026-11-07 16:31:37.298114: saving checkpoint... +2026-11-07 16:31:39.886441: done, saving took 2.61 seconds +2026-11-07 16:31:39.888767: This epoch took 297.313004 s + +2026-11-07 16:31:39.889273: +epoch: 35 +2026-11-07 16:36:04.197485: train loss : -0.5926 +2026-11-07 16:36:12.293019: validation loss: -0.4640 +2026-11-07 16:36:12.293980: Average global foreground Dice: [np.float32(0.8929), np.float32(0.5149), np.float32(0.5039), np.float32(0.4678), np.float32(0.4272), np.float32(0.5432), np.float32(0.4628), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3678), np.float32(0.4372), np.float32(0.4638), np.float32(0.4299), np.float32(0.0), np.float32(0.0), np.float32(0.3975), np.float32(0.3122), np.float32(0.3999), np.float32(0.3484), np.float32(0.4725), np.float32(0.3267), np.float32(0.6991), np.float32(0.6593), np.float32(0.2761), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4845), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5495), np.float32(0.5793)] +2026-11-07 16:36:12.294348: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:36:12.575816: lr: 0.000968 +2026-11-07 16:36:12.605065: saving checkpoint... +2026-11-07 16:36:13.170093: done, saving took 0.59 seconds +2026-11-07 16:36:13.176375: This epoch took 273.286949 s + +2026-11-07 16:36:13.176559: +epoch: 36 +2026-11-07 16:40:03.524301: train loss : -0.6053 +2026-11-07 16:40:11.636562: validation loss: -0.4055 +2026-11-07 16:40:11.637525: Average global foreground Dice: [np.float32(0.8813), np.float32(0.4172), np.float32(0.4051), np.float32(0.3872), np.float32(0.3141), np.float32(0.4569), np.float32(0.3572), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3108), np.float32(0.3889), np.float32(0.3998), np.float32(0.3995), np.float32(0.0), np.float32(0.0), np.float32(0.2791), np.float32(0.2203), np.float32(0.281), np.float32(0.2462), np.float32(0.3712), np.float32(0.2749), np.float32(0.5686), np.float32(0.5256), np.float32(0.2166), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3815), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4363), np.float32(0.479)] +2026-11-07 16:40:11.637692: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:40:11.924823: lr: 0.000967 +2026-11-07 16:40:11.925250: This epoch took 238.748608 s + +2026-11-07 16:40:11.925603: +epoch: 37 +2026-11-07 16:44:19.200822: train loss : -0.5924 +2026-11-07 16:44:27.290789: validation loss: -0.2690 +2026-11-07 16:44:27.291871: Average global foreground Dice: [np.float32(0.9035), np.float32(0.1809), np.float32(0.1527), np.float32(0.1285), np.float32(0.0222), np.float32(0.183), np.float32(0.1088), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1852), np.float32(0.2006), np.float32(0.2682), np.float32(0.1676), np.float32(0.0), np.float32(0.0), np.float32(0.0451), np.float32(0.0324), np.float32(0.0941), np.float32(0.1403), np.float32(0.1596), np.float32(0.1101), np.float32(0.2118), np.float32(0.2578), np.float32(0.0716), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2129), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1291), np.float32(0.2034)] +2026-11-07 16:44:27.292358: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:44:27.575297: lr: 0.000966 +2026-11-07 16:44:27.575530: This epoch took 255.649619 s + +2026-11-07 16:44:27.575614: +epoch: 38 +2026-11-07 16:48:42.936675: train loss : -0.5900 +2026-11-07 16:48:51.047153: validation loss: -0.5161 +2026-11-07 16:48:51.048460: Average global foreground Dice: [np.float32(0.8797), np.float32(0.5728), np.float32(0.6847), np.float32(0.65), np.float32(0.6519), np.float32(0.7281), np.float32(0.6321), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5148), np.float32(0.558), np.float32(0.4876), np.float32(0.416), np.float32(0.0), np.float32(0.0), np.float32(0.6599), np.float32(0.5271), np.float32(0.6709), np.float32(0.5716), np.float32(0.4745), np.float32(0.5336), np.float32(0.7046), np.float32(0.7098), np.float32(0.359), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5083), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7299), np.float32(0.7512)] +2026-11-07 16:48:51.048713: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:48:51.337303: lr: 0.000965 +2026-11-07 16:48:51.337757: This epoch took 263.762089 s + +2026-11-07 16:48:51.338104: +epoch: 39 +2026-11-07 16:52:59.968443: train loss : -0.5962 +2026-11-07 16:53:08.105816: validation loss: -0.4170 +2026-11-07 16:53:08.107024: Average global foreground Dice: [np.float32(0.9048), np.float32(0.3912), np.float32(0.4092), np.float32(0.3516), np.float32(0.2223), np.float32(0.3968), np.float32(0.3795), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3694), np.float32(0.4422), np.float32(0.5117), np.float32(0.4689), np.float32(0.0), np.float32(0.0), np.float32(0.2321), np.float32(0.2215), np.float32(0.3622), np.float32(0.3965), np.float32(0.4334), np.float32(0.2985), np.float32(0.5536), np.float32(0.5475), np.float32(0.1859), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3822), np.float32(0.3897)] +2026-11-07 16:53:08.107238: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:53:08.398466: lr: 0.000964 +2026-11-07 16:53:08.398834: This epoch took 257.060466 s + +2026-11-07 16:53:08.399013: +epoch: 40 +2026-11-07 16:57:42.419325: train loss : -0.5994 +2026-11-07 16:57:50.511064: validation loss: -0.3875 +2026-11-07 16:57:50.512366: Average global foreground Dice: [np.float32(0.8875), np.float32(0.4188), np.float32(0.4205), np.float32(0.367), np.float32(0.3781), np.float32(0.384), np.float32(0.3562), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2329), np.float32(0.3329), np.float32(0.4128), np.float32(0.3662), np.float32(0.0), np.float32(0.0), np.float32(0.3296), np.float32(0.2783), np.float32(0.348), np.float32(0.3411), np.float32(0.2761), np.float32(0.1471), np.float32(0.6081), np.float32(0.5664), np.float32(0.2623), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4332), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4252), np.float32(0.44)] +2026-11-07 16:57:50.512682: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 16:57:50.814276: lr: 0.000963 +2026-11-07 16:57:50.814572: This epoch took 282.415464 s + +2026-11-07 16:57:50.814668: +epoch: 41 +2026-11-07 17:01:59.785404: train loss : -0.6056 +2026-11-07 17:02:07.889692: validation loss: -0.5685 +2026-11-07 17:02:07.890993: Average global foreground Dice: [np.float32(0.9051), np.float32(0.6351), np.float32(0.7705), np.float32(0.7928), np.float32(0.7119), np.float32(0.8223), np.float32(0.7452), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6697), np.float32(0.7086), np.float32(0.4336), np.float32(0.4575), np.float32(0.0), np.float32(0.0), np.float32(0.7362), np.float32(0.5444), np.float32(0.7347), np.float32(0.5503), np.float32(0.5428), np.float32(0.6009), np.float32(0.8592), np.float32(0.8338), np.float32(0.3882), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5867), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8265), np.float32(0.812)] +2026-11-07 17:02:07.891224: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:02:08.191671: lr: 0.000962 +2026-11-07 17:02:08.225160: saving checkpoint... +2026-11-07 17:02:10.734878: done, saving took 2.54 seconds +2026-11-07 17:02:10.738095: This epoch took 259.923368 s + +2026-11-07 17:02:10.738301: +epoch: 42 +2026-11-07 17:06:13.285943: train loss : -0.6083 +2026-11-07 17:06:21.381836: validation loss: -0.2057 +2026-11-07 17:06:21.382758: Average global foreground Dice: [np.float32(0.8742), np.float32(0.0892), np.float32(0.0018), np.float32(0.0067), np.float32(0.0301), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0107), np.float32(0.1994), np.float32(0.1973), np.float32(0.0691), np.float32(0.0), np.float32(0.0), np.float32(0.0035), np.float32(0.0006), np.float32(0.0027), np.float32(0.0099), np.float32(0.1103), np.float32(0.0045), np.float32(0.244), np.float32(0.1843), np.float32(0.0612), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1962), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0229), np.float32(0.0653)] +2026-11-07 17:06:21.383097: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:06:21.699631: lr: 0.000961 +2026-11-07 17:06:21.699883: This epoch took 250.961500 s + +2026-11-07 17:06:21.700083: +epoch: 43 +2026-11-07 17:10:33.769932: train loss : -0.6026 +2026-11-07 17:10:41.872159: validation loss: -0.4404 +2026-11-07 17:10:41.873420: Average global foreground Dice: [np.float32(0.9007), np.float32(0.4714), np.float32(0.5404), np.float32(0.5234), np.float32(0.4909), np.float32(0.5873), np.float32(0.5074), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3669), np.float32(0.4346), np.float32(0.4554), np.float32(0.3074), np.float32(0.0), np.float32(0.0), np.float32(0.394), np.float32(0.3125), np.float32(0.404), np.float32(0.3049), np.float32(0.4349), np.float32(0.2964), np.float32(0.6394), np.float32(0.6423), np.float32(0.3773), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3338), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5826), np.float32(0.6387)] +2026-11-07 17:10:41.873679: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:10:42.164933: lr: 0.00096 +2026-11-07 17:10:42.165201: This epoch took 260.465024 s + +2026-11-07 17:10:42.165288: +epoch: 44 +2026-11-07 17:14:41.042378: train loss : -0.6298 +2026-11-07 17:14:49.155887: validation loss: -0.2838 +2026-11-07 17:14:49.156967: Average global foreground Dice: [np.float32(0.9013), np.float32(0.2167), np.float32(0.1621), np.float32(0.2821), np.float32(0.2886), np.float32(0.1291), np.float32(0.1659), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1466), np.float32(0.1409), np.float32(0.3143), np.float32(0.1951), np.float32(0.0), np.float32(0.0), np.float32(0.2622), np.float32(0.1956), np.float32(0.0838), np.float32(0.1196), np.float32(0.2417), np.float32(0.0922), np.float32(0.1545), np.float32(0.3857), np.float32(0.1781), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2843), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1653), np.float32(0.222)] +2026-11-07 17:14:49.157324: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:14:49.455935: lr: 0.000959 +2026-11-07 17:14:49.456321: This epoch took 247.290962 s + +2026-11-07 17:14:49.456453: +epoch: 45 +2026-11-07 17:18:58.839030: train loss : -0.6125 +2026-11-07 17:19:06.950946: validation loss: -0.4250 +2026-11-07 17:19:06.951855: Average global foreground Dice: [np.float32(0.9154), np.float32(0.4406), np.float32(0.4346), np.float32(0.4261), np.float32(0.5164), np.float32(0.458), np.float32(0.4031), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3037), np.float32(0.3789), np.float32(0.5254), np.float32(0.4544), np.float32(0.0), np.float32(0.0), np.float32(0.3136), np.float32(0.3047), np.float32(0.3312), np.float32(0.3041), np.float32(0.3588), np.float32(0.3414), np.float32(0.5282), np.float32(0.5099), np.float32(0.3208), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4072), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.473), np.float32(0.5048)] +2026-11-07 17:19:06.952016: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:19:07.280089: lr: 0.000959 +2026-11-07 17:19:07.280365: This epoch took 257.823812 s + +2026-11-07 17:19:07.280559: +epoch: 46 +2026-11-07 17:23:38.318666: train loss : -0.6156 +2026-11-07 17:23:46.437368: validation loss: -0.4226 +2026-11-07 17:23:46.438357: Average global foreground Dice: [np.float32(0.8996), np.float32(0.4188), np.float32(0.4737), np.float32(0.428), np.float32(0.4201), np.float32(0.4991), np.float32(0.4523), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3164), np.float32(0.4197), np.float32(0.4196), np.float32(0.4113), np.float32(0.0), np.float32(0.0), np.float32(0.3552), np.float32(0.3274), np.float32(0.4202), np.float32(0.414), np.float32(0.2142), np.float32(0.2358), np.float32(0.6299), np.float32(0.6041), np.float32(0.3231), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4567), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5012), np.float32(0.5309)] +2026-11-07 17:23:46.438675: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:23:46.730657: lr: 0.000958 +2026-11-07 17:23:46.730922: This epoch took 279.449951 s + +2026-11-07 17:23:46.731011: +epoch: 47 +2026-11-07 17:28:01.106686: train loss : -0.6337 +2026-11-07 17:28:09.247277: validation loss: -0.4591 +2026-11-07 17:28:09.248353: Average global foreground Dice: [np.float32(0.9239), np.float32(0.5134), np.float32(0.4991), np.float32(0.5039), np.float32(0.5099), np.float32(0.5675), np.float32(0.5277), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3912), np.float32(0.4614), np.float32(0.5078), np.float32(0.4573), np.float32(0.0), np.float32(0.0), np.float32(0.4707), np.float32(0.3978), np.float32(0.4496), np.float32(0.3955), np.float32(0.4379), np.float32(0.3967), np.float32(0.6966), np.float32(0.6454), np.float32(0.2627), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3574), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5644), np.float32(0.6003)] +2026-11-07 17:28:09.248528: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:28:09.543330: lr: 0.000957 +2026-11-07 17:28:09.543689: This epoch took 262.812610 s + +2026-11-07 17:28:09.543934: +epoch: 48 +2026-11-07 17:32:31.386820: train loss : -0.6382 +2026-11-07 17:32:39.503600: validation loss: -0.3656 +2026-11-07 17:32:39.504789: Average global foreground Dice: [np.float32(0.9216), np.float32(0.3406), np.float32(0.3333), np.float32(0.3158), np.float32(0.23), np.float32(0.3947), np.float32(0.3089), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3219), np.float32(0.3573), np.float32(0.4261), np.float32(0.4078), np.float32(0.0), np.float32(0.0), np.float32(0.1882), np.float32(0.2212), np.float32(0.2621), np.float32(0.2575), np.float32(0.2952), np.float32(0.1493), np.float32(0.4784), np.float32(0.4865), np.float32(0.1508), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4307), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3377), np.float32(0.399)] +2026-11-07 17:32:39.505013: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:32:39.826182: lr: 0.000956 +2026-11-07 17:32:39.826447: This epoch took 270.282316 s + +2026-11-07 17:32:39.826530: +epoch: 49 +2026-11-07 17:36:45.174874: train loss : -0.5988 +2026-11-07 17:36:53.311676: validation loss: -0.4376 +2026-11-07 17:36:53.312640: Average global foreground Dice: [np.float32(0.9038), np.float32(0.4872), np.float32(0.5703), np.float32(0.5099), np.float32(0.2902), np.float32(0.6423), np.float32(0.5288), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4778), np.float32(0.5518), np.float32(0.5272), np.float32(0.4569), np.float32(0.0), np.float32(0.0), np.float32(0.311), np.float32(0.3277), np.float32(0.4449), np.float32(0.3408), np.float32(0.3715), np.float32(0.2059), np.float32(0.5717), np.float32(0.5867), np.float32(0.3826), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3324), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5517), np.float32(0.6333)] +2026-11-07 17:36:53.312973: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:36:53.597503: lr: 0.000955 +2026-11-07 17:36:53.597718: saving scheduled checkpoint file... +2026-11-07 17:36:53.621170: saving checkpoint... +2026-11-07 17:36:54.874681: done, saving took 1.28 seconds +2026-11-07 17:36:54.877506: done +2026-11-07 17:36:54.877858: This epoch took 255.051268 s + +2026-11-07 17:36:54.878019: +epoch: 50 +2026-11-07 17:40:58.697113: train loss : -0.6259 +2026-11-07 17:41:06.841113: validation loss: -0.4335 +2026-11-07 17:41:06.842219: Average global foreground Dice: [np.float32(0.9247), np.float32(0.4304), np.float32(0.4376), np.float32(0.4596), np.float32(0.2972), np.float32(0.5135), np.float32(0.4124), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.399), np.float32(0.4511), np.float32(0.4596), np.float32(0.4419), np.float32(0.0), np.float32(0.0), np.float32(0.2623), np.float32(0.2384), np.float32(0.3387), np.float32(0.3383), np.float32(0.4217), np.float32(0.3193), np.float32(0.5809), np.float32(0.506), np.float32(0.1891), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4521), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4697), np.float32(0.5398)] +2026-11-07 17:41:06.842571: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:41:07.131857: lr: 0.000954 +2026-11-07 17:41:07.132320: This epoch took 252.254181 s + +2026-11-07 17:41:07.132583: +epoch: 51 +2026-11-07 17:46:03.407323: train loss : -0.6280 +2026-11-07 17:46:11.561630: validation loss: -0.4401 +2026-11-07 17:46:11.562630: Average global foreground Dice: [np.float32(0.8664), np.float32(0.4631), np.float32(0.4906), np.float32(0.4595), np.float32(0.4524), np.float32(0.5441), np.float32(0.4669), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3619), np.float32(0.4454), np.float32(0.5108), np.float32(0.4013), np.float32(0.0), np.float32(0.0), np.float32(0.3616), np.float32(0.2955), np.float32(0.4071), np.float32(0.3387), np.float32(0.2841), np.float32(0.3428), np.float32(0.6238), np.float32(0.5603), np.float32(0.2273), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4231), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5304), np.float32(0.5639)] +2026-11-07 17:46:11.562823: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:46:11.859328: lr: 0.000953 +2026-11-07 17:46:11.859677: This epoch took 304.727000 s + +2026-11-07 17:46:11.859885: +epoch: 52 +2026-11-07 17:50:08.127918: train loss : -0.6358 +2026-11-07 17:50:16.223721: validation loss: -0.4504 +2026-11-07 17:50:16.224681: Average global foreground Dice: [np.float32(0.9099), np.float32(0.4778), np.float32(0.509), np.float32(0.4718), np.float32(0.4458), np.float32(0.5344), np.float32(0.4633), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3863), np.float32(0.4667), np.float32(0.5116), np.float32(0.5), np.float32(0.0), np.float32(0.0), np.float32(0.3954), np.float32(0.3391), np.float32(0.3917), np.float32(0.358), np.float32(0.4434), np.float32(0.3234), np.float32(0.638), np.float32(0.5003), np.float32(0.252), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5177), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5339), np.float32(0.5738)] +2026-11-07 17:50:16.224859: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:50:16.528968: lr: 0.000952 +2026-11-07 17:50:16.529252: This epoch took 244.669270 s + +2026-11-07 17:50:16.529346: +epoch: 53 +2026-11-07 17:54:35.900881: train loss : -0.6228 +2026-11-07 17:54:44.028333: validation loss: -0.4600 +2026-11-07 17:54:44.029325: Average global foreground Dice: [np.float32(0.8918), np.float32(0.5087), np.float32(0.4963), np.float32(0.5104), np.float32(0.4227), np.float32(0.5718), np.float32(0.4706), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.399), np.float32(0.499), np.float32(0.5013), np.float32(0.5787), np.float32(0.0), np.float32(0.0), np.float32(0.3485), np.float32(0.3033), np.float32(0.378), np.float32(0.3111), np.float32(0.4474), np.float32(0.2399), np.float32(0.6898), np.float32(0.6045), np.float32(0.2225), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5575), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5578), np.float32(0.6057)] +2026-11-07 17:54:44.029513: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:54:44.335464: lr: 0.000951 +2026-11-07 17:54:44.335767: This epoch took 267.806364 s + +2026-11-07 17:54:44.335896: +epoch: 54 +2026-11-07 17:58:51.855672: train loss : -0.6354 +2026-11-07 17:59:00.006952: validation loss: -0.4603 +2026-11-07 17:59:00.007901: Average global foreground Dice: [np.float32(0.9121), np.float32(0.5478), np.float32(0.5335), np.float32(0.5083), np.float32(0.4393), np.float32(0.5724), np.float32(0.4461), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4413), np.float32(0.4846), np.float32(0.5274), np.float32(0.5043), np.float32(0.0), np.float32(0.0), np.float32(0.427), np.float32(0.3662), np.float32(0.4327), np.float32(0.4442), np.float32(0.4813), np.float32(0.3246), np.float32(0.6158), np.float32(0.5306), np.float32(0.2519), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4825), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5478), np.float32(0.5332)] +2026-11-07 17:59:00.008197: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 17:59:00.301668: lr: 0.00095 +2026-11-07 17:59:00.302027: This epoch took 255.966059 s + +2026-11-07 17:59:00.302204: +epoch: 55 +2026-11-07 18:03:17.908767: train loss : -0.6266 +2026-11-07 18:03:26.037961: validation loss: -0.3437 +2026-11-07 18:03:26.039045: Average global foreground Dice: [np.float32(0.9071), np.float32(0.3013), np.float32(0.2781), np.float32(0.2556), np.float32(0.3712), np.float32(0.303), np.float32(0.3108), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.203), np.float32(0.2578), np.float32(0.3714), np.float32(0.2712), np.float32(0.0), np.float32(0.0), np.float32(0.2487), np.float32(0.2129), np.float32(0.2481), np.float32(0.2106), np.float32(0.2761), np.float32(0.1154), np.float32(0.4675), np.float32(0.3672), np.float32(0.2052), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3436), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2942), np.float32(0.3307)] +2026-11-07 18:03:26.039270: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:03:26.328923: lr: 0.000949 +2026-11-07 18:03:26.329271: This epoch took 266.026984 s + +2026-11-07 18:03:26.329490: +epoch: 56 +2026-11-07 18:07:38.954218: train loss : -0.6345 +2026-11-07 18:07:47.075860: validation loss: -0.4687 +2026-11-07 18:07:47.077066: Average global foreground Dice: [np.float32(0.9147), np.float32(0.5215), np.float32(0.4943), np.float32(0.5104), np.float32(0.4733), np.float32(0.5832), np.float32(0.5023), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3919), np.float32(0.4756), np.float32(0.4721), np.float32(0.4102), np.float32(0.0), np.float32(0.0), np.float32(0.4516), np.float32(0.3762), np.float32(0.4369), np.float32(0.4174), np.float32(0.3506), np.float32(0.275), np.float32(0.6922), np.float32(0.6364), np.float32(0.2895), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4393), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5595), np.float32(0.5964)] +2026-11-07 18:07:47.077293: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:07:47.368805: lr: 0.000949 +2026-11-07 18:07:47.369099: This epoch took 261.039505 s + +2026-11-07 18:07:47.369195: +epoch: 57 +2026-11-07 18:11:38.789477: train loss : -0.6360 +2026-11-07 18:11:46.895690: validation loss: -0.4474 +2026-11-07 18:11:46.896651: Average global foreground Dice: [np.float32(0.9164), np.float32(0.4882), np.float32(0.4722), np.float32(0.4872), np.float32(0.4116), np.float32(0.5411), np.float32(0.4392), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4032), np.float32(0.455), np.float32(0.4954), np.float32(0.4199), np.float32(0.0), np.float32(0.0), np.float32(0.3369), np.float32(0.2689), np.float32(0.3609), np.float32(0.3642), np.float32(0.3696), np.float32(0.2673), np.float32(0.6638), np.float32(0.628), np.float32(0.2296), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4797), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5447), np.float32(0.57)] +2026-11-07 18:11:46.897136: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:11:47.194119: lr: 0.000948 +2026-11-07 18:11:47.194459: This epoch took 239.825181 s + +2026-11-07 18:11:47.194666: +epoch: 58 +2026-11-07 18:15:40.763292: train loss : -0.6414 +2026-11-07 18:15:48.863135: validation loss: -0.5219 +2026-11-07 18:15:48.864323: Average global foreground Dice: [np.float32(0.887), np.float32(0.5786), np.float32(0.6958), np.float32(0.7504), np.float32(0.6174), np.float32(0.7667), np.float32(0.6658), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5787), np.float32(0.6022), np.float32(0.5593), np.float32(0.471), np.float32(0.0), np.float32(0.0), np.float32(0.6736), np.float32(0.5322), np.float32(0.6774), np.float32(0.5766), np.float32(0.3396), np.float32(0.4854), np.float32(0.8064), np.float32(0.814), np.float32(0.4621), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5038), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7702), np.float32(0.7895)] +2026-11-07 18:15:48.864698: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:15:49.156250: lr: 0.000947 +2026-11-07 18:15:49.180523: saving checkpoint... +2026-11-07 18:15:49.769298: done, saving took 0.61 seconds +2026-11-07 18:15:49.771565: This epoch took 242.576671 s + +2026-11-07 18:15:49.771696: +epoch: 59 +2026-11-07 18:19:58.088458: train loss : -0.6083 +2026-11-07 18:20:06.297027: validation loss: -0.4608 +2026-11-07 18:20:06.298125: Average global foreground Dice: [np.float32(0.8997), np.float32(0.5037), np.float32(0.5715), np.float32(0.5415), np.float32(0.4374), np.float32(0.6172), np.float32(0.5321), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.426), np.float32(0.4558), np.float32(0.4895), np.float32(0.4526), np.float32(0.0), np.float32(0.0), np.float32(0.3771), np.float32(0.2852), np.float32(0.4179), np.float32(0.3502), np.float32(0.2563), np.float32(0.3429), np.float32(0.7055), np.float32(0.6746), np.float32(0.2743), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4976), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.601), np.float32(0.6252)] +2026-11-07 18:20:06.298370: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:20:06.579707: lr: 0.000946 +2026-11-07 18:20:06.609579: saving checkpoint... +2026-11-07 18:20:07.149377: done, saving took 0.57 seconds +2026-11-07 18:20:07.152265: This epoch took 257.380377 s + +2026-11-07 18:20:07.152483: +epoch: 60 +2026-11-07 18:24:10.575087: train loss : -0.6454 +2026-11-07 18:24:18.674152: validation loss: -0.4785 +2026-11-07 18:24:18.675073: Average global foreground Dice: [np.float32(0.8966), np.float32(0.5386), np.float32(0.551), np.float32(0.5435), np.float32(0.4732), np.float32(0.6108), np.float32(0.5122), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4151), np.float32(0.4866), np.float32(0.4863), np.float32(0.4376), np.float32(0.0), np.float32(0.0), np.float32(0.4242), np.float32(0.3641), np.float32(0.46), np.float32(0.427), np.float32(0.3978), np.float32(0.3998), np.float32(0.7567), np.float32(0.7265), np.float32(0.3092), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5038), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6189), np.float32(0.6565)] +2026-11-07 18:24:18.675422: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:24:18.962608: lr: 0.000945 +2026-11-07 18:24:18.990736: saving checkpoint... +2026-11-07 18:24:19.540301: done, saving took 0.58 seconds +2026-11-07 18:24:19.542477: This epoch took 252.389894 s + +2026-11-07 18:24:19.542766: +epoch: 61 +2026-11-07 18:28:41.882161: train loss : -0.6412 +2026-11-07 18:28:50.041728: validation loss: -0.5319 +2026-11-07 18:28:50.042828: Average global foreground Dice: [np.float32(0.8902), np.float32(0.6333), np.float32(0.7274), np.float32(0.6937), np.float32(0.6551), np.float32(0.7117), np.float32(0.6517), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5148), np.float32(0.5716), np.float32(0.5111), np.float32(0.4778), np.float32(0.0), np.float32(0.0), np.float32(0.6376), np.float32(0.5077), np.float32(0.6216), np.float32(0.5795), np.float32(0.418), np.float32(0.5985), np.float32(0.8076), np.float32(0.7942), np.float32(0.5371), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5314), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7861), np.float32(0.8066)] +2026-11-07 18:28:50.043068: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:28:50.331863: lr: 0.000944 +2026-11-07 18:28:50.361550: saving checkpoint... +2026-11-07 18:28:52.920581: done, saving took 2.59 seconds +2026-11-07 18:28:52.923273: This epoch took 273.380394 s + +2026-11-07 18:28:52.923476: +epoch: 62 +2026-11-07 18:32:49.153597: train loss : -0.6368 +2026-11-07 18:32:57.261557: validation loss: -0.5222 +2026-11-07 18:32:57.262691: Average global foreground Dice: [np.float32(0.8889), np.float32(0.6215), np.float32(0.6931), np.float32(0.7308), np.float32(0.6294), np.float32(0.7912), np.float32(0.6837), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5962), np.float32(0.6137), np.float32(0.5185), np.float32(0.411), np.float32(0.0), np.float32(0.0), np.float32(0.7345), np.float32(0.5805), np.float32(0.6967), np.float32(0.603), np.float32(0.33), np.float32(0.4707), np.float32(0.7717), np.float32(0.7453), np.float32(0.4718), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4313), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7639), np.float32(0.7866)] +2026-11-07 18:32:57.263333: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:32:57.562050: lr: 0.000943 +2026-11-07 18:32:57.594164: saving checkpoint... +2026-11-07 18:33:00.717183: done, saving took 3.15 seconds +2026-11-07 18:33:00.719145: This epoch took 247.795547 s + +2026-11-07 18:33:00.719301: +epoch: 63 +2026-11-07 18:36:48.909863: train loss : -0.6511 +2026-11-07 18:36:57.015702: validation loss: -0.4397 +2026-11-07 18:36:57.016669: Average global foreground Dice: [np.float32(0.8998), np.float32(0.4701), np.float32(0.4954), np.float32(0.4979), np.float32(0.4228), np.float32(0.5797), np.float32(0.488), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3849), np.float32(0.4754), np.float32(0.4975), np.float32(0.4425), np.float32(0.0), np.float32(0.0), np.float32(0.3841), np.float32(0.3462), np.float32(0.4185), np.float32(0.3809), np.float32(0.0494), np.float32(0.141), np.float32(0.6939), np.float32(0.6624), np.float32(0.2565), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5017), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5643), np.float32(0.5936)] +2026-11-07 18:36:57.016844: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:36:57.308721: lr: 0.000942 +2026-11-07 18:36:57.308949: This epoch took 236.589578 s + +2026-11-07 18:36:57.309041: +epoch: 64 +2026-11-07 18:41:19.432870: train loss : -0.6356 +2026-11-07 18:41:27.563822: validation loss: -0.5010 +2026-11-07 18:41:27.564772: Average global foreground Dice: [np.float32(0.8998), np.float32(0.579), np.float32(0.6445), np.float32(0.6108), np.float32(0.5321), np.float32(0.6403), np.float32(0.5539), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4343), np.float32(0.5278), np.float32(0.4762), np.float32(0.463), np.float32(0.0), np.float32(0.0), np.float32(0.5244), np.float32(0.4189), np.float32(0.5043), np.float32(0.4751), np.float32(0.4618), np.float32(0.5031), np.float32(0.7865), np.float32(0.7687), np.float32(0.4118), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5289), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7267), np.float32(0.7323)] +2026-11-07 18:41:27.565108: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:41:27.871950: lr: 0.000941 +2026-11-07 18:41:27.898411: saving checkpoint... +2026-11-07 18:41:30.524790: done, saving took 2.65 seconds +2026-11-07 18:41:30.528215: This epoch took 273.218944 s + +2026-11-07 18:41:30.528959: +epoch: 65 +2026-11-07 18:45:29.724965: train loss : -0.6580 +2026-11-07 18:45:37.829911: validation loss: -0.5449 +2026-11-07 18:45:37.830768: Average global foreground Dice: [np.float32(0.8958), np.float32(0.6134), np.float32(0.723), np.float32(0.7415), np.float32(0.6191), np.float32(0.8025), np.float32(0.6814), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5929), np.float32(0.6481), np.float32(0.5607), np.float32(0.4681), np.float32(0.0), np.float32(0.0), np.float32(0.7306), np.float32(0.5728), np.float32(0.725), np.float32(0.5619), np.float32(0.5029), np.float32(0.5049), np.float32(0.8204), np.float32(0.7818), np.float32(0.4151), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5088), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.797), np.float32(0.813)] +2026-11-07 18:45:37.830905: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:45:38.130155: lr: 0.00094 +2026-11-07 18:45:38.158744: saving checkpoint... +2026-11-07 18:45:40.658688: done, saving took 2.53 seconds +2026-11-07 18:45:40.661022: This epoch took 250.131062 s + +2026-11-07 18:45:40.661119: +epoch: 66 +2026-11-07 18:49:42.046971: train loss : -0.6222 +2026-11-07 18:49:50.174972: validation loss: -0.4857 +2026-11-07 18:49:50.176013: Average global foreground Dice: [np.float32(0.8952), np.float32(0.4964), np.float32(0.581), np.float32(0.5503), np.float32(0.4696), np.float32(0.6112), np.float32(0.5173), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4547), np.float32(0.5152), np.float32(0.5103), np.float32(0.4841), np.float32(0.0), np.float32(0.0), np.float32(0.392), np.float32(0.3269), np.float32(0.4466), np.float32(0.3505), np.float32(0.2845), np.float32(0.3757), np.float32(0.732), np.float32(0.6992), np.float32(0.3629), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5016), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6318), np.float32(0.6406)] +2026-11-07 18:49:50.176467: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:49:50.486582: lr: 0.000939 +2026-11-07 18:49:50.486844: This epoch took 249.825665 s + +2026-11-07 18:49:50.486919: +epoch: 67 +2026-11-07 18:53:56.337158: train loss : -0.6404 +2026-11-07 18:54:04.436710: validation loss: -0.4898 +2026-11-07 18:54:04.437703: Average global foreground Dice: [np.float32(0.9097), np.float32(0.5154), np.float32(0.5626), np.float32(0.5834), np.float32(0.4676), np.float32(0.6365), np.float32(0.5686), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4916), np.float32(0.5115), np.float32(0.5223), np.float32(0.4976), np.float32(0.0), np.float32(0.0), np.float32(0.4432), np.float32(0.3728), np.float32(0.5125), np.float32(0.3918), np.float32(0.3584), np.float32(0.4236), np.float32(0.7168), np.float32(0.6814), np.float32(0.2873), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.64), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6203), np.float32(0.6573)] +2026-11-07 18:54:04.437882: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:54:04.744709: lr: 0.000939 +2026-11-07 18:54:04.776330: saving checkpoint... +2026-11-07 18:54:05.314473: done, saving took 0.57 seconds +2026-11-07 18:54:05.317605: This epoch took 254.830634 s + +2026-11-07 18:54:05.317795: +epoch: 68 +2026-11-07 18:58:15.084702: train loss : -0.6395 +2026-11-07 18:58:23.239455: validation loss: -0.4503 +2026-11-07 18:58:23.240716: Average global foreground Dice: [np.float32(0.9059), np.float32(0.4904), np.float32(0.5599), np.float32(0.4955), np.float32(0.3866), np.float32(0.5652), np.float32(0.4604), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4196), np.float32(0.4652), np.float32(0.5398), np.float32(0.4747), np.float32(0.0), np.float32(0.0), np.float32(0.3177), np.float32(0.2613), np.float32(0.3531), np.float32(0.3451), np.float32(0.2564), np.float32(0.2295), np.float32(0.7152), np.float32(0.6956), np.float32(0.2249), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5596), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5536), np.float32(0.6119)] +2026-11-07 18:58:23.241147: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 18:58:23.541605: lr: 0.000938 +2026-11-07 18:58:23.541874: This epoch took 258.223962 s + +2026-11-07 18:58:23.541975: +epoch: 69 +2026-11-07 19:02:34.647233: train loss : -0.6572 +2026-11-07 19:02:42.770557: validation loss: -0.5496 +2026-11-07 19:02:42.771534: Average global foreground Dice: [np.float32(0.9072), np.float32(0.6178), np.float32(0.7215), np.float32(0.7462), np.float32(0.6549), np.float32(0.7814), np.float32(0.6874), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.606), np.float32(0.6458), np.float32(0.5338), np.float32(0.4777), np.float32(0.0), np.float32(0.0), np.float32(0.6507), np.float32(0.5108), np.float32(0.6745), np.float32(0.582), np.float32(0.5383), np.float32(0.527), np.float32(0.8049), np.float32(0.7593), np.float32(0.4587), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5838), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7672), np.float32(0.7848)] +2026-11-07 19:02:42.771839: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:02:43.095345: lr: 0.000937 +2026-11-07 19:02:43.120677: saving checkpoint... +2026-11-07 19:02:43.657009: done, saving took 0.56 seconds +2026-11-07 19:02:43.660199: This epoch took 260.118157 s + +2026-11-07 19:02:43.660505: +epoch: 70 +2026-11-07 19:06:43.945331: train loss : -0.6397 +2026-11-07 19:06:52.072767: validation loss: -0.4916 +2026-11-07 19:06:52.073826: Average global foreground Dice: [np.float32(0.9063), np.float32(0.5443), np.float32(0.5772), np.float32(0.545), np.float32(0.4589), np.float32(0.6138), np.float32(0.5313), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4679), np.float32(0.5072), np.float32(0.4869), np.float32(0.4849), np.float32(0.0), np.float32(0.0), np.float32(0.4208), np.float32(0.3393), np.float32(0.4592), np.float32(0.4401), np.float32(0.5261), np.float32(0.4367), np.float32(0.7847), np.float32(0.748), np.float32(0.2668), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4789), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6208), np.float32(0.6743)] +2026-11-07 19:06:52.073994: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:06:52.370200: lr: 0.000936 +2026-11-07 19:06:52.394369: saving checkpoint... +2026-11-07 19:06:52.936313: done, saving took 0.57 seconds +2026-11-07 19:06:52.938784: This epoch took 249.278110 s + +2026-11-07 19:06:52.938982: +epoch: 71 +2026-11-07 19:11:04.996150: train loss : -0.6643 +2026-11-07 19:11:13.098827: validation loss: -0.4180 +2026-11-07 19:11:13.099797: Average global foreground Dice: [np.float32(0.9117), np.float32(0.427), np.float32(0.4516), np.float32(0.4419), np.float32(0.3634), np.float32(0.5006), np.float32(0.402), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3424), np.float32(0.4286), np.float32(0.4367), np.float32(0.3678), np.float32(0.0), np.float32(0.0), np.float32(0.3047), np.float32(0.2341), np.float32(0.3345), np.float32(0.3349), np.float32(0.2658), np.float32(0.3246), np.float32(0.5995), np.float32(0.5732), np.float32(0.2113), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4169), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4894), np.float32(0.5234)] +2026-11-07 19:11:13.099967: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:11:13.408090: lr: 0.000935 +2026-11-07 19:11:13.408309: This epoch took 260.469233 s + +2026-11-07 19:11:13.408386: +epoch: 72 +2026-11-07 19:15:32.928902: train loss : -0.6520 +2026-11-07 19:15:41.037926: validation loss: -0.4807 +2026-11-07 19:15:41.038797: Average global foreground Dice: [np.float32(0.8998), np.float32(0.5428), np.float32(0.6087), np.float32(0.596), np.float32(0.4708), np.float32(0.6494), np.float32(0.5318), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5), np.float32(0.5406), np.float32(0.501), np.float32(0.3687), np.float32(0.0), np.float32(0.0), np.float32(0.4367), np.float32(0.3717), np.float32(0.4798), np.float32(0.4409), np.float32(0.4446), np.float32(0.4009), np.float32(0.7096), np.float32(0.6879), np.float32(0.2763), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5206), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6201), np.float32(0.6655)] +2026-11-07 19:15:41.038940: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:15:41.337569: lr: 0.000934 +2026-11-07 19:15:41.338013: This epoch took 267.929563 s + +2026-11-07 19:15:41.338331: +epoch: 73 +2026-11-07 19:19:55.852809: train loss : -0.6377 +2026-11-07 19:20:03.945891: validation loss: -0.2090 +2026-11-07 19:20:03.946893: Average global foreground Dice: [np.float32(0.7665), np.float32(0.0125), np.float32(0.013), np.float32(0.0229), np.float32(0.0469), np.float32(0.0291), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1126), np.float32(0.1692), np.float32(0.1277), np.float32(0.0287), np.float32(0.0), np.float32(0.0), np.float32(0.0302), np.float32(0.0), np.float32(0.0068), np.float32(0.0193), np.float32(0.0392), np.float32(0.049), np.float32(0.0676), np.float32(0.0049), np.float32(0.0918), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1849), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0043), np.float32(0.0385)] +2026-11-07 19:20:03.947069: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:20:04.235341: lr: 0.000933 +2026-11-07 19:20:04.235567: This epoch took 262.896983 s + +2026-11-07 19:20:04.235646: +epoch: 74 +2026-11-07 19:23:54.596210: train loss : -0.5760 +2026-11-07 19:24:02.691977: validation loss: -0.4913 +2026-11-07 19:24:02.693138: Average global foreground Dice: [np.float32(0.9133), np.float32(0.565), np.float32(0.5963), np.float32(0.5812), np.float32(0.4976), np.float32(0.6281), np.float32(0.5231), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4651), np.float32(0.5402), np.float32(0.5106), np.float32(0.4706), np.float32(0.0), np.float32(0.0), np.float32(0.4458), np.float32(0.3676), np.float32(0.4849), np.float32(0.4124), np.float32(0.4045), np.float32(0.316), np.float32(0.732), np.float32(0.7292), np.float32(0.3317), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5426), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6584), np.float32(0.7008)] +2026-11-07 19:24:02.693440: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:24:03.003401: lr: 0.000932 +2026-11-07 19:24:03.003659: This epoch took 238.767959 s + +2026-11-07 19:24:03.003790: +epoch: 75 +2026-11-07 19:28:10.835361: train loss : -0.6447 +2026-11-07 19:28:18.937350: validation loss: -0.4334 +2026-11-07 19:28:18.938496: Average global foreground Dice: [np.float32(0.8802), np.float32(0.4688), np.float32(0.4918), np.float32(0.4531), np.float32(0.3745), np.float32(0.5133), np.float32(0.4381), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3435), np.float32(0.4467), np.float32(0.4491), np.float32(0.3359), np.float32(0.0), np.float32(0.0), np.float32(0.3031), np.float32(0.1941), np.float32(0.3615), np.float32(0.364), np.float32(0.3085), np.float32(0.2572), np.float32(0.6449), np.float32(0.6004), np.float32(0.223), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4904), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5241), np.float32(0.5504)] +2026-11-07 19:28:18.938701: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:28:19.236056: lr: 0.000931 +2026-11-07 19:28:19.236391: This epoch took 256.232427 s + +2026-11-07 19:28:19.236636: +epoch: 76 +2026-11-07 19:32:36.279135: train loss : -0.6376 +2026-11-07 19:32:44.377564: validation loss: -0.4348 +2026-11-07 19:32:44.378691: Average global foreground Dice: [np.float32(0.894), np.float32(0.4396), np.float32(0.4621), np.float32(0.4528), np.float32(0.3747), np.float32(0.5393), np.float32(0.4507), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3583), np.float32(0.4591), np.float32(0.4592), np.float32(0.4435), np.float32(0.0), np.float32(0.0), np.float32(0.2954), np.float32(0.2888), np.float32(0.3368), np.float32(0.3025), np.float32(0.3459), np.float32(0.2799), np.float32(0.6502), np.float32(0.6289), np.float32(0.1923), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4934), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5122), np.float32(0.5504)] +2026-11-07 19:32:44.379042: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:32:44.667433: lr: 0.00093 +2026-11-07 19:32:44.667683: This epoch took 265.430942 s + +2026-11-07 19:32:44.667774: +epoch: 77 +2026-11-07 19:36:58.521823: train loss : -0.6344 +2026-11-07 19:37:06.657585: validation loss: -0.3494 +2026-11-07 19:37:06.658716: Average global foreground Dice: [np.float32(0.9127), np.float32(0.384), np.float32(0.3562), np.float32(0.308), np.float32(0.3167), np.float32(0.345), np.float32(0.2988), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2273), np.float32(0.259), np.float32(0.3733), np.float32(0.2983), np.float32(0.0), np.float32(0.0), np.float32(0.3095), np.float32(0.2635), np.float32(0.2921), np.float32(0.2767), np.float32(0.157), np.float32(0.1578), np.float32(0.4976), np.float32(0.4547), np.float32(0.1739), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4012), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3622), np.float32(0.3787)] +2026-11-07 19:37:06.659145: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:37:07.013310: lr: 0.00093 +2026-11-07 19:37:07.013513: This epoch took 262.345667 s + +2026-11-07 19:37:07.013588: +epoch: 78 +2026-11-07 19:41:44.966862: train loss : -0.6163 +2026-11-07 19:41:53.119383: validation loss: -0.4430 +2026-11-07 19:41:53.120473: Average global foreground Dice: [np.float32(0.9085), np.float32(0.5095), np.float32(0.4938), np.float32(0.4743), np.float32(0.3788), np.float32(0.5509), np.float32(0.46), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4243), np.float32(0.4456), np.float32(0.4523), np.float32(0.4322), np.float32(0.0), np.float32(0.0), np.float32(0.3464), np.float32(0.3444), np.float32(0.3867), np.float32(0.3587), np.float32(0.3734), np.float32(0.2528), np.float32(0.637), np.float32(0.586), np.float32(0.2679), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4455), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5252), np.float32(0.556)] +2026-11-07 19:41:53.120679: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:41:53.425829: lr: 0.000929 +2026-11-07 19:41:53.426287: This epoch took 286.412642 s + +2026-11-07 19:41:53.426610: +epoch: 79 +2026-11-07 19:45:58.269973: train loss : -0.6306 +2026-11-07 19:46:06.390322: validation loss: -0.4626 +2026-11-07 19:46:06.391527: Average global foreground Dice: [np.float32(0.9183), np.float32(0.5232), np.float32(0.5133), np.float32(0.5176), np.float32(0.4154), np.float32(0.5861), np.float32(0.5068), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4478), np.float32(0.505), np.float32(0.4785), np.float32(0.4622), np.float32(0.0), np.float32(0.0), np.float32(0.3365), np.float32(0.3225), np.float32(0.3871), np.float32(0.3235), np.float32(0.4693), np.float32(0.2213), np.float32(0.7076), np.float32(0.6636), np.float32(0.2593), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4499), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.556), np.float32(0.5927)] +2026-11-07 19:46:06.392049: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:46:06.685774: lr: 0.000928 +2026-11-07 19:46:06.686151: This epoch took 253.259295 s + +2026-11-07 19:46:06.686460: +epoch: 80 +2026-11-07 19:50:05.450716: train loss : -0.6542 +2026-11-07 19:50:13.546475: validation loss: -0.5228 +2026-11-07 19:50:13.547537: Average global foreground Dice: [np.float32(0.9103), np.float32(0.5989), np.float32(0.6713), np.float32(0.6452), np.float32(0.6231), np.float32(0.6613), np.float32(0.6175), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4932), np.float32(0.5543), np.float32(0.548), np.float32(0.4725), np.float32(0.0), np.float32(0.0), np.float32(0.6256), np.float32(0.5249), np.float32(0.5986), np.float32(0.5735), np.float32(0.4868), np.float32(0.4963), np.float32(0.7807), np.float32(0.762), np.float32(0.3987), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4104), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7074), np.float32(0.7331)] +2026-11-07 19:50:13.547983: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:50:13.851980: lr: 0.000927 +2026-11-07 19:50:13.852207: This epoch took 247.165607 s + +2026-11-07 19:50:13.852285: +epoch: 81 +2026-11-07 19:54:08.244415: train loss : -0.6696 +2026-11-07 19:54:16.344942: validation loss: -0.5003 +2026-11-07 19:54:16.345933: Average global foreground Dice: [np.float32(0.8843), np.float32(0.5483), np.float32(0.6334), np.float32(0.6049), np.float32(0.5569), np.float32(0.6621), np.float32(0.5836), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4495), np.float32(0.5265), np.float32(0.4859), np.float32(0.4574), np.float32(0.0), np.float32(0.0), np.float32(0.5425), np.float32(0.4029), np.float32(0.5405), np.float32(0.5), np.float32(0.4364), np.float32(0.4207), np.float32(0.7681), np.float32(0.7254), np.float32(0.3756), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4899), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6828), np.float32(0.7037)] +2026-11-07 19:54:16.346088: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:54:16.650229: lr: 0.000926 +2026-11-07 19:54:16.650656: This epoch took 242.798096 s + +2026-11-07 19:54:16.650867: +epoch: 82 +2026-11-07 19:58:34.425620: train loss : -0.6608 +2026-11-07 19:58:42.568385: validation loss: -0.5594 +2026-11-07 19:58:42.569658: Average global foreground Dice: [np.float32(0.9124), np.float32(0.609), np.float32(0.763), np.float32(0.8005), np.float32(0.6908), np.float32(0.8424), np.float32(0.7304), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6148), np.float32(0.6385), np.float32(0.6051), np.float32(0.5477), np.float32(0.0), np.float32(0.0), np.float32(0.7443), np.float32(0.5646), np.float32(0.7225), np.float32(0.6367), np.float32(0.4319), np.float32(0.5319), np.float32(0.8532), np.float32(0.8441), np.float32(0.5244), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5645), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8316), np.float32(0.8409)] +2026-11-07 19:58:42.570064: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 19:58:42.870257: lr: 0.000925 +2026-11-07 19:58:42.870588: This epoch took 266.219622 s + +2026-11-07 19:58:42.870774: +epoch: 83 +2026-11-07 20:02:55.518389: train loss : -0.6389 +2026-11-07 20:03:03.704080: validation loss: -0.4255 +2026-11-07 20:03:03.705805: Average global foreground Dice: [np.float32(0.9022), np.float32(0.458), np.float32(0.4842), np.float32(0.4705), np.float32(0.3158), np.float32(0.522), np.float32(0.4442), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3862), np.float32(0.4696), np.float32(0.5076), np.float32(0.425), np.float32(0.0), np.float32(0.0), np.float32(0.2529), np.float32(0.2352), np.float32(0.3238), np.float32(0.2604), np.float32(0.2398), np.float32(0.1737), np.float32(0.6418), np.float32(0.623), np.float32(0.2197), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3974), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4802), np.float32(0.5728)] +2026-11-07 20:03:03.706396: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:03:04.008828: lr: 0.000924 +2026-11-07 20:03:04.009083: This epoch took 261.138226 s + +2026-11-07 20:03:04.009185: +epoch: 84 +2026-11-07 20:07:08.357559: train loss : -0.6651 +2026-11-07 20:07:16.473638: validation loss: -0.4317 +2026-11-07 20:07:16.474698: Average global foreground Dice: [np.float32(0.9041), np.float32(0.472), np.float32(0.4686), np.float32(0.4362), np.float32(0.4338), np.float32(0.4944), np.float32(0.4259), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3129), np.float32(0.3789), np.float32(0.3841), np.float32(0.3079), np.float32(0.0), np.float32(0.0), np.float32(0.4432), np.float32(0.3623), np.float32(0.436), np.float32(0.4098), np.float32(0.2984), np.float32(0.4466), np.float32(0.596), np.float32(0.5714), np.float32(0.313), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4062), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4916), np.float32(0.5179)] +2026-11-07 20:07:16.475080: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:07:16.771702: lr: 0.000923 +2026-11-07 20:07:16.772153: This epoch took 252.762885 s + +2026-11-07 20:07:16.772318: +epoch: 85 +2026-11-07 20:11:46.782694: train loss : -0.6374 +2026-11-07 20:11:54.897505: validation loss: -0.4977 +2026-11-07 20:11:54.898418: Average global foreground Dice: [np.float32(0.8824), np.float32(0.5316), np.float32(0.6204), np.float32(0.6691), np.float32(0.5733), np.float32(0.7126), np.float32(0.5944), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5353), np.float32(0.5688), np.float32(0.5141), np.float32(0.4422), np.float32(0.0), np.float32(0.0), np.float32(0.6057), np.float32(0.5105), np.float32(0.6074), np.float32(0.5428), np.float32(0.0228), np.float32(0.3883), np.float32(0.7278), np.float32(0.7234), np.float32(0.442), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5343), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6823), np.float32(0.7048)] +2026-11-07 20:11:54.898563: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:11:55.192393: lr: 0.000922 +2026-11-07 20:11:55.192623: This epoch took 278.420228 s + +2026-11-07 20:11:55.192710: +epoch: 86 +2026-11-07 20:16:22.942284: train loss : -0.6330 +2026-11-07 20:16:31.083046: validation loss: -0.4591 +2026-11-07 20:16:31.084230: Average global foreground Dice: [np.float32(0.883), np.float32(0.5008), np.float32(0.6404), np.float32(0.5464), np.float32(0.5109), np.float32(0.6154), np.float32(0.5745), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3181), np.float32(0.3901), np.float32(0.4278), np.float32(0.2742), np.float32(0.0), np.float32(0.0), np.float32(0.5692), np.float32(0.4289), np.float32(0.523), np.float32(0.4604), np.float32(0.2134), np.float32(0.4163), np.float32(0.6733), np.float32(0.6734), np.float32(0.3833), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4955), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6669), np.float32(0.6981)] +2026-11-07 20:16:31.084616: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:16:31.379318: lr: 0.000921 +2026-11-07 20:16:31.379649: This epoch took 276.186882 s + +2026-11-07 20:16:31.379873: +epoch: 87 +2026-11-07 20:20:35.596925: train loss : -0.6578 +2026-11-07 20:20:43.724171: validation loss: -0.5196 +2026-11-07 20:20:43.725388: Average global foreground Dice: [np.float32(0.9017), np.float32(0.5511), np.float32(0.6662), np.float32(0.6757), np.float32(0.5813), np.float32(0.7259), np.float32(0.6242), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5766), np.float32(0.6006), np.float32(0.5258), np.float32(0.443), np.float32(0.0), np.float32(0.0), np.float32(0.657), np.float32(0.5075), np.float32(0.6627), np.float32(0.5809), np.float32(0.3341), np.float32(0.4416), np.float32(0.7378), np.float32(0.7108), np.float32(0.4513), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6189), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7234), np.float32(0.7526)] +2026-11-07 20:20:43.725697: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:20:44.037585: lr: 0.00092 +2026-11-07 20:20:44.038007: This epoch took 252.658043 s + +2026-11-07 20:20:44.038263: +epoch: 88 +2026-11-07 20:24:45.295298: train loss : -0.6282 +2026-11-07 20:24:53.399214: validation loss: -0.4452 +2026-11-07 20:24:53.400276: Average global foreground Dice: [np.float32(0.8992), np.float32(0.5118), np.float32(0.5025), np.float32(0.5282), np.float32(0.4553), np.float32(0.5497), np.float32(0.4657), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4137), np.float32(0.4784), np.float32(0.4714), np.float32(0.4205), np.float32(0.0), np.float32(0.0), np.float32(0.4178), np.float32(0.3665), np.float32(0.4246), np.float32(0.3826), np.float32(0.3705), np.float32(0.2244), np.float32(0.6009), np.float32(0.5083), np.float32(0.2106), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3982), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5632), np.float32(0.554)] +2026-11-07 20:24:53.400778: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:24:53.701171: lr: 0.00092 +2026-11-07 20:24:53.701436: This epoch took 249.662960 s + +2026-11-07 20:24:53.701536: +epoch: 89 +2026-11-07 20:29:11.811862: train loss : -0.6409 +2026-11-07 20:29:19.948711: validation loss: -0.4331 +2026-11-07 20:29:19.949710: Average global foreground Dice: [np.float32(0.9057), np.float32(0.5087), np.float32(0.4875), np.float32(0.4836), np.float32(0.4125), np.float32(0.5395), np.float32(0.4457), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.46), np.float32(0.4583), np.float32(0.5712), np.float32(0.4507), np.float32(0.0), np.float32(0.0), np.float32(0.3159), np.float32(0.3073), np.float32(0.3843), np.float32(0.3833), np.float32(0.2249), np.float32(0.1884), np.float32(0.609), np.float32(0.5604), np.float32(0.1859), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4585), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5111), np.float32(0.5058)] +2026-11-07 20:29:19.949862: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:29:20.248667: lr: 0.000919 +2026-11-07 20:29:20.248968: This epoch took 266.547356 s + +2026-11-07 20:29:20.249273: +epoch: 90 +2026-11-07 20:34:05.802571: train loss : -0.6298 +2026-11-07 20:34:13.910694: validation loss: -0.4987 +2026-11-07 20:34:13.911849: Average global foreground Dice: [np.float32(0.9019), np.float32(0.565), np.float32(0.6719), np.float32(0.6627), np.float32(0.5891), np.float32(0.7229), np.float32(0.6023), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4993), np.float32(0.5407), np.float32(0.491), np.float32(0.4331), np.float32(0.0), np.float32(0.0), np.float32(0.5937), np.float32(0.4357), np.float32(0.5643), np.float32(0.5092), np.float32(0.241), np.float32(0.418), np.float32(0.7063), np.float32(0.679), np.float32(0.3918), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5566), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.699), np.float32(0.6916)] +2026-11-07 20:34:13.912243: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:34:14.224530: lr: 0.000918 +2026-11-07 20:34:14.224886: This epoch took 293.975493 s + +2026-11-07 20:34:14.225099: +epoch: 91 +2026-11-07 20:38:30.931052: train loss : -0.6442 +2026-11-07 20:38:39.060977: validation loss: -0.4240 +2026-11-07 20:38:39.061850: Average global foreground Dice: [np.float32(0.9077), np.float32(0.4113), np.float32(0.4815), np.float32(0.4576), np.float32(0.311), np.float32(0.4937), np.float32(0.4619), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4108), np.float32(0.4429), np.float32(0.4952), np.float32(0.4307), np.float32(0.0), np.float32(0.0), np.float32(0.3244), np.float32(0.2882), np.float32(0.4236), np.float32(0.3729), np.float32(0.4225), np.float32(0.2654), np.float32(0.5165), np.float32(0.5293), np.float32(0.3003), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.462), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4553), np.float32(0.5203)] +2026-11-07 20:38:39.062026: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:38:39.355488: lr: 0.000917 +2026-11-07 20:38:39.355816: This epoch took 265.130427 s + +2026-11-07 20:38:39.356075: +epoch: 92 +2026-11-07 20:42:46.844976: train loss : -0.6470 +2026-11-07 20:42:54.952373: validation loss: -0.5183 +2026-11-07 20:42:54.953503: Average global foreground Dice: [np.float32(0.9112), np.float32(0.5801), np.float32(0.6704), np.float32(0.7092), np.float32(0.6767), np.float32(0.7474), np.float32(0.6826), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5009), np.float32(0.5382), np.float32(0.4998), np.float32(0.2484), np.float32(0.0), np.float32(0.0), np.float32(0.7025), np.float32(0.5165), np.float32(0.6766), np.float32(0.5684), np.float32(0.4484), np.float32(0.5679), np.float32(0.8316), np.float32(0.8225), np.float32(0.5019), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4872), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7723), np.float32(0.787)] +2026-11-07 20:42:54.953949: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:42:55.255966: lr: 0.000916 +2026-11-07 20:42:55.256217: This epoch took 255.900027 s + +2026-11-07 20:42:55.256318: +epoch: 93 +2026-11-07 20:47:01.909657: train loss : -0.6766 +2026-11-07 20:47:10.045245: validation loss: -0.5419 +2026-11-07 20:47:10.046120: Average global foreground Dice: [np.float32(0.909), np.float32(0.6188), np.float32(0.7202), np.float32(0.7386), np.float32(0.6322), np.float32(0.783), np.float32(0.6743), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5566), np.float32(0.6302), np.float32(0.6663), np.float32(0.3863), np.float32(0.0), np.float32(0.0), np.float32(0.6987), np.float32(0.5844), np.float32(0.6624), np.float32(0.6049), np.float32(0.4976), np.float32(0.4789), np.float32(0.8129), np.float32(0.7982), np.float32(0.5136), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5353), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7752), np.float32(0.8047)] +2026-11-07 20:47:10.046373: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:47:10.352866: lr: 0.000915 +2026-11-07 20:47:10.384915: saving checkpoint... +2026-11-07 20:47:11.137046: done, saving took 0.78 seconds +2026-11-07 20:47:11.139377: This epoch took 255.882993 s + +2026-11-07 20:47:11.139813: +epoch: 94 +2026-11-07 20:51:14.526106: train loss : -0.6716 +2026-11-07 20:51:22.631748: validation loss: -0.5439 +2026-11-07 20:51:22.632787: Average global foreground Dice: [np.float32(0.9093), np.float32(0.6), np.float32(0.7327), np.float32(0.7401), np.float32(0.6578), np.float32(0.7932), np.float32(0.6841), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6144), np.float32(0.64), np.float32(0.5735), np.float32(0.4721), np.float32(0.0), np.float32(0.0), np.float32(0.7278), np.float32(0.5714), np.float32(0.7213), np.float32(0.6195), np.float32(0.5122), np.float32(0.4931), np.float32(0.8192), np.float32(0.8141), np.float32(0.4895), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6018), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7919), np.float32(0.8144)] +2026-11-07 20:51:22.632965: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:51:22.935920: lr: 0.000914 +2026-11-07 20:51:22.967059: saving checkpoint... +2026-11-07 20:51:25.942964: done, saving took 3.01 seconds +2026-11-07 20:51:25.946012: This epoch took 254.805992 s + +2026-11-07 20:51:25.946252: +epoch: 95 +2026-11-07 20:55:41.172396: train loss : -0.6070 +2026-11-07 20:55:49.340811: validation loss: -0.4172 +2026-11-07 20:55:49.341970: Average global foreground Dice: [np.float32(0.8974), np.float32(0.3722), np.float32(0.4283), np.float32(0.3932), np.float32(0.3872), np.float32(0.4498), np.float32(0.3727), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3688), np.float32(0.4311), np.float32(0.4771), np.float32(0.4167), np.float32(0.0), np.float32(0.0), np.float32(0.43), np.float32(0.4398), np.float32(0.4349), np.float32(0.4369), np.float32(0.3168), np.float32(0.2222), np.float32(0.5454), np.float32(0.5671), np.float32(0.2793), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4152), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4549), np.float32(0.5293)] +2026-11-07 20:55:49.342452: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 20:55:49.653641: lr: 0.000913 +2026-11-07 20:55:49.654107: This epoch took 263.707666 s + +2026-11-07 20:55:49.654395: +epoch: 96 +2026-11-07 21:00:09.479715: train loss : -0.6678 +2026-11-07 21:00:17.621790: validation loss: -0.5028 +2026-11-07 21:00:17.622731: Average global foreground Dice: [np.float32(0.9114), np.float32(0.5411), np.float32(0.6379), np.float32(0.5612), np.float32(0.5827), np.float32(0.6305), np.float32(0.5788), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3974), np.float32(0.4928), np.float32(0.573), np.float32(0.4824), np.float32(0.0), np.float32(0.0), np.float32(0.5361), np.float32(0.4919), np.float32(0.5246), np.float32(0.5055), np.float32(0.4893), np.float32(0.5001), np.float32(0.7745), np.float32(0.7617), np.float32(0.3969), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5704), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6772), np.float32(0.7264)] +2026-11-07 21:00:17.622883: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:00:17.935137: lr: 0.000912 +2026-11-07 21:00:17.935602: This epoch took 268.280898 s + +2026-11-07 21:00:17.935952: +epoch: 97 +2026-11-07 21:04:20.641607: train loss : -0.6754 +2026-11-07 21:04:28.756532: validation loss: -0.5063 +2026-11-07 21:04:28.757895: Average global foreground Dice: [np.float32(0.8923), np.float32(0.4695), np.float32(0.6845), np.float32(0.6599), np.float32(0.6499), np.float32(0.7196), np.float32(0.6656), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4053), np.float32(0.5303), np.float32(0.5866), np.float32(0.4163), np.float32(0.0), np.float32(0.0), np.float32(0.6822), np.float32(0.5275), np.float32(0.6704), np.float32(0.5481), np.float32(0.4746), np.float32(0.4953), np.float32(0.7356), np.float32(0.7274), np.float32(0.4985), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4409), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7567), np.float32(0.7728)] +2026-11-07 21:04:28.758211: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:04:29.077877: lr: 0.000911 +2026-11-07 21:04:29.106535: saving checkpoint... +2026-11-07 21:04:29.647702: done, saving took 0.57 seconds +2026-11-07 21:04:29.649785: This epoch took 251.713617 s + +2026-11-07 21:04:29.649971: +epoch: 98 +2026-11-07 21:08:23.712032: train loss : -0.6773 +2026-11-07 21:08:31.803646: validation loss: -0.5093 +2026-11-07 21:08:31.804935: Average global foreground Dice: [np.float32(0.9024), np.float32(0.5467), np.float32(0.6217), np.float32(0.6289), np.float32(0.522), np.float32(0.6898), np.float32(0.5707), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5268), np.float32(0.5934), np.float32(0.4998), np.float32(0.4801), np.float32(0.0), np.float32(0.0), np.float32(0.5185), np.float32(0.4211), np.float32(0.5194), np.float32(0.5014), np.float32(0.4664), np.float32(0.3834), np.float32(0.7182), np.float32(0.692), np.float32(0.3237), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5335), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6693), np.float32(0.7049)] +2026-11-07 21:08:31.805261: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:08:32.106393: lr: 0.00091 +2026-11-07 21:08:32.130697: saving checkpoint... +2026-11-07 21:08:32.715015: done, saving took 0.61 seconds +2026-11-07 21:08:32.717752: This epoch took 243.067700 s + +2026-11-07 21:08:32.718142: +epoch: 99 +2026-11-07 21:12:49.862339: train loss : -0.6756 +2026-11-07 21:12:57.999642: validation loss: -0.3821 +2026-11-07 21:12:58.000806: Average global foreground Dice: [np.float32(0.9088), np.float32(0.4494), np.float32(0.4773), np.float32(0.4198), np.float32(0.3732), np.float32(0.318), np.float32(0.383), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3322), np.float32(0.4467), np.float32(0.4243), np.float32(0.4209), np.float32(0.0), np.float32(0.0), np.float32(0.3513), np.float32(0.2816), np.float32(0.3493), np.float32(0.3639), np.float32(0.152), np.float32(0.2703), np.float32(0.3575), np.float32(0.339), np.float32(0.2084), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4035), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3649), np.float32(0.3943)] +2026-11-07 21:12:58.001207: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:12:58.304766: lr: 0.00091 +2026-11-07 21:12:58.305019: saving scheduled checkpoint file... +2026-11-07 21:12:58.330326: saving checkpoint... +2026-11-07 21:12:58.868479: done, saving took 0.56 seconds +2026-11-07 21:12:58.870929: done +2026-11-07 21:12:58.871186: This epoch took 266.152674 s + +2026-11-07 21:12:58.871292: +epoch: 100 +2026-11-07 21:17:25.550436: train loss : -0.6557 +2026-11-07 21:17:33.664549: validation loss: -0.4637 +2026-11-07 21:17:33.665473: Average global foreground Dice: [np.float32(0.8906), np.float32(0.4992), np.float32(0.5304), np.float32(0.52), np.float32(0.4197), np.float32(0.5978), np.float32(0.5057), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3976), np.float32(0.4834), np.float32(0.4673), np.float32(0.3673), np.float32(0.0), np.float32(0.0), np.float32(0.4236), np.float32(0.3704), np.float32(0.4545), np.float32(0.4289), np.float32(0.4593), np.float32(0.4892), np.float32(0.6043), np.float32(0.5739), np.float32(0.311), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4889), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5673), np.float32(0.6061)] +2026-11-07 21:17:33.665618: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:17:33.962556: lr: 0.000909 +2026-11-07 21:17:33.962768: This epoch took 275.091414 s + +2026-11-07 21:17:33.962840: +epoch: 101 +2026-11-07 21:21:39.261272: train loss : -0.6743 +2026-11-07 21:21:47.367359: validation loss: -0.4435 +2026-11-07 21:21:47.368469: Average global foreground Dice: [np.float32(0.8924), np.float32(0.4755), np.float32(0.5218), np.float32(0.5345), np.float32(0.4399), np.float32(0.5736), np.float32(0.5091), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3844), np.float32(0.3952), np.float32(0.5151), np.float32(0.0178), np.float32(0.0), np.float32(0.0), np.float32(0.4352), np.float32(0.345), np.float32(0.4661), np.float32(0.4577), np.float32(0.3761), np.float32(0.3835), np.float32(0.6942), np.float32(0.6462), np.float32(0.3384), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5327), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5891), np.float32(0.6153)] +2026-11-07 21:21:47.368699: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:21:47.667410: lr: 0.000908 +2026-11-07 21:21:47.667737: This epoch took 253.704832 s + +2026-11-07 21:21:47.667864: +epoch: 102 +2026-11-07 21:25:46.287582: train loss : -0.6760 +2026-11-07 21:25:54.438184: validation loss: -0.5229 +2026-11-07 21:25:54.439114: Average global foreground Dice: [np.float32(0.9245), np.float32(0.5474), np.float32(0.6927), np.float32(0.7328), np.float32(0.6532), np.float32(0.7818), np.float32(0.6542), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5399), np.float32(0.5959), np.float32(0.5449), np.float32(0.4148), np.float32(0.0), np.float32(0.0), np.float32(0.7371), np.float32(0.4976), np.float32(0.7146), np.float32(0.4953), np.float32(0.3403), np.float32(0.5081), np.float32(0.8159), np.float32(0.8063), np.float32(0.5303), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5566), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7717), np.float32(0.8012)] +2026-11-07 21:25:54.439451: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:25:54.744870: lr: 0.000907 +2026-11-07 21:25:54.745129: This epoch took 247.077197 s + +2026-11-07 21:25:54.745224: +epoch: 103 +2026-11-07 21:30:06.000455: train loss : -0.6595 +2026-11-07 21:30:14.154448: validation loss: -0.5621 +2026-11-07 21:30:14.155517: Average global foreground Dice: [np.float32(0.9102), np.float32(0.6433), np.float32(0.7467), np.float32(0.8037), np.float32(0.707), np.float32(0.8494), np.float32(0.7195), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6009), np.float32(0.6505), np.float32(0.5788), np.float32(0.4942), np.float32(0.0), np.float32(0.0), np.float32(0.7544), np.float32(0.551), np.float32(0.7244), np.float32(0.5639), np.float32(0.53), np.float32(0.604), np.float32(0.8521), np.float32(0.8491), np.float32(0.515), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5963), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8271), np.float32(0.8469)] +2026-11-07 21:30:14.155808: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:30:14.472239: lr: 0.000906 +2026-11-07 21:30:14.505053: saving checkpoint... +2026-11-07 21:30:15.312794: done, saving took 0.84 seconds +2026-11-07 21:30:15.315108: This epoch took 260.569813 s + +2026-11-07 21:30:15.315312: +epoch: 104 +2026-11-07 21:34:47.842261: train loss : -0.6774 +2026-11-07 21:34:55.995422: validation loss: -0.5536 +2026-11-07 21:34:55.996606: Average global foreground Dice: [np.float32(0.9153), np.float32(0.6152), np.float32(0.7043), np.float32(0.7466), np.float32(0.6586), np.float32(0.803), np.float32(0.6743), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5675), np.float32(0.6301), np.float32(0.5982), np.float32(0.4989), np.float32(0.0), np.float32(0.0), np.float32(0.6913), np.float32(0.5642), np.float32(0.6698), np.float32(0.5934), np.float32(0.535), np.float32(0.5709), np.float32(0.8022), np.float32(0.7883), np.float32(0.4476), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6154), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7712), np.float32(0.7956)] +2026-11-07 21:34:55.996820: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:34:56.313483: lr: 0.000905 +2026-11-07 21:34:56.345406: saving checkpoint... +2026-11-07 21:34:59.063482: done, saving took 2.75 seconds +2026-11-07 21:34:59.065779: This epoch took 283.750369 s + +2026-11-07 21:34:59.065984: +epoch: 105 +2026-11-07 21:38:49.135328: train loss : -0.6820 +2026-11-07 21:38:57.274755: validation loss: -0.4831 +2026-11-07 21:38:57.275744: Average global foreground Dice: [np.float32(0.8978), np.float32(0.5316), np.float32(0.5303), np.float32(0.5672), np.float32(0.5092), np.float32(0.6503), np.float32(0.5513), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4236), np.float32(0.5163), np.float32(0.4962), np.float32(0.4647), np.float32(0.0), np.float32(0.0), np.float32(0.4382), np.float32(0.3566), np.float32(0.4387), np.float32(0.4374), np.float32(0.4047), np.float32(0.2524), np.float32(0.7156), np.float32(0.65), np.float32(0.2683), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5728), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6145), np.float32(0.6485)] +2026-11-07 21:38:57.276024: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:38:57.579643: lr: 0.000904 +2026-11-07 21:38:57.579859: This epoch took 238.513786 s + +2026-11-07 21:38:57.579948: +epoch: 106 +2026-11-07 21:43:02.365535: train loss : -0.6639 +2026-11-07 21:43:10.507909: validation loss: -0.3276 +2026-11-07 21:43:10.509068: Average global foreground Dice: [np.float32(0.8877), np.float32(0.3039), np.float32(0.3641), np.float32(0.1939), np.float32(0.1883), np.float32(0.3104), np.float32(0.2893), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1793), np.float32(0.287), np.float32(0.4186), np.float32(0.2598), np.float32(0.0), np.float32(0.0), np.float32(0.1577), np.float32(0.1448), np.float32(0.2035), np.float32(0.1463), np.float32(0.0523), np.float32(0.1926), np.float32(0.4224), np.float32(0.4251), np.float32(0.1983), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.335), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2392), np.float32(0.4043)] +2026-11-07 21:43:10.509379: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:43:10.814777: lr: 0.000903 +2026-11-07 21:43:10.815024: This epoch took 253.235011 s + +2026-11-07 21:43:10.815228: +epoch: 107 +2026-11-07 21:47:31.878503: train loss : -0.6515 +2026-11-07 21:47:40.040225: validation loss: -0.5725 +2026-11-07 21:47:40.041179: Average global foreground Dice: [np.float32(0.8877), np.float32(0.6767), np.float32(0.7828), np.float32(0.8508), np.float32(0.8072), np.float32(0.9079), np.float32(0.7935), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6958), np.float32(0.6796), np.float32(0.6201), np.float32(0.5111), np.float32(0.0), np.float32(0.0), np.float32(0.7068), np.float32(0.5583), np.float32(0.7251), np.float32(0.611), np.float32(0.0), np.float32(0.0), np.float32(0.8788), np.float32(0.8413), np.float32(0.4699), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3367), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8822), np.float32(0.8824)] +2026-11-07 21:47:40.041721: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:47:40.380707: lr: 0.000902 +2026-11-07 21:47:40.380967: This epoch took 269.565453 s + +2026-11-07 21:47:40.381242: +epoch: 108 +2026-11-07 21:51:37.014930: train loss : -0.6441 +2026-11-07 21:51:45.175547: validation loss: -0.5718 +2026-11-07 21:51:45.176724: Average global foreground Dice: [np.float32(0.8895), np.float32(0.663), np.float32(0.7689), np.float32(0.7967), np.float32(0.7073), np.float32(0.8432), np.float32(0.7067), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6054), np.float32(0.6413), np.float32(0.6031), np.float32(0.4748), np.float32(0.0), np.float32(0.0), np.float32(0.7054), np.float32(0.5662), np.float32(0.7203), np.float32(0.5595), np.float32(0.4446), np.float32(0.6951), np.float32(0.8563), np.float32(0.8314), np.float32(0.4862), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5922), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.847), np.float32(0.8535)] +2026-11-07 21:51:45.177039: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:51:45.480494: lr: 0.000901 +2026-11-07 21:51:45.480751: This epoch took 245.099362 s + +2026-11-07 21:51:45.480825: +epoch: 109 +2026-11-07 21:55:36.474763: train loss : -0.6549 +2026-11-07 21:55:44.636193: validation loss: -0.5658 +2026-11-07 21:55:44.637155: Average global foreground Dice: [np.float32(0.9207), np.float32(0.643), np.float32(0.7602), np.float32(0.7935), np.float32(0.709), np.float32(0.8389), np.float32(0.7019), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6427), np.float32(0.6657), np.float32(0.6073), np.float32(0.5598), np.float32(0.0), np.float32(0.0), np.float32(0.7228), np.float32(0.5762), np.float32(0.7367), np.float32(0.618), np.float32(0.527), np.float32(0.6006), np.float32(0.8524), np.float32(0.842), np.float32(0.4625), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6861), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8174), np.float32(0.8459)] +2026-11-07 21:55:44.637569: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:55:44.956699: lr: 0.0009 +2026-11-07 21:55:44.988368: saving checkpoint... +2026-11-07 21:55:45.537481: done, saving took 0.58 seconds +2026-11-07 21:55:45.540061: This epoch took 240.059182 s + +2026-11-07 21:55:45.540296: +epoch: 110 +2026-11-07 21:59:50.575121: train loss : -0.6775 +2026-11-07 21:59:58.714767: validation loss: -0.4932 +2026-11-07 21:59:58.716307: Average global foreground Dice: [np.float32(0.9161), np.float32(0.5296), np.float32(0.6247), np.float32(0.5845), np.float32(0.5677), np.float32(0.6653), np.float32(0.6027), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4435), np.float32(0.4772), np.float32(0.5063), np.float32(0.4316), np.float32(0.0), np.float32(0.0), np.float32(0.6794), np.float32(0.5341), np.float32(0.6677), np.float32(0.5417), np.float32(0.3684), np.float32(0.4784), np.float32(0.7424), np.float32(0.7256), np.float32(0.4341), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4546), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6668), np.float32(0.7002)] +2026-11-07 21:59:58.716996: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 21:59:59.023637: lr: 0.0009 +2026-11-07 21:59:59.056102: saving checkpoint... +2026-11-07 21:59:59.623899: done, saving took 0.60 seconds +2026-11-07 21:59:59.626732: This epoch took 254.086258 s + +2026-11-07 21:59:59.626931: +epoch: 111 +2026-11-07 22:04:26.915290: train loss : -0.6571 +2026-11-07 22:04:35.069989: validation loss: -0.5245 +2026-11-07 22:04:35.070926: Average global foreground Dice: [np.float32(0.9097), np.float32(0.6019), np.float32(0.6869), np.float32(0.7141), np.float32(0.62), np.float32(0.7592), np.float32(0.6389), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5316), np.float32(0.5734), np.float32(0.5031), np.float32(0.4572), np.float32(0.0), np.float32(0.0), np.float32(0.7021), np.float32(0.5368), np.float32(0.6864), np.float32(0.5845), np.float32(0.5182), np.float32(0.5454), np.float32(0.7658), np.float32(0.746), np.float32(0.4397), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5835), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7364), np.float32(0.7706)] +2026-11-07 22:04:35.071208: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:04:35.378892: lr: 0.000899 +2026-11-07 22:04:35.411775: saving checkpoint... +2026-11-07 22:04:38.180290: done, saving took 2.80 seconds +2026-11-07 22:04:38.182547: This epoch took 278.555471 s + +2026-11-07 22:04:38.182707: +epoch: 112 +2026-11-07 22:08:57.062242: train loss : -0.6811 +2026-11-07 22:09:05.175616: validation loss: -0.5449 +2026-11-07 22:09:05.176589: Average global foreground Dice: [np.float32(0.9113), np.float32(0.6173), np.float32(0.7333), np.float32(0.7672), np.float32(0.6728), np.float32(0.8016), np.float32(0.7073), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5044), np.float32(0.5948), np.float32(0.5245), np.float32(0.4039), np.float32(0.0), np.float32(0.0), np.float32(0.7433), np.float32(0.5706), np.float32(0.7342), np.float32(0.6324), np.float32(0.5162), np.float32(0.5501), np.float32(0.7975), np.float32(0.7919), np.float32(0.4902), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4676), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7931), np.float32(0.8137)] +2026-11-07 22:09:05.176857: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:09:05.484204: lr: 0.000898 +2026-11-07 22:09:05.518288: saving checkpoint... +2026-11-07 22:09:06.081054: done, saving took 0.60 seconds +2026-11-07 22:09:06.083780: This epoch took 267.901011 s + +2026-11-07 22:09:06.084200: +epoch: 113 +2026-11-07 22:13:13.299426: train loss : -0.6568 +2026-11-07 22:13:21.403640: validation loss: -0.5374 +2026-11-07 22:13:21.404776: Average global foreground Dice: [np.float32(0.9171), np.float32(0.5911), np.float32(0.7171), np.float32(0.7275), np.float32(0.6602), np.float32(0.7681), np.float32(0.7048), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5423), np.float32(0.6091), np.float32(0.6096), np.float32(0.4825), np.float32(0.0), np.float32(0.0), np.float32(0.6821), np.float32(0.5469), np.float32(0.673), np.float32(0.5488), np.float32(0.4539), np.float32(0.5839), np.float32(0.8063), np.float32(0.7941), np.float32(0.5081), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5367), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7654), np.float32(0.7916)] +2026-11-07 22:13:21.405189: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:13:21.713604: lr: 0.000897 +2026-11-07 22:13:21.741753: saving checkpoint... +2026-11-07 22:13:22.312635: done, saving took 0.60 seconds +2026-11-07 22:13:22.316068: This epoch took 256.231699 s + +2026-11-07 22:13:22.316293: +epoch: 114 +2026-11-07 22:17:51.956546: train loss : -0.6692 +2026-11-07 22:18:00.092117: validation loss: -0.5653 +2026-11-07 22:18:00.093122: Average global foreground Dice: [np.float32(0.9162), np.float32(0.6642), np.float32(0.7364), np.float32(0.7677), np.float32(0.7149), np.float32(0.8197), np.float32(0.725), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6029), np.float32(0.6351), np.float32(0.5468), np.float32(0.5074), np.float32(0.0), np.float32(0.0), np.float32(0.7495), np.float32(0.5844), np.float32(0.7295), np.float32(0.6183), np.float32(0.5379), np.float32(0.6487), np.float32(0.8234), np.float32(0.802), np.float32(0.462), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5182), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8064), np.float32(0.8202)] +2026-11-07 22:18:00.093481: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:18:00.382812: lr: 0.000896 +2026-11-07 22:18:00.415765: saving checkpoint... +2026-11-07 22:18:02.953352: done, saving took 2.57 seconds +2026-11-07 22:18:02.956161: This epoch took 280.639770 s + +2026-11-07 22:18:02.956575: +epoch: 115 +2026-11-07 22:21:58.121608: train loss : -0.6766 +2026-11-07 22:22:06.224243: validation loss: -0.4403 +2026-11-07 22:22:06.225285: Average global foreground Dice: [np.float32(0.9113), np.float32(0.4747), np.float32(0.447), np.float32(0.459), np.float32(0.4289), np.float32(0.5334), np.float32(0.4444), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4045), np.float32(0.4109), np.float32(0.5042), np.float32(0.4906), np.float32(0.0), np.float32(0.0), np.float32(0.3576), np.float32(0.3092), np.float32(0.3588), np.float32(0.3642), np.float32(0.4497), np.float32(0.2268), np.float32(0.649), np.float32(0.5828), np.float32(0.1989), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2839), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.515), np.float32(0.5514)] +2026-11-07 22:22:06.225475: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:22:06.523489: lr: 0.000895 +2026-11-07 22:22:06.523837: This epoch took 243.566923 s + +2026-11-07 22:22:06.523960: +epoch: 116 +2026-11-07 22:26:34.395200: train loss : -0.6736 +2026-11-07 22:26:42.554533: validation loss: -0.4882 +2026-11-07 22:26:42.555600: Average global foreground Dice: [np.float32(0.9054), np.float32(0.5474), np.float32(0.5582), np.float32(0.6131), np.float32(0.5617), np.float32(0.6459), np.float32(0.5205), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4555), np.float32(0.4772), np.float32(0.5294), np.float32(0.4544), np.float32(0.0), np.float32(0.0), np.float32(0.4855), np.float32(0.3704), np.float32(0.4672), np.float32(0.4305), np.float32(0.4404), np.float32(0.409), np.float32(0.6816), np.float32(0.6394), np.float32(0.3605), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5315), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6286), np.float32(0.6541)] +2026-11-07 22:26:42.555947: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:26:42.859475: lr: 0.000894 +2026-11-07 22:26:42.859738: This epoch took 276.335721 s + +2026-11-07 22:26:42.859991: +epoch: 117 +2026-11-07 22:31:10.133313: train loss : -0.6773 +2026-11-07 22:31:18.270903: validation loss: -0.5661 +2026-11-07 22:31:18.271853: Average global foreground Dice: [np.float32(0.9204), np.float32(0.6455), np.float32(0.7541), np.float32(0.7822), np.float32(0.7047), np.float32(0.816), np.float32(0.7213), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6457), np.float32(0.66), np.float32(0.5788), np.float32(0.4558), np.float32(0.0), np.float32(0.0), np.float32(0.7414), np.float32(0.6032), np.float32(0.7138), np.float32(0.6465), np.float32(0.5194), np.float32(0.6103), np.float32(0.8302), np.float32(0.8139), np.float32(0.5326), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6228), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8169), np.float32(0.8262)] +2026-11-07 22:31:18.272016: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:31:18.581253: lr: 0.000893 +2026-11-07 22:31:18.581488: This epoch took 275.721386 s + +2026-11-07 22:31:18.581573: +epoch: 118 +2026-11-07 22:35:23.391901: train loss : -0.6720 +2026-11-07 22:35:31.507334: validation loss: -0.6162 +2026-11-07 22:35:31.508307: Average global foreground Dice: [np.float32(0.8997), np.float32(0.6593), np.float32(0.8182), np.float32(0.8657), np.float32(0.8347), np.float32(0.9019), np.float32(0.8034), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6859), np.float32(0.6967), np.float32(0.664), np.float32(0.5836), np.float32(0.0), np.float32(0.0), np.float32(0.8121), np.float32(0.5846), np.float32(0.7781), np.float32(0.6539), np.float32(0.5218), np.float32(0.6256), np.float32(0.9117), np.float32(0.8815), np.float32(0.5271), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6356), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8997), np.float32(0.8978)] +2026-11-07 22:35:31.508664: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:35:31.828811: lr: 0.000892 +2026-11-07 22:35:31.855306: saving checkpoint... +2026-11-07 22:35:32.393529: done, saving took 0.56 seconds +2026-11-07 22:35:32.396029: This epoch took 253.814401 s + +2026-11-07 22:35:32.396228: +epoch: 119 +2026-11-07 22:39:45.074496: train loss : -0.6788 +2026-11-07 22:39:53.182527: validation loss: -0.4598 +2026-11-07 22:39:53.183441: Average global foreground Dice: [np.float32(0.8857), np.float32(0.5021), np.float32(0.55), np.float32(0.5552), np.float32(0.4652), np.float32(0.5983), np.float32(0.4838), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3935), np.float32(0.4447), np.float32(0.5134), np.float32(0.411), np.float32(0.0), np.float32(0.0), np.float32(0.4191), np.float32(0.329), np.float32(0.4269), np.float32(0.4058), np.float32(0.3992), np.float32(0.3819), np.float32(0.7172), np.float32(0.633), np.float32(0.3396), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5847), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6104), np.float32(0.6274)] +2026-11-07 22:39:53.183617: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:39:53.479349: lr: 0.000891 +2026-11-07 22:39:53.479609: This epoch took 261.083218 s + +2026-11-07 22:39:53.479699: +epoch: 120 +2026-11-07 22:43:56.063460: train loss : -0.6893 +2026-11-07 22:44:04.207166: validation loss: -0.4712 +2026-11-07 22:44:04.208270: Average global foreground Dice: [np.float32(0.8998), np.float32(0.4876), np.float32(0.5879), np.float32(0.5855), np.float32(0.4804), np.float32(0.624), np.float32(0.5105), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.418), np.float32(0.4843), np.float32(0.5152), np.float32(0.4199), np.float32(0.0), np.float32(0.0), np.float32(0.4219), np.float32(0.3315), np.float32(0.4428), np.float32(0.3962), np.float32(0.4649), np.float32(0.4309), np.float32(0.7041), np.float32(0.6735), np.float32(0.411), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6217), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6479), np.float32(0.6818)] +2026-11-07 22:44:04.208643: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:44:04.511631: lr: 0.00089 +2026-11-07 22:44:04.511876: This epoch took 251.032108 s + +2026-11-07 22:44:04.511956: +epoch: 121 +2026-11-07 22:48:36.255420: train loss : -0.6936 +2026-11-07 22:48:44.391322: validation loss: -0.4439 +2026-11-07 22:48:44.392598: Average global foreground Dice: [np.float32(0.8977), np.float32(0.441), np.float32(0.4857), np.float32(0.4705), np.float32(0.3633), np.float32(0.5445), np.float32(0.439), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3888), np.float32(0.4479), np.float32(0.5215), np.float32(0.3808), np.float32(0.0), np.float32(0.0), np.float32(0.3452), np.float32(0.2915), np.float32(0.37), np.float32(0.3608), np.float32(0.4337), np.float32(0.3756), np.float32(0.696), np.float32(0.6646), np.float32(0.2419), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5585), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5437), np.float32(0.5904)] +2026-11-07 22:48:44.392792: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:48:44.703787: lr: 0.000889 +2026-11-07 22:48:44.704358: This epoch took 280.192231 s + +2026-11-07 22:48:44.706139: +epoch: 122 +2026-11-07 22:52:40.701420: train loss : -0.6925 +2026-11-07 22:52:48.805626: validation loss: -0.5963 +2026-11-07 22:52:48.806593: Average global foreground Dice: [np.float32(0.9199), np.float32(0.6629), np.float32(0.7611), np.float32(0.8083), np.float32(0.7592), np.float32(0.8483), np.float32(0.7412), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6535), np.float32(0.648), np.float32(0.6428), np.float32(0.5868), np.float32(0.0), np.float32(0.0), np.float32(0.7413), np.float32(0.6135), np.float32(0.7161), np.float32(0.6274), np.float32(0.6156), np.float32(0.6136), np.float32(0.8861), np.float32(0.8773), np.float32(0.4684), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6795), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8564), np.float32(0.8669)] +2026-11-07 22:52:48.806778: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:52:49.104561: lr: 0.000889 +2026-11-07 22:52:49.104803: This epoch took 244.398378 s + +2026-11-07 22:52:49.104884: +epoch: 123 +2026-11-07 22:56:45.222488: train loss : -0.6817 +2026-11-07 22:56:53.346779: validation loss: -0.5526 +2026-11-07 22:56:53.347720: Average global foreground Dice: [np.float32(0.9186), np.float32(0.619), np.float32(0.7256), np.float32(0.7718), np.float32(0.6767), np.float32(0.805), np.float32(0.684), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5699), np.float32(0.6361), np.float32(0.5885), np.float32(0.4804), np.float32(0.0), np.float32(0.0), np.float32(0.7333), np.float32(0.5857), np.float32(0.701), np.float32(0.6509), np.float32(0.4852), np.float32(0.5633), np.float32(0.8316), np.float32(0.8283), np.float32(0.5174), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.571), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7898), np.float32(0.811)] +2026-11-07 22:56:53.347976: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 22:56:53.648473: lr: 0.000888 +2026-11-07 22:56:53.648701: This epoch took 244.543757 s + +2026-11-07 22:56:53.648787: +epoch: 124 +2026-11-07 23:00:51.992075: train loss : -0.6964 +2026-11-07 23:01:00.104102: validation loss: -0.5332 +2026-11-07 23:01:00.105074: Average global foreground Dice: [np.float32(0.9114), np.float32(0.6102), np.float32(0.6685), np.float32(0.6998), np.float32(0.6084), np.float32(0.747), np.float32(0.6302), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5203), np.float32(0.6125), np.float32(0.5575), np.float32(0.5212), np.float32(0.0), np.float32(0.0), np.float32(0.5839), np.float32(0.4879), np.float32(0.5946), np.float32(0.5664), np.float32(0.5179), np.float32(0.5122), np.float32(0.7889), np.float32(0.7577), np.float32(0.3907), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6131), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.731), np.float32(0.7579)] +2026-11-07 23:01:00.105289: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:01:00.414128: lr: 0.000887 +2026-11-07 23:01:00.414405: This epoch took 246.765555 s + +2026-11-07 23:01:00.414527: +epoch: 125 +2026-11-07 23:05:19.329514: train loss : -0.6893 +2026-11-07 23:05:27.452243: validation loss: -0.5694 +2026-11-07 23:05:27.453202: Average global foreground Dice: [np.float32(0.8944), np.float32(0.6809), np.float32(0.7711), np.float32(0.8023), np.float32(0.6552), np.float32(0.8344), np.float32(0.6279), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5774), np.float32(0.6532), np.float32(0.5178), np.float32(0.0041), np.float32(0.0), np.float32(0.0), np.float32(0.7183), np.float32(0.58), np.float32(0.6601), np.float32(0.5824), np.float32(0.4633), np.float32(0.6512), np.float32(0.8709), np.float32(0.8297), np.float32(0.4458), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5833), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8591), np.float32(0.8565)] +2026-11-07 23:05:27.453435: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:05:27.758606: lr: 0.000886 +2026-11-07 23:05:27.790958: saving checkpoint... +2026-11-07 23:05:30.359710: done, saving took 2.60 seconds +2026-11-07 23:05:30.362873: This epoch took 269.948139 s + +2026-11-07 23:05:30.363080: +epoch: 126 +2026-11-07 23:09:30.431953: train loss : -0.6907 +2026-11-07 23:09:38.562825: validation loss: -0.5575 +2026-11-07 23:09:38.563743: Average global foreground Dice: [np.float32(0.9087), np.float32(0.6338), np.float32(0.7162), np.float32(0.7431), np.float32(0.6571), np.float32(0.7903), np.float32(0.6752), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5724), np.float32(0.6335), np.float32(0.6067), np.float32(0.4583), np.float32(0.0), np.float32(0.0), np.float32(0.7287), np.float32(0.5808), np.float32(0.6934), np.float32(0.5893), np.float32(0.5199), np.float32(0.6167), np.float32(0.8195), np.float32(0.8042), np.float32(0.4748), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6353), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7842), np.float32(0.8056)] +2026-11-07 23:09:38.563878: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:09:38.870407: lr: 0.000885 +2026-11-07 23:09:38.898750: saving checkpoint... +2026-11-07 23:09:39.771788: done, saving took 0.90 seconds +2026-11-07 23:09:39.773916: This epoch took 249.410628 s + +2026-11-07 23:09:39.774188: +epoch: 127 +2026-11-07 23:13:43.426444: train loss : -0.6896 +2026-11-07 23:13:51.546737: validation loss: -0.4696 +2026-11-07 23:13:51.547936: Average global foreground Dice: [np.float32(0.9013), np.float32(0.5038), np.float32(0.5257), np.float32(0.529), np.float32(0.4201), np.float32(0.5559), np.float32(0.4861), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4017), np.float32(0.4686), np.float32(0.5838), np.float32(0.4353), np.float32(0.0), np.float32(0.0), np.float32(0.3829), np.float32(0.3336), np.float32(0.4164), np.float32(0.4001), np.float32(0.4298), np.float32(0.4068), np.float32(0.6469), np.float32(0.6233), np.float32(0.278), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6307), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5833), np.float32(0.6136)] +2026-11-07 23:13:51.548454: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:13:51.859810: lr: 0.000884 +2026-11-07 23:13:51.860214: This epoch took 252.085936 s + +2026-11-07 23:13:51.860423: +epoch: 128 +2026-11-07 23:17:53.045207: train loss : -0.7025 +2026-11-07 23:18:01.162939: validation loss: -0.5128 +2026-11-07 23:18:01.163899: Average global foreground Dice: [np.float32(0.9046), np.float32(0.5953), np.float32(0.5904), np.float32(0.6176), np.float32(0.5183), np.float32(0.6651), np.float32(0.5582), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5004), np.float32(0.5462), np.float32(0.6001), np.float32(0.517), np.float32(0.0), np.float32(0.0), np.float32(0.4665), np.float32(0.4037), np.float32(0.4923), np.float32(0.4754), np.float32(0.5162), np.float32(0.4943), np.float32(0.7371), np.float32(0.691), np.float32(0.297), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.596), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.656), np.float32(0.689)] +2026-11-07 23:18:01.164417: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:18:01.484509: lr: 0.000883 +2026-11-07 23:18:01.484787: This epoch took 249.624275 s + +2026-11-07 23:18:01.484881: +epoch: 129 +2026-11-07 23:21:55.716665: train loss : -0.6949 +2026-11-07 23:22:03.832256: validation loss: -0.5381 +2026-11-07 23:22:03.833247: Average global foreground Dice: [np.float32(0.8893), np.float32(0.6235), np.float32(0.7232), np.float32(0.7744), np.float32(0.6976), np.float32(0.7607), np.float32(0.6831), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5626), np.float32(0.5855), np.float32(0.6206), np.float32(0.4023), np.float32(0.0), np.float32(0.0), np.float32(0.7111), np.float32(0.548), np.float32(0.7229), np.float32(0.6302), np.float32(0.111), np.float32(0.5162), np.float32(0.8235), np.float32(0.8056), np.float32(0.517), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6274), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8039), np.float32(0.8092)] +2026-11-07 23:22:03.833423: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:22:04.138950: lr: 0.000882 +2026-11-07 23:22:04.139205: This epoch took 242.654257 s + +2026-11-07 23:22:04.139400: +epoch: 130 +2026-11-07 23:25:56.275896: train loss : -0.6671 +2026-11-07 23:26:04.382518: validation loss: -0.4875 +2026-11-07 23:26:04.383738: Average global foreground Dice: [np.float32(0.8953), np.float32(0.5988), np.float32(0.6273), np.float32(0.5775), np.float32(0.4929), np.float32(0.6459), np.float32(0.5483), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4645), np.float32(0.5391), np.float32(0.5072), np.float32(0.4315), np.float32(0.0), np.float32(0.0), np.float32(0.4292), np.float32(0.3724), np.float32(0.5253), np.float32(0.486), np.float32(0.4067), np.float32(0.5684), np.float32(0.7539), np.float32(0.7303), np.float32(0.3665), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4448), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6525), np.float32(0.7106)] +2026-11-07 23:26:04.384242: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:26:04.697388: lr: 0.000881 +2026-11-07 23:26:04.697618: This epoch took 240.558019 s + +2026-11-07 23:26:04.697733: +epoch: 131 +2026-11-07 23:30:02.001438: train loss : -0.6704 +2026-11-07 23:30:10.137715: validation loss: -0.3793 +2026-11-07 23:30:10.138787: Average global foreground Dice: [np.float32(0.9081), np.float32(0.4584), np.float32(0.3831), np.float32(0.4058), np.float32(0.4749), np.float32(0.4431), np.float32(0.4201), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.294), np.float32(0.3415), np.float32(0.4811), np.float32(0.396), np.float32(0.0), np.float32(0.0), np.float32(0.4166), np.float32(0.3158), np.float32(0.3724), np.float32(0.4016), np.float32(0.0656), np.float32(0.1665), np.float32(0.6019), np.float32(0.5172), np.float32(0.2405), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0463), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4594), np.float32(0.5045)] +2026-11-07 23:30:10.138986: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:30:10.444681: lr: 0.00088 +2026-11-07 23:30:10.444911: This epoch took 245.747008 s + +2026-11-07 23:30:10.444991: +epoch: 132 +2026-11-07 23:34:13.585438: train loss : -0.6625 +2026-11-07 23:34:21.694416: validation loss: -0.5258 +2026-11-07 23:34:21.695352: Average global foreground Dice: [np.float32(0.8897), np.float32(0.5903), np.float32(0.6283), np.float32(0.7074), np.float32(0.6806), np.float32(0.7542), np.float32(0.684), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.569), np.float32(0.5914), np.float32(0.6232), np.float32(0.5047), np.float32(0.0), np.float32(0.0), np.float32(0.7372), np.float32(0.5899), np.float32(0.7112), np.float32(0.6141), np.float32(0.3921), np.float32(0.3905), np.float32(0.7626), np.float32(0.7289), np.float32(0.4009), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5904), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7074), np.float32(0.7265)] +2026-11-07 23:34:21.695731: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:34:22.005344: lr: 0.000879 +2026-11-07 23:34:22.005572: This epoch took 251.560523 s + +2026-11-07 23:34:22.005652: +epoch: 133 +2026-11-07 23:38:58.573446: train loss : -0.6649 +2026-11-07 23:39:06.718882: validation loss: -0.4357 +2026-11-07 23:39:06.720005: Average global foreground Dice: [np.float32(0.8796), np.float32(0.4335), np.float32(0.473), np.float32(0.4479), np.float32(0.2806), np.float32(0.5278), np.float32(0.4214), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3628), np.float32(0.4394), np.float32(0.521), np.float32(0.4715), np.float32(0.0), np.float32(0.0), np.float32(0.2168), np.float32(0.2024), np.float32(0.3201), np.float32(0.323), np.float32(0.3962), np.float32(0.3519), np.float32(0.5727), np.float32(0.5584), np.float32(0.1722), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4792), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4895), np.float32(0.5779)] +2026-11-07 23:39:06.720218: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:39:07.052501: lr: 0.000879 +2026-11-07 23:39:07.052939: This epoch took 285.047164 s + +2026-11-07 23:39:07.053120: +epoch: 134 +2026-11-07 23:42:58.768688: train loss : -0.6901 +2026-11-07 23:43:06.906811: validation loss: -0.4629 +2026-11-07 23:43:06.907899: Average global foreground Dice: [np.float32(0.8873), np.float32(0.5101), np.float32(0.4691), np.float32(0.538), np.float32(0.4313), np.float32(0.5842), np.float32(0.4707), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4331), np.float32(0.4996), np.float32(0.5308), np.float32(0.468), np.float32(0.0), np.float32(0.0), np.float32(0.4091), np.float32(0.3856), np.float32(0.4124), np.float32(0.4024), np.float32(0.3877), np.float32(0.2685), np.float32(0.6714), np.float32(0.6202), np.float32(0.2521), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6298), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5629), np.float32(0.6148)] +2026-11-07 23:43:06.908417: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:43:07.218149: lr: 0.000878 +2026-11-07 23:43:07.218370: This epoch took 240.165040 s + +2026-11-07 23:43:07.218438: +epoch: 135 +2026-11-07 23:47:12.824703: train loss : -0.6707 +2026-11-07 23:47:20.948867: validation loss: -0.5608 +2026-11-07 23:47:20.949978: Average global foreground Dice: [np.float32(0.9061), np.float32(0.6657), np.float32(0.7593), np.float32(0.7653), np.float32(0.7205), np.float32(0.8227), np.float32(0.73), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.511), np.float32(0.658), np.float32(0.5552), np.float32(0.4806), np.float32(0.0), np.float32(0.0), np.float32(0.7642), np.float32(0.608), np.float32(0.7249), np.float32(0.6386), np.float32(0.4934), np.float32(0.548), np.float32(0.8643), np.float32(0.8562), np.float32(0.5125), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6453), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8145), np.float32(0.8423)] +2026-11-07 23:47:20.950390: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:47:21.260801: lr: 0.000877 +2026-11-07 23:47:21.261013: This epoch took 254.042521 s + +2026-11-07 23:47:21.261089: +epoch: 136 +2026-11-07 23:51:30.550544: train loss : -0.6905 +2026-11-07 23:51:38.662110: validation loss: -0.5688 +2026-11-07 23:51:38.663225: Average global foreground Dice: [np.float32(0.9031), np.float32(0.6435), np.float32(0.739), np.float32(0.8037), np.float32(0.7074), np.float32(0.8433), np.float32(0.7542), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5941), np.float32(0.6617), np.float32(0.5546), np.float32(0.4582), np.float32(0.0), np.float32(0.0), np.float32(0.7594), np.float32(0.6103), np.float32(0.7338), np.float32(0.6466), np.float32(0.552), np.float32(0.6167), np.float32(0.8293), np.float32(0.8016), np.float32(0.5068), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5021), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8246), np.float32(0.8399)] +2026-11-07 23:51:38.663627: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:51:38.965964: lr: 0.000876 +2026-11-07 23:51:38.966242: This epoch took 257.705097 s + +2026-11-07 23:51:38.966423: +epoch: 137 +2026-11-07 23:55:42.730895: train loss : -0.6916 +2026-11-07 23:55:50.832858: validation loss: -0.5376 +2026-11-07 23:55:50.833969: Average global foreground Dice: [np.float32(0.8957), np.float32(0.5712), np.float32(0.7108), np.float32(0.731), np.float32(0.6529), np.float32(0.7793), np.float32(0.7086), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.598), np.float32(0.5803), np.float32(0.5255), np.float32(0.3987), np.float32(0.0), np.float32(0.0), np.float32(0.7419), np.float32(0.5756), np.float32(0.7285), np.float32(0.5954), np.float32(0.4892), np.float32(0.5202), np.float32(0.7928), np.float32(0.7393), np.float32(0.5074), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.585), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7714), np.float32(0.7984)] +2026-11-07 23:55:50.834402: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-07 23:55:51.140776: lr: 0.000875 +2026-11-07 23:55:51.141333: This epoch took 252.174836 s + +2026-11-07 23:55:51.141571: +epoch: 138 +2026-11-07 23:59:54.334900: train loss : -0.6998 +2026-11-08 00:00:02.426520: validation loss: -0.5400 +2026-11-08 00:00:02.427480: Average global foreground Dice: [np.float32(0.9178), np.float32(0.6101), np.float32(0.6911), np.float32(0.6929), np.float32(0.6188), np.float32(0.7399), np.float32(0.6508), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5696), np.float32(0.5958), np.float32(0.5974), np.float32(0.4655), np.float32(0.0), np.float32(0.0), np.float32(0.6644), np.float32(0.5338), np.float32(0.6532), np.float32(0.5935), np.float32(0.4927), np.float32(0.4956), np.float32(0.7781), np.float32(0.7258), np.float32(0.4256), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6701), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7433), np.float32(0.7722)] +2026-11-08 00:00:02.427656: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:00:02.732468: lr: 0.000874 +2026-11-08 00:00:02.732732: This epoch took 251.591037 s + +2026-11-08 00:00:02.732828: +epoch: 139 +2026-11-08 00:04:15.624618: train loss : -0.6978 +2026-11-08 00:04:23.758339: validation loss: -0.5449 +2026-11-08 00:04:23.759484: Average global foreground Dice: [np.float32(0.9148), np.float32(0.6049), np.float32(0.701), np.float32(0.6973), np.float32(0.6073), np.float32(0.748), np.float32(0.6539), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5801), np.float32(0.6103), np.float32(0.5635), np.float32(0.5199), np.float32(0.0), np.float32(0.0), np.float32(0.652), np.float32(0.5344), np.float32(0.6487), np.float32(0.5983), np.float32(0.5165), np.float32(0.5208), np.float32(0.8175), np.float32(0.7942), np.float32(0.4738), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6191), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7662), np.float32(0.7883)] +2026-11-08 00:04:23.760041: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:04:24.067263: lr: 0.000873 +2026-11-08 00:04:24.067497: This epoch took 261.334603 s + +2026-11-08 00:04:24.067577: +epoch: 140 +2026-11-08 00:08:04.114200: train loss : -0.7083 +2026-11-08 00:08:12.217424: validation loss: -0.5162 +2026-11-08 00:08:12.218399: Average global foreground Dice: [np.float32(0.9128), np.float32(0.5497), np.float32(0.6812), np.float32(0.6891), np.float32(0.6147), np.float32(0.7297), np.float32(0.6354), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5338), np.float32(0.586), np.float32(0.5201), np.float32(0.3911), np.float32(0.0), np.float32(0.0), np.float32(0.7014), np.float32(0.5305), np.float32(0.6843), np.float32(0.6154), np.float32(0.4601), np.float32(0.5434), np.float32(0.7532), np.float32(0.7362), np.float32(0.4785), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6033), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7239), np.float32(0.7564)] +2026-11-08 00:08:12.218554: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:08:12.526527: lr: 0.000872 +2026-11-08 00:08:12.526819: This epoch took 228.459185 s + +2026-11-08 00:08:12.526917: +epoch: 141 +2026-11-08 00:12:28.094705: train loss : -0.6839 +2026-11-08 00:12:36.203985: validation loss: -0.4522 +2026-11-08 00:12:36.205148: Average global foreground Dice: [np.float32(0.8942), np.float32(0.4677), np.float32(0.4918), np.float32(0.5276), np.float32(0.4965), np.float32(0.5647), np.float32(0.4911), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3969), np.float32(0.4894), np.float32(0.511), np.float32(0.4845), np.float32(0.0), np.float32(0.0), np.float32(0.4664), np.float32(0.3943), np.float32(0.4782), np.float32(0.4422), np.float32(0.2808), np.float32(0.2892), np.float32(0.6667), np.float32(0.5956), np.float32(0.2579), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6166), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5744), np.float32(0.5932)] +2026-11-08 00:12:36.205450: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:12:36.520194: lr: 0.000871 +2026-11-08 00:12:36.520638: This epoch took 263.993649 s + +2026-11-08 00:12:36.520845: +epoch: 142 +2026-11-08 00:17:14.243966: train loss : -0.6728 +2026-11-08 00:17:22.388710: validation loss: -0.5456 +2026-11-08 00:17:22.389615: Average global foreground Dice: [np.float32(0.9111), np.float32(0.6338), np.float32(0.72), np.float32(0.7264), np.float32(0.6567), np.float32(0.7865), np.float32(0.6591), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5384), np.float32(0.6302), np.float32(0.6066), np.float32(0.4902), np.float32(0.0), np.float32(0.0), np.float32(0.728), np.float32(0.5669), np.float32(0.7005), np.float32(0.5994), np.float32(0.5093), np.float32(0.5106), np.float32(0.82), np.float32(0.789), np.float32(0.4185), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5764), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7744), np.float32(0.7986)] +2026-11-08 00:17:22.389768: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:17:22.693991: lr: 0.00087 +2026-11-08 00:17:22.694227: This epoch took 286.173284 s + +2026-11-08 00:17:22.694306: +epoch: 143 +2026-11-08 00:21:24.733522: train loss : -0.6917 +2026-11-08 00:21:32.859850: validation loss: -0.5487 +2026-11-08 00:21:32.860836: Average global foreground Dice: [np.float32(0.9124), np.float32(0.6527), np.float32(0.7457), np.float32(0.7559), np.float32(0.6884), np.float32(0.8022), np.float32(0.6792), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5914), np.float32(0.6234), np.float32(0.5782), np.float32(0.5344), np.float32(0.0), np.float32(0.0), np.float32(0.7442), np.float32(0.5984), np.float32(0.7092), np.float32(0.6275), np.float32(0.5232), np.float32(0.5126), np.float32(0.8162), np.float32(0.8009), np.float32(0.4442), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5661), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7895), np.float32(0.8154)] +2026-11-08 00:21:32.861013: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:21:33.172909: lr: 0.000869 +2026-11-08 00:21:33.173563: This epoch took 250.479199 s + +2026-11-08 00:21:33.173776: +epoch: 144 +2026-11-08 00:25:38.068101: train loss : -0.6368 +2026-11-08 00:25:46.228391: validation loss: -0.4306 +2026-11-08 00:25:46.229319: Average global foreground Dice: [np.float32(0.8881), np.float32(0.4538), np.float32(0.4688), np.float32(0.4611), np.float32(0.3753), np.float32(0.5221), np.float32(0.4714), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3322), np.float32(0.4043), np.float32(0.5131), np.float32(0.4014), np.float32(0.0), np.float32(0.0), np.float32(0.3968), np.float32(0.3489), np.float32(0.4248), np.float32(0.3716), np.float32(0.4174), np.float32(0.3776), np.float32(0.6704), np.float32(0.6201), np.float32(0.2414), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5296), np.float32(0.5724)] +2026-11-08 00:25:46.229475: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:25:46.535684: lr: 0.000868 +2026-11-08 00:25:46.536090: This epoch took 253.362232 s + +2026-11-08 00:25:46.536309: +epoch: 145 +2026-11-08 00:29:58.262534: train loss : -0.6599 +2026-11-08 00:30:06.354741: validation loss: -0.3717 +2026-11-08 00:30:06.355694: Average global foreground Dice: [np.float32(0.8862), np.float32(0.4256), np.float32(0.3528), np.float32(0.3915), np.float32(0.368), np.float32(0.4352), np.float32(0.3767), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2791), np.float32(0.351), np.float32(0.4084), np.float32(0.3828), np.float32(0.0), np.float32(0.0), np.float32(0.3323), np.float32(0.3074), np.float32(0.3092), np.float32(0.2606), np.float32(0.1829), np.float32(0.1909), np.float32(0.5736), np.float32(0.473), np.float32(0.2055), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1369), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4274), np.float32(0.4668)] +2026-11-08 00:30:06.355975: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:30:06.661771: lr: 0.000868 +2026-11-08 00:30:06.662135: This epoch took 260.125718 s + +2026-11-08 00:30:06.662356: +epoch: 146 +2026-11-08 00:34:14.337683: train loss : -0.6828 +2026-11-08 00:34:22.481069: validation loss: -0.5564 +2026-11-08 00:34:22.482195: Average global foreground Dice: [np.float32(0.906), np.float32(0.6289), np.float32(0.7212), np.float32(0.7657), np.float32(0.6787), np.float32(0.8067), np.float32(0.6946), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6153), np.float32(0.6606), np.float32(0.5486), np.float32(0.4737), np.float32(0.0), np.float32(0.0), np.float32(0.7509), np.float32(0.6037), np.float32(0.7164), np.float32(0.6299), np.float32(0.5446), np.float32(0.5638), np.float32(0.8189), np.float32(0.7989), np.float32(0.5187), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6199), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8062), np.float32(0.8222)] +2026-11-08 00:34:22.482454: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:34:22.802998: lr: 0.000867 +2026-11-08 00:34:22.803336: This epoch took 256.140899 s + +2026-11-08 00:34:22.803661: +epoch: 147 +2026-11-08 00:38:14.127847: train loss : -0.6921 +2026-11-08 00:38:22.228864: validation loss: -0.5403 +2026-11-08 00:38:22.229755: Average global foreground Dice: [np.float32(0.9145), np.float32(0.6216), np.float32(0.7161), np.float32(0.7808), np.float32(0.6971), np.float32(0.8077), np.float32(0.6956), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5899), np.float32(0.5986), np.float32(0.564), np.float32(0.538), np.float32(0.0), np.float32(0.0), np.float32(0.738), np.float32(0.5831), np.float32(0.714), np.float32(0.5935), np.float32(0.3976), np.float32(0.498), np.float32(0.7972), np.float32(0.7848), np.float32(0.5065), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4752), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8074), np.float32(0.8222)] +2026-11-08 00:38:22.229918: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:38:22.584956: lr: 0.000866 +2026-11-08 00:38:22.585195: This epoch took 239.781428 s + +2026-11-08 00:38:22.585420: +epoch: 148 +2026-11-08 00:41:43.619550: train loss : -0.6946 +2026-11-08 00:41:51.696074: validation loss: -0.3305 +2026-11-08 00:41:51.696962: Average global foreground Dice: [np.float32(0.8891), np.float32(0.3173), np.float32(0.2568), np.float32(0.2318), np.float32(0.2237), np.float32(0.2868), np.float32(0.2266), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2142), np.float32(0.2932), np.float32(0.3532), np.float32(0.3029), np.float32(0.0), np.float32(0.0), np.float32(0.2233), np.float32(0.2234), np.float32(0.207), np.float32(0.274), np.float32(0.2422), np.float32(0.0287), np.float32(0.4993), np.float32(0.3854), np.float32(0.0944), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4387), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0003), np.float32(0.3273), np.float32(0.326)] +2026-11-08 00:41:51.697098: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:41:52.026907: lr: 0.000865 +2026-11-08 00:41:52.027237: This epoch took 209.441731 s + +2026-11-08 00:41:52.027470: +epoch: 149 +2026-11-08 00:45:15.374653: train loss : -0.6765 +2026-11-08 00:45:23.460213: validation loss: -0.4745 +2026-11-08 00:45:23.461189: Average global foreground Dice: [np.float32(0.9095), np.float32(0.5309), np.float32(0.5169), np.float32(0.5349), np.float32(0.4279), np.float32(0.5958), np.float32(0.4938), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4214), np.float32(0.5154), np.float32(0.5254), np.float32(0.4806), np.float32(0.0), np.float32(0.0), np.float32(0.3911), np.float32(0.3378), np.float32(0.4641), np.float32(0.3938), np.float32(0.438), np.float32(0.3138), np.float32(0.7282), np.float32(0.6963), np.float32(0.2508), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6138), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3168), np.float32(0.5936), np.float32(0.6433)] +2026-11-08 00:45:23.461485: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:45:23.778555: lr: 0.000864 +2026-11-08 00:45:23.778764: saving scheduled checkpoint file... +2026-11-08 00:45:23.802916: saving checkpoint... +2026-11-08 00:45:24.334625: done, saving took 0.56 seconds +2026-11-08 00:45:24.336704: done +2026-11-08 00:45:24.336862: This epoch took 212.309299 s + +2026-11-08 00:45:24.336930: +epoch: 150 +2026-11-08 00:49:00.609138: train loss : -0.6807 +2026-11-08 00:49:08.721565: validation loss: -0.5426 +2026-11-08 00:49:08.722542: Average global foreground Dice: [np.float32(0.8968), np.float32(0.6136), np.float32(0.6777), np.float32(0.7229), np.float32(0.6281), np.float32(0.7692), np.float32(0.6761), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.526), np.float32(0.5948), np.float32(0.5905), np.float32(0.4963), np.float32(0.0), np.float32(0.0), np.float32(0.7146), np.float32(0.5403), np.float32(0.6747), np.float32(0.594), np.float32(0.4845), np.float32(0.4924), np.float32(0.787), np.float32(0.7356), np.float32(0.3931), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6131), np.float32(0.0), np.float32(0.0), np.float32(0.3336), np.float32(0.4097), np.float32(0.7472), np.float32(0.7577)] +2026-11-08 00:49:08.722844: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:49:09.077307: lr: 0.000863 +2026-11-08 00:49:09.077574: This epoch took 224.740522 s + +2026-11-08 00:49:09.077654: +epoch: 151 +2026-11-08 00:53:02.575773: train loss : -0.7074 +2026-11-08 00:53:10.783151: validation loss: -0.3600 +2026-11-08 00:53:10.784096: Average global foreground Dice: [np.float32(0.886), np.float32(0.3663), np.float32(0.3758), np.float32(0.2851), np.float32(0.346), np.float32(0.4083), np.float32(0.3787), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.225), np.float32(0.3459), np.float32(0.3954), np.float32(0.3372), np.float32(0.0), np.float32(0.0), np.float32(0.2958), np.float32(0.2693), np.float32(0.3189), np.float32(0.312), np.float32(0.2539), np.float32(0.2464), np.float32(0.612), np.float32(0.4519), np.float32(0.1345), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4034), np.float32(0.0), np.float32(0.0), np.float32(0.1319), np.float32(0.0616), np.float32(0.3718), np.float32(0.3998)] +2026-11-08 00:53:10.784267: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:53:11.161405: lr: 0.000862 +2026-11-08 00:53:11.161678: This epoch took 242.083970 s + +2026-11-08 00:53:11.161784: +epoch: 152 +2026-11-08 00:57:53.185445: train loss : -0.7136 +2026-11-08 00:58:01.427711: validation loss: -0.5636 +2026-11-08 00:58:01.428903: Average global foreground Dice: [np.float32(0.9141), np.float32(0.6457), np.float32(0.7288), np.float32(0.7532), np.float32(0.6982), np.float32(0.7911), np.float32(0.7209), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5473), np.float32(0.6104), np.float32(0.579), np.float32(0.466), np.float32(0.0), np.float32(0.0), np.float32(0.7545), np.float32(0.6199), np.float32(0.7177), np.float32(0.6324), np.float32(0.4773), np.float32(0.5431), np.float32(0.828), np.float32(0.8184), np.float32(0.4628), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5967), np.float32(0.0), np.float32(0.0), np.float32(0.5052), np.float32(0.5585), np.float32(0.8166), np.float32(0.8357)] +2026-11-08 00:58:01.429128: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 00:58:01.797966: lr: 0.000861 +2026-11-08 00:58:01.798578: This epoch took 290.636725 s + +2026-11-08 00:58:01.798712: +epoch: 153 +2026-11-08 01:02:03.082705: train loss : -0.7197 +2026-11-08 01:02:11.219410: validation loss: -0.5550 +2026-11-08 01:02:11.220333: Average global foreground Dice: [np.float32(0.9261), np.float32(0.595), np.float32(0.7066), np.float32(0.7772), np.float32(0.721), np.float32(0.8187), np.float32(0.7234), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.539), np.float32(0.6131), np.float32(0.5638), np.float32(0.4276), np.float32(0.0), np.float32(0.0), np.float32(0.7688), np.float32(0.6139), np.float32(0.7472), np.float32(0.6292), np.float32(0.5066), np.float32(0.609), np.float32(0.8196), np.float32(0.8072), np.float32(0.4176), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5766), np.float32(0.0), np.float32(0.0), np.float32(0.5114), np.float32(0.5025), np.float32(0.8142), np.float32(0.8336)] +2026-11-08 01:02:11.220515: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 01:02:11.537056: lr: 0.00086 +2026-11-08 01:02:11.537286: This epoch took 249.738496 s + +2026-11-08 01:02:11.537365: +epoch: 154 +2026-11-08 01:05:36.421265: train loss : -0.7183 +2026-11-08 01:05:44.516699: validation loss: -0.5275 +2026-11-08 01:05:44.517678: Average global foreground Dice: [np.float32(0.9123), np.float32(0.5989), np.float32(0.6749), np.float32(0.6821), np.float32(0.598), np.float32(0.7584), np.float32(0.6207), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5241), np.float32(0.5629), np.float32(0.483), np.float32(0.4773), np.float32(0.0), np.float32(0.0), np.float32(0.6775), np.float32(0.4544), np.float32(0.6813), np.float32(0.5495), np.float32(0.5379), np.float32(0.5111), np.float32(0.7477), np.float32(0.7238), np.float32(0.4681), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6381), np.float32(0.0), np.float32(0.0), np.float32(0.3424), np.float32(0.1601), np.float32(0.7208), np.float32(0.758)] +2026-11-08 01:05:44.518008: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 01:05:44.828491: lr: 0.000859 +2026-11-08 01:05:44.828847: This epoch took 213.291429 s + +2026-11-08 01:05:44.829105: +epoch: 155 +2026-11-08 01:08:48.425722: train loss : -0.7216 +2026-11-08 01:08:56.543947: validation loss: -0.5723 +2026-11-08 01:08:56.544811: Average global foreground Dice: [np.float32(0.9216), np.float32(0.6011), np.float32(0.743), np.float32(0.7987), np.float32(0.7205), np.float32(0.8403), np.float32(0.7098), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5847), np.float32(0.5889), np.float32(0.5657), np.float32(0.4579), np.float32(0.0), np.float32(0.0), np.float32(0.7552), np.float32(0.5534), np.float32(0.7361), np.float32(0.652), np.float32(0.4761), np.float32(0.555), np.float32(0.8105), np.float32(0.811), np.float32(0.5039), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6454), np.float32(0.0), np.float32(0.0), np.float32(0.5478), np.float32(0.6186), np.float32(0.8318), np.float32(0.8354)] +2026-11-08 01:08:56.544987: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 01:08:56.857345: lr: 0.000858 +2026-11-08 01:08:56.857565: This epoch took 192.028266 s + +2026-11-08 01:08:56.857633: +epoch: 156 +2026-11-08 01:12:41.277463: train loss : -0.7234 +2026-11-08 01:12:49.429908: validation loss: -0.5767 +2026-11-08 01:12:49.430930: Average global foreground Dice: [np.float32(0.9079), np.float32(0.6401), np.float32(0.7206), np.float32(0.7659), np.float32(0.684), np.float32(0.814), np.float32(0.686), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5917), np.float32(0.633), np.float32(0.567), np.float32(0.505), np.float32(0.0), np.float32(0.0), np.float32(0.7149), np.float32(0.5526), np.float32(0.7037), np.float32(0.6148), np.float32(0.5388), np.float32(0.5635), np.float32(0.8489), np.float32(0.8291), np.float32(0.4214), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.615), np.float32(0.0), np.float32(0.0), np.float32(0.5044), np.float32(0.5187), np.float32(0.8022), np.float32(0.8192)] +2026-11-08 01:12:49.431222: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 01:12:49.783513: lr: 0.000858 +2026-11-08 01:12:49.783775: This epoch took 232.926089 s + +2026-11-08 01:12:49.783961: +epoch: 157 +2026-11-08 01:16:42.303727: train loss : -0.7278 +2026-11-08 01:16:50.414345: validation loss: -0.5435 +2026-11-08 01:16:50.415414: Average global foreground Dice: [np.float32(0.9064), np.float32(0.5816), np.float32(0.6917), np.float32(0.726), np.float32(0.6521), np.float32(0.7721), np.float32(0.6574), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4986), np.float32(0.5809), np.float32(0.5411), np.float32(0.4205), np.float32(0.0), np.float32(0.0), np.float32(0.7102), np.float32(0.5234), np.float32(0.6951), np.float32(0.6171), np.float32(0.4789), np.float32(0.6045), np.float32(0.8219), np.float32(0.7878), np.float32(0.4924), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.593), np.float32(0.0), np.float32(0.0), np.float32(0.4888), np.float32(0.4876), np.float32(0.756), np.float32(0.7773)] +2026-11-08 01:16:50.415804: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 01:16:50.733309: lr: 0.000857 +2026-11-08 01:16:50.756961: saving checkpoint... +2026-11-08 01:16:51.290270: done, saving took 0.56 seconds +2026-11-08 01:16:51.292198: This epoch took 241.508140 s + +2026-11-08 01:16:51.292366: +epoch: 158 +2026-11-08 01:20:58.845249: train loss : -0.7302 +2026-11-08 01:21:06.953732: validation loss: -0.5985 +2026-11-08 01:21:06.955102: Average global foreground Dice: [np.float32(0.9218), np.float32(0.6516), np.float32(0.7516), np.float32(0.8292), np.float32(0.772), np.float32(0.8604), np.float32(0.7729), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6233), np.float32(0.6452), np.float32(0.5952), np.float32(0.5485), np.float32(0.0), np.float32(0.0), np.float32(0.7911), np.float32(0.609), np.float32(0.7504), np.float32(0.6705), np.float32(0.4976), np.float32(0.5895), np.float32(0.8839), np.float32(0.8747), np.float32(0.4987), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.084), np.float32(0.5226), np.float32(0.0), np.float32(0.0), np.float32(0.6076), np.float32(0.6324), np.float32(0.8536), np.float32(0.8671)] +2026-11-08 01:21:06.955335: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 01:21:07.271624: lr: 0.000856 +2026-11-08 01:21:07.296119: saving checkpoint... +2026-11-08 01:21:07.818315: done, saving took 0.55 seconds +2026-11-08 01:21:07.820120: This epoch took 256.527681 s + +2026-11-08 01:21:07.820289: +epoch: 159 +2026-11-08 01:24:48.162234: train loss : -0.7238 +2026-11-08 01:24:56.291388: validation loss: -0.5241 +2026-11-08 01:24:56.292432: Average global foreground Dice: [np.float32(0.9026), np.float32(0.5512), np.float32(0.6234), np.float32(0.6951), np.float32(0.5938), np.float32(0.7458), np.float32(0.624), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.573), np.float32(0.5983), np.float32(0.58), np.float32(0.4765), np.float32(0.0), np.float32(0.0), np.float32(0.6549), np.float32(0.4682), np.float32(0.6567), np.float32(0.5514), np.float32(0.4137), np.float32(0.3788), np.float32(0.7738), np.float32(0.7177), np.float32(0.4103), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0241), np.float32(0.1608), np.float32(0.0), np.float32(0.0), np.float32(0.1771), np.float32(0.229), np.float32(0.7235), np.float32(0.7436)] +2026-11-08 01:24:56.292704: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 01:24:56.617545: lr: 0.000855 +2026-11-08 01:24:56.617810: This epoch took 228.797445 s + +2026-11-08 01:24:56.617909: +epoch: 160 +2026-11-08 01:29:18.834675: train loss : -0.7180 +2026-11-08 01:29:26.994314: validation loss: -0.4681 +2026-11-08 01:29:26.995378: Average global foreground Dice: [np.float32(0.8932), np.float32(0.509), np.float32(0.4997), np.float32(0.5209), np.float32(0.4402), np.float32(0.5898), np.float32(0.4835), np.float32(0.0131), np.float32(0.0), np.float32(0.0), np.float32(0.4213), np.float32(0.4975), np.float32(0.526), np.float32(0.4555), np.float32(0.0), np.float32(0.0), np.float32(0.3572), np.float32(0.3136), np.float32(0.3895), np.float32(0.3849), np.float32(0.3749), np.float32(0.3674), np.float32(0.6321), np.float32(0.5787), np.float32(0.2096), np.float32(0.1723), np.float32(0.0), np.float32(0.0), np.float32(0.0996), np.float32(0.6029), np.float32(0.0), np.float32(0.0), np.float32(0.4075), np.float32(0.4872), np.float32(0.5526), np.float32(0.5782)] +2026-11-08 01:29:26.995738: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 01:29:27.307192: lr: 0.000854 +2026-11-08 01:29:27.307451: This epoch took 270.689247 s + +2026-11-08 01:29:27.307549: +epoch: 161 +2026-11-08 01:33:07.587696: train loss : -0.7372 +2026-11-08 01:33:15.731727: validation loss: -0.5791 +2026-11-08 01:33:15.732984: Average global foreground Dice: [np.float32(0.9204), np.float32(0.6358), np.float32(0.74), np.float32(0.7898), np.float32(0.6853), np.float32(0.8254), np.float32(0.7041), np.float32(0.1388), np.float32(0.0), np.float32(0.0), np.float32(0.5984), np.float32(0.6199), np.float32(0.5721), np.float32(0.4519), np.float32(0.0), np.float32(0.0), np.float32(0.7345), np.float32(0.5652), np.float32(0.7187), np.float32(0.6635), np.float32(0.5571), np.float32(0.5952), np.float32(0.8047), np.float32(0.8098), np.float32(0.5375), np.float32(0.3705), np.float32(0.0), np.float32(0.0), np.float32(0.2237), np.float32(0.6143), np.float32(0.0), np.float32(0.0), np.float32(0.5218), np.float32(0.622), np.float32(0.8075), np.float32(0.8245)] +2026-11-08 01:33:15.733505: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 01:33:16.054258: lr: 0.000853 +2026-11-08 01:33:16.078871: saving checkpoint... +2026-11-08 01:33:18.722817: done, saving took 2.67 seconds +2026-11-08 01:33:18.724857: This epoch took 231.417230 s + +2026-11-08 01:33:18.724990: +epoch: 162 +2026-11-08 01:37:16.420299: train loss : -0.7439 +2026-11-08 01:37:24.566638: validation loss: -0.5856 +2026-11-08 01:37:24.567509: Average global foreground Dice: [np.float32(0.9194), np.float32(0.5984), np.float32(0.7322), np.float32(0.7436), np.float32(0.6595), np.float32(0.8056), np.float32(0.7142), np.float32(0.5943), np.float32(0.0), np.float32(0.0), np.float32(0.563), np.float32(0.632), np.float32(0.5808), np.float32(0.4912), np.float32(0.0), np.float32(0.0), np.float32(0.7344), np.float32(0.5448), np.float32(0.7263), np.float32(0.649), np.float32(0.5372), np.float32(0.5826), np.float32(0.8216), np.float32(0.8006), np.float32(0.5231), np.float32(0.3473), np.float32(0.0), np.float32(0.0), np.float32(0.2831), np.float32(0.6813), np.float32(0.0), np.float32(0.0), np.float32(0.5908), np.float32(0.6345), np.float32(0.7794), np.float32(0.8047)] +2026-11-08 01:37:24.567823: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 01:37:24.885746: lr: 0.000852 +2026-11-08 01:37:24.911138: saving checkpoint... +2026-11-08 01:37:25.436564: done, saving took 0.55 seconds +2026-11-08 01:37:25.439147: This epoch took 246.713985 s + +2026-11-08 01:37:25.439348: +epoch: 163 +2026-11-08 01:41:29.096492: train loss : -0.7454 +2026-11-08 01:41:37.232954: validation loss: -0.5713 +2026-11-08 01:41:37.233973: Average global foreground Dice: [np.float32(0.8941), np.float32(0.6035), np.float32(0.7196), np.float32(0.7388), np.float32(0.673), np.float32(0.7941), np.float32(0.6987), np.float32(0.2774), np.float32(0.0), np.float32(0.3698), np.float32(0.5592), np.float32(0.5968), np.float32(0.5857), np.float32(0.4899), np.float32(0.0), np.float32(0.0), np.float32(0.7158), np.float32(0.5516), np.float32(0.69), np.float32(0.6051), np.float32(0.4708), np.float32(0.4877), np.float32(0.7879), np.float32(0.7383), np.float32(0.457), np.float32(0.3842), np.float32(0.0), np.float32(0.0), np.float32(0.2265), np.float32(0.6882), np.float32(0.0), np.float32(0.0), np.float32(0.5681), np.float32(0.641), np.float32(0.7616), np.float32(0.7854)] +2026-11-08 01:41:37.234141: (interpret this as an estimate for the Dice of the different classes. This is not exact.) +2026-11-08 01:41:37.566386: lr: 0.000851 +2026-11-08 01:41:37.594740: saving checkpoint... +2026-11-08 01:41:38.155840: done, saving took 0.59 seconds +2026-11-08 01:41:38.158228: This epoch took 252.718805 s + +2026-11-08 01:41:38.158450: +epoch: 164 diff --git a/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/plans.pkl b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/plans.pkl new file mode 100644 index 0000000000000000000000000000000000000000..9831252072712250dcc3633e9bc7105bb169daef --- /dev/null +++ b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/plans.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19b6f010813fa97c33e26842b697d7196a3f46050bdffe68808c010215b55713 +size 12979