diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..1988b35c43333956b21df59f1bb3ec9cdf2fa97b 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+MedNeXt_Base/fold_0/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
diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json
new file mode 100644
index 0000000000000000000000000000000000000000..85db40ddca0a5b527547dc0fc0d9650a6b98be51
--- /dev/null
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_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), np.float64(0.25), np.float64(0.25)], [np.float64(0.125), np.float64(0.125), np.float64(0.125)], [np.float64(0.0625), np.float64(0.0625), np.float64(0.0625)], [np.float64(0.03125), np.float64(0.03125), np.float64(0.03125)]]",
+    "deterministic": "False",
+    "dl_tr": "<nnunet_mednext.training.dataloading.dataset_loading.DataLoader3D object at 0x7f0bdd3423c0>",
+    "dl_val": "<nnunet_mednext.training.dataloading.dataset_loading.DataLoader3D object at 0x7f0bdd164e10>",
+    "do_dummy_2D_aug": "False",
+    "ds_loss_weights": "[0.51612903 0.25806452 0.12903226 0.06451613 0.03225806 0.        ]",
+    "epoch": "0",
+    "experiment_name": "nnUNetTrainerV2_MedNeXt_B_kernel3",
+    "fold": "0",
+    "folder_with_preprocessed_data": "/marimo/TopBrain2026/nnUNet_preprocessed/Task001_Brain2026/nnUNetData_plans_v2.1_trgSp_1x1x1_stage0",
+    "fp16": "True",
+    "gt_niftis_folder": "/marimo/TopBrain2026/nnUNet_preprocessed/Task001_Brain2026/gt_segmentations",
+    "inference_pad_border_mode": "constant",
+    "inference_pad_kwargs": "{'constant_values': 0}",
+    "init_args": "('/marimo/TopBrain2026/nnUNet_preprocessed/Task001_Brain2026/nnUNetPlansv2.1_trgSp_1x1x1_plans_3D.pkl', 0, '/marimo/TopBrain2026/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1', '/marimo/TopBrain2026/nnUNet_preprocessed/Task001_Brain2026', False, 0, True, False, True)",
+    "initial_lr": "0.001",
+    "log_file": "/marimo/TopBrain2026/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_10_14_17_59_39.txt",
+    "lr_scheduler": "None",
+    "lr_scheduler_eps": "0.001",
+    "lr_scheduler_patience": "30",
+    "lr_threshold": "1e-06",
+    "max_num_epochs": "1000",
+    "min_region_size_per_class": "None",
+    "min_size_per_class": "None",
+    "net_conv_kernel_sizes": "[[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]]",
+    "net_num_pool_op_kernel_sizes": "[[2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]]",
+    "net_pool_per_axis": "[5, 5, 4]",
+    "normalization_schemes": "OrderedDict({0: 'CT'})",
+    "num_batches_per_epoch": "250",
+    "num_classes": "37",
+    "num_input_channels": "1",
+    "num_val_batches_per_epoch": "50",
+    "online_eval_fn": "[]",
+    "online_eval_foreground_dc": "[]",
+    "online_eval_fp": "[]",
+    "online_eval_tp": "[]",
+    "only_keep_largest_connected_component": "None",
+    "optimizer": "AdamW (\nParameter Group 0\n    amsgrad: False\n    betas: (0.9, 0.999)\n    capturable: False\n    decoupled_weight_decay: True\n    differentiable: False\n    eps: 0.0001\n    foreach: None\n    fused: None\n    lr: 0.001\n    maximize: False\n    weight_decay: 3e-05\n)",
+    "output_folder": "/marimo/TopBrain2026/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0",
+    "output_folder_base": "/marimo/TopBrain2026/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1",
+    "oversample_foreground_percent": "0.33",
+    "pad_all_sides": "None",
+    "patch_size": "[128 128 128]",
+    "patience": "50",
+    "pin_memory": "True",
+    "plans_file": "/marimo/TopBrain2026/nnUNet_preprocessed/Task001_Brain2026/nnUNetPlansv2.1_trgSp_1x1x1_plans_3D.pkl",
+    "regions_class_order": "None",
+    "sample_by_frequency": "False",
+    "save_best_checkpoint": "True",
+    "save_every": "50",
+    "save_final_checkpoint": "True",
+    "save_intermediate_checkpoints": "True",
+    "save_latest_only": "True",
+    "stage": "0",
+    "threeD": "True",
+    "tr_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7f0bdd343a10>",
+    "train_loss_MA": "None",
+    "train_loss_MA_alpha": "0.93",
+    "train_loss_MA_eps": "0.0005",
+    "transpose_backward": "[np.int64(0), np.int64(1), np.int64(2)]",
+    "transpose_forward": "[np.int64(0), 1, 2]",
+    "unpack_data": "True",
+    "use_mask_for_norm": "OrderedDict({0: False})",
+    "use_progress_bar": "False",
+    "val_eval_criterion_MA": "None",
+    "val_eval_criterion_alpha": "0.9",
+    "val_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7f0bdd165bd0>",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}
\ No newline at end of file
diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model
new file mode 100644
index 0000000000000000000000000000000000000000..fb6bc9afa1a5ff3bc53e4bd3727d66ce42e4353f
--- /dev/null
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5e4833231da769ea890195f72985ef0a4e9dfa46f798e79288d0fd34f9a0b0d
+size 127055170
diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model.pkl b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..808d080cbac0248b558ec8072677d5ee9cd83ccf
--- /dev/null
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f3fa3a299828dc75cb081015adadea4745f5a06edab5066c21072dca8a8576b
+size 13544
diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model
new file mode 100644
index 0000000000000000000000000000000000000000..c220f229dd92a5971a842a276c55968765f825e8
--- /dev/null
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d307523c8b0a5b40e46fb6ca84ce1894088fb2a5aaf5a213dc39f40b2fcb0cb5
+size 127068464
diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model.pkl b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..808d080cbac0248b558ec8072677d5ee9cd83ccf
--- /dev/null
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f3fa3a299828dc75cb081015adadea4745f5a06edab5066c21072dca8a8576b
+size 13544
diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/progress.png b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/progress.png
new file mode 100644
index 0000000000000000000000000000000000000000..9ce527f993c199da555307b5a3a366667ff3dd90
--- /dev/null
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/progress.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d794ee913d4c24393287e1cb5a7f9f5d4242e4003d06c066d4049acd8bdafba7
+size 590870
diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_10_14_17_59_39.txt b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_10_14_17_59_39.txt
new file mode 100644
index 0000000000000000000000000000000000000000..137437f095a58fbeb8cb574ec0fcda714348bba9
--- /dev/null
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_10_14_17_59_39.txt
@@ -0,0 +1,1712 @@
+Starting... 
+2026-10-14 17:59:39.075278: Creating new 5-fold cross-validation split... 
+2026-10-14 17:59:39.076068: Desired fold for training: 0 
+2026-10-14 17:59:39.076128: This split has 20 training and 5 validation cases. 
+2026-10-14 17:59:39.754614: 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')]) 
+2026-10-14 17:59:39.754983: 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')]) 
+2026-10-14 17:59:41.201739: lr: 0.001 
+2026-10-14 17:59:44.730106: Unable to plot network architecture: 
+2026-10-14 17:59:44.730468: No module named 'hiddenlayer' 
+2026-10-14 17:59:44.730687: 
+printing the network instead:
+ 
+2026-10-14 17:59:44.730882: 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, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(64, 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, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(64, 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, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(96, 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, 192, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(192, 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, 192, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(192, 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, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(256, 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, 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))
+    )
+    (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, 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))
+    )
+  )
+  (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, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(512, 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, 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))
+    )
+    (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, 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))
+    )
+  )
+  (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, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(1024, 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, 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))
+    )
+    (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, 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))
+    )
+  )
+  (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, 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))
+    (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, 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))
+    )
+    (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, 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))
+    )
+  )
+  (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, 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))
+    (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, 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))
+    )
+    (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, 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))
+    )
+  )
+  (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, 384, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(384, 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, 192, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(192, 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, 192, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(192, 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, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(128, 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, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(64, 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, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(64, 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))
+  )
+) 
+2026-10-14 17:59:44.742759: 
+ 
+2026-10-14 17:59:44.743196: 
+epoch:  0 
+2026-10-14 18:04:26.654129: train loss : -0.0070 
+2026-10-14 18:04:32.076790: validation loss: -0.0892 
+2026-10-14 18:04:32.077753: 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(0.0307), np.float32(0.0113)] 
+2026-10-14 18:04:32.078076: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:04:32.409157: lr: 0.000999 
+2026-10-14 18:04:32.409386: This epoch took 287.665794 s
+ 
+2026-10-14 18:04:32.409466: 
+epoch:  1 
+2026-10-14 18:08:23.661861: train loss : -0.1129 
+2026-10-14 18:08:29.002704: validation loss: -0.1214 
+2026-10-14 18:08:29.003797: Average global foreground Dice: [np.float32(0.2431), np.float32(0.0), np.float32(0.0013), np.float32(0.0), np.float32(0.0231), np.float32(0.0), np.float32(0.038), 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(0.0016), np.float32(0.0), np.float32(0.0748), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0584), np.float32(0.0789), 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(0.3485), np.float32(0.2051)] 
+2026-10-14 18:08:29.004395: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:08:29.294241: lr: 0.000998 
+2026-10-14 18:08:29.384632: saving checkpoint... 
+2026-10-14 18:08:29.511432: done, saving took 0.22 seconds 
+2026-10-14 18:08:29.512877: This epoch took 237.103352 s
+ 
+2026-10-14 18:08:29.512961: 
+epoch:  2 
+2026-10-14 18:12:47.058778: train loss : -0.1494 
+2026-10-14 18:12:52.445385: validation loss: -0.1905 
+2026-10-14 18:12:52.446603: Average global foreground Dice: [np.float32(0.8007), np.float32(0.0037), np.float32(0.1047), np.float32(0.3146), np.float32(0.1845), np.float32(0.3942), np.float32(0.3374), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0019), np.float32(0.1216), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2136), np.float32(0.0359), np.float32(0.2689), np.float32(0.0619), np.float32(0.0), np.float32(0.0), np.float32(0.1071), np.float32(0.3525), 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(0.0498), np.float32(0.5176)] 
+2026-10-14 18:12:52.447065: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:12:52.742826: lr: 0.000997 
+2026-10-14 18:12:52.750801: saving checkpoint... 
+2026-10-14 18:12:52.883221: done, saving took 0.14 seconds 
+2026-10-14 18:12:52.884955: This epoch took 263.371940 s
+ 
+2026-10-14 18:12:52.885123: 
+epoch:  3 
+2026-10-14 18:16:51.803208: train loss : -0.2493 
+2026-10-14 18:16:57.156435: validation loss: -0.2720 
+2026-10-14 18:16:57.157627: Average global foreground Dice: [np.float32(0.7271), np.float32(0.2484), np.float32(0.3466), np.float32(0.3368), np.float32(0.4206), np.float32(0.4914), np.float32(0.4547), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0899), np.float32(0.3072), np.float32(0.3456), np.float32(0.0431), np.float32(0.0), np.float32(0.0), np.float32(0.3207), np.float32(0.0711), np.float32(0.2595), np.float32(0.2554), np.float32(0.0), np.float32(0.0457), np.float32(0.1751), np.float32(0.2956), 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(0.3497), np.float32(0.2323)] 
+2026-10-14 18:16:57.157988: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:16:57.435953: lr: 0.000996 
+2026-10-14 18:16:57.460410: saving checkpoint... 
+2026-10-14 18:16:57.594770: done, saving took 0.16 seconds 
+2026-10-14 18:16:57.596456: This epoch took 244.711139 s
+ 
+2026-10-14 18:16:57.596575: 
+epoch:  4 
+2026-10-14 18:21:10.049217: train loss : -0.3654 
+2026-10-14 18:21:15.384619: validation loss: -0.3585 
+2026-10-14 18:21:15.385770: Average global foreground Dice: [np.float32(0.8121), np.float32(0.421), np.float32(0.3314), np.float32(0.4562), np.float32(0.2859), np.float32(0.4276), np.float32(0.2547), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2299), np.float32(0.358), np.float32(0.3821), np.float32(0.1261), np.float32(0.0), np.float32(0.0), np.float32(0.2048), np.float32(0.236), np.float32(0.3927), np.float32(0.2191), np.float32(0.0), np.float32(0.229), np.float32(0.2754), np.float32(0.4328), np.float32(0.0006), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.11), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3512), np.float32(0.1537)] 
+2026-10-14 18:21:15.386263: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:21:15.683782: lr: 0.000995 
+2026-10-14 18:21:15.691788: saving checkpoint... 
+2026-10-14 18:21:15.822492: done, saving took 0.14 seconds 
+2026-10-14 18:21:15.824160: This epoch took 258.227520 s
+ 
+2026-10-14 18:21:15.824355: 
+epoch:  5 
+2026-10-14 18:24:52.846317: train loss : -0.4054 
+2026-10-14 18:24:58.181383: validation loss: -0.3656 
+2026-10-14 18:24:58.182499: Average global foreground Dice: [np.float32(0.8514), np.float32(0.471), np.float32(0.4158), np.float32(0.5209), np.float32(0.3605), np.float32(0.348), np.float32(0.3792), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3868), np.float32(0.4045), np.float32(0.3583), np.float32(0.1376), np.float32(0.0), np.float32(0.0), np.float32(0.068), np.float32(0.0973), np.float32(0.36), np.float32(0.2028), np.float32(0.0), np.float32(0.3336), np.float32(0.4877), np.float32(0.1947), np.float32(0.1841), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1548), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3358), np.float32(0.3148)] 
+2026-10-14 18:24:58.182860: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:24:58.475380: lr: 0.000995 
+2026-10-14 18:24:58.512909: saving checkpoint... 
+2026-10-14 18:24:58.643714: done, saving took 0.17 seconds 
+2026-10-14 18:24:58.645292: This epoch took 222.820806 s
+ 
+2026-10-14 18:24:58.645499: 
+epoch:  6 
+2026-10-14 18:28:59.245060: train loss : -0.4184 
+2026-10-14 18:29:04.592295: validation loss: -0.3837 
+2026-10-14 18:29:04.593451: Average global foreground Dice: [np.float32(0.8605), np.float32(0.3342), np.float32(0.3673), np.float32(0.2846), np.float32(0.4341), np.float32(0.2662), np.float32(0.2646), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1984), np.float32(0.3968), np.float32(0.3556), np.float32(0.2828), np.float32(0.0), np.float32(0.0), np.float32(0.4594), np.float32(0.2761), np.float32(0.382), np.float32(0.1672), np.float32(0.0), np.float32(0.4013), np.float32(0.4979), np.float32(0.42), np.float32(0.2711), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2891), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4613), np.float32(0.421)] 
+2026-10-14 18:29:04.593924: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:29:04.863016: lr: 0.000994 
+2026-10-14 18:29:04.920056: saving checkpoint... 
+2026-10-14 18:29:05.054671: done, saving took 0.19 seconds 
+2026-10-14 18:29:05.056570: This epoch took 246.410990 s
+ 
+2026-10-14 18:29:05.056884: 
+epoch:  7 
+2026-10-14 18:32:41.932259: train loss : -0.4408 
+2026-10-14 18:32:47.270240: validation loss: -0.3713 
+2026-10-14 18:32:47.271232: Average global foreground Dice: [np.float32(0.8125), np.float32(0.3655), np.float32(0.3713), np.float32(0.237), np.float32(0.3797), np.float32(0.2906), np.float32(0.1895), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2761), np.float32(0.3249), np.float32(0.4105), np.float32(0.2331), np.float32(0.0), np.float32(0.0), np.float32(0.1531), np.float32(0.1256), np.float32(0.3087), np.float32(0.1859), np.float32(0.0), np.float32(0.3907), np.float32(0.3618), np.float32(0.4153), np.float32(0.2055), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3151), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3454), np.float32(0.3156)] 
+2026-10-14 18:32:47.271480: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:32:47.543357: lr: 0.000993 
+2026-10-14 18:32:47.609448: saving checkpoint... 
+2026-10-14 18:32:47.740861: done, saving took 0.20 seconds 
+2026-10-14 18:32:47.742503: This epoch took 222.680609 s
+ 
+2026-10-14 18:32:47.742733: 
+epoch:  8 
+2026-10-14 18:36:22.964673: train loss : -0.4538 
+2026-10-14 18:36:28.317353: validation loss: -0.3808 
+2026-10-14 18:36:28.318625: Average global foreground Dice: [np.float32(0.8644), np.float32(0.4113), np.float32(0.262), np.float32(0.3248), np.float32(0.5298), np.float32(0.166), np.float32(0.3492), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2465), np.float32(0.3739), np.float32(0.4735), np.float32(0.2873), np.float32(0.0), np.float32(0.0), np.float32(0.5235), np.float32(0.4045), np.float32(0.3122), np.float32(0.2717), np.float32(0.0), np.float32(0.4282), np.float32(0.3405), np.float32(0.415), np.float32(0.3578), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2741), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4986), np.float32(0.2278)] 
+2026-10-14 18:36:28.318903: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:36:28.609095: lr: 0.000992 
+2026-10-14 18:36:28.641861: saving checkpoint... 
+2026-10-14 18:36:28.776643: done, saving took 0.17 seconds 
+2026-10-14 18:36:28.778322: This epoch took 221.035490 s
+ 
+2026-10-14 18:36:28.778508: 
+epoch:  9 
+2026-10-14 18:40:21.137057: train loss : -0.4668 
+2026-10-14 18:40:26.498519: validation loss: -0.3866 
+2026-10-14 18:40:26.499605: Average global foreground Dice: [np.float32(0.788), np.float32(0.4881), np.float32(0.4659), np.float32(0.5041), np.float32(0.4332), np.float32(0.227), np.float32(0.302), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4035), np.float32(0.2043), np.float32(0.396), np.float32(0.2437), np.float32(0.0), np.float32(0.0), np.float32(0.5456), np.float32(0.4565), np.float32(0.3052), np.float32(0.3078), np.float32(0.0), np.float32(0.369), np.float32(0.4374), np.float32(0.2262), np.float32(0.3538), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2235), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5558), np.float32(0.261)] 
+2026-10-14 18:40:26.500126: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:40:26.760792: lr: 0.000991 
+2026-10-14 18:40:26.811938: saving checkpoint... 
+2026-10-14 18:40:26.946078: done, saving took 0.18 seconds 
+2026-10-14 18:40:26.947667: This epoch took 238.169083 s
+ 
+2026-10-14 18:40:26.947881: 
+epoch:  10 
+2026-10-14 18:44:07.911762: train loss : -0.4926 
+2026-10-14 18:44:13.257499: validation loss: -0.3463 
+2026-10-14 18:44:13.258344: Average global foreground Dice: [np.float32(0.8313), np.float32(0.4145), np.float32(0.2276), np.float32(0.4498), np.float32(0.4258), np.float32(0.226), np.float32(0.2086), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3053), np.float32(0.2678), np.float32(0.3242), np.float32(0.3062), np.float32(0.0), np.float32(0.0), np.float32(0.4333), np.float32(0.3541), np.float32(0.2912), np.float32(0.2392), np.float32(0.0), np.float32(0.3614), np.float32(0.2941), np.float32(0.2399), np.float32(0.2671), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2565), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4159), np.float32(0.1842)] 
+2026-10-14 18:44:13.258492: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:44:13.577041: lr: 0.00099 
+2026-10-14 18:44:13.647947: saving checkpoint... 
+2026-10-14 18:44:13.780548: done, saving took 0.20 seconds 
+2026-10-14 18:44:13.782035: This epoch took 226.834006 s
+ 
+2026-10-14 18:44:13.782185: 
+epoch:  11 
+2026-10-14 18:47:52.664362: train loss : -0.4972 
+2026-10-14 18:47:57.998629: validation loss: -0.4382 
+2026-10-14 18:47:57.999517: Average global foreground Dice: [np.float32(0.8691), np.float32(0.5343), np.float32(0.606), np.float32(0.6805), np.float32(0.6439), np.float32(0.6806), np.float32(0.6072), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5159), np.float32(0.5463), np.float32(0.477), np.float32(0.07), np.float32(0.0), np.float32(0.0), np.float32(0.447), np.float32(0.3147), np.float32(0.4739), np.float32(0.3258), np.float32(0.0), np.float32(0.471), np.float32(0.4576), np.float32(0.5758), np.float32(0.3988), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3206), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5691), np.float32(0.5976)] 
+2026-10-14 18:47:57.999745: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:47:58.273182: lr: 0.000989 
+2026-10-14 18:47:58.330935: saving checkpoint... 
+2026-10-14 18:47:58.464319: done, saving took 0.19 seconds 
+2026-10-14 18:47:58.465893: This epoch took 224.683648 s
+ 
+2026-10-14 18:47:58.466076: 
+epoch:  12 
+2026-10-14 18:51:33.548774: train loss : -0.5038 
+2026-10-14 18:51:38.887233: validation loss: -0.3496 
+2026-10-14 18:51:38.888100: Average global foreground Dice: [np.float32(0.8987), np.float32(0.3337), np.float32(0.3671), np.float32(0.2228), np.float32(0.1035), np.float32(0.3585), np.float32(0.2601), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.22), np.float32(0.393), np.float32(0.3762), np.float32(0.1621), np.float32(0.0), np.float32(0.0), np.float32(0.2628), np.float32(0.2311), np.float32(0.2926), np.float32(0.2432), np.float32(0.0), np.float32(0.2839), np.float32(0.4288), np.float32(0.3887), np.float32(0.1617), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2297), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2862), np.float32(0.3341)] 
+2026-10-14 18:51:38.888251: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:51:39.162905: lr: 0.000988 
+2026-10-14 18:51:39.212893: saving checkpoint... 
+2026-10-14 18:51:39.352785: done, saving took 0.19 seconds 
+2026-10-14 18:51:39.354475: This epoch took 220.888326 s
+ 
+2026-10-14 18:51:39.354697: 
+epoch:  13 
+2026-10-14 18:55:21.912976: train loss : -0.5237 
+2026-10-14 18:55:27.240250: validation loss: -0.4470 
+2026-10-14 18:55:27.241200: Average global foreground Dice: [np.float32(0.8834), np.float32(0.4794), np.float32(0.5738), np.float32(0.602), np.float32(0.441), np.float32(0.6), np.float32(0.4648), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3473), np.float32(0.5278), np.float32(0.4077), np.float32(0.3832), np.float32(0.0), np.float32(0.0), np.float32(0.4505), np.float32(0.4081), np.float32(0.4439), np.float32(0.4173), np.float32(0.0), np.float32(0.4945), np.float32(0.6293), np.float32(0.617), np.float32(0.3476), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3806), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6082), np.float32(0.6444)] 
+2026-10-14 18:55:27.241395: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:55:27.761393: lr: 0.000987 
+2026-10-14 18:55:27.770510: saving checkpoint... 
+2026-10-14 18:55:27.896082: done, saving took 0.13 seconds 
+2026-10-14 18:55:27.898065: This epoch took 228.543270 s
+ 
+2026-10-14 18:55:27.898247: 
+epoch:  14 
+2026-10-14 18:59:08.280940: train loss : -0.5304 
+2026-10-14 18:59:13.629462: validation loss: -0.3550 
+2026-10-14 18:59:13.630362: Average global foreground Dice: [np.float32(0.8726), np.float32(0.3141), np.float32(0.3358), np.float32(0.3274), np.float32(0.194), np.float32(0.2801), np.float32(0.2816), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3001), np.float32(0.3033), np.float32(0.2923), np.float32(0.3427), np.float32(0.0), np.float32(0.0), np.float32(0.2052), np.float32(0.2045), np.float32(0.1819), np.float32(0.249), np.float32(0.0), np.float32(0.2278), np.float32(0.4873), np.float32(0.4635), np.float32(0.1971), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3573), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3462), np.float32(0.3641)] 
+2026-10-14 18:59:13.630813: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 18:59:13.906008: lr: 0.000986 
+2026-10-14 18:59:13.914979: saving checkpoint... 
+2026-10-14 18:59:14.042293: done, saving took 0.14 seconds 
+2026-10-14 18:59:14.043891: This epoch took 226.145559 s
+ 
+2026-10-14 18:59:14.044055: 
+epoch:  15 
+2026-10-14 19:02:57.317995: train loss : -0.5431 
+2026-10-14 19:03:02.669469: validation loss: -0.3479 
+2026-10-14 19:03:02.670419: Average global foreground Dice: [np.float32(0.8801), np.float32(0.4086), np.float32(0.3349), np.float32(0.3695), np.float32(0.1987), np.float32(0.2353), np.float32(0.1632), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2284), np.float32(0.3104), np.float32(0.2331), np.float32(0.2538), np.float32(0.0), np.float32(0.0), np.float32(0.1704), np.float32(0.2089), np.float32(0.1516), np.float32(0.2238), np.float32(0.0), np.float32(0.3467), np.float32(0.4485), np.float32(0.45), np.float32(0.2691), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3393), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3802), np.float32(0.3181)] 
+2026-10-14 19:03:02.670585: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:03:02.964859: lr: 0.000986 
+2026-10-14 19:03:02.972849: saving checkpoint... 
+2026-10-14 19:03:03.100354: done, saving took 0.14 seconds 
+2026-10-14 19:03:03.102134: This epoch took 229.058003 s
+ 
+2026-10-14 19:03:03.102315: 
+epoch:  16 
+2026-10-14 19:06:41.298910: train loss : -0.5221 
+2026-10-14 19:06:46.649858: validation loss: -0.3289 
+2026-10-14 19:06:46.650907: Average global foreground Dice: [np.float32(0.8835), np.float32(0.2776), np.float32(0.2631), np.float32(0.3239), np.float32(0.2947), np.float32(0.3074), np.float32(0.2363), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3011), np.float32(0.4131), np.float32(0.2782), np.float32(0.2415), np.float32(0.0), np.float32(0.0), np.float32(0.3796), np.float32(0.2799), np.float32(0.2735), np.float32(0.3466), np.float32(0.0), np.float32(0.2616), np.float32(0.3224), np.float32(0.3666), np.float32(0.3453), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1199), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2281), np.float32(0.1594)] 
+2026-10-14 19:06:46.651645: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:06:46.937601: lr: 0.000985 
+2026-10-14 19:06:46.945750: saving checkpoint... 
+2026-10-14 19:06:47.071645: done, saving took 0.13 seconds 
+2026-10-14 19:06:47.073623: This epoch took 223.971167 s
+ 
+2026-10-14 19:06:47.073831: 
+epoch:  17 
+2026-10-14 19:10:25.585919: train loss : -0.5473 
+2026-10-14 19:10:30.928199: validation loss: -0.3866 
+2026-10-14 19:10:30.929197: Average global foreground Dice: [np.float32(0.8846), np.float32(0.4029), np.float32(0.4243), np.float32(0.3823), np.float32(0.1349), np.float32(0.4495), np.float32(0.3201), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3819), np.float32(0.4515), np.float32(0.4362), np.float32(0.3669), np.float32(0.0), np.float32(0.0), np.float32(0.2923), np.float32(0.2664), np.float32(0.2441), np.float32(0.2872), np.float32(0.0), np.float32(0.4177), np.float32(0.5241), np.float32(0.4624), np.float32(0.2569), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3635), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3379), np.float32(0.4548)] 
+2026-10-14 19:10:30.929363: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:10:31.216192: lr: 0.000984 
+2026-10-14 19:10:31.224401: saving checkpoint... 
+2026-10-14 19:10:31.356190: done, saving took 0.14 seconds 
+2026-10-14 19:10:31.358183: This epoch took 224.284259 s
+ 
+2026-10-14 19:10:31.358347: 
+epoch:  18 
+2026-10-14 19:14:08.441905: train loss : -0.5517 
+2026-10-14 19:14:13.793303: validation loss: -0.3429 
+2026-10-14 19:14:13.794465: Average global foreground Dice: [np.float32(0.87), np.float32(0.2356), np.float32(0.2323), np.float32(0.2571), np.float32(0.2988), np.float32(0.2571), np.float32(0.193), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1787), np.float32(0.3086), np.float32(0.3438), np.float32(0.2705), np.float32(0.0), np.float32(0.0), np.float32(0.3158), np.float32(0.1798), np.float32(0.267), np.float32(0.2632), np.float32(0.0), np.float32(0.3907), np.float32(0.5319), np.float32(0.3885), np.float32(0.1854), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.35), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2736), np.float32(0.2979)] 
+2026-10-14 19:14:13.794737: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:14:14.090866: lr: 0.000983 
+2026-10-14 19:14:14.098923: saving checkpoint... 
+2026-10-14 19:14:14.220562: done, saving took 0.13 seconds 
+2026-10-14 19:14:14.222657: This epoch took 222.864146 s
+ 
+2026-10-14 19:14:14.222815: 
+epoch:  19 
+2026-10-14 19:18:05.842209: train loss : -0.5454 
+2026-10-14 19:18:11.165760: validation loss: -0.3403 
+2026-10-14 19:18:11.166608: Average global foreground Dice: [np.float32(0.8586), np.float32(0.2985), np.float32(0.262), np.float32(0.1964), np.float32(0.2008), np.float32(0.2089), np.float32(0.1592), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2224), np.float32(0.2508), np.float32(0.453), np.float32(0.3526), np.float32(0.0), np.float32(0.0), np.float32(0.2221), np.float32(0.1886), np.float32(0.1773), np.float32(0.1349), np.float32(0.0), np.float32(0.3235), np.float32(0.4876), np.float32(0.4858), np.float32(0.1895), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3245), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2271), np.float32(0.2352)] 
+2026-10-14 19:18:11.166854: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:18:11.446225: lr: 0.000982 
+2026-10-14 19:18:11.446565: This epoch took 237.223674 s
+ 
+2026-10-14 19:18:11.446848: 
+epoch:  20 
+2026-10-14 19:22:02.337753: train loss : -0.5562 
+2026-10-14 19:22:07.676476: validation loss: -0.4306 
+2026-10-14 19:22:07.677525: Average global foreground Dice: [np.float32(0.9055), np.float32(0.511), np.float32(0.511), np.float32(0.4992), np.float32(0.4355), np.float32(0.5359), np.float32(0.4317), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3925), np.float32(0.4433), np.float32(0.4908), np.float32(0.359), np.float32(0.0), np.float32(0.0), np.float32(0.3794), np.float32(0.2934), np.float32(0.386), np.float32(0.3861), np.float32(0.0), np.float32(0.244), np.float32(0.6611), np.float32(0.4215), np.float32(0.2695), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3923), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5461), np.float32(0.5358)] 
+2026-10-14 19:22:07.677814: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:22:07.960040: lr: 0.000981 
+2026-10-14 19:22:07.968625: saving checkpoint... 
+2026-10-14 19:22:08.089265: done, saving took 0.13 seconds 
+2026-10-14 19:22:08.091234: This epoch took 236.644263 s
+ 
+2026-10-14 19:22:08.091421: 
+epoch:  21 
+2026-10-14 19:25:38.187545: train loss : -0.5696 
+2026-10-14 19:25:43.522384: validation loss: -0.4289 
+2026-10-14 19:25:43.523527: Average global foreground Dice: [np.float32(0.8921), np.float32(0.4691), np.float32(0.4446), np.float32(0.491), np.float32(0.3586), np.float32(0.5288), np.float32(0.3854), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3829), np.float32(0.4765), np.float32(0.4418), np.float32(0.4167), np.float32(0.0), np.float32(0.0), np.float32(0.3774), np.float32(0.2478), np.float32(0.3843), np.float32(0.3586), np.float32(0.0), np.float32(0.3123), np.float32(0.4842), np.float32(0.5064), np.float32(0.2063), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4485), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4727), np.float32(0.4737)] 
+2026-10-14 19:25:43.523987: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:25:43.799351: lr: 0.00098 
+2026-10-14 19:25:43.819165: saving checkpoint... 
+2026-10-14 19:25:43.958254: done, saving took 0.16 seconds 
+2026-10-14 19:25:43.959997: This epoch took 215.868490 s
+ 
+2026-10-14 19:25:43.960196: 
+epoch:  22 
+2026-10-14 19:29:32.436741: train loss : -0.5603 
+2026-10-14 19:29:37.790775: validation loss: -0.2438 
+2026-10-14 19:29:37.791907: Average global foreground Dice: [np.float32(0.8783), np.float32(0.1327), np.float32(0.0374), np.float32(0.052), np.float32(0.0173), np.float32(0.0154), np.float32(0.0161), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0773), np.float32(0.1737), np.float32(0.2774), np.float32(0.1294), np.float32(0.0), np.float32(0.0), np.float32(0.0365), np.float32(0.0273), np.float32(0.0285), np.float32(0.1111), np.float32(0.0), np.float32(0.2964), np.float32(0.1755), np.float32(0.4174), np.float32(0.1196), 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(0.0417), np.float32(0.0512)] 
+2026-10-14 19:29:37.792160: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:29:38.072429: lr: 0.000979 
+2026-10-14 19:29:38.072711: This epoch took 234.112412 s
+ 
+2026-10-14 19:29:38.072903: 
+epoch:  23 
+2026-10-14 19:34:02.383273: train loss : -0.5587 
+2026-10-14 19:34:07.713003: validation loss: -0.3189 
+2026-10-14 19:34:07.713958: Average global foreground Dice: [np.float32(0.9013), np.float32(0.3019), np.float32(0.2308), np.float32(0.2041), np.float32(0.1629), np.float32(0.1894), np.float32(0.134), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2741), np.float32(0.271), np.float32(0.2741), np.float32(0.295), np.float32(0.0), np.float32(0.0), np.float32(0.1858), np.float32(0.1532), np.float32(0.1316), np.float32(0.1786), np.float32(0.0), np.float32(0.0981), np.float32(0.3802), np.float32(0.3702), np.float32(0.1674), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3387), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2441), np.float32(0.2608)] 
+2026-10-14 19:34:07.714136: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:34:08.007995: lr: 0.000978 
+2026-10-14 19:34:08.008589: This epoch took 269.935604 s
+ 
+2026-10-14 19:34:08.008982: 
+epoch:  24 
+2026-10-14 19:37:51.385917: train loss : -0.5834 
+2026-10-14 19:37:56.737369: validation loss: -0.4135 
+2026-10-14 19:37:56.738487: Average global foreground Dice: [np.float32(0.8958), np.float32(0.4399), np.float32(0.4299), np.float32(0.4024), np.float32(0.3243), np.float32(0.4594), np.float32(0.3574), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3683), np.float32(0.4719), np.float32(0.3963), np.float32(0.4062), np.float32(0.0), np.float32(0.0), np.float32(0.2855), np.float32(0.2252), np.float32(0.3137), np.float32(0.2747), np.float32(0.0), np.float32(0.3513), np.float32(0.5674), np.float32(0.5366), np.float32(0.2082), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3998), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4334), np.float32(0.4387)] 
+2026-10-14 19:37:56.738805: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:37:57.031389: lr: 0.000977 
+2026-10-14 19:37:57.031659: This epoch took 229.022508 s
+ 
+2026-10-14 19:37:57.031872: 
+epoch:  25 
+2026-10-14 19:42:09.191603: train loss : -0.5746 
+2026-10-14 19:42:14.548573: validation loss: -0.3663 
+2026-10-14 19:42:14.549443: Average global foreground Dice: [np.float32(0.8967), np.float32(0.3677), np.float32(0.3107), np.float32(0.3093), np.float32(0.4015), np.float32(0.3482), np.float32(0.3503), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3014), np.float32(0.381), np.float32(0.5059), np.float32(0.3703), np.float32(0.0), np.float32(0.0), np.float32(0.353), np.float32(0.2017), np.float32(0.2438), np.float32(0.2763), np.float32(0.0), np.float32(0.2276), np.float32(0.513), np.float32(0.4343), np.float32(0.272), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3542), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3482), np.float32(0.3082)] 
+2026-10-14 19:42:14.549589: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:42:14.894267: lr: 0.000977 
+2026-10-14 19:42:14.894508: This epoch took 257.862539 s
+ 
+2026-10-14 19:42:14.894594: 
+epoch:  26 
+2026-10-14 19:46:19.364180: train loss : -0.5676 
+2026-10-14 19:46:24.714874: validation loss: -0.5144 
+2026-10-14 19:46:24.716035: Average global foreground Dice: [np.float32(0.8752), np.float32(0.5944), np.float32(0.7095), np.float32(0.7418), np.float32(0.6746), np.float32(0.7616), np.float32(0.6676), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5599), np.float32(0.6425), np.float32(0.5309), np.float32(0.4496), np.float32(0.0), np.float32(0.0), np.float32(0.7163), np.float32(0.4497), np.float32(0.6752), np.float32(0.551), np.float32(0.0), np.float32(0.5764), np.float32(0.7031), np.float32(0.7307), np.float32(0.4503), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4613), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7121), np.float32(0.7424)] 
+2026-10-14 19:46:24.716265: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:46:25.006795: lr: 0.000976 
+2026-10-14 19:46:25.041254: saving checkpoint... 
+2026-10-14 19:46:25.170327: done, saving took 0.16 seconds 
+2026-10-14 19:46:25.172074: This epoch took 250.277410 s
+ 
+2026-10-14 19:46:25.172216: 
+epoch:  27 
+2026-10-14 19:50:19.261594: train loss : -0.5763 
+2026-10-14 19:50:24.601502: validation loss: -0.5033 
+2026-10-14 19:50:24.602686: Average global foreground Dice: [np.float32(0.9037), np.float32(0.5559), np.float32(0.688), np.float32(0.7259), np.float32(0.6401), np.float32(0.7343), np.float32(0.6148), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5866), np.float32(0.6614), np.float32(0.5102), np.float32(0.4755), np.float32(0.0), np.float32(0.0), np.float32(0.6974), np.float32(0.512), np.float32(0.6484), np.float32(0.5796), np.float32(0.0), np.float32(0.5678), np.float32(0.6804), np.float32(0.6497), np.float32(0.4841), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4464), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6827), np.float32(0.7021)] 
+2026-10-14 19:50:24.602913: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:50:24.888325: lr: 0.000975 
+2026-10-14 19:50:24.916849: saving checkpoint... 
+2026-10-14 19:50:25.048609: done, saving took 0.16 seconds 
+2026-10-14 19:50:25.050315: This epoch took 239.878020 s
+ 
+2026-10-14 19:50:25.050503: 
+epoch:  28 
+2026-10-14 19:54:22.058718: train loss : -0.5728 
+2026-10-14 19:54:27.396672: validation loss: -0.4029 
+2026-10-14 19:54:27.397960: Average global foreground Dice: [np.float32(0.8966), np.float32(0.4621), np.float32(0.4834), np.float32(0.4898), np.float32(0.3051), np.float32(0.5211), np.float32(0.5203), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.231), np.float32(0.346), np.float32(0.3532), np.float32(0.052), np.float32(0.0), np.float32(0.0), np.float32(0.2765), np.float32(0.2058), np.float32(0.3507), np.float32(0.2319), np.float32(0.0), np.float32(0.2775), np.float32(0.6004), np.float32(0.6012), np.float32(0.2868), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3341), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.484), np.float32(0.5954)] 
+2026-10-14 19:54:27.398199: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:54:27.694445: lr: 0.000974 
+2026-10-14 19:54:27.720899: saving checkpoint... 
+2026-10-14 19:54:27.846835: done, saving took 0.15 seconds 
+2026-10-14 19:54:27.848929: This epoch took 242.798343 s
+ 
+2026-10-14 19:54:27.849102: 
+epoch:  29 
+2026-10-14 19:58:42.891813: train loss : -0.5672 
+2026-10-14 19:58:48.260203: validation loss: -0.5247 
+2026-10-14 19:58:48.261367: Average global foreground Dice: [np.float32(0.8996), np.float32(0.5789), np.float32(0.74), np.float32(0.7497), np.float32(0.7255), np.float32(0.7576), np.float32(0.6937), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5506), np.float32(0.6373), np.float32(0.5886), np.float32(0.4524), np.float32(0.0), np.float32(0.0), np.float32(0.658), np.float32(0.487), np.float32(0.6506), np.float32(0.4956), np.float32(0.0), np.float32(0.412), np.float32(0.8227), np.float32(0.8024), np.float32(0.4376), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4135), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7961), np.float32(0.7947)] 
+2026-10-14 19:58:48.261830: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 19:58:48.601002: lr: 0.000973 
+2026-10-14 19:58:48.683923: saving checkpoint... 
+2026-10-14 19:58:48.819705: done, saving took 0.22 seconds 
+2026-10-14 19:58:48.821632: This epoch took 260.972452 s
+ 
+2026-10-14 19:58:48.821865: 
+epoch:  30 
+2026-10-14 20:02:37.902802: train loss : -0.5757 
+2026-10-14 20:02:43.268317: validation loss: -0.3841 
+2026-10-14 20:02:43.269344: Average global foreground Dice: [np.float32(0.8778), np.float32(0.4049), np.float32(0.3911), np.float32(0.3585), np.float32(0.269), np.float32(0.4109), np.float32(0.3698), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3399), np.float32(0.3863), np.float32(0.407), np.float32(0.4091), np.float32(0.0), np.float32(0.0), np.float32(0.2654), np.float32(0.2288), np.float32(0.3582), np.float32(0.283), np.float32(0.0), np.float32(0.2842), np.float32(0.4789), np.float32(0.3541), np.float32(0.2667), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3301), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3553), np.float32(0.3431)] 
+2026-10-14 20:02:43.269679: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:02:43.553670: lr: 0.000972 
+2026-10-14 20:02:43.554178: This epoch took 234.732234 s
+ 
+2026-10-14 20:02:43.554522: 
+epoch:  31 
+2026-10-14 20:06:37.825998: train loss : -0.5869 
+2026-10-14 20:06:43.165433: validation loss: -0.4758 
+2026-10-14 20:06:43.166404: Average global foreground Dice: [np.float32(0.9017), np.float32(0.5533), np.float32(0.5655), np.float32(0.591), np.float32(0.5257), np.float32(0.6315), np.float32(0.5134), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4635), np.float32(0.5318), np.float32(0.5131), np.float32(0.4281), np.float32(0.0), np.float32(0.0), np.float32(0.4785), np.float32(0.3861), np.float32(0.4723), np.float32(0.4202), np.float32(0.0), np.float32(0.3831), np.float32(0.7018), np.float32(0.6209), np.float32(0.2981), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4357), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5954), np.float32(0.6009)] 
+2026-10-14 20:06:43.166701: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:06:43.499425: lr: 0.000971 
+2026-10-14 20:06:43.507317: saving checkpoint... 
+2026-10-14 20:06:43.634274: done, saving took 0.13 seconds 
+2026-10-14 20:06:43.635890: This epoch took 240.081122 s
+ 
+2026-10-14 20:06:43.636118: 
+epoch:  32 
+2026-10-14 20:10:43.019830: train loss : -0.6032 
+2026-10-14 20:10:48.361565: validation loss: -0.4415 
+2026-10-14 20:10:48.362493: Average global foreground Dice: [np.float32(0.8997), np.float32(0.4782), np.float32(0.4627), np.float32(0.4975), np.float32(0.4319), np.float32(0.5635), np.float32(0.4355), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4374), np.float32(0.4876), np.float32(0.5119), np.float32(0.4462), np.float32(0.0), np.float32(0.0), np.float32(0.4248), np.float32(0.3485), np.float32(0.4173), np.float32(0.3551), np.float32(0.0), np.float32(0.3665), np.float32(0.6055), np.float32(0.4273), np.float32(0.1726), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5004), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5101), np.float32(0.5216)] 
+2026-10-14 20:10:48.362666: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:10:48.659512: lr: 0.00097 
+2026-10-14 20:10:48.676595: saving checkpoint... 
+2026-10-14 20:10:48.814430: done, saving took 0.15 seconds 
+2026-10-14 20:10:48.816265: This epoch took 245.180056 s
+ 
+2026-10-14 20:10:48.816438: 
+epoch:  33 
+2026-10-14 20:14:53.387968: train loss : -0.5818 
+2026-10-14 20:14:58.741778: validation loss: -0.4853 
+2026-10-14 20:14:58.742896: Average global foreground Dice: [np.float32(0.9045), np.float32(0.518), np.float32(0.5462), np.float32(0.5925), np.float32(0.4651), np.float32(0.6586), np.float32(0.5612), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4653), np.float32(0.599), np.float32(0.4825), np.float32(0.4377), np.float32(0.0), np.float32(0.0), np.float32(0.4414), np.float32(0.3978), np.float32(0.5148), np.float32(0.417), np.float32(0.0), np.float32(0.4326), np.float32(0.7349), np.float32(0.688), np.float32(0.2843), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3783), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5807), np.float32(0.6032)] 
+2026-10-14 20:14:58.743167: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:14:59.027436: lr: 0.000969 
+2026-10-14 20:14:59.055317: saving checkpoint... 
+2026-10-14 20:14:59.185416: done, saving took 0.16 seconds 
+2026-10-14 20:14:59.187137: This epoch took 250.370616 s
+ 
+2026-10-14 20:14:59.187347: 
+epoch:  34 
+2026-10-14 20:19:02.745304: train loss : -0.5954 
+2026-10-14 20:19:08.097213: validation loss: -0.4696 
+2026-10-14 20:19:08.098274: Average global foreground Dice: [np.float32(0.8791), np.float32(0.4646), np.float32(0.5031), np.float32(0.6007), np.float32(0.4548), np.float32(0.5769), np.float32(0.4483), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4606), np.float32(0.4705), np.float32(0.575), np.float32(0.5169), np.float32(0.0), np.float32(0.0), np.float32(0.502), np.float32(0.4495), np.float32(0.5022), np.float32(0.4207), np.float32(0.0), np.float32(0.3205), np.float32(0.6862), np.float32(0.625), np.float32(0.2706), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4056), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5403), np.float32(0.5555)] 
+2026-10-14 20:19:08.098467: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:19:08.400408: lr: 0.000968 
+2026-10-14 20:19:08.408741: saving checkpoint... 
+2026-10-14 20:19:08.529931: done, saving took 0.13 seconds 
+2026-10-14 20:19:08.532618: This epoch took 249.345177 s
+ 
+2026-10-14 20:19:08.532797: 
+epoch:  35 
+2026-10-14 20:23:13.113665: train loss : -0.5962 
+2026-10-14 20:23:18.473572: validation loss: -0.4169 
+2026-10-14 20:23:18.474668: Average global foreground Dice: [np.float32(0.8993), np.float32(0.4531), np.float32(0.4019), np.float32(0.4532), np.float32(0.38), np.float32(0.4813), np.float32(0.4118), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3628), np.float32(0.408), np.float32(0.4598), np.float32(0.4017), np.float32(0.0), np.float32(0.0), np.float32(0.337), np.float32(0.2647), np.float32(0.3332), np.float32(0.3097), np.float32(0.0), np.float32(0.3963), np.float32(0.5837), np.float32(0.4463), np.float32(0.3229), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3355), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.466), np.float32(0.4677)] 
+2026-10-14 20:23:18.474943: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:23:18.766658: lr: 0.000968 
+2026-10-14 20:23:18.766924: This epoch took 250.234049 s
+ 
+2026-10-14 20:23:18.767106: 
+epoch:  36 
+2026-10-14 20:27:33.537534: train loss : -0.6024 
+2026-10-14 20:27:38.893006: validation loss: -0.3078 
+2026-10-14 20:27:38.894281: Average global foreground Dice: [np.float32(0.8624), np.float32(0.1454), np.float32(0.2024), np.float32(0.2146), np.float32(0.1304), np.float32(0.1033), np.float32(0.1095), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1526), np.float32(0.21), np.float32(0.4105), np.float32(0.1022), np.float32(0.0), np.float32(0.0), np.float32(0.344), np.float32(0.3969), np.float32(0.2526), np.float32(0.3643), np.float32(0.0), np.float32(0.363), np.float32(0.3564), np.float32(0.3077), np.float32(0.1126), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2249), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1793), np.float32(0.1952)] 
+2026-10-14 20:27:38.894556: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:27:39.195402: lr: 0.000967 
+2026-10-14 20:27:39.195829: This epoch took 260.428652 s
+ 
+2026-10-14 20:27:39.195930: 
+epoch:  37 
+2026-10-14 20:32:02.471121: train loss : -0.5788 
+2026-10-14 20:32:07.802978: validation loss: -0.4663 
+2026-10-14 20:32:07.803966: Average global foreground Dice: [np.float32(0.9129), np.float32(0.5164), np.float32(0.5773), np.float32(0.6556), np.float32(0.5666), np.float32(0.6104), np.float32(0.5444), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5021), np.float32(0.5852), np.float32(0.4414), np.float32(0.3464), np.float32(0.0), np.float32(0.0), np.float32(0.4834), np.float32(0.3266), np.float32(0.5478), np.float32(0.4141), np.float32(0.0), np.float32(0.4802), np.float32(0.6069), np.float32(0.5725), np.float32(0.4404), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3441), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6122), np.float32(0.6107)] 
+2026-10-14 20:32:07.804118: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:32:08.141566: lr: 0.000966 
+2026-10-14 20:32:08.141814: This epoch took 268.945820 s
+ 
+2026-10-14 20:32:08.141901: 
+epoch:  38 
+2026-10-14 20:36:09.236511: train loss : -0.5963 
+2026-10-14 20:36:14.575104: validation loss: -0.5561 
+2026-10-14 20:36:14.576218: Average global foreground Dice: [np.float32(0.8826), np.float32(0.6018), np.float32(0.7851), np.float32(0.7968), np.float32(0.6936), np.float32(0.8227), np.float32(0.7131), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6435), np.float32(0.6962), np.float32(0.5671), np.float32(0.4645), np.float32(0.0), np.float32(0.0), np.float32(0.7358), np.float32(0.5549), np.float32(0.6962), np.float32(0.6286), np.float32(0.0), np.float32(0.6197), np.float32(0.8524), np.float32(0.8371), np.float32(0.48), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4292), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8455), np.float32(0.8379)] 
+2026-10-14 20:36:14.576464: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:36:14.868125: lr: 0.000965 
+2026-10-14 20:36:14.910242: saving checkpoint... 
+2026-10-14 20:36:15.048267: done, saving took 0.18 seconds 
+2026-10-14 20:36:15.050080: This epoch took 246.908116 s
+ 
+2026-10-14 20:36:15.050217: 
+epoch:  39 
+2026-10-14 20:40:24.673599: train loss : -0.5978 
+2026-10-14 20:40:30.032999: validation loss: -0.4502 
+2026-10-14 20:40:30.034265: Average global foreground Dice: [np.float32(0.8859), np.float32(0.5309), np.float32(0.4904), np.float32(0.5554), np.float32(0.5068), np.float32(0.554), np.float32(0.4464), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.301), np.float32(0.4653), np.float32(0.3315), np.float32(0.2666), np.float32(0.0), np.float32(0.0), np.float32(0.5735), np.float32(0.4294), np.float32(0.4422), np.float32(0.3815), np.float32(0.0), np.float32(0.4994), np.float32(0.701), np.float32(0.6633), np.float32(0.4469), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3991), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6539), np.float32(0.6549)] 
+2026-10-14 20:40:30.034482: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:40:30.319279: lr: 0.000964 
+2026-10-14 20:40:30.382749: saving checkpoint... 
+2026-10-14 20:40:31.611293: done, saving took 1.29 seconds 
+2026-10-14 20:40:31.614513: This epoch took 256.564229 s
+ 
+2026-10-14 20:40:31.614625: 
+epoch:  40 
+2026-10-14 20:44:31.870238: train loss : -0.6084 
+2026-10-14 20:44:37.208985: validation loss: -0.3766 
+2026-10-14 20:44:37.210014: Average global foreground Dice: [np.float32(0.9065), np.float32(0.4694), np.float32(0.3262), np.float32(0.5164), np.float32(0.3563), np.float32(0.0745), np.float32(0.2774), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3502), np.float32(0.3715), np.float32(0.4966), np.float32(0.4296), np.float32(0.0), np.float32(0.0), np.float32(0.2912), np.float32(0.2106), np.float32(0.2353), np.float32(0.2532), np.float32(0.0), np.float32(0.3412), np.float32(0.5478), np.float32(0.4559), np.float32(0.3648), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3135), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4803), np.float32(0.2802)] 
+2026-10-14 20:44:37.210203: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:44:37.497298: lr: 0.000963 
+2026-10-14 20:44:37.497560: This epoch took 245.882876 s
+ 
+2026-10-14 20:44:37.497646: 
+epoch:  41 
+2026-10-14 20:48:28.537994: train loss : -0.5959 
+2026-10-14 20:48:33.887258: validation loss: -0.3769 
+2026-10-14 20:48:33.888706: Average global foreground Dice: [np.float32(0.8938), np.float32(0.334), np.float32(0.3771), np.float32(0.3442), np.float32(0.2573), np.float32(0.3571), np.float32(0.3174), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2776), np.float32(0.2973), np.float32(0.3122), np.float32(0.1767), np.float32(0.0), np.float32(0.0), np.float32(0.4184), np.float32(0.3951), np.float32(0.3199), np.float32(0.3216), np.float32(0.0), np.float32(0.4286), np.float32(0.6004), np.float32(0.5879), np.float32(0.2826), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3734), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.414), np.float32(0.4916)] 
+2026-10-14 20:48:33.889045: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:48:34.224116: lr: 0.000962 
+2026-10-14 20:48:34.224419: This epoch took 236.726710 s
+ 
+2026-10-14 20:48:34.224517: 
+epoch:  42 
+2026-10-14 20:52:20.804507: train loss : -0.5953 
+2026-10-14 20:52:26.153430: validation loss: -0.3587 
+2026-10-14 20:52:26.154422: Average global foreground Dice: [np.float32(0.9119), np.float32(0.3354), np.float32(0.3005), np.float32(0.2913), np.float32(0.2711), np.float32(0.3551), np.float32(0.2739), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2778), np.float32(0.3502), np.float32(0.4823), np.float32(0.4755), np.float32(0.0), np.float32(0.0), np.float32(0.2461), np.float32(0.2532), np.float32(0.2109), np.float32(0.2196), np.float32(0.0), np.float32(0.1639), np.float32(0.4805), np.float32(0.4), np.float32(0.2245), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3042), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3148), np.float32(0.3546)] 
+2026-10-14 20:52:26.154732: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:52:26.507200: lr: 0.000961 
+2026-10-14 20:52:26.507467: This epoch took 232.282885 s
+ 
+2026-10-14 20:52:26.507756: 
+epoch:  43 
+2026-10-14 20:56:37.675580: train loss : -0.5971 
+2026-10-14 20:56:43.031018: validation loss: -0.4158 
+2026-10-14 20:56:43.032192: Average global foreground Dice: [np.float32(0.8988), np.float32(0.4217), np.float32(0.4586), np.float32(0.458), np.float32(0.4422), np.float32(0.4846), np.float32(0.4519), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2792), np.float32(0.4522), np.float32(0.3161), np.float32(0.3462), np.float32(0.0), np.float32(0.0), np.float32(0.5149), np.float32(0.3503), np.float32(0.4089), np.float32(0.3819), np.float32(0.0), np.float32(0.4052), np.float32(0.5921), np.float32(0.516), np.float32(0.3822), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4282), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4947), np.float32(0.5695)] 
+2026-10-14 20:56:43.032422: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 20:56:43.323989: lr: 0.00096 
+2026-10-14 20:56:43.324248: This epoch took 256.816388 s
+ 
+2026-10-14 20:56:43.324455: 
+epoch:  44 
+2026-10-14 21:00:39.549799: train loss : -0.5994 
+2026-10-14 21:00:44.887140: validation loss: -0.4228 
+2026-10-14 21:00:44.888176: Average global foreground Dice: [np.float32(0.8891), np.float32(0.4726), np.float32(0.4108), np.float32(0.4865), np.float32(0.3722), np.float32(0.3981), np.float32(0.3442), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4283), np.float32(0.4441), np.float32(0.5276), np.float32(0.4394), np.float32(0.0), np.float32(0.0), np.float32(0.3677), np.float32(0.2817), np.float32(0.3304), np.float32(0.3221), np.float32(0.0), np.float32(0.3721), np.float32(0.5981), np.float32(0.5264), np.float32(0.2979), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.449), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5065), np.float32(0.4487)] 
+2026-10-14 21:00:44.888495: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:00:45.171456: lr: 0.000959 
+2026-10-14 21:00:45.171705: This epoch took 241.847154 s
+ 
+2026-10-14 21:00:45.171798: 
+epoch:  45 
+2026-10-14 21:04:48.294557: train loss : -0.6108 
+2026-10-14 21:04:53.647937: validation loss: -0.2344 
+2026-10-14 21:04:53.648944: Average global foreground Dice: [np.float32(0.8981), np.float32(0.0717), np.float32(0.0877), np.float32(0.0838), np.float32(0.0536), np.float32(0.0673), np.float32(0.0491), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0887), np.float32(0.0952), np.float32(0.2427), np.float32(0.0762), np.float32(0.0), np.float32(0.0), np.float32(0.0808), np.float32(0.0625), np.float32(0.0388), np.float32(0.0757), np.float32(0.0), np.float32(0.1187), np.float32(0.2251), np.float32(0.0916), np.float32(0.1358), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0777), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.09), np.float32(0.0625)] 
+2026-10-14 21:04:53.649336: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:04:53.930453: lr: 0.000959 
+2026-10-14 21:04:53.930725: This epoch took 248.758868 s
+ 
+2026-10-14 21:04:53.930821: 
+epoch:  46 
+2026-10-14 21:08:55.017574: train loss : -0.6068 
+2026-10-14 21:09:00.372816: validation loss: -0.4102 
+2026-10-14 21:09:00.374143: Average global foreground Dice: [np.float32(0.9062), np.float32(0.408), np.float32(0.4236), np.float32(0.4259), np.float32(0.3496), np.float32(0.4734), np.float32(0.3666), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3623), np.float32(0.4302), np.float32(0.39), np.float32(0.3175), np.float32(0.0), np.float32(0.0), np.float32(0.3022), np.float32(0.2135), np.float32(0.2948), np.float32(0.3114), np.float32(0.0), np.float32(0.3118), np.float32(0.5866), np.float32(0.5047), np.float32(0.1666), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4113), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4514), np.float32(0.4562)] 
+2026-10-14 21:09:00.374619: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:09:00.714057: lr: 0.000958 
+2026-10-14 21:09:00.714483: This epoch took 246.783606 s
+ 
+2026-10-14 21:09:00.714787: 
+epoch:  47 
+2026-10-14 21:12:49.012327: train loss : -0.6081 
+2026-10-14 21:12:54.356194: validation loss: -0.5214 
+2026-10-14 21:12:54.357705: Average global foreground Dice: [np.float32(0.9011), np.float32(0.5663), np.float32(0.6884), np.float32(0.7464), np.float32(0.666), np.float32(0.783), np.float32(0.6711), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5733), np.float32(0.6323), np.float32(0.528), np.float32(0.4171), np.float32(0.0), np.float32(0.0), np.float32(0.7057), np.float32(0.5457), np.float32(0.6697), np.float32(0.5691), np.float32(0.0), np.float32(0.5892), np.float32(0.8194), np.float32(0.7775), np.float32(0.4712), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4014), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7798), np.float32(0.7972)] 
+2026-10-14 21:12:54.358135: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:12:54.679155: lr: 0.000957 
+2026-10-14 21:12:54.679615: This epoch took 233.964591 s
+ 
+2026-10-14 21:12:54.679933: 
+epoch:  48 
+2026-10-14 21:16:46.271750: train loss : -0.6225 
+2026-10-14 21:16:51.611741: validation loss: -0.4140 
+2026-10-14 21:16:51.612823: Average global foreground Dice: [np.float32(0.8951), np.float32(0.3743), np.float32(0.5051), np.float32(0.4766), np.float32(0.4318), np.float32(0.4625), np.float32(0.3904), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2846), np.float32(0.4223), np.float32(0.4274), np.float32(0.3027), np.float32(0.0), np.float32(0.0), np.float32(0.4149), np.float32(0.2775), np.float32(0.4015), np.float32(0.4159), np.float32(0.0), np.float32(0.4628), np.float32(0.6337), np.float32(0.5745), np.float32(0.3696), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3592), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5264), np.float32(0.5528)] 
+2026-10-14 21:16:51.613115: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:16:51.913174: lr: 0.000956 
+2026-10-14 21:16:51.913428: This epoch took 237.233304 s
+ 
+2026-10-14 21:16:51.913526: 
+epoch:  49 
+2026-10-14 21:20:49.595344: train loss : -0.5996 
+2026-10-14 21:20:54.950999: validation loss: -0.3954 
+2026-10-14 21:20:54.951983: Average global foreground Dice: [np.float32(0.9122), np.float32(0.4434), np.float32(0.3861), np.float32(0.3326), np.float32(0.3317), np.float32(0.3256), np.float32(0.3793), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2296), np.float32(0.3021), np.float32(0.352), np.float32(0.3413), np.float32(0.0), np.float32(0.0), np.float32(0.3192), np.float32(0.2645), np.float32(0.3347), np.float32(0.2928), np.float32(0.0), np.float32(0.3382), np.float32(0.5241), np.float32(0.5564), np.float32(0.2408), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.44), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4242), np.float32(0.3941)] 
+2026-10-14 21:20:54.952532: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:20:55.267880: lr: 0.000955 
+2026-10-14 21:20:55.268134: saving scheduled checkpoint file... 
+2026-10-14 21:20:55.361499: saving checkpoint... 
+2026-10-14 21:20:55.501475: done, saving took 0.23 seconds 
+2026-10-14 21:20:55.503188: done 
+2026-10-14 21:20:55.503308: This epoch took 243.589726 s
+ 
+2026-10-14 21:20:55.503427: 
+epoch:  50 
+2026-10-14 21:24:57.986111: train loss : -0.6047 
+2026-10-14 21:25:03.336803: validation loss: -0.3968 
+2026-10-14 21:25:03.337926: Average global foreground Dice: [np.float32(0.9116), np.float32(0.4129), np.float32(0.3945), np.float32(0.3766), np.float32(0.3707), np.float32(0.3989), np.float32(0.3322), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3023), np.float32(0.3582), np.float32(0.4743), np.float32(0.3653), np.float32(0.0), np.float32(0.0), np.float32(0.3579), np.float32(0.3362), np.float32(0.3618), np.float32(0.3282), np.float32(0.0), np.float32(0.1878), np.float32(0.4977), np.float32(0.4489), np.float32(0.1994), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3425), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3796), np.float32(0.4246)] 
+2026-10-14 21:25:03.338169: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:25:03.627116: lr: 0.000954 
+2026-10-14 21:25:03.627367: This epoch took 248.123869 s
+ 
+2026-10-14 21:25:03.627451: 
+epoch:  51 
+2026-10-14 21:29:01.521707: train loss : -0.6234 
+2026-10-14 21:29:06.857436: validation loss: -0.3690 
+2026-10-14 21:29:06.858475: Average global foreground Dice: [np.float32(0.8985), np.float32(0.3821), np.float32(0.3167), np.float32(0.3146), np.float32(0.3586), np.float32(0.2983), np.float32(0.2793), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.273), np.float32(0.282), np.float32(0.4412), np.float32(0.387), np.float32(0.0), np.float32(0.0), np.float32(0.3291), np.float32(0.3189), np.float32(0.2923), np.float32(0.3446), np.float32(0.0), np.float32(0.3291), np.float32(0.5271), np.float32(0.4142), np.float32(0.2103), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4197), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3262), np.float32(0.3468)] 
+2026-10-14 21:29:06.858900: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:29:07.173726: lr: 0.000953 
+2026-10-14 21:29:07.173950: This epoch took 243.546446 s
+ 
+2026-10-14 21:29:07.174026: 
+epoch:  52 
+2026-10-14 21:32:59.406006: train loss : -0.6092 
+2026-10-14 21:33:04.767399: validation loss: -0.4384 
+2026-10-14 21:33:04.768380: Average global foreground Dice: [np.float32(0.9046), np.float32(0.4088), np.float32(0.4984), np.float32(0.4406), np.float32(0.3549), np.float32(0.5668), np.float32(0.4078), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4056), np.float32(0.5078), np.float32(0.4862), np.float32(0.3803), np.float32(0.0), np.float32(0.0), np.float32(0.4486), np.float32(0.3854), np.float32(0.4481), np.float32(0.4289), np.float32(0.0), np.float32(0.3954), np.float32(0.6538), np.float32(0.6231), np.float32(0.2886), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4243), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4453), np.float32(0.5078)] 
+2026-10-14 21:33:04.768801: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:33:05.056590: lr: 0.000952 
+2026-10-14 21:33:05.056915: This epoch took 237.882835 s
+ 
+2026-10-14 21:33:05.057088: 
+epoch:  53 
+2026-10-14 21:36:52.621475: train loss : -0.6072 
+2026-10-14 21:36:57.973217: validation loss: -0.4929 
+2026-10-14 21:36:57.974335: Average global foreground Dice: [np.float32(0.9002), np.float32(0.5666), np.float32(0.6803), np.float32(0.6897), np.float32(0.5872), np.float32(0.6995), np.float32(0.6156), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.508), np.float32(0.5645), np.float32(0.4603), np.float32(0.3401), np.float32(0.0), np.float32(0.0), np.float32(0.6296), np.float32(0.489), np.float32(0.5837), np.float32(0.5121), np.float32(0.0), np.float32(0.5357), np.float32(0.6772), np.float32(0.5552), np.float32(0.4041), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.455), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6572), np.float32(0.7102)] 
+2026-10-14 21:36:57.974740: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:36:58.300346: lr: 0.000951 
+2026-10-14 21:36:58.300833: This epoch took 233.243600 s
+ 
+2026-10-14 21:36:58.300989: 
+epoch:  54 
+2026-10-14 21:41:05.252383: train loss : -0.6055 
+2026-10-14 21:41:10.591285: validation loss: -0.4066 
+2026-10-14 21:41:10.592391: Average global foreground Dice: [np.float32(0.8896), np.float32(0.4703), np.float32(0.4921), np.float32(0.491), np.float32(0.3772), np.float32(0.4325), np.float32(0.2555), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3294), np.float32(0.3178), np.float32(0.4478), np.float32(0.3037), np.float32(0.0), np.float32(0.0), np.float32(0.3512), np.float32(0.2864), np.float32(0.3172), np.float32(0.3349), np.float32(0.0), np.float32(0.2911), np.float32(0.6032), np.float32(0.5307), np.float32(0.208), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5067), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5173), np.float32(0.5219)] 
+2026-10-14 21:41:10.592816: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:41:10.902163: lr: 0.00095 
+2026-10-14 21:41:10.902529: This epoch took 252.601450 s
+ 
+2026-10-14 21:41:10.902717: 
+epoch:  55 
+2026-10-14 21:45:13.821817: train loss : -0.6084 
+2026-10-14 21:45:19.169412: validation loss: -0.2985 
+2026-10-14 21:45:19.170416: Average global foreground Dice: [np.float32(0.892), np.float32(0.2968), np.float32(0.1975), np.float32(0.3345), np.float32(0.3385), np.float32(0.1194), np.float32(0.0865), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1982), np.float32(0.1352), np.float32(0.3351), np.float32(0.2596), np.float32(0.0), np.float32(0.0), np.float32(0.2125), np.float32(0.1298), np.float32(0.1148), np.float32(0.1378), np.float32(0.0), np.float32(0.1165), np.float32(0.4017), np.float32(0.2764), np.float32(0.3049), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2852), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2542), np.float32(0.184)] 
+2026-10-14 21:45:19.170601: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:45:19.458041: lr: 0.000949 
+2026-10-14 21:45:19.458550: This epoch took 248.555712 s
+ 
+2026-10-14 21:45:19.458966: 
+epoch:  56 
+2026-10-14 21:49:32.618017: train loss : -0.6141 
+2026-10-14 21:49:37.966717: validation loss: -0.3556 
+2026-10-14 21:49:37.967972: Average global foreground Dice: [np.float32(0.9141), np.float32(0.3214), np.float32(0.2677), np.float32(0.275), np.float32(0.2313), np.float32(0.2563), np.float32(0.2337), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2822), np.float32(0.326), np.float32(0.4314), np.float32(0.2978), np.float32(0.0), np.float32(0.0), np.float32(0.3225), np.float32(0.209), np.float32(0.1854), np.float32(0.1803), np.float32(0.0), np.float32(0.2758), np.float32(0.5057), np.float32(0.4374), np.float32(0.1975), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.394), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3153), np.float32(0.3788)] 
+2026-10-14 21:49:37.968171: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:49:38.261575: lr: 0.000949 
+2026-10-14 21:49:38.261868: This epoch took 258.802564 s
+ 
+2026-10-14 21:49:38.261967: 
+epoch:  57 
+2026-10-14 21:53:44.084389: train loss : -0.6173 
+2026-10-14 21:53:49.417702: validation loss: -0.5088 
+2026-10-14 21:53:49.418800: Average global foreground Dice: [np.float32(0.9139), np.float32(0.5862), np.float32(0.6488), np.float32(0.7108), np.float32(0.5992), np.float32(0.7166), np.float32(0.5741), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5822), np.float32(0.6207), np.float32(0.5277), np.float32(0.4928), np.float32(0.0), np.float32(0.0), np.float32(0.6716), np.float32(0.5138), np.float32(0.6105), np.float32(0.5301), np.float32(0.0), np.float32(0.4733), np.float32(0.7324), np.float32(0.6941), np.float32(0.4156), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5524), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7103), np.float32(0.6986)] 
+2026-10-14 21:53:49.419095: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:53:49.820355: lr: 0.000948 
+2026-10-14 21:53:49.820573: This epoch took 251.558537 s
+ 
+2026-10-14 21:53:49.820660: 
+epoch:  58 
+2026-10-14 21:57:46.427906: train loss : -0.6240 
+2026-10-14 21:57:51.780484: validation loss: -0.3639 
+2026-10-14 21:57:51.781442: Average global foreground Dice: [np.float32(0.8934), np.float32(0.3759), np.float32(0.3416), np.float32(0.3554), np.float32(0.2363), np.float32(0.2949), np.float32(0.2127), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2961), np.float32(0.3477), np.float32(0.4638), np.float32(0.41), np.float32(0.0), np.float32(0.0), np.float32(0.1905), np.float32(0.1895), np.float32(0.1966), np.float32(0.2153), np.float32(0.0), np.float32(0.2451), np.float32(0.5291), np.float32(0.5054), np.float32(0.1588), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4563), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3606), np.float32(0.3609)] 
+2026-10-14 21:57:51.781612: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 21:57:52.070649: lr: 0.000947 
+2026-10-14 21:57:52.071182: This epoch took 242.250455 s
+ 
+2026-10-14 21:57:52.071519: 
+epoch:  59 
+2026-10-14 22:02:03.643316: train loss : -0.6157 
+2026-10-14 22:02:08.991827: validation loss: -0.3527 
+2026-10-14 22:02:08.993158: Average global foreground Dice: [np.float32(0.893), np.float32(0.3739), np.float32(0.2715), np.float32(0.2381), np.float32(0.2045), np.float32(0.3874), np.float32(0.2344), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2533), np.float32(0.2497), np.float32(0.3404), np.float32(0.2524), np.float32(0.0), np.float32(0.0), np.float32(0.1675), np.float32(0.2137), np.float32(0.1847), np.float32(0.205), np.float32(0.0), np.float32(0.3124), np.float32(0.554), np.float32(0.4805), np.float32(0.3658), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4556), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3153), np.float32(0.3526)] 
+2026-10-14 22:02:08.993412: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:02:09.288200: lr: 0.000946 
+2026-10-14 22:02:09.288452: This epoch took 257.216788 s
+ 
+2026-10-14 22:02:09.288544: 
+epoch:  60 
+2026-10-14 22:06:31.943985: train loss : -0.5953 
+2026-10-14 22:06:37.289846: validation loss: -0.3923 
+2026-10-14 22:06:37.290789: Average global foreground Dice: [np.float32(0.9212), np.float32(0.3936), np.float32(0.373), np.float32(0.3866), np.float32(0.2959), np.float32(0.3831), np.float32(0.3142), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3285), np.float32(0.3866), np.float32(0.4019), np.float32(0.3253), np.float32(0.0), np.float32(0.0), np.float32(0.2615), np.float32(0.2386), np.float32(0.2471), np.float32(0.2377), np.float32(0.0), np.float32(0.3479), np.float32(0.6077), np.float32(0.5266), np.float32(0.2135), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3756), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4575), np.float32(0.4283)] 
+2026-10-14 22:06:37.291164: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:06:37.617897: lr: 0.000945 
+2026-10-14 22:06:37.618282: This epoch took 268.329679 s
+ 
+2026-10-14 22:06:37.618421: 
+epoch:  61 
+2026-10-14 22:10:44.353569: train loss : -0.6134 
+2026-10-14 22:10:49.692286: validation loss: -0.4206 
+2026-10-14 22:10:49.693544: Average global foreground Dice: [np.float32(0.907), np.float32(0.4185), np.float32(0.4513), np.float32(0.3167), np.float32(0.3557), np.float32(0.4922), np.float32(0.5293), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2941), np.float32(0.3783), np.float32(0.3884), np.float32(0.4104), np.float32(0.0), np.float32(0.0), np.float32(0.3746), np.float32(0.3398), np.float32(0.4379), np.float32(0.2906), np.float32(0.0), np.float32(0.3875), np.float32(0.6552), np.float32(0.6131), np.float32(0.2808), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4648), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.418), np.float32(0.5633)] 
+2026-10-14 22:10:49.693813: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:10:49.993691: lr: 0.000944 
+2026-10-14 22:10:49.994095: This epoch took 252.375612 s
+ 
+2026-10-14 22:10:49.994298: 
+epoch:  62 
+2026-10-14 22:14:53.487706: train loss : -0.6247 
+2026-10-14 22:14:58.840069: validation loss: -0.4695 
+2026-10-14 22:14:58.841317: Average global foreground Dice: [np.float32(0.9004), np.float32(0.5127), np.float32(0.5704), np.float32(0.6217), np.float32(0.5135), np.float32(0.6618), np.float32(0.538), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4558), np.float32(0.538), np.float32(0.4446), np.float32(0.3659), np.float32(0.0), np.float32(0.0), np.float32(0.568), np.float32(0.3594), np.float32(0.4905), np.float32(0.4019), np.float32(0.0), np.float32(0.3999), np.float32(0.6783), np.float32(0.6379), np.float32(0.3654), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5032), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6332), np.float32(0.6345)] 
+2026-10-14 22:14:58.841576: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:14:59.128985: lr: 0.000943 
+2026-10-14 22:14:59.129222: This epoch took 249.134839 s
+ 
+2026-10-14 22:14:59.129300: 
+epoch:  63 
+2026-10-14 22:18:52.035460: train loss : -0.6276 
+2026-10-14 22:18:57.384550: validation loss: -0.3373 
+2026-10-14 22:18:57.385604: Average global foreground Dice: [np.float32(0.9075), np.float32(0.3407), np.float32(0.2739), np.float32(0.2601), np.float32(0.1865), np.float32(0.2767), np.float32(0.2446), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2701), np.float32(0.2764), np.float32(0.4283), np.float32(0.2872), np.float32(0.0), np.float32(0.0), np.float32(0.1581), np.float32(0.1798), np.float32(0.2663), np.float32(0.1957), np.float32(0.0), np.float32(0.1979), np.float32(0.4624), np.float32(0.3635), np.float32(0.1596), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3736), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2663), np.float32(0.2896)] 
+2026-10-14 22:18:57.386018: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:18:57.676791: lr: 0.000942 
+2026-10-14 22:18:57.677077: This epoch took 238.547721 s
+ 
+2026-10-14 22:18:57.677172: 
+epoch:  64 
+2026-10-14 22:22:58.975713: train loss : -0.6137 
+2026-10-14 22:23:04.314379: validation loss: -0.2558 
+2026-10-14 22:23:04.315568: Average global foreground Dice: [np.float32(0.9015), np.float32(0.1822), np.float32(0.1761), np.float32(0.1305), np.float32(0.0837), np.float32(0.0992), np.float32(0.0693), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1782), np.float32(0.0509), np.float32(0.3635), np.float32(0.0841), np.float32(0.0), np.float32(0.0), np.float32(0.0223), np.float32(0.0131), np.float32(0.0483), np.float32(0.0229), np.float32(0.0), np.float32(0.1321), np.float32(0.3836), np.float32(0.3205), np.float32(0.1501), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2363), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1623), np.float32(0.1576)] 
+2026-10-14 22:23:04.315808: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:23:04.617142: lr: 0.000941 
+2026-10-14 22:23:04.617404: This epoch took 246.940164 s
+ 
+2026-10-14 22:23:04.617595: 
+epoch:  65 
+2026-10-14 22:27:21.521471: train loss : -0.6179 
+2026-10-14 22:27:26.863116: validation loss: -0.5086 
+2026-10-14 22:27:26.864095: Average global foreground Dice: [np.float32(0.8885), np.float32(0.5449), np.float32(0.6261), np.float32(0.6478), np.float32(0.5823), np.float32(0.6885), np.float32(0.5997), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5105), np.float32(0.554), np.float32(0.5481), np.float32(0.5289), np.float32(0.0), np.float32(0.0), np.float32(0.5404), np.float32(0.4138), np.float32(0.524), np.float32(0.4514), np.float32(0.283), np.float32(0.4494), np.float32(0.712), np.float32(0.6734), np.float32(0.3327), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5634), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6735), np.float32(0.6836)] 
+2026-10-14 22:27:26.864254: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:27:27.157746: lr: 0.00094 
+2026-10-14 22:27:27.158141: This epoch took 262.540462 s
+ 
+2026-10-14 22:27:27.158344: 
+epoch:  66 
+2026-10-14 22:31:14.766673: train loss : -0.6315 
+2026-10-14 22:31:20.121618: validation loss: -0.4713 
+2026-10-14 22:31:20.123004: Average global foreground Dice: [np.float32(0.9062), np.float32(0.5188), np.float32(0.5589), np.float32(0.5315), np.float32(0.4194), np.float32(0.5725), np.float32(0.4886), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4225), np.float32(0.446), np.float32(0.5544), np.float32(0.4294), np.float32(0.0), np.float32(0.0), np.float32(0.3835), np.float32(0.274), np.float32(0.387), np.float32(0.3066), np.float32(0.4195), np.float32(0.4746), np.float32(0.7016), np.float32(0.6795), np.float32(0.3269), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5203), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6055), np.float32(0.6382)] 
+2026-10-14 22:31:20.123494: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:31:20.416713: lr: 0.000939 
+2026-10-14 22:31:20.416980: This epoch took 233.258542 s
+ 
+2026-10-14 22:31:20.417062: 
+epoch:  67 
+2026-10-14 22:35:47.678960: train loss : -0.6231 
+2026-10-14 22:35:53.017216: validation loss: -0.4074 
+2026-10-14 22:35:53.018097: Average global foreground Dice: [np.float32(0.9142), np.float32(0.4399), np.float32(0.525), np.float32(0.4295), np.float32(0.3239), np.float32(0.4686), np.float32(0.2283), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3565), np.float32(0.4239), np.float32(0.4025), np.float32(0.1919), np.float32(0.0), np.float32(0.0), np.float32(0.2377), np.float32(0.2282), np.float32(0.2062), np.float32(0.202), np.float32(0.2688), np.float32(0.4234), np.float32(0.6836), np.float32(0.6627), np.float32(0.2933), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4592), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4817), np.float32(0.5606)] 
+2026-10-14 22:35:53.018456: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:35:53.380984: lr: 0.000939 
+2026-10-14 22:35:53.381233: This epoch took 272.964112 s
+ 
+2026-10-14 22:35:53.381425: 
+epoch:  68 
+2026-10-14 22:40:00.329146: train loss : -0.6217 
+2026-10-14 22:40:05.668413: validation loss: -0.3954 
+2026-10-14 22:40:05.669450: Average global foreground Dice: [np.float32(0.8983), np.float32(0.3967), np.float32(0.4013), np.float32(0.3711), np.float32(0.2684), np.float32(0.4507), np.float32(0.343), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3658), np.float32(0.4393), np.float32(0.5256), np.float32(0.4613), np.float32(0.0), np.float32(0.0), np.float32(0.2036), np.float32(0.2009), np.float32(0.2502), np.float32(0.1595), np.float32(0.323), np.float32(0.2991), np.float32(0.4899), np.float32(0.3998), np.float32(0.1259), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4516), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3696), np.float32(0.4237)] 
+2026-10-14 22:40:05.669844: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:40:05.966254: lr: 0.000938 
+2026-10-14 22:40:05.966532: This epoch took 252.584875 s
+ 
+2026-10-14 22:40:05.966622: 
+epoch:  69 
+2026-10-14 22:44:28.415880: train loss : -0.6283 
+2026-10-14 22:44:33.767113: validation loss: -0.5605 
+2026-10-14 22:44:33.768120: Average global foreground Dice: [np.float32(0.9135), np.float32(0.6632), np.float32(0.7956), np.float32(0.7829), np.float32(0.7457), np.float32(0.7969), np.float32(0.7699), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6209), np.float32(0.6841), np.float32(0.6215), np.float32(0.4844), np.float32(0.0), np.float32(0.0), np.float32(0.7534), np.float32(0.6014), np.float32(0.7031), np.float32(0.622), np.float32(0.1085), np.float32(0.652), np.float32(0.8078), np.float32(0.7817), np.float32(0.4706), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4366), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7828), np.float32(0.7849)] 
+2026-10-14 22:44:33.768502: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:44:34.078101: lr: 0.000937 
+2026-10-14 22:44:34.078367: This epoch took 268.111543 s
+ 
+2026-10-14 22:44:34.078457: 
+epoch:  70 
+2026-10-14 22:48:47.139577: train loss : -0.6335 
+2026-10-14 22:48:52.477585: validation loss: -0.5256 
+2026-10-14 22:48:52.478694: Average global foreground Dice: [np.float32(0.8911), np.float32(0.5965), np.float32(0.6974), np.float32(0.7074), np.float32(0.6273), np.float32(0.7375), np.float32(0.6558), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5583), np.float32(0.6431), np.float32(0.5616), np.float32(0.4484), np.float32(0.0), np.float32(0.0), np.float32(0.7024), np.float32(0.5459), np.float32(0.688), np.float32(0.6192), np.float32(0.4852), np.float32(0.5838), np.float32(0.7675), np.float32(0.7279), np.float32(0.458), 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(0.707), np.float32(0.74)] 
+2026-10-14 22:48:52.478854: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:48:52.818131: lr: 0.000936 
+2026-10-14 22:48:52.860590: saving checkpoint... 
+2026-10-14 22:48:52.999916: done, saving took 0.18 seconds 
+2026-10-14 22:48:53.001637: This epoch took 258.923114 s
+ 
+2026-10-14 22:48:53.001910: 
+epoch:  71 
+2026-10-14 22:52:58.820760: train loss : -0.6401 
+2026-10-14 22:53:04.188107: validation loss: -0.3794 
+2026-10-14 22:53:04.189017: Average global foreground Dice: [np.float32(0.8772), np.float32(0.4166), np.float32(0.3588), np.float32(0.4318), np.float32(0.3092), np.float32(0.4505), np.float32(0.3402), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3749), np.float32(0.4248), np.float32(0.4733), np.float32(0.4028), np.float32(0.0), np.float32(0.0), np.float32(0.2282), np.float32(0.2041), np.float32(0.2447), np.float32(0.1775), np.float32(0.2737), np.float32(0.2862), np.float32(0.4199), np.float32(0.3912), np.float32(0.2138), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4687), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4128), np.float32(0.3539)] 
+2026-10-14 22:53:04.189259: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:53:04.481122: lr: 0.000935 
+2026-10-14 22:53:04.481354: This epoch took 251.479347 s
+ 
+2026-10-14 22:53:04.481432: 
+epoch:  72 
+2026-10-14 22:56:59.095762: train loss : -0.6302 
+2026-10-14 22:57:04.444447: validation loss: -0.4961 
+2026-10-14 22:57:04.446153: Average global foreground Dice: [np.float32(0.894), np.float32(0.5518), np.float32(0.5537), np.float32(0.5994), np.float32(0.4829), np.float32(0.6411), np.float32(0.5172), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4574), np.float32(0.5268), np.float32(0.5718), np.float32(0.4291), np.float32(0.0), np.float32(0.0), np.float32(0.5025), np.float32(0.4429), np.float32(0.5193), np.float32(0.4953), np.float32(0.4241), np.float32(0.4805), np.float32(0.7088), np.float32(0.6621), np.float32(0.3249), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4969), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6298), np.float32(0.6352)] 
+2026-10-14 22:57:04.448093: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 22:57:04.799565: lr: 0.000934 
+2026-10-14 22:57:04.902382: saving checkpoint... 
+2026-10-14 22:57:05.048876: done, saving took 0.25 seconds 
+2026-10-14 22:57:05.050807: This epoch took 240.569318 s
+ 
+2026-10-14 22:57:05.050972: 
+epoch:  73 
+2026-10-14 23:01:17.147965: train loss : -0.6454 
+2026-10-14 23:01:22.491004: validation loss: -0.5156 
+2026-10-14 23:01:22.491983: Average global foreground Dice: [np.float32(0.9153), np.float32(0.5875), np.float32(0.7148), np.float32(0.6874), np.float32(0.5777), np.float32(0.7453), np.float32(0.6363), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4743), np.float32(0.6287), np.float32(0.4384), np.float32(0.3444), np.float32(0.0), np.float32(0.0), np.float32(0.664), np.float32(0.534), np.float32(0.6355), np.float32(0.5831), np.float32(0.5333), np.float32(0.5756), np.float32(0.7784), np.float32(0.7489), np.float32(0.4906), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4834), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7453), np.float32(0.7539)] 
+2026-10-14 23:01:22.492351: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:01:22.781003: lr: 0.000933 
+2026-10-14 23:01:22.837955: saving checkpoint... 
+2026-10-14 23:01:22.971548: done, saving took 0.19 seconds 
+2026-10-14 23:01:22.973549: This epoch took 257.922509 s
+ 
+2026-10-14 23:01:22.973775: 
+epoch:  74 
+2026-10-14 23:05:28.726715: train loss : -0.6435 
+2026-10-14 23:05:34.070234: validation loss: -0.4452 
+2026-10-14 23:05:34.071088: Average global foreground Dice: [np.float32(0.9075), np.float32(0.474), np.float32(0.5504), np.float32(0.5651), np.float32(0.4092), np.float32(0.4494), np.float32(0.4094), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4551), np.float32(0.4099), np.float32(0.512), np.float32(0.4802), np.float32(0.0), np.float32(0.0), np.float32(0.4635), np.float32(0.3899), np.float32(0.4029), np.float32(0.377), np.float32(0.4356), np.float32(0.4984), np.float32(0.6171), np.float32(0.6161), np.float32(0.2966), 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(0.5586), np.float32(0.532)] 
+2026-10-14 23:05:34.071262: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:05:34.370149: lr: 0.000932 
+2026-10-14 23:05:34.403058: saving checkpoint... 
+2026-10-14 23:05:34.532734: done, saving took 0.16 seconds 
+2026-10-14 23:05:34.534365: This epoch took 251.560513 s
+ 
+2026-10-14 23:05:34.534554: 
+epoch:  75 
+2026-10-14 23:09:38.979761: train loss : -0.6460 
+2026-10-14 23:09:44.323867: validation loss: -0.4999 
+2026-10-14 23:09:44.325022: Average global foreground Dice: [np.float32(0.9163), np.float32(0.5814), np.float32(0.7071), np.float32(0.6731), np.float32(0.5685), np.float32(0.6858), np.float32(0.5925), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5174), np.float32(0.5667), np.float32(0.5822), np.float32(0.4617), np.float32(0.0), np.float32(0.0), np.float32(0.6275), np.float32(0.4735), np.float32(0.5776), np.float32(0.5159), np.float32(0.4985), np.float32(0.5145), np.float32(0.7756), np.float32(0.7512), np.float32(0.3427), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.504), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7049), np.float32(0.7105)] 
+2026-10-14 23:09:44.325386: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:09:44.637708: lr: 0.000931 
+2026-10-14 23:09:44.693549: saving checkpoint... 
+2026-10-14 23:09:44.835124: done, saving took 0.20 seconds 
+2026-10-14 23:09:44.837145: This epoch took 250.302518 s
+ 
+2026-10-14 23:09:44.837559: 
+epoch:  76 
+2026-10-14 23:13:52.275289: train loss : -0.6291 
+2026-10-14 23:13:57.613165: validation loss: -0.5334 
+2026-10-14 23:13:57.614215: Average global foreground Dice: [np.float32(0.9168), np.float32(0.6055), np.float32(0.7491), np.float32(0.7562), np.float32(0.6596), np.float32(0.7602), np.float32(0.7047), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5822), np.float32(0.6589), np.float32(0.4509), np.float32(0.4412), np.float32(0.0), np.float32(0.0), np.float32(0.7003), np.float32(0.5384), np.float32(0.6908), np.float32(0.5617), np.float32(0.4967), np.float32(0.5578), np.float32(0.8611), np.float32(0.8474), np.float32(0.4373), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5423), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7994), np.float32(0.7932)] 
+2026-10-14 23:13:57.614499: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:13:57.934190: lr: 0.00093 
+2026-10-14 23:13:57.995038: saving checkpoint... 
+2026-10-14 23:13:58.132594: done, saving took 0.20 seconds 
+2026-10-14 23:13:58.137806: This epoch took 253.299707 s
+ 
+2026-10-14 23:13:58.138005: 
+epoch:  77 
+2026-10-14 23:17:52.936298: train loss : -0.6367 
+2026-10-14 23:17:58.291626: validation loss: -0.5054 
+2026-10-14 23:17:58.292690: Average global foreground Dice: [np.float32(0.8869), np.float32(0.582), np.float32(0.6454), np.float32(0.693), np.float32(0.5869), np.float32(0.6414), np.float32(0.6082), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5355), np.float32(0.6171), np.float32(0.4937), np.float32(0.1786), np.float32(0.0), np.float32(0.0), np.float32(0.6698), np.float32(0.4979), np.float32(0.6518), np.float32(0.5214), np.float32(0.4761), np.float32(0.4965), np.float32(0.7631), np.float32(0.7137), np.float32(0.3737), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5446), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7286), np.float32(0.7399)] 
+2026-10-14 23:17:58.293038: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:17:58.766137: lr: 0.00093 
+2026-10-14 23:17:58.773890: saving checkpoint... 
+2026-10-14 23:17:58.904195: done, saving took 0.14 seconds 
+2026-10-14 23:17:58.905957: This epoch took 240.767866 s
+ 
+2026-10-14 23:17:58.906285: 
+epoch:  78 
+2026-10-14 23:21:46.447029: train loss : -0.6494 
+2026-10-14 23:21:51.794137: validation loss: -0.5113 
+2026-10-14 23:21:51.795078: Average global foreground Dice: [np.float32(0.9082), np.float32(0.5631), np.float32(0.6769), np.float32(0.7017), np.float32(0.6135), np.float32(0.7283), np.float32(0.6456), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5389), np.float32(0.65), np.float32(0.5202), np.float32(0.4409), np.float32(0.0), np.float32(0.0), np.float32(0.6736), np.float32(0.549), np.float32(0.6587), np.float32(0.591), np.float32(0.4395), np.float32(0.5184), np.float32(0.7526), np.float32(0.7109), np.float32(0.4278), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5438), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7046), np.float32(0.6976)] 
+2026-10-14 23:21:51.795251: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:21:52.089833: lr: 0.000929 
+2026-10-14 23:21:52.097502: saving checkpoint... 
+2026-10-14 23:21:52.230867: done, saving took 0.14 seconds 
+2026-10-14 23:21:52.232916: This epoch took 233.326478 s
+ 
+2026-10-14 23:21:52.233227: 
+epoch:  79 
+2026-10-14 23:26:23.949288: train loss : -0.6235 
+2026-10-14 23:26:29.303058: validation loss: -0.5289 
+2026-10-14 23:26:29.304185: Average global foreground Dice: [np.float32(0.9008), np.float32(0.606), np.float32(0.7296), np.float32(0.7312), np.float32(0.6478), np.float32(0.7593), np.float32(0.6779), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5737), np.float32(0.6501), np.float32(0.4433), np.float32(0.4254), np.float32(0.0), np.float32(0.0), np.float32(0.6963), np.float32(0.5615), np.float32(0.6962), np.float32(0.5936), np.float32(0.4656), np.float32(0.582), np.float32(0.7458), np.float32(0.712), np.float32(0.4185), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5551), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7597), np.float32(0.7698)] 
+2026-10-14 23:26:29.304502: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:26:29.604325: lr: 0.000928 
+2026-10-14 23:26:29.612958: saving checkpoint... 
+2026-10-14 23:26:29.740848: done, saving took 0.14 seconds 
+2026-10-14 23:26:29.742456: This epoch took 277.508878 s
+ 
+2026-10-14 23:26:29.742611: 
+epoch:  80 
+2026-10-14 23:30:45.991376: train loss : -0.6210 
+2026-10-14 23:30:51.336277: validation loss: -0.4588 
+2026-10-14 23:30:51.337219: Average global foreground Dice: [np.float32(0.9069), np.float32(0.5081), np.float32(0.526), np.float32(0.5373), np.float32(0.4338), np.float32(0.5873), np.float32(0.4925), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4575), np.float32(0.5133), np.float32(0.5428), np.float32(0.5025), np.float32(0.0), np.float32(0.0), np.float32(0.4431), np.float32(0.3623), np.float32(0.4164), np.float32(0.3595), np.float32(0.4427), np.float32(0.4316), np.float32(0.699), np.float32(0.6558), np.float32(0.2575), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5165), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5651), np.float32(0.5927)] 
+2026-10-14 23:30:51.337375: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:30:51.635199: lr: 0.000927 
+2026-10-14 23:30:51.635589: This epoch took 261.892901 s
+ 
+2026-10-14 23:30:51.635696: 
+epoch:  81 
+2026-10-14 23:34:45.556967: train loss : -0.6384 
+2026-10-14 23:34:50.912713: validation loss: -0.4827 
+2026-10-14 23:34:50.913686: Average global foreground Dice: [np.float32(0.9031), np.float32(0.5441), np.float32(0.6384), np.float32(0.5963), np.float32(0.4451), np.float32(0.6152), np.float32(0.5382), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4666), np.float32(0.5572), np.float32(0.5201), np.float32(0.409), np.float32(0.0), np.float32(0.0), np.float32(0.462), np.float32(0.3745), np.float32(0.4806), np.float32(0.4116), np.float32(0.4719), np.float32(0.5512), np.float32(0.71), np.float32(0.7032), np.float32(0.3596), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5333), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6293), np.float32(0.6834)] 
+2026-10-14 23:34:50.914048: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:34:51.213096: lr: 0.000926 
+2026-10-14 23:34:51.213436: This epoch took 239.577662 s
+ 
+2026-10-14 23:34:51.213560: 
+epoch:  82 
+2026-10-14 23:38:54.355437: train loss : -0.6581 
+2026-10-14 23:38:59.710008: validation loss: -0.5523 
+2026-10-14 23:38:59.710866: Average global foreground Dice: [np.float32(0.908), np.float32(0.6366), np.float32(0.7516), np.float32(0.7665), np.float32(0.6929), np.float32(0.7829), np.float32(0.7069), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5806), np.float32(0.6573), np.float32(0.5554), np.float32(0.4388), np.float32(0.0), np.float32(0.0), np.float32(0.7247), np.float32(0.5447), np.float32(0.6891), np.float32(0.6284), np.float32(0.5202), np.float32(0.5777), np.float32(0.8301), np.float32(0.8144), np.float32(0.4814), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5658), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8099), np.float32(0.8197)] 
+2026-10-14 23:38:59.711099: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:39:00.011764: lr: 0.000925 
+2026-10-14 23:39:00.019537: saving checkpoint... 
+2026-10-14 23:39:00.148698: done, saving took 0.14 seconds 
+2026-10-14 23:39:00.150294: This epoch took 248.936667 s
+ 
+2026-10-14 23:39:00.150496: 
+epoch:  83 
+2026-10-14 23:42:56.880692: train loss : -0.6527 
+2026-10-14 23:43:02.218908: validation loss: -0.5404 
+2026-10-14 23:43:02.219758: Average global foreground Dice: [np.float32(0.9091), np.float32(0.6262), np.float32(0.7522), np.float32(0.7361), np.float32(0.6444), np.float32(0.7757), np.float32(0.677), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5441), np.float32(0.6362), np.float32(0.5862), np.float32(0.4854), np.float32(0.0), np.float32(0.0), np.float32(0.7284), np.float32(0.5416), np.float32(0.7003), np.float32(0.5617), np.float32(0.4739), np.float32(0.6506), np.float32(0.8115), np.float32(0.7965), np.float32(0.4243), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5404), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8019), np.float32(0.8078)] 
+2026-10-14 23:43:02.219905: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:43:02.515881: lr: 0.000924 
+2026-10-14 23:43:02.524390: saving checkpoint... 
+2026-10-14 23:43:02.647556: done, saving took 0.13 seconds 
+2026-10-14 23:43:02.649417: This epoch took 242.498829 s
+ 
+2026-10-14 23:43:02.649644: 
+epoch:  84 
+2026-10-14 23:47:02.956201: train loss : -0.6439 
+2026-10-14 23:47:08.310807: validation loss: -0.5419 
+2026-10-14 23:47:08.311891: Average global foreground Dice: [np.float32(0.8946), np.float32(0.6025), np.float32(0.7372), np.float32(0.751), np.float32(0.6776), np.float32(0.7779), np.float32(0.676), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5592), np.float32(0.6057), np.float32(0.5724), np.float32(0.4619), np.float32(0.0), np.float32(0.0), np.float32(0.7333), np.float32(0.5574), np.float32(0.7054), np.float32(0.6325), np.float32(0.5082), np.float32(0.6133), np.float32(0.8039), np.float32(0.7812), np.float32(0.3983), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5587), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7815), np.float32(0.81)] 
+2026-10-14 23:47:08.312349: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:47:08.666599: lr: 0.000923 
+2026-10-14 23:47:08.674621: saving checkpoint... 
+2026-10-14 23:47:08.803154: done, saving took 0.14 seconds 
+2026-10-14 23:47:08.805178: This epoch took 246.155459 s
+ 
+2026-10-14 23:47:08.805427: 
+epoch:  85 
+2026-10-14 23:50:52.126328: train loss : -0.6257 
+2026-10-14 23:50:57.481666: validation loss: -0.5423 
+2026-10-14 23:50:57.482580: Average global foreground Dice: [np.float32(0.9079), np.float32(0.6094), np.float32(0.7311), np.float32(0.7616), np.float32(0.6145), np.float32(0.7842), np.float32(0.6574), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5743), np.float32(0.6284), np.float32(0.512), np.float32(0.4407), np.float32(0.0), np.float32(0.0), np.float32(0.609), np.float32(0.4539), np.float32(0.6089), np.float32(0.5421), np.float32(0.4442), np.float32(0.573), np.float32(0.8487), np.float32(0.8308), np.float32(0.4071), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5789), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7776), np.float32(0.7948)] 
+2026-10-14 23:50:57.482727: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:50:57.781644: lr: 0.000922 
+2026-10-14 23:50:57.790270: saving checkpoint... 
+2026-10-14 23:50:57.912416: done, saving took 0.13 seconds 
+2026-10-14 23:50:57.914493: This epoch took 229.108875 s
+ 
+2026-10-14 23:50:57.914677: 
+epoch:  86 
+2026-10-14 23:55:11.105032: train loss : -0.6478 
+2026-10-14 23:55:16.461810: validation loss: -0.5271 
+2026-10-14 23:55:16.462850: Average global foreground Dice: [np.float32(0.9057), np.float32(0.5468), np.float32(0.6801), np.float32(0.7069), np.float32(0.6119), np.float32(0.7355), np.float32(0.6333), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5354), np.float32(0.6227), np.float32(0.5093), np.float32(0.3841), np.float32(0.0), np.float32(0.0), np.float32(0.6599), np.float32(0.5045), np.float32(0.6455), np.float32(0.5864), np.float32(0.4948), np.float32(0.5807), np.float32(0.7791), np.float32(0.7523), np.float32(0.4204), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5567), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7297), np.float32(0.7521)] 
+2026-10-14 23:55:16.463114: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:55:16.777549: lr: 0.000921 
+2026-10-14 23:55:16.785814: saving checkpoint... 
+2026-10-14 23:55:16.916855: done, saving took 0.14 seconds 
+2026-10-14 23:55:16.918810: This epoch took 259.004043 s
+ 
+2026-10-14 23:55:16.919032: 
+epoch:  87 
+2026-10-14 23:59:45.385786: train loss : -0.6536 
+2026-10-14 23:59:50.756915: validation loss: -0.4429 
+2026-10-14 23:59:50.757900: Average global foreground Dice: [np.float32(0.8906), np.float32(0.4567), np.float32(0.4935), np.float32(0.4969), np.float32(0.3722), np.float32(0.5539), np.float32(0.4717), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3602), np.float32(0.4083), np.float32(0.4463), np.float32(0.3337), np.float32(0.0), np.float32(0.0), np.float32(0.3647), np.float32(0.2784), np.float32(0.3868), np.float32(0.3734), np.float32(0.104), np.float32(0.2997), np.float32(0.6937), np.float32(0.6574), np.float32(0.2739), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5467), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5519), np.float32(0.5852)] 
+2026-10-14 23:59:50.758158: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-14 23:59:51.059685: lr: 0.00092 
+2026-10-14 23:59:51.059958: This epoch took 274.140717 s
+ 
+2026-10-14 23:59:51.060081: 
+epoch:  88 
+2026-10-15 00:03:56.011925: train loss : -0.6356 
+2026-10-15 00:04:01.361971: validation loss: -0.2747 
+2026-10-15 00:04:01.362938: Average global foreground Dice: [np.float32(0.8835), np.float32(0.2862), np.float32(0.1503), np.float32(0.1073), np.float32(0.1067), np.float32(0.2078), np.float32(0.1504), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1359), np.float32(0.2204), np.float32(0.2827), np.float32(0.1609), np.float32(0.0), np.float32(0.0), np.float32(0.0961), np.float32(0.1156), np.float32(0.1194), np.float32(0.1099), np.float32(0.2391), np.float32(0.1121), np.float32(0.3206), np.float32(0.2213), np.float32(0.257), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3864), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1411), np.float32(0.188)] 
+2026-10-15 00:04:01.363372: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:04:01.698785: lr: 0.00092 
+2026-10-15 00:04:01.699293: This epoch took 250.639014 s
+ 
+2026-10-15 00:04:01.699637: 
+epoch:  89 
+2026-10-15 00:08:18.103355: train loss : -0.6454 
+2026-10-15 00:08:23.453871: validation loss: -0.3181 
+2026-10-15 00:08:23.454946: Average global foreground Dice: [np.float32(0.8868), np.float32(0.3306), np.float32(0.2738), np.float32(0.3027), np.float32(0.1793), np.float32(0.2232), np.float32(0.1539), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2488), np.float32(0.2187), np.float32(0.4094), np.float32(0.2762), np.float32(0.0), np.float32(0.0), np.float32(0.1561), np.float32(0.1401), np.float32(0.1781), np.float32(0.1314), np.float32(0.0842), np.float32(0.2595), np.float32(0.3344), np.float32(0.3925), np.float32(0.168), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4423), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2394), np.float32(0.3094)] 
+2026-10-15 00:08:23.455198: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:08:23.746989: lr: 0.000919 
+2026-10-15 00:08:23.747363: This epoch took 262.047461 s
+ 
+2026-10-15 00:08:23.747570: 
+epoch:  90 
+2026-10-15 00:13:03.933651: train loss : -0.6441 
+2026-10-15 00:13:09.264885: validation loss: -0.4321 
+2026-10-15 00:13:09.265851: Average global foreground Dice: [np.float32(0.907), np.float32(0.467), np.float32(0.4575), np.float32(0.4764), np.float32(0.3549), np.float32(0.5077), np.float32(0.3802), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3741), np.float32(0.4485), np.float32(0.4443), np.float32(0.4008), np.float32(0.0), np.float32(0.0), np.float32(0.3313), np.float32(0.2776), np.float32(0.3325), np.float32(0.3248), np.float32(0.3535), np.float32(0.3655), np.float32(0.672), np.float32(0.5873), np.float32(0.2606), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4961), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4892), np.float32(0.5061)] 
+2026-10-15 00:13:09.266112: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:13:09.560215: lr: 0.000918 
+2026-10-15 00:13:09.560469: This epoch took 285.812793 s
+ 
+2026-10-15 00:13:09.560553: 
+epoch:  91 
+2026-10-15 00:17:35.731706: train loss : -0.6550 
+2026-10-15 00:17:41.089356: validation loss: -0.2968 
+2026-10-15 00:17:41.090284: Average global foreground Dice: [np.float32(0.8957), np.float32(0.2201), np.float32(0.2567), np.float32(0.2477), np.float32(0.1892), np.float32(0.2726), np.float32(0.2221), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1771), np.float32(0.247), np.float32(0.2644), np.float32(0.1098), np.float32(0.0), np.float32(0.0), np.float32(0.1792), np.float32(0.1194), np.float32(0.1829), np.float32(0.1996), np.float32(0.1599), np.float32(0.1713), np.float32(0.2769), np.float32(0.1729), np.float32(0.1644), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2607), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2497), np.float32(0.213)] 
+2026-10-15 00:17:41.090623: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:17:41.390011: lr: 0.000917 
+2026-10-15 00:17:41.390301: This epoch took 271.829693 s
+ 
+2026-10-15 00:17:41.390388: 
+epoch:  92 
+2026-10-15 00:21:35.816682: train loss : -0.6479 
+2026-10-15 00:21:41.173973: validation loss: -0.4845 
+2026-10-15 00:21:41.175297: Average global foreground Dice: [np.float32(0.9075), np.float32(0.528), np.float32(0.6014), np.float32(0.5897), np.float32(0.5133), np.float32(0.6215), np.float32(0.5155), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4404), np.float32(0.3819), np.float32(0.41), np.float32(0.3685), np.float32(0.0), np.float32(0.0), np.float32(0.5784), np.float32(0.4469), np.float32(0.4635), np.float32(0.4372), np.float32(0.4217), np.float32(0.5433), np.float32(0.6992), np.float32(0.5864), np.float32(0.4064), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.502), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6575), np.float32(0.6254)] 
+2026-10-15 00:21:41.175581: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:21:41.465633: lr: 0.000916 
+2026-10-15 00:21:41.465924: This epoch took 240.075462 s
+ 
+2026-10-15 00:21:41.466225: 
+epoch:  93 
+2026-10-15 00:25:52.155824: train loss : -0.6609 
+2026-10-15 00:25:57.511193: validation loss: -0.5024 
+2026-10-15 00:25:57.512311: Average global foreground Dice: [np.float32(0.9157), np.float32(0.5665), np.float32(0.715), np.float32(0.6599), np.float32(0.5555), np.float32(0.6916), np.float32(0.57), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.546), np.float32(0.6155), np.float32(0.484), np.float32(0.446), np.float32(0.0), np.float32(0.0), np.float32(0.6018), np.float32(0.4939), np.float32(0.5962), np.float32(0.5126), np.float32(0.4996), np.float32(0.6171), np.float32(0.6933), np.float32(0.6198), np.float32(0.3894), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4914), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6937), np.float32(0.7017)] 
+2026-10-15 00:25:57.512840: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:25:57.806758: lr: 0.000915 
+2026-10-15 00:25:57.807217: This epoch took 256.340865 s
+ 
+2026-10-15 00:25:57.807486: 
+epoch:  94 
+2026-10-15 00:30:40.937431: train loss : -0.6604 
+2026-10-15 00:30:46.276409: validation loss: -0.4993 
+2026-10-15 00:30:46.277297: Average global foreground Dice: [np.float32(0.8993), np.float32(0.5571), np.float32(0.5993), np.float32(0.6213), np.float32(0.5078), np.float32(0.639), np.float32(0.4995), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4633), np.float32(0.5589), np.float32(0.5382), np.float32(0.4518), np.float32(0.0), np.float32(0.0), np.float32(0.5144), np.float32(0.4413), np.float32(0.4947), np.float32(0.466), np.float32(0.5082), np.float32(0.5044), np.float32(0.726), np.float32(0.6679), np.float32(0.3332), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5472), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6265), np.float32(0.6172)] 
+2026-10-15 00:30:46.277447: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:30:46.597367: lr: 0.000914 
+2026-10-15 00:30:46.597763: This epoch took 288.790083 s
+ 
+2026-10-15 00:30:46.597979: 
+epoch:  95 
+2026-10-15 00:35:09.327552: train loss : -0.6558 
+2026-10-15 00:35:14.699591: validation loss: -0.5860 
+2026-10-15 00:35:14.700557: Average global foreground Dice: [np.float32(0.914), np.float32(0.6227), np.float32(0.8056), np.float32(0.8262), np.float32(0.7636), np.float32(0.8578), np.float32(0.7713), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6742), np.float32(0.6829), np.float32(0.6185), np.float32(0.5003), np.float32(0.0), np.float32(0.0), np.float32(0.7072), np.float32(0.5309), np.float32(0.6975), np.float32(0.61), np.float32(0.4999), np.float32(0.6097), np.float32(0.8467), np.float32(0.8366), np.float32(0.4834), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3855), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8479), np.float32(0.8533)] 
+2026-10-15 00:35:14.700736: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:35:15.001781: lr: 0.000913 
+2026-10-15 00:35:15.002041: This epoch took 268.403937 s
+ 
+2026-10-15 00:35:15.002128: 
+epoch:  96 
+2026-10-15 00:39:46.327650: train loss : -0.6577 
+2026-10-15 00:39:51.664531: validation loss: -0.3966 
+2026-10-15 00:39:51.665561: Average global foreground Dice: [np.float32(0.8803), np.float32(0.4053), np.float32(0.3775), np.float32(0.421), np.float32(0.3451), np.float32(0.3612), np.float32(0.342), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3174), np.float32(0.3309), np.float32(0.4032), np.float32(0.2981), np.float32(0.0), np.float32(0.0), np.float32(0.3193), np.float32(0.2268), np.float32(0.285), np.float32(0.267), np.float32(0.4084), np.float32(0.3368), np.float32(0.699), np.float32(0.6617), np.float32(0.218), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4462), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4813), np.float32(0.4856)] 
+2026-10-15 00:39:51.665957: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:39:51.971698: lr: 0.000912 
+2026-10-15 00:39:51.971961: This epoch took 276.969777 s
+ 
+2026-10-15 00:39:51.972235: 
+epoch:  97 
+2026-10-15 00:43:45.893946: train loss : -0.6407 
+2026-10-15 00:43:51.236224: validation loss: -0.5046 
+2026-10-15 00:43:51.241804: Average global foreground Dice: [np.float32(0.902), np.float32(0.5352), np.float32(0.6596), np.float32(0.6462), np.float32(0.5179), np.float32(0.6574), np.float32(0.5661), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4625), np.float32(0.5328), np.float32(0.5684), np.float32(0.3671), np.float32(0.0), np.float32(0.0), np.float32(0.5871), np.float32(0.4771), np.float32(0.5703), np.float32(0.5131), np.float32(0.4077), np.float32(0.5271), np.float32(0.8202), np.float32(0.808), np.float32(0.4432), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4842), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7207), np.float32(0.7528)] 
+2026-10-15 00:43:51.244514: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:43:51.639225: lr: 0.000911 
+2026-10-15 00:43:51.639470: This epoch took 239.667121 s
+ 
+2026-10-15 00:43:51.639571: 
+epoch:  98 
+2026-10-15 00:47:35.725787: train loss : -0.6361 
+2026-10-15 00:47:41.094635: validation loss: -0.4044 
+2026-10-15 00:47:41.095750: Average global foreground Dice: [np.float32(0.9169), np.float32(0.3802), np.float32(0.4151), np.float32(0.4731), np.float32(0.3283), np.float32(0.4527), np.float32(0.3601), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3617), np.float32(0.4078), np.float32(0.4725), np.float32(0.3731), np.float32(0.0), np.float32(0.0), np.float32(0.3432), np.float32(0.2712), np.float32(0.37), np.float32(0.3126), np.float32(0.3352), np.float32(0.3124), np.float32(0.7133), np.float32(0.682), np.float32(0.3774), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4634), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5191), np.float32(0.5369)] 
+2026-10-15 00:47:41.095992: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:47:41.403088: lr: 0.00091 
+2026-10-15 00:47:41.403341: This epoch took 229.763702 s
+ 
+2026-10-15 00:47:41.403427: 
+epoch:  99 
+2026-10-15 00:51:46.159743: train loss : -0.6430 
+2026-10-15 00:51:51.502493: validation loss: -0.4338 
+2026-10-15 00:51:51.503942: Average global foreground Dice: [np.float32(0.8991), np.float32(0.4869), np.float32(0.4864), np.float32(0.4579), np.float32(0.3511), np.float32(0.4766), np.float32(0.3947), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3835), np.float32(0.4259), np.float32(0.4955), np.float32(0.4172), np.float32(0.0), np.float32(0.0), np.float32(0.3144), np.float32(0.2826), np.float32(0.3481), np.float32(0.2936), np.float32(0.3811), np.float32(0.3727), np.float32(0.5305), np.float32(0.5786), np.float32(0.2419), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5156), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4797), np.float32(0.538)] 
+2026-10-15 00:51:51.504345: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:51:51.801082: lr: 0.00091 
+2026-10-15 00:51:51.801529: saving scheduled checkpoint file... 
+2026-10-15 00:51:51.810015: saving checkpoint... 
+2026-10-15 00:51:51.937318: done, saving took 0.14 seconds 
+2026-10-15 00:51:51.939219: done 
+2026-10-15 00:51:51.939439: This epoch took 250.535952 s
+ 
+2026-10-15 00:51:51.939521: 
+epoch:  100 
+2026-10-15 00:55:54.475900: train loss : -0.6576 
+2026-10-15 00:55:59.829810: validation loss: -0.4776 
+2026-10-15 00:55:59.830887: Average global foreground Dice: [np.float32(0.909), np.float32(0.5113), np.float32(0.5478), np.float32(0.5542), np.float32(0.4188), np.float32(0.5923), np.float32(0.5131), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4267), np.float32(0.5123), np.float32(0.5392), np.float32(0.421), np.float32(0.0), np.float32(0.0), np.float32(0.3916), np.float32(0.3356), np.float32(0.4127), np.float32(0.4086), np.float32(0.417), np.float32(0.4354), np.float32(0.7167), np.float32(0.6602), np.float32(0.2659), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5165), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6171), np.float32(0.6361)] 
+2026-10-15 00:55:59.831265: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 00:56:00.134408: lr: 0.000909 
+2026-10-15 00:56:00.134786: This epoch took 248.195206 s
+ 
+2026-10-15 00:56:00.135108: 
+epoch:  101 
+2026-10-15 01:00:18.462971: train loss : -0.6631 
+2026-10-15 01:00:23.849628: validation loss: -0.4919 
+2026-10-15 01:00:23.850584: Average global foreground Dice: [np.float32(0.9125), np.float32(0.5325), np.float32(0.6143), np.float32(0.6063), np.float32(0.5233), np.float32(0.6115), np.float32(0.5765), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4457), np.float32(0.4801), np.float32(0.5036), np.float32(0.5065), np.float32(0.0), np.float32(0.0), np.float32(0.488), np.float32(0.4209), np.float32(0.5278), np.float32(0.407), np.float32(0.3084), np.float32(0.4772), np.float32(0.7839), np.float32(0.7263), np.float32(0.3192), 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(0.6362), np.float32(0.6607)] 
+2026-10-15 01:00:23.850746: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:00:24.162111: lr: 0.000908 
+2026-10-15 01:00:24.162374: This epoch took 264.027025 s
+ 
+2026-10-15 01:00:24.162470: 
+epoch:  102 
+2026-10-15 01:04:30.999456: train loss : -0.6574 
+2026-10-15 01:04:36.356623: validation loss: -0.5391 
+2026-10-15 01:04:36.357664: Average global foreground Dice: [np.float32(0.9014), np.float32(0.5907), np.float32(0.7167), np.float32(0.7462), np.float32(0.6548), np.float32(0.7807), np.float32(0.6872), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5633), np.float32(0.683), np.float32(0.5953), np.float32(0.4533), np.float32(0.0), np.float32(0.0), np.float32(0.6666), np.float32(0.4908), np.float32(0.6497), np.float32(0.5498), np.float32(0.569), np.float32(0.5674), np.float32(0.7772), np.float32(0.7294), np.float32(0.4123), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5613), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7595), np.float32(0.7688)] 
+2026-10-15 01:04:36.357949: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:04:36.666662: lr: 0.000907 
+2026-10-15 01:04:36.667073: This epoch took 252.504535 s
+ 
+2026-10-15 01:04:36.667390: 
+epoch:  103 
+2026-10-15 01:08:44.411689: train loss : -0.6618 
+2026-10-15 01:08:49.746204: validation loss: -0.4986 
+2026-10-15 01:08:49.747226: Average global foreground Dice: [np.float32(0.9153), np.float32(0.5205), np.float32(0.6252), np.float32(0.6175), np.float32(0.5099), np.float32(0.6855), np.float32(0.565), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4918), np.float32(0.561), np.float32(0.5072), np.float32(0.4266), np.float32(0.0), np.float32(0.0), np.float32(0.5225), np.float32(0.4312), np.float32(0.5385), np.float32(0.5125), np.float32(0.5065), np.float32(0.521), np.float32(0.7693), np.float32(0.7436), np.float32(0.4226), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5769), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6635), np.float32(0.6859)] 
+2026-10-15 01:08:49.747611: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:08:50.083109: lr: 0.000906 
+2026-10-15 01:08:50.083699: This epoch took 253.416184 s
+ 
+2026-10-15 01:08:50.084176: 
+epoch:  104 
+2026-10-15 01:12:46.946357: train loss : -0.6655 
+2026-10-15 01:12:52.287631: validation loss: -0.5059 
+2026-10-15 01:12:52.288758: Average global foreground Dice: [np.float32(0.912), np.float32(0.5854), np.float32(0.6605), np.float32(0.6719), np.float32(0.5634), np.float32(0.6851), np.float32(0.5738), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5105), np.float32(0.5493), np.float32(0.5385), np.float32(0.4532), np.float32(0.0), np.float32(0.0), np.float32(0.5196), np.float32(0.3752), np.float32(0.5103), np.float32(0.4381), np.float32(0.3574), np.float32(0.3666), np.float32(0.7901), np.float32(0.7312), np.float32(0.3481), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5198), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7011), np.float32(0.7009)] 
+2026-10-15 01:12:52.289146: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:12:52.594490: lr: 0.000905 
+2026-10-15 01:12:52.594750: This epoch took 242.510328 s
+ 
+2026-10-15 01:12:52.594844: 
+epoch:  105 
+2026-10-15 01:17:30.313049: train loss : -0.6500 
+2026-10-15 01:17:35.660228: validation loss: -0.3826 
+2026-10-15 01:17:35.661300: Average global foreground Dice: [np.float32(0.8946), np.float32(0.4237), np.float32(0.4084), np.float32(0.3754), np.float32(0.2679), np.float32(0.4223), np.float32(0.3622), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.257), np.float32(0.3599), np.float32(0.4012), np.float32(0.2707), np.float32(0.0), np.float32(0.0), np.float32(0.266), np.float32(0.2407), np.float32(0.2742), np.float32(0.304), np.float32(0.3848), np.float32(0.3673), np.float32(0.5911), np.float32(0.4456), np.float32(0.277), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3469), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.424), np.float32(0.4109)] 
+2026-10-15 01:17:35.661632: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:17:35.965923: lr: 0.000904 
+2026-10-15 01:17:35.966190: This epoch took 283.371277 s
+ 
+2026-10-15 01:17:35.966271: 
+epoch:  106 
+2026-10-15 01:21:35.081282: train loss : -0.6743 
+2026-10-15 01:21:40.442945: validation loss: -0.2319 
+2026-10-15 01:21:40.444129: Average global foreground Dice: [np.float32(0.9136), np.float32(0.1174), np.float32(0.1132), np.float32(0.076), np.float32(0.0846), np.float32(0.1003), np.float32(0.1145), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1042), np.float32(0.1267), np.float32(0.3009), np.float32(0.1551), np.float32(0.0), np.float32(0.0), np.float32(0.0674), np.float32(0.0412), np.float32(0.1121), np.float32(0.1405), np.float32(0.0852), np.float32(0.0719), np.float32(0.2496), np.float32(0.1551), np.float32(0.0901), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2053), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0993), np.float32(0.1175)] 
+2026-10-15 01:21:40.444624: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:21:40.759878: lr: 0.000903 
+2026-10-15 01:21:40.760126: This epoch took 244.793792 s
+ 
+2026-10-15 01:21:40.760205: 
+epoch:  107 
+2026-10-15 01:25:31.275507: train loss : -0.6764 
+2026-10-15 01:25:36.637403: validation loss: -0.4964 
+2026-10-15 01:25:36.638404: Average global foreground Dice: [np.float32(0.9054), np.float32(0.5479), np.float32(0.6023), np.float32(0.5998), np.float32(0.4663), np.float32(0.6022), np.float32(0.5104), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4638), np.float32(0.5435), np.float32(0.5336), np.float32(0.4559), np.float32(0.0), np.float32(0.0), np.float32(0.4575), np.float32(0.3202), np.float32(0.4714), np.float32(0.474), np.float32(0.4846), np.float32(0.455), np.float32(0.7228), np.float32(0.7062), np.float32(0.3785), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5363), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6272), np.float32(0.6371)] 
+2026-10-15 01:25:36.638573: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:25:36.949758: lr: 0.000902 
+2026-10-15 01:25:36.950121: This epoch took 236.189848 s
+ 
+2026-10-15 01:25:36.950249: 
+epoch:  108 
+2026-10-15 01:29:55.262044: train loss : -0.6640 
+2026-10-15 01:30:00.601421: validation loss: -0.4428 
+2026-10-15 01:30:00.602340: Average global foreground Dice: [np.float32(0.9161), np.float32(0.4865), np.float32(0.5573), np.float32(0.4457), np.float32(0.3736), np.float32(0.4867), np.float32(0.4061), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3629), np.float32(0.4102), np.float32(0.5108), np.float32(0.3392), np.float32(0.0), np.float32(0.0), np.float32(0.3188), np.float32(0.2713), np.float32(0.341), np.float32(0.3543), np.float32(0.3801), np.float32(0.48), np.float32(0.6959), np.float32(0.6495), np.float32(0.3133), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5323), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4937), np.float32(0.5357)] 
+2026-10-15 01:30:00.602566: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:30:00.899550: lr: 0.000901 
+2026-10-15 01:30:00.899994: This epoch took 263.949672 s
+ 
+2026-10-15 01:30:00.900339: 
+epoch:  109 
+2026-10-15 01:34:19.115520: train loss : -0.6474 
+2026-10-15 01:34:24.460412: validation loss: -0.4501 
+2026-10-15 01:34:24.461366: Average global foreground Dice: [np.float32(0.9043), np.float32(0.4351), np.float32(0.4953), np.float32(0.5045), np.float32(0.3716), np.float32(0.5589), np.float32(0.4664), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4553), np.float32(0.4981), np.float32(0.4935), np.float32(0.3998), np.float32(0.0), np.float32(0.0), np.float32(0.3277), np.float32(0.2659), np.float32(0.3731), np.float32(0.298), np.float32(0.4061), np.float32(0.2181), np.float32(0.6262), np.float32(0.6492), np.float32(0.2349), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5506), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5448), np.float32(0.5693)] 
+2026-10-15 01:34:24.461535: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:34:24.765025: lr: 0.0009 
+2026-10-15 01:34:24.765441: This epoch took 263.864789 s
+ 
+2026-10-15 01:34:24.765888: 
+epoch:  110 
+2026-10-15 01:38:33.802496: train loss : -0.6507 
+2026-10-15 01:38:39.161773: validation loss: -0.3781 
+2026-10-15 01:38:39.162950: Average global foreground Dice: [np.float32(0.9058), np.float32(0.3659), np.float32(0.3695), np.float32(0.3342), np.float32(0.2619), np.float32(0.4002), np.float32(0.3397), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.342), np.float32(0.3859), np.float32(0.5236), np.float32(0.5235), np.float32(0.0), np.float32(0.0), np.float32(0.2148), np.float32(0.2357), np.float32(0.2765), np.float32(0.2229), np.float32(0.298), np.float32(0.2026), np.float32(0.5136), np.float32(0.4684), np.float32(0.1435), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4334), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3572), np.float32(0.4197)] 
+2026-10-15 01:38:39.163165: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:38:39.475607: lr: 0.0009 
+2026-10-15 01:38:39.475962: This epoch took 254.709854 s
+ 
+2026-10-15 01:38:39.476197: 
+epoch:  111 
+2026-10-15 01:42:40.698297: train loss : -0.6460 
+2026-10-15 01:42:46.054001: validation loss: -0.4612 
+2026-10-15 01:42:46.054892: Average global foreground Dice: [np.float32(0.8961), np.float32(0.5261), np.float32(0.514), np.float32(0.5396), np.float32(0.4751), np.float32(0.5821), np.float32(0.4958), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4131), np.float32(0.4369), np.float32(0.5006), np.float32(0.3184), np.float32(0.0), np.float32(0.0), np.float32(0.4601), np.float32(0.3865), np.float32(0.459), np.float32(0.3922), np.float32(0.3938), np.float32(0.4566), np.float32(0.6183), np.float32(0.5982), np.float32(0.2842), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4442), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5588), np.float32(0.5972)] 
+2026-10-15 01:42:46.055052: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:42:46.364272: lr: 0.000899 
+2026-10-15 01:42:46.364714: This epoch took 246.888340 s
+ 
+2026-10-15 01:42:46.364917: 
+epoch:  112 
+2026-10-15 01:46:42.241654: train loss : -0.6572 
+2026-10-15 01:46:47.584388: validation loss: -0.4904 
+2026-10-15 01:46:47.585401: Average global foreground Dice: [np.float32(0.906), np.float32(0.5746), np.float32(0.655), np.float32(0.5842), np.float32(0.5348), np.float32(0.5726), np.float32(0.5354), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4174), np.float32(0.5378), np.float32(0.5368), np.float32(0.4077), np.float32(0.0), np.float32(0.0), np.float32(0.5667), np.float32(0.4893), np.float32(0.5143), np.float32(0.5344), np.float32(0.4719), np.float32(0.4794), np.float32(0.6788), np.float32(0.6909), np.float32(0.4061), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5316), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6196), np.float32(0.6579)] 
+2026-10-15 01:46:47.585561: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:46:47.897498: lr: 0.000898 
+2026-10-15 01:46:47.897891: This epoch took 241.532891 s
+ 
+2026-10-15 01:46:47.898080: 
+epoch:  113 
+2026-10-15 01:50:50.146333: train loss : -0.6672 
+2026-10-15 01:50:55.501073: validation loss: -0.4458 
+2026-10-15 01:50:55.502052: Average global foreground Dice: [np.float32(0.8912), np.float32(0.4644), np.float32(0.6143), np.float32(0.5224), np.float32(0.4659), np.float32(0.4553), np.float32(0.4028), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3415), np.float32(0.4174), np.float32(0.4962), np.float32(0.3184), np.float32(0.0), np.float32(0.0), np.float32(0.5347), np.float32(0.4056), np.float32(0.493), np.float32(0.5331), np.float32(0.3792), np.float32(0.5594), np.float32(0.7631), np.float32(0.714), np.float32(0.4065), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5121), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5996), np.float32(0.5817)] 
+2026-10-15 01:50:55.502337: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:50:55.805012: lr: 0.000897 
+2026-10-15 01:50:55.805361: This epoch took 247.907189 s
+ 
+2026-10-15 01:50:55.805623: 
+epoch:  114 
+2026-10-15 01:55:18.025169: train loss : -0.6728 
+2026-10-15 01:55:23.381359: validation loss: -0.5579 
+2026-10-15 01:55:23.382354: Average global foreground Dice: [np.float32(0.9134), np.float32(0.6267), np.float32(0.7591), np.float32(0.7419), np.float32(0.6678), np.float32(0.7819), np.float32(0.7064), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5728), np.float32(0.6324), np.float32(0.5941), np.float32(0.5066), np.float32(0.0), np.float32(0.0), np.float32(0.7173), np.float32(0.5642), np.float32(0.6951), np.float32(0.5975), np.float32(0.5802), np.float32(0.6234), np.float32(0.791), np.float32(0.7713), np.float32(0.4469), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5859), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7694), np.float32(0.7838)] 
+2026-10-15 01:55:23.382556: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:55:23.717545: lr: 0.000896 
+2026-10-15 01:55:23.717817: This epoch took 267.911983 s
+ 
+2026-10-15 01:55:23.717917: 
+epoch:  115 
+2026-10-15 01:59:48.415387: train loss : -0.6650 
+2026-10-15 01:59:53.767586: validation loss: -0.4830 
+2026-10-15 01:59:53.768556: Average global foreground Dice: [np.float32(0.8946), np.float32(0.5195), np.float32(0.5982), np.float32(0.6286), np.float32(0.5735), np.float32(0.6517), np.float32(0.527), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4441), np.float32(0.5611), np.float32(0.4898), np.float32(0.4186), np.float32(0.0), np.float32(0.0), np.float32(0.6371), np.float32(0.5431), np.float32(0.5521), np.float32(0.5357), np.float32(0.4818), np.float32(0.5173), np.float32(0.691), np.float32(0.6064), np.float32(0.4465), 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(0.6108), np.float32(0.6415)] 
+2026-10-15 01:59:53.768793: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 01:59:54.104178: lr: 0.000895 
+2026-10-15 01:59:54.104478: This epoch took 270.386492 s
+ 
+2026-10-15 01:59:54.104808: 
+epoch:  116 
+2026-10-15 02:04:06.755107: train loss : -0.6631 
+2026-10-15 02:04:12.092650: validation loss: -0.5317 
+2026-10-15 02:04:12.093786: Average global foreground Dice: [np.float32(0.9092), np.float32(0.6097), np.float32(0.7451), np.float32(0.7295), np.float32(0.628), np.float32(0.7249), np.float32(0.6658), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.488), np.float32(0.6028), np.float32(0.5449), np.float32(0.4237), np.float32(0.0), np.float32(0.0), np.float32(0.7145), np.float32(0.5895), np.float32(0.654), np.float32(0.603), np.float32(0.52), np.float32(0.6409), np.float32(0.8467), np.float32(0.8181), np.float32(0.4021), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5633), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7809), np.float32(0.8098)] 
+2026-10-15 02:04:12.094061: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:04:12.400794: lr: 0.000894 
+2026-10-15 02:04:12.401127: This epoch took 258.296103 s
+ 
+2026-10-15 02:04:12.401218: 
+epoch:  117 
+2026-10-15 02:08:30.715820: train loss : -0.6554 
+2026-10-15 02:08:36.056675: validation loss: -0.5558 
+2026-10-15 02:08:36.057957: Average global foreground Dice: [np.float32(0.9038), np.float32(0.5719), np.float32(0.7571), np.float32(0.7542), np.float32(0.6997), np.float32(0.7745), np.float32(0.7005), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5632), np.float32(0.6601), np.float32(0.544), np.float32(0.4285), np.float32(0.0), np.float32(0.0), np.float32(0.7437), np.float32(0.5814), np.float32(0.7019), np.float32(0.6153), np.float32(0.5037), np.float32(0.6676), np.float32(0.8943), np.float32(0.8617), np.float32(0.4707), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5211), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8046), np.float32(0.8211)] 
+2026-10-15 02:08:36.058423: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:08:36.361402: lr: 0.000893 
+2026-10-15 02:08:36.361771: This epoch took 263.960490 s
+ 
+2026-10-15 02:08:36.361998: 
+epoch:  118 
+2026-10-15 02:12:30.661413: train loss : -0.6785 
+2026-10-15 02:12:36.017745: validation loss: -0.5574 
+2026-10-15 02:12:36.018584: Average global foreground Dice: [np.float32(0.9119), np.float32(0.6666), np.float32(0.7647), np.float32(0.7787), np.float32(0.7122), np.float32(0.7706), np.float32(0.7122), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6238), np.float32(0.6731), np.float32(0.5232), np.float32(0.328), np.float32(0.0), np.float32(0.0), np.float32(0.7467), np.float32(0.6037), np.float32(0.6951), np.float32(0.6334), np.float32(0.4645), np.float32(0.5283), np.float32(0.8516), np.float32(0.8418), np.float32(0.4928), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5743), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8017), np.float32(0.8229)] 
+2026-10-15 02:12:36.018744: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:12:36.315588: lr: 0.000892 
+2026-10-15 02:12:36.316047: This epoch took 239.953955 s
+ 
+2026-10-15 02:12:36.316302: 
+epoch:  119 
+2026-10-15 02:16:59.024264: train loss : -0.6787 
+2026-10-15 02:17:04.381833: validation loss: -0.4519 
+2026-10-15 02:17:04.383075: Average global foreground Dice: [np.float32(0.8982), np.float32(0.5016), np.float32(0.5279), np.float32(0.5483), np.float32(0.4428), np.float32(0.3797), np.float32(0.3606), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.361), np.float32(0.425), np.float32(0.5058), np.float32(0.3959), np.float32(0.0), np.float32(0.0), np.float32(0.4402), np.float32(0.3597), np.float32(0.4662), np.float32(0.4296), np.float32(0.4107), np.float32(0.5011), np.float32(0.7546), np.float32(0.7146), np.float32(0.3497), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5933), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5879), np.float32(0.5451)] 
+2026-10-15 02:17:04.383751: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:17:04.688451: lr: 0.000891 
+2026-10-15 02:17:04.688746: This epoch took 268.372309 s
+ 
+2026-10-15 02:17:04.688842: 
+epoch:  120 
+2026-10-15 02:20:49.109681: train loss : -0.6834 
+2026-10-15 02:20:54.482153: validation loss: -0.5539 
+2026-10-15 02:20:54.483126: Average global foreground Dice: [np.float32(0.9117), np.float32(0.6136), np.float32(0.7186), np.float32(0.7239), np.float32(0.6475), np.float32(0.7168), np.float32(0.6765), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5552), np.float32(0.6139), np.float32(0.5881), np.float32(0.5633), np.float32(0.0), np.float32(0.0), np.float32(0.6387), np.float32(0.5045), np.float32(0.623), np.float32(0.589), np.float32(0.4724), np.float32(0.6431), np.float32(0.8598), np.float32(0.8403), np.float32(0.4091), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5915), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7872), np.float32(0.8007)] 
+2026-10-15 02:20:54.483301: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:20:54.779431: lr: 0.00089 
+2026-10-15 02:20:54.779662: This epoch took 230.090755 s
+ 
+2026-10-15 02:20:54.779746: 
+epoch:  121 
+2026-10-15 02:24:56.887841: train loss : -0.6702 
+2026-10-15 02:25:02.231996: validation loss: -0.4660 
+2026-10-15 02:25:02.233116: Average global foreground Dice: [np.float32(0.9174), np.float32(0.5136), np.float32(0.491), np.float32(0.5322), np.float32(0.4478), np.float32(0.561), np.float32(0.4944), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4146), np.float32(0.4655), np.float32(0.517), np.float32(0.4698), np.float32(0.0), np.float32(0.0), np.float32(0.4122), np.float32(0.3452), np.float32(0.4038), np.float32(0.3674), np.float32(0.4393), np.float32(0.4057), np.float32(0.723), np.float32(0.6699), np.float32(0.2544), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6146), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5819), np.float32(0.5945)] 
+2026-10-15 02:25:02.233559: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:25:02.557434: lr: 0.000889 
+2026-10-15 02:25:02.557666: This epoch took 247.777857 s
+ 
+2026-10-15 02:25:02.557760: 
+epoch:  122 
+2026-10-15 02:29:18.388973: train loss : -0.6618 
+2026-10-15 02:29:23.769218: validation loss: -0.4929 
+2026-10-15 02:29:23.770134: Average global foreground Dice: [np.float32(0.9025), np.float32(0.5542), np.float32(0.6226), np.float32(0.6412), np.float32(0.5025), np.float32(0.6564), np.float32(0.5622), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.48), np.float32(0.5978), np.float32(0.3899), np.float32(0.319), np.float32(0.0), np.float32(0.0), np.float32(0.5703), np.float32(0.4453), np.float32(0.5596), np.float32(0.51), np.float32(0.346), np.float32(0.5485), np.float32(0.6599), np.float32(0.6057), np.float32(0.414), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5798), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6294), np.float32(0.6424)] 
+2026-10-15 02:29:23.770417: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:29:24.081220: lr: 0.000889 
+2026-10-15 02:29:24.081442: This epoch took 261.523602 s
+ 
+2026-10-15 02:29:24.081520: 
+epoch:  123 
+2026-10-15 02:33:29.482888: train loss : -0.6638 
+2026-10-15 02:33:34.820255: validation loss: -0.5496 
+2026-10-15 02:33:34.821507: Average global foreground Dice: [np.float32(0.9076), np.float32(0.6174), np.float32(0.7561), np.float32(0.6225), np.float32(0.7344), np.float32(0.7317), np.float32(0.7274), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5286), np.float32(0.6147), np.float32(0.4443), np.float32(0.3965), np.float32(0.0), np.float32(0.0), np.float32(0.7369), np.float32(0.5541), np.float32(0.7101), np.float32(0.6172), np.float32(0.5386), np.float32(0.6753), np.float32(0.8704), np.float32(0.8665), np.float32(0.4337), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5932), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7755), np.float32(0.8089)] 
+2026-10-15 02:33:34.821737: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:33:35.131305: lr: 0.000888 
+2026-10-15 02:33:35.131596: This epoch took 251.050020 s
+ 
+2026-10-15 02:33:35.131705: 
+epoch:  124 
+2026-10-15 02:37:47.888099: train loss : -0.6534 
+2026-10-15 02:37:53.240556: validation loss: -0.4895 
+2026-10-15 02:37:53.241561: Average global foreground Dice: [np.float32(0.9058), np.float32(0.5318), np.float32(0.6338), np.float32(0.5568), np.float32(0.4759), np.float32(0.5893), np.float32(0.5475), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4446), np.float32(0.5433), np.float32(0.5516), np.float32(0.4296), np.float32(0.0), np.float32(0.0), np.float32(0.4832), np.float32(0.3894), np.float32(0.4919), np.float32(0.4719), np.float32(0.3969), np.float32(0.5208), np.float32(0.7629), np.float32(0.7184), np.float32(0.3791), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5523), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6265), np.float32(0.6571)] 
+2026-10-15 02:37:53.241910: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:37:53.553551: lr: 0.000887 
+2026-10-15 02:37:53.553753: This epoch took 258.421969 s
+ 
+2026-10-15 02:37:53.553812: 
+epoch:  125 
+2026-10-15 02:42:01.765753: train loss : -0.6811 
+2026-10-15 02:42:07.114850: validation loss: -0.4871 
+2026-10-15 02:42:07.116494: Average global foreground Dice: [np.float32(0.8997), np.float32(0.5447), np.float32(0.5375), np.float32(0.5598), np.float32(0.4792), np.float32(0.5834), np.float32(0.5141), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4302), np.float32(0.5177), np.float32(0.5912), np.float32(0.5834), np.float32(0.0), np.float32(0.0), np.float32(0.4305), np.float32(0.3495), np.float32(0.4407), np.float32(0.4011), np.float32(0.5098), np.float32(0.4335), np.float32(0.7355), np.float32(0.7246), np.float32(0.2487), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5108), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6029), np.float32(0.6509)] 
+2026-10-15 02:42:07.116909: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:42:07.445794: lr: 0.000886 
+2026-10-15 02:42:07.446171: This epoch took 253.892300 s
+ 
+2026-10-15 02:42:07.446372: 
+epoch:  126 
+2026-10-15 02:46:29.962098: train loss : -0.6713 
+2026-10-15 02:46:35.310047: validation loss: -0.5623 
+2026-10-15 02:46:35.311031: Average global foreground Dice: [np.float32(0.8984), np.float32(0.635), np.float32(0.7325), np.float32(0.7318), np.float32(0.654), np.float32(0.7233), np.float32(0.6697), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5842), np.float32(0.6502), np.float32(0.5959), np.float32(0.5648), np.float32(0.0), np.float32(0.0), np.float32(0.6945), np.float32(0.5566), np.float32(0.6446), np.float32(0.5898), np.float32(0.4966), np.float32(0.6504), np.float32(0.8295), np.float32(0.8117), np.float32(0.4473), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5807), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7719), np.float32(0.7952)] 
+2026-10-15 02:46:35.311167: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:46:35.620473: lr: 0.000885 
+2026-10-15 02:46:35.620721: This epoch took 268.174247 s
+ 
+2026-10-15 02:46:35.620815: 
+epoch:  127 
+2026-10-15 02:50:41.356506: train loss : -0.6593 
+2026-10-15 02:50:46.699057: validation loss: -0.4426 
+2026-10-15 02:50:46.700206: Average global foreground Dice: [np.float32(0.8989), np.float32(0.4866), np.float32(0.4959), np.float32(0.4637), np.float32(0.3595), np.float32(0.5035), np.float32(0.3885), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3669), np.float32(0.3739), np.float32(0.4552), np.float32(0.3573), np.float32(0.0), np.float32(0.0), np.float32(0.3739), np.float32(0.2776), np.float32(0.3612), np.float32(0.3869), np.float32(0.4111), np.float32(0.4034), np.float32(0.6867), np.float32(0.625), np.float32(0.2412), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5223), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5392), np.float32(0.5768)] 
+2026-10-15 02:50:46.700484: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:50:47.007799: lr: 0.000884 
+2026-10-15 02:50:47.008237: This epoch took 251.387367 s
+ 
+2026-10-15 02:50:47.008547: 
+epoch:  128 
+2026-10-15 02:55:01.099953: train loss : -0.6833 
+2026-10-15 02:55:06.439578: validation loss: -0.4794 
+2026-10-15 02:55:06.440525: Average global foreground Dice: [np.float32(0.8903), np.float32(0.5084), np.float32(0.549), np.float32(0.5217), np.float32(0.4125), np.float32(0.5591), np.float32(0.4686), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4084), np.float32(0.4828), np.float32(0.5076), np.float32(0.4672), np.float32(0.0), np.float32(0.0), np.float32(0.4055), np.float32(0.3285), np.float32(0.4), np.float32(0.4204), np.float32(0.475), np.float32(0.5208), np.float32(0.7647), np.float32(0.7135), np.float32(0.228), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4437), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5875), np.float32(0.62)] 
+2026-10-15 02:55:06.440938: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:55:06.744878: lr: 0.000883 
+2026-10-15 02:55:06.745135: This epoch took 259.736398 s
+ 
+2026-10-15 02:55:06.745231: 
+epoch:  129 
+2026-10-15 02:59:23.701615: train loss : -0.6764 
+2026-10-15 02:59:29.057204: validation loss: -0.3538 
+2026-10-15 02:59:29.058250: Average global foreground Dice: [np.float32(0.8862), np.float32(0.3443), np.float32(0.334), np.float32(0.3352), np.float32(0.2151), np.float32(0.243), np.float32(0.274), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2929), np.float32(0.3365), np.float32(0.4751), np.float32(0.3454), np.float32(0.0), np.float32(0.0), np.float32(0.1949), np.float32(0.1717), np.float32(0.2281), np.float32(0.205), np.float32(0.3134), np.float32(0.2487), np.float32(0.5441), np.float32(0.4669), np.float32(0.1064), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4474), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3781), np.float32(0.4024)] 
+2026-10-15 02:59:29.058516: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 02:59:29.371243: lr: 0.000882 
+2026-10-15 02:59:29.371694: This epoch took 262.626391 s
+ 
+2026-10-15 02:59:29.372000: 
+epoch:  130 
+2026-10-15 03:03:44.004953: train loss : -0.6800 
+2026-10-15 03:03:49.340925: validation loss: -0.4493 
+2026-10-15 03:03:49.342112: Average global foreground Dice: [np.float32(0.8929), np.float32(0.4741), np.float32(0.5056), np.float32(0.4907), np.float32(0.398), np.float32(0.5411), np.float32(0.458), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3654), np.float32(0.4517), np.float32(0.5182), np.float32(0.3936), np.float32(0.0), np.float32(0.0), np.float32(0.3804), np.float32(0.3012), np.float32(0.3725), np.float32(0.3144), np.float32(0.438), np.float32(0.4455), np.float32(0.7049), np.float32(0.6751), np.float32(0.2721), 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(0.5518), np.float32(0.6097)] 
+2026-10-15 03:03:49.342481: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:03:49.659622: lr: 0.000881 
+2026-10-15 03:03:49.660110: This epoch took 260.287914 s
+ 
+2026-10-15 03:03:49.660358: 
+epoch:  131 
+2026-10-15 03:08:00.763216: train loss : -0.6754 
+2026-10-15 03:08:06.109542: validation loss: -0.3819 
+2026-10-15 03:08:06.110506: Average global foreground Dice: [np.float32(0.9111), np.float32(0.3822), np.float32(0.3722), np.float32(0.3379), np.float32(0.2408), np.float32(0.4091), np.float32(0.3067), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3129), np.float32(0.3574), np.float32(0.5339), np.float32(0.3611), np.float32(0.0), np.float32(0.0), np.float32(0.2307), np.float32(0.2275), np.float32(0.2534), np.float32(0.259), np.float32(0.2517), np.float32(0.1965), np.float32(0.557), np.float32(0.4493), np.float32(0.1244), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4876), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4103), np.float32(0.4043)] 
+2026-10-15 03:08:06.110842: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:08:06.423934: lr: 0.00088 
+2026-10-15 03:08:06.424208: This epoch took 256.763729 s
+ 
+2026-10-15 03:08:06.424313: 
+epoch:  132 
+2026-10-15 03:12:24.857278: train loss : -0.6701 
+2026-10-15 03:12:30.215759: validation loss: -0.4111 
+2026-10-15 03:12:30.216704: Average global foreground Dice: [np.float32(0.885), np.float32(0.4451), np.float32(0.4556), np.float32(0.4243), np.float32(0.3095), np.float32(0.3289), np.float32(0.3195), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3661), np.float32(0.4605), np.float32(0.5394), np.float32(0.4567), np.float32(0.0), np.float32(0.0), np.float32(0.3129), np.float32(0.2765), np.float32(0.3115), np.float32(0.3171), np.float32(0.2728), np.float32(0.27), np.float32(0.7105), np.float32(0.6558), np.float32(0.236), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5188), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4742), np.float32(0.5193)] 
+2026-10-15 03:12:30.217190: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:12:30.523555: lr: 0.000879 
+2026-10-15 03:12:30.523808: This epoch took 264.099425 s
+ 
+2026-10-15 03:12:30.523900: 
+epoch:  133 
+2026-10-15 03:16:36.014453: train loss : -0.6828 
+2026-10-15 03:16:41.359389: validation loss: -0.3194 
+2026-10-15 03:16:41.360576: Average global foreground Dice: [np.float32(0.9127), np.float32(0.3033), np.float32(0.2703), np.float32(0.2264), np.float32(0.1761), np.float32(0.3124), np.float32(0.2552), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2293), np.float32(0.2866), np.float32(0.3841), np.float32(0.2685), np.float32(0.0), np.float32(0.0), np.float32(0.165), np.float32(0.1771), np.float32(0.1759), np.float32(0.1584), np.float32(0.2637), np.float32(0.1814), np.float32(0.4678), np.float32(0.2961), np.float32(0.1287), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3822), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2833), np.float32(0.2934)] 
+2026-10-15 03:16:41.360800: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:16:41.690504: lr: 0.000879 
+2026-10-15 03:16:41.690746: This epoch took 251.166776 s
+ 
+2026-10-15 03:16:41.690831: 
+epoch:  134 
+2026-10-15 03:21:06.113811: train loss : -0.6779 
+2026-10-15 03:21:11.458066: validation loss: -0.3048 
+2026-10-15 03:21:11.459007: Average global foreground Dice: [np.float32(0.9183), np.float32(0.233), np.float32(0.2251), np.float32(0.1882), np.float32(0.2122), np.float32(0.2353), np.float32(0.2485), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.2146), np.float32(0.2864), np.float32(0.3835), np.float32(0.3252), np.float32(0.0), np.float32(0.0), np.float32(0.2185), np.float32(0.245), np.float32(0.2183), np.float32(0.1858), np.float32(0.2178), np.float32(0.0689), np.float32(0.3993), np.float32(0.2618), np.float32(0.1714), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3301), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.1954), np.float32(0.2475)] 
+2026-10-15 03:21:11.459164: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:21:11.790084: lr: 0.000878 
+2026-10-15 03:21:11.790325: This epoch took 270.099441 s
+ 
+2026-10-15 03:21:11.790517: 
+epoch:  135 
+2026-10-15 03:25:16.476724: train loss : -0.6719 
+2026-10-15 03:25:21.820123: validation loss: -0.5190 
+2026-10-15 03:25:21.821109: Average global foreground Dice: [np.float32(0.9163), np.float32(0.578), np.float32(0.6673), np.float32(0.6702), np.float32(0.5999), np.float32(0.673), np.float32(0.6088), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4553), np.float32(0.5281), np.float32(0.494), np.float32(0.4609), np.float32(0.0), np.float32(0.0), np.float32(0.5489), np.float32(0.4875), np.float32(0.5191), np.float32(0.4482), np.float32(0.5487), np.float32(0.5644), np.float32(0.7864), np.float32(0.7644), np.float32(0.4375), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6023), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.734), np.float32(0.7574)] 
+2026-10-15 03:25:21.822026: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:25:22.187054: lr: 0.000877 
+2026-10-15 03:25:22.187366: This epoch took 250.396762 s
+ 
+2026-10-15 03:25:22.187629: 
+epoch:  136 
+2026-10-15 03:29:33.325137: train loss : -0.6801 
+2026-10-15 03:29:38.698257: validation loss: -0.5632 
+2026-10-15 03:29:38.699421: Average global foreground Dice: [np.float32(0.9078), np.float32(0.6241), np.float32(0.7566), np.float32(0.7633), np.float32(0.6819), np.float32(0.7884), np.float32(0.7069), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5635), np.float32(0.6175), np.float32(0.6047), np.float32(0.3586), np.float32(0.0), np.float32(0.0), np.float32(0.7242), np.float32(0.5839), np.float32(0.6866), np.float32(0.6074), np.float32(0.5184), np.float32(0.6475), np.float32(0.8704), np.float32(0.8448), np.float32(0.4854), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5664), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8146), np.float32(0.8286)] 
+2026-10-15 03:29:38.699680: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:29:39.011058: lr: 0.000876 
+2026-10-15 03:29:39.011334: This epoch took 256.823554 s
+ 
+2026-10-15 03:29:39.011517: 
+epoch:  137 
+2026-10-15 03:34:00.810196: train loss : -0.6842 
+2026-10-15 03:34:06.146335: validation loss: -0.4066 
+2026-10-15 03:34:06.147838: Average global foreground Dice: [np.float32(0.9094), np.float32(0.447), np.float32(0.4636), np.float32(0.4038), np.float32(0.297), np.float32(0.3998), np.float32(0.3793), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3409), np.float32(0.3293), np.float32(0.5241), np.float32(0.4111), np.float32(0.0), np.float32(0.0), np.float32(0.28), np.float32(0.2548), np.float32(0.3104), np.float32(0.3242), np.float32(0.3962), np.float32(0.4298), np.float32(0.5433), np.float32(0.5027), np.float32(0.2654), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4941), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.417), np.float32(0.4563)] 
+2026-10-15 03:34:06.148193: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:34:06.459163: lr: 0.000875 
+2026-10-15 03:34:06.459641: This epoch took 267.447890 s
+ 
+2026-10-15 03:34:06.459974: 
+epoch:  138 
+2026-10-15 03:38:18.180821: train loss : -0.6899 
+2026-10-15 03:38:23.527043: validation loss: -0.3852 
+2026-10-15 03:38:23.528079: Average global foreground Dice: [np.float32(0.9087), np.float32(0.3939), np.float32(0.3954), np.float32(0.3799), np.float32(0.2643), np.float32(0.3605), np.float32(0.3225), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3549), np.float32(0.3517), np.float32(0.5351), np.float32(0.4299), np.float32(0.0), np.float32(0.0), np.float32(0.2345), np.float32(0.2173), np.float32(0.2812), np.float32(0.2463), np.float32(0.256), np.float32(0.2575), np.float32(0.5082), np.float32(0.4856), np.float32(0.2016), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.545), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.3719), np.float32(0.4544)] 
+2026-10-15 03:38:23.528305: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:38:23.905044: lr: 0.000874 
+2026-10-15 03:38:23.905459: This epoch took 257.445233 s
+ 
+2026-10-15 03:38:23.905640: 
+epoch:  139 
+2026-10-15 03:42:18.089385: train loss : -0.6861 
+2026-10-15 03:42:23.455429: validation loss: -0.5373 
+2026-10-15 03:42:23.456409: Average global foreground Dice: [np.float32(0.8873), np.float32(0.6451), np.float32(0.7221), np.float32(0.7609), np.float32(0.6837), np.float32(0.7729), np.float32(0.7028), np.float32(0.0), np.float32(0.0), np.float32(0.0018), np.float32(0.5693), np.float32(0.5934), np.float32(0.6109), np.float32(0.4797), np.float32(0.0), np.float32(0.0), np.float32(0.7012), np.float32(0.6278), np.float32(0.6818), np.float32(0.6338), np.float32(0.4022), np.float32(0.5638), np.float32(0.7577), np.float32(0.7203), np.float32(0.4569), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5825), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.779), np.float32(0.7683)] 
+2026-10-15 03:42:23.456809: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:42:23.795864: lr: 0.000873 
+2026-10-15 03:42:23.796269: This epoch took 239.890552 s
+ 
+2026-10-15 03:42:23.796496: 
+epoch:  140 
+2026-10-15 03:46:20.425123: train loss : -0.6737 
+2026-10-15 03:46:25.767858: validation loss: -0.4343 
+2026-10-15 03:46:25.768974: Average global foreground Dice: [np.float32(0.9162), np.float32(0.4746), np.float32(0.4523), np.float32(0.4807), np.float32(0.3321), np.float32(0.5121), np.float32(0.4222), np.float32(0.0), np.float32(0.0), np.float32(0.0474), np.float32(0.396), np.float32(0.4511), np.float32(0.51), np.float32(0.4299), np.float32(0.0), np.float32(0.0), np.float32(0.348), np.float32(0.2531), np.float32(0.3675), np.float32(0.3084), np.float32(0.3397), np.float32(0.2739), np.float32(0.6007), np.float32(0.5334), np.float32(0.2484), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5179), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5052), np.float32(0.5198)] 
+2026-10-15 03:46:25.769569: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:46:26.089710: lr: 0.000872 
+2026-10-15 03:46:26.089952: This epoch took 242.293236 s
+ 
+2026-10-15 03:46:26.090035: 
+epoch:  141 
+2026-10-15 03:49:59.727031: train loss : -0.6735 
+2026-10-15 03:50:05.100635: validation loss: -0.4556 
+2026-10-15 03:50:05.101608: Average global foreground Dice: [np.float32(0.9133), np.float32(0.4841), np.float32(0.4844), np.float32(0.5126), np.float32(0.37), np.float32(0.5771), np.float32(0.4251), np.float32(0.0), np.float32(0.0), np.float32(0.0651), np.float32(0.4158), np.float32(0.4525), np.float32(0.5973), np.float32(0.5142), np.float32(0.0), np.float32(0.0), np.float32(0.3619), np.float32(0.3202), np.float32(0.3745), np.float32(0.3588), np.float32(0.3667), np.float32(0.3861), np.float32(0.6548), np.float32(0.6119), np.float32(0.2359), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4589), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5572), np.float32(0.5938)] 
+2026-10-15 03:50:05.101798: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:50:05.462144: lr: 0.000871 
+2026-10-15 03:50:05.462414: This epoch took 219.372318 s
+ 
+2026-10-15 03:50:05.462508: 
+epoch:  142 
+2026-10-15 03:54:06.936211: train loss : -0.6717 
+2026-10-15 03:54:12.275579: validation loss: -0.5265 
+2026-10-15 03:54:12.276608: Average global foreground Dice: [np.float32(0.907), np.float32(0.5828), np.float32(0.6591), np.float32(0.6678), np.float32(0.5804), np.float32(0.7015), np.float32(0.6632), np.float32(0.0), np.float32(0.0), np.float32(0.3785), np.float32(0.433), np.float32(0.582), np.float32(0.57), np.float32(0.4577), np.float32(0.0), np.float32(0.0), np.float32(0.5919), np.float32(0.4942), np.float32(0.5578), np.float32(0.4212), np.float32(0.3983), np.float32(0.6114), np.float32(0.8027), np.float32(0.7814), np.float32(0.41), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4605), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7613), np.float32(0.7868)] 
+2026-10-15 03:54:12.276814: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:54:12.618980: lr: 0.00087 
+2026-10-15 03:54:12.619356: This epoch took 247.156780 s
+ 
+2026-10-15 03:54:12.619582: 
+epoch:  143 
+2026-10-15 03:57:57.778055: train loss : -0.6758 
+2026-10-15 03:58:03.124497: validation loss: -0.4255 
+2026-10-15 03:58:03.125489: Average global foreground Dice: [np.float32(0.9184), np.float32(0.4926), np.float32(0.4782), np.float32(0.4322), np.float32(0.3047), np.float32(0.474), np.float32(0.4046), np.float32(0.0), np.float32(0.0), np.float32(0.4147), np.float32(0.388), np.float32(0.4424), np.float32(0.458), np.float32(0.4006), np.float32(0.0), np.float32(0.0), np.float32(0.3236), np.float32(0.3235), np.float32(0.3855), np.float32(0.3578), np.float32(0.4075), np.float32(0.3392), np.float32(0.6324), np.float32(0.5672), np.float32(0.2031), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4522), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4553), np.float32(0.4755)] 
+2026-10-15 03:58:03.125893: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 03:58:03.439430: lr: 0.000869 
+2026-10-15 03:58:03.439911: This epoch took 230.820255 s
+ 
+2026-10-15 03:58:03.440030: 
+epoch:  144 
+2026-10-15 04:02:17.034218: train loss : -0.6863 
+2026-10-15 04:02:22.379766: validation loss: -0.4010 
+2026-10-15 04:02:22.380815: Average global foreground Dice: [np.float32(0.9116), np.float32(0.4323), np.float32(0.4434), np.float32(0.3456), np.float32(0.213), np.float32(0.4485), np.float32(0.3739), np.float32(0.0), np.float32(0.0), np.float32(0.0962), np.float32(0.3188), np.float32(0.408), np.float32(0.4755), np.float32(0.3312), np.float32(0.0), np.float32(0.0), np.float32(0.2209), np.float32(0.2299), np.float32(0.3296), np.float32(0.3618), np.float32(0.2844), np.float32(0.3124), np.float32(0.6174), np.float32(0.5684), np.float32(0.1575), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5252), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4119), np.float32(0.4469)] 
+2026-10-15 04:02:22.380986: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 04:02:22.680364: lr: 0.000868 
+2026-10-15 04:02:22.680615: This epoch took 259.240294 s
+ 
+2026-10-15 04:02:22.680869: 
+epoch:  145 
+2026-10-15 04:06:00.853508: train loss : -0.6816 
+2026-10-15 04:06:06.192470: validation loss: -0.5992 
+2026-10-15 04:06:06.193449: Average global foreground Dice: [np.float32(0.9221), np.float32(0.6494), np.float32(0.8282), np.float32(0.8574), np.float32(0.8014), np.float32(0.8814), np.float32(0.8145), np.float32(0.0), np.float32(0.0), np.float32(0.098), np.float32(0.6024), np.float32(0.6797), np.float32(0.6198), np.float32(0.533), np.float32(0.0), np.float32(0.0), np.float32(0.7986), np.float32(0.6281), np.float32(0.7712), np.float32(0.6437), np.float32(0.4364), np.float32(0.6273), np.float32(0.9026), np.float32(0.8917), np.float32(0.4887), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5171), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.8939), np.float32(0.8949)] 
+2026-10-15 04:06:06.193762: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 04:06:06.690588: lr: 0.000868 
+2026-10-15 04:06:06.690908: This epoch took 224.009944 s
+ 
+2026-10-15 04:06:06.691018: 
+epoch:  146 
+2026-10-15 04:09:54.015275: train loss : -0.6847 
+2026-10-15 04:09:59.352454: validation loss: -0.5566 
+2026-10-15 04:09:59.353542: Average global foreground Dice: [np.float32(0.8852), np.float32(0.6332), np.float32(0.7733), np.float32(0.7657), np.float32(0.6718), np.float32(0.7811), np.float32(0.6943), np.float32(0.0), np.float32(0.0), np.float32(0.4473), np.float32(0.5571), np.float32(0.5878), np.float32(0.6266), np.float32(0.5632), np.float32(0.0), np.float32(0.0), np.float32(0.6995), np.float32(0.5758), np.float32(0.6662), np.float32(0.6334), np.float32(0.5691), np.float32(0.592), np.float32(0.8083), np.float32(0.7851), np.float32(0.4265), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.4819), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7566), np.float32(0.7663)] 
+2026-10-15 04:09:59.353723: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 04:09:59.671279: lr: 0.000867 
+2026-10-15 04:09:59.671530: This epoch took 232.980456 s
+ 
+2026-10-15 04:09:59.671611: 
+epoch:  147 
+2026-10-15 04:14:10.227778: train loss : -0.6721 
+2026-10-15 04:14:15.562090: validation loss: -0.5409 
+2026-10-15 04:14:15.563088: Average global foreground Dice: [np.float32(0.9158), np.float32(0.6093), np.float32(0.6722), np.float32(0.7), np.float32(0.5921), np.float32(0.7462), np.float32(0.6314), np.float32(0.0), np.float32(0.0), np.float32(0.2209), np.float32(0.5065), np.float32(0.6049), np.float32(0.5881), np.float32(0.4286), np.float32(0.0), np.float32(0.0), np.float32(0.6491), np.float32(0.5291), np.float32(0.6048), np.float32(0.5483), np.float32(0.3611), np.float32(0.6179), np.float32(0.8276), np.float32(0.8281), np.float32(0.4266), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.632), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7424), np.float32(0.751)] 
+2026-10-15 04:14:15.563270: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 04:14:15.875121: lr: 0.000866 
+2026-10-15 04:14:15.875362: This epoch took 256.203699 s
+ 
+2026-10-15 04:14:15.875441: 
+epoch:  148 
+2026-10-15 04:18:02.192838: train loss : -0.6929 
+2026-10-15 04:18:07.570267: validation loss: -0.4754 
+2026-10-15 04:18:07.571388: Average global foreground Dice: [np.float32(0.9165), np.float32(0.4985), np.float32(0.5153), np.float32(0.5299), np.float32(0.3995), np.float32(0.5601), np.float32(0.4494), np.float32(0.0), np.float32(0.0), np.float32(0.3925), np.float32(0.4292), np.float32(0.4569), np.float32(0.5618), np.float32(0.5243), np.float32(0.0), np.float32(0.0), np.float32(0.3805), np.float32(0.3177), np.float32(0.3942), np.float32(0.3733), np.float32(0.5285), np.float32(0.4853), np.float32(0.6881), np.float32(0.6434), np.float32(0.204), 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(0.5761), np.float32(0.5845)] 
+2026-10-15 04:18:07.571818: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 04:18:07.883392: lr: 0.000865 
+2026-10-15 04:18:07.883877: This epoch took 232.008381 s
+ 
+2026-10-15 04:18:07.884288: 
+epoch:  149 
+2026-10-15 04:21:56.555550: train loss : -0.6923 
+2026-10-15 04:22:01.911295: validation loss: -0.4754 
+2026-10-15 04:22:01.912416: Average global foreground Dice: [np.float32(0.9158), np.float32(0.5178), np.float32(0.5248), np.float32(0.5363), np.float32(0.4218), np.float32(0.5766), np.float32(0.4902), np.float32(0.0), np.float32(0.0), np.float32(0.1345), np.float32(0.4209), np.float32(0.4602), np.float32(0.5656), np.float32(0.4552), np.float32(0.0), np.float32(0.0), np.float32(0.4463), np.float32(0.4035), np.float32(0.436), np.float32(0.4114), np.float32(0.4612), np.float32(0.3676), np.float32(0.6443), np.float32(0.6213), np.float32(0.273), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5378), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.5708), np.float32(0.5845)] 
+2026-10-15 04:22:01.913002: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 04:22:02.228544: lr: 0.000864 
+2026-10-15 04:22:02.229058: saving scheduled checkpoint file... 
+2026-10-15 04:22:02.237340: saving checkpoint... 
+2026-10-15 04:22:02.367493: done, saving took 0.14 seconds 
+2026-10-15 04:22:02.369927: done 
+2026-10-15 04:22:02.370175: This epoch took 234.485651 s
+ 
+2026-10-15 04:22:02.370335: 
+epoch:  150 
+2026-10-15 04:26:05.800967: train loss : -0.6907 
+2026-10-15 04:26:11.160895: validation loss: -0.5417 
+2026-10-15 04:26:11.161887: Average global foreground Dice: [np.float32(0.8969), np.float32(0.6076), np.float32(0.7203), np.float32(0.6705), np.float32(0.6135), np.float32(0.6695), np.float32(0.6257), np.float32(0.0), np.float32(0.0), np.float32(0.2842), np.float32(0.4516), np.float32(0.4438), np.float32(0.5966), np.float32(0.5153), np.float32(0.0), np.float32(0.0), np.float32(0.6166), np.float32(0.5421), np.float32(0.5559), np.float32(0.5), np.float32(0.5937), np.float32(0.6033), np.float32(0.8053), np.float32(0.758), np.float32(0.4434), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.6001), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.0), np.float32(0.7499), np.float32(0.7578)] 
+2026-10-15 04:26:11.162080: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 04:26:11.471158: lr: 0.000863 
+2026-10-15 04:26:11.471680: This epoch took 249.101275 s
+ 
+2026-10-15 04:26:11.472368: 
+epoch:  151 
+2026-10-15 04:30:24.991035: train loss : -0.6933 
+2026-10-15 04:30:30.334813: validation loss: -0.4901 
+2026-10-15 04:30:30.336051: Average global foreground Dice: [np.float32(0.8751), np.float32(0.5474), np.float32(0.6598), np.float32(0.6418), np.float32(0.5573), np.float32(0.6236), np.float32(0.5705), np.float32(0.0), np.float32(0.0), np.float32(0.4697), np.float32(0.4081), np.float32(0.4377), np.float32(0.5558), np.float32(0.4338), np.float32(0.0), np.float32(0.0), np.float32(0.6084), np.float32(0.4866), np.float32(0.5122), np.float32(0.5179), np.float32(0.4357), np.float32(0.5764), np.float32(0.7033), np.float32(0.6852), np.float32(0.4602), 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(0.6746), np.float32(0.6886)] 
+2026-10-15 04:30:30.336250: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-15 04:30:30.647898: lr: 0.000862 
+2026-10-15 04:30:30.648195: This epoch took 259.175664 s
+ 
+2026-10-15 04:30:30.648300: 
+epoch:  152 
diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/plans.pkl b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/plans.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..9831252072712250dcc3633e9bc7105bb169daef
--- /dev/null
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/plans.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19b6f010813fa97c33e26842b697d7196a3f46050bdffe68808c010215b55713
+size 12979