diff --git a/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json
index 95cb2e329b3b9b609fea94960d4b97940a25d774..e0b88233f0383b183609b7ff6e59288de5c61135 100644
--- a/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json
+++ b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json
@@ -1,28 +1,28 @@
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np.float32(-0.54934716), np.float32(-0.53050643), np.float32(-0.6134094), np.float32(-0.596375), np.float32(-0.61513555), np.float32(-0.63561773), np.float32(-0.5121949), np.float32(-0.53081083)]",
     "all_val_losses_tr_mode": "[]",
     "also_val_in_tr_mode": "False",
-    "amp_grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7fb6739e0ad0>",
+    "amp_grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7fa1f5dd4ad0>",
     "base_num_features": "30",
     "basic_generator_patch_size": "[205 205 205]",
     "batch_dice": "False",
     "batch_size": "2",
-    "best_MA_tr_loss_for_patience": "-0.7926375",
-    "best_epoch_based_on_MA_tr_loss": "296",
-    "best_val_eval_criterion_MA": "0.524023",
+    "best_MA_tr_loss_for_patience": "-0.83409584",
+    "best_epoch_based_on_MA_tr_loss": "444",
+    "best_val_eval_criterion_MA": "0.5641129",
     "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 0x7fb673b5dfd0>",
-    "dl_val": "<nnunet_mednext.training.dataloading.dataset_loading.DataLoader3D object at 0x7fb6739d91d0>",
+    "dl_tr": "<nnunet_mednext.training.dataloading.dataset_loading.DataLoader3D object at 0x7fa1f5fcdfd0>",
+    "dl_val": "<nnunet_mednext.training.dataloading.dataset_loading.DataLoader3D object at 0x7fa1f5df91d0>",
     "do_dummy_2D_aug": "False",
     "ds_loss_weights": "[0.51612903 0.25806452 0.12903226 0.06451613 0.03225806 0.        ]",
-    "epoch": "300",
+    "epoch": "450",
     "experiment_name": "nnUNetTrainerV2_MedNeXt_L_kernel3",
     "fold": "0",
     "folder_with_preprocessed_data": "/marimo/TopBrain2026/nnUNet_preprocessed/Task001_Brain2026/nnUNetData_plans_v2.1_trgSp_1x1x1_stage0",
@@ -32,7 +32,7 @@
     "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_L_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_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_11_8_18_31_24.txt",
+    "log_file": "/marimo/TopBrain2026/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_11_9_07_02_33.txt",
     "lr_scheduler": "None",
     "lr_scheduler_eps": "0.001",
     "lr_scheduler_patience": "30",
@@ -53,7 +53,7 @@
     "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: 0.0007254178469372199\n    maximize: False\n    weight_decay: 3e-05\n)",
+    "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: 0.0005838837929199803\n    maximize: False\n    weight_decay: 3e-05\n)",
     "output_folder": "/marimo/TopBrain2026/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0",
     "output_folder_base": "/marimo/TopBrain2026/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1",
     "oversample_foreground_percent": "0.33",
@@ -71,7 +71,7 @@
     "save_latest_only": "True",
     "stage": "0",
     "threeD": "True",
-    "tr_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7fb673b5f620>",
+    "tr_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7fa1f5fcf620>",
     "train_loss_MA": "None",
     "train_loss_MA_alpha": "0.93",
     "train_loss_MA_eps": "0.0005",
@@ -82,7 +82,7 @@
     "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 0x7fb6739d9f90>",
+    "val_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7fa1f5df9f90>",
     "was_initialized": "True",
     "weight_decay": "3e-05"
 }
\ No newline at end of file
diff --git a/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model
index 38bc3d5ab27f9ba9323d56161cb4ed8f01f89636..3df4d69f753aae3fc9b2372454efe549d2c1da7b 100644
--- a/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model
+++ b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model
@@ -1,3 +1,3 @@
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-oid sha256:3d8eecfb2d4f78d2bb5ef6a77089534d9ad30e04cfa2a0bce728b455019014c1
-size 742496523
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+size 742526859
diff --git a/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model
index d7fbcf16a3d2b014c076d7348a3b0e5a0d7b72ea..54bb2ca6628a4173235a3feee388d585dae90de5 100644
--- a/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model
+++ b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model
@@ -1,3 +1,3 @@
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+size 742533561
diff --git a/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/progress.png b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/progress.png
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+++ b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/progress.png
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diff --git a/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_11_9_07_02_33.txt b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_11_9_07_02_33.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fde369eac56274868ed1dc12a71f018da8950d34
--- /dev/null
+++ b/MedNeXt_Large/fold_0_CT/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_11_9_07_02_33.txt
@@ -0,0 +1,1878 @@
+Starting... 
+2026-11-09 07:02:33.314473: Creating new 5-fold cross-validation split... 
+2026-11-09 07:02:33.315291: Desired fold for training: 0 
+2026-11-09 07:02:33.315361: This split has 20 training and 5 validation cases. 
+2026-11-09 07:02:34.023812: TRAINING KEYS:
+ odict_keys([np.str_('topcow_ct_001'), np.str_('topcow_ct_002'), np.str_('topcow_ct_003'), np.str_('topcow_ct_004'), np.str_('topcow_ct_005'), np.str_('topcow_ct_006'), np.str_('topcow_ct_007'), np.str_('topcow_ct_008'), np.str_('topcow_ct_011'), np.str_('topcow_ct_012'), np.str_('topcow_ct_013'), np.str_('topcow_ct_014'), np.str_('topcow_ct_016'), np.str_('topcow_ct_017'), np.str_('topcow_ct_020'), np.str_('topcow_ct_021'), np.str_('topcow_ct_023'), np.str_('topcow_ct_025'), np.str_('topcow_ct_026'), np.str_('topcow_ct_027')]) 
+2026-11-09 07:02:34.024174: VALIDATION KEYS:
+ odict_keys([np.str_('topcow_ct_010'), np.str_('topcow_ct_015'), np.str_('topcow_ct_018'), np.str_('topcow_ct_022'), np.str_('topcow_ct_024')]) 
+2026-11-09 07:02:35.847771: loading checkpoint /marimo/TopBrain2026/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_L_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model train= True 
+2026-11-09 07:02:36.532887: lr: 0.000584 
+2026-11-09 07:02:49.246507: Unable to plot network architecture: 
+2026-11-09 07:02:49.251242: No module named 'hiddenlayer' 
+2026-11-09 07:02:49.251635: 
+printing the network instead:
+ 
+2026-11-09 07:02:49.252105: MedNeXt(
+  (stem): Conv3d(1, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+  (enc_block_0): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32)
+      (norm): GroupNorm(32, 32, eps=1e-05, affine=True)
+      (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(96, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32)
+      (norm): GroupNorm(32, 32, eps=1e-05, affine=True)
+      (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(96, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (2): MedNeXtBlock(
+      (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32)
+      (norm): GroupNorm(32, 32, eps=1e-05, affine=True)
+      (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(96, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (down_0): MedNeXtDownBlock(
+    (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=32)
+    (norm): GroupNorm(32, 32, eps=1e-05, affine=True)
+    (conv2): Conv3d(32, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(128, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (enc_block_1): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64)
+      (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+      (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64)
+      (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+      (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (2): MedNeXtBlock(
+      (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64)
+      (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+      (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (3): MedNeXtBlock(
+      (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64)
+      (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+      (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (down_1): MedNeXtDownBlock(
+    (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=64)
+    (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+    (conv2): Conv3d(64, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (enc_block_2): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (2): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (3): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (4): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (5): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (6): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (7): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (down_2): MedNeXtDownBlock(
+    (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=128)
+    (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+    (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(1024, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (enc_block_3): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (2): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (3): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (4): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (5): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (6): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (7): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (down_3): MedNeXtDownBlock(
+    (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=256)
+    (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+    (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(2048, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (bottleneck): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512)
+      (norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+      (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512)
+      (norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+      (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (2): MedNeXtBlock(
+      (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512)
+      (norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+      (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (3): MedNeXtBlock(
+      (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512)
+      (norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+      (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (4): MedNeXtBlock(
+      (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512)
+      (norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+      (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (5): MedNeXtBlock(
+      (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512)
+      (norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+      (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (6): MedNeXtBlock(
+      (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512)
+      (norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+      (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (7): MedNeXtBlock(
+      (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512)
+      (norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+      (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(4096, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (up_3): MedNeXtUpBlock(
+    (conv1): ConvTranspose3d(512, 512, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=512)
+    (norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+    (conv2): Conv3d(512, 4096, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(4096, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): ConvTranspose3d(512, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (dec_block_3): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (2): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (3): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (4): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (5): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (6): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (7): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (up_2): MedNeXtUpBlock(
+    (conv1): ConvTranspose3d(256, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=256)
+    (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+    (conv2): Conv3d(256, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(2048, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): ConvTranspose3d(256, 128, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (dec_block_2): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (2): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (3): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (4): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (5): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (6): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (7): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (up_1): MedNeXtUpBlock(
+    (conv1): ConvTranspose3d(128, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=128)
+    (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+    (conv2): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(512, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): ConvTranspose3d(128, 64, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (dec_block_1): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64)
+      (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+      (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64)
+      (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+      (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (2): MedNeXtBlock(
+      (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64)
+      (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+      (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (3): MedNeXtBlock(
+      (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64)
+      (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+      (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (up_0): MedNeXtUpBlock(
+    (conv1): ConvTranspose3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=64)
+    (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+    (conv2): Conv3d(64, 192, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(192, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): ConvTranspose3d(64, 32, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (dec_block_0): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32)
+      (norm): GroupNorm(32, 32, eps=1e-05, affine=True)
+      (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(96, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32)
+      (norm): GroupNorm(32, 32, eps=1e-05, affine=True)
+      (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(96, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (2): MedNeXtBlock(
+      (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32)
+      (norm): GroupNorm(32, 32, eps=1e-05, affine=True)
+      (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(96, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (out_0): OutBlock(
+    (conv_out): ConvTranspose3d(32, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+  )
+  (out_1): OutBlock(
+    (conv_out): ConvTranspose3d(64, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+  )
+  (out_2): OutBlock(
+    (conv_out): ConvTranspose3d(128, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+  )
+  (out_3): OutBlock(
+    (conv_out): ConvTranspose3d(256, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+  )
+  (out_4): OutBlock(
+    (conv_out): ConvTranspose3d(512, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+  )
+) 
+2026-11-09 07:02:49.268792: 
+ 
+2026-11-09 07:02:49.271140: 
+epoch:  450 
+2026-11-09 07:07:10.134666: train loss : -0.8421 
+2026-11-09 07:07:18.327390: validation loss: -0.6015 
+2026-11-09 07:07:18.328259: Average global foreground Dice: [np.float32(0.9321), np.float32(0.6499), np.float32(0.7053), np.float32(0.696), np.float32(0.6365), np.float32(0.7598), np.float32(0.6594), np.float32(0.6634), np.float32(0.5153), np.float32(0.5005), np.float32(0.5952), np.float32(0.6608), np.float32(0.6355), np.float32(0.5979), np.float32(0.0025), np.float32(0.0), np.float32(0.6039), np.float32(0.4891), np.float32(0.6007), np.float32(0.5245), np.float32(0.5311), np.float32(0.4803), np.float32(0.8323), np.float32(0.812), np.float32(0.347), np.float32(0.4479), np.float32(0.0545), np.float32(0.2716), np.float32(0.2376), np.float32(0.6871), np.float32(0.0), np.float32(0.0), np.float32(0.5502), np.float32(0.5779), np.float32(0.7511), np.float32(0.7769)] 
+2026-11-09 07:07:18.328420: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 07:07:18.689980: lr: 0.000583 
+2026-11-09 07:07:18.690224: This epoch took 269.418463 s
+ 
+2026-11-09 07:07:18.690341: 
+epoch:  451 
+2026-11-09 07:11:43.186536: train loss : -0.8385 
+2026-11-09 07:11:51.333947: validation loss: -0.4995 
+2026-11-09 07:11:51.335014: Average global foreground Dice: [np.float32(0.922), np.float32(0.5137), np.float32(0.5582), np.float32(0.5421), np.float32(0.4844), np.float32(0.5931), np.float32(0.5175), np.float32(0.7288), np.float32(0.4845), np.float32(0.4097), np.float32(0.4153), np.float32(0.504), np.float32(0.5721), np.float32(0.5037), np.float32(0.0012), np.float32(0.0), np.float32(0.4142), np.float32(0.332), np.float32(0.421), np.float32(0.3942), np.float32(0.4073), np.float32(0.4494), np.float32(0.7375), np.float32(0.6957), np.float32(0.2463), np.float32(0.3043), np.float32(0.0783), np.float32(0.2465), np.float32(0.1497), np.float32(0.5807), np.float32(0.0), np.float32(0.0), np.float32(0.3909), np.float32(0.4581), np.float32(0.5913), np.float32(0.6325)] 
+2026-11-09 07:11:51.335309: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 07:11:51.681057: lr: 0.000582 
+2026-11-09 07:11:51.681295: This epoch took 272.990761 s
+ 
+2026-11-09 07:11:51.681382: 
+epoch:  452 
+2026-11-09 07:16:32.599310: train loss : -0.8366 
+2026-11-09 07:16:40.789792: validation loss: -0.5186 
+2026-11-09 07:16:40.790729: Average global foreground Dice: [np.float32(0.929), np.float32(0.5631), np.float32(0.5881), np.float32(0.5879), np.float32(0.5269), np.float32(0.6412), np.float32(0.548), np.float32(0.7108), np.float32(0.5253), np.float32(0.4923), np.float32(0.4836), np.float32(0.5247), np.float32(0.6035), np.float32(0.5679), np.float32(0.0125), np.float32(0.0), np.float32(0.467), np.float32(0.3784), np.float32(0.4851), np.float32(0.4353), np.float32(0.3993), np.float32(0.4048), np.float32(0.7697), np.float32(0.7248), np.float32(0.2529), np.float32(0.3524), np.float32(0.0666), np.float32(0.1022), np.float32(0.2165), np.float32(0.6316), np.float32(0.0), np.float32(0.0), np.float32(0.488), np.float32(0.5289), np.float32(0.6362), np.float32(0.6769)] 
+2026-11-09 07:16:40.791134: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 07:16:41.126348: lr: 0.000581 
+2026-11-09 07:16:41.126588: This epoch took 289.445148 s
+ 
+2026-11-09 07:16:41.126670: 
+epoch:  453 
+2026-11-09 07:20:34.786186: train loss : -0.8397 
+2026-11-09 07:20:42.942263: validation loss: -0.5286 
+2026-11-09 07:20:42.943162: Average global foreground Dice: [np.float32(0.9294), np.float32(0.5602), np.float32(0.6008), np.float32(0.5936), np.float32(0.5027), np.float32(0.6488), np.float32(0.5753), np.float32(0.528), np.float32(0.4805), np.float32(0.4999), np.float32(0.4983), np.float32(0.5631), np.float32(0.5932), np.float32(0.5092), np.float32(0.0055), np.float32(0.0), np.float32(0.4391), np.float32(0.3835), np.float32(0.4602), np.float32(0.4295), np.float32(0.4197), np.float32(0.3801), np.float32(0.7358), np.float32(0.7072), np.float32(0.3097), np.float32(0.296), np.float32(0.0412), np.float32(0.0807), np.float32(0.1715), np.float32(0.6731), np.float32(0.0), np.float32(0.0), np.float32(0.3937), np.float32(0.4731), np.float32(0.6454), np.float32(0.6699)] 
+2026-11-09 07:20:42.943314: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 07:20:43.268348: lr: 0.00058 
+2026-11-09 07:20:43.268755: This epoch took 242.142014 s
+ 
+2026-11-09 07:20:43.268973: 
+epoch:  454 
+2026-11-09 07:24:58.929435: train loss : -0.8458 
+2026-11-09 07:25:07.109067: validation loss: -0.5179 
+2026-11-09 07:25:07.110158: Average global foreground Dice: [np.float32(0.9244), np.float32(0.5321), np.float32(0.5498), np.float32(0.5621), np.float32(0.4044), np.float32(0.6158), np.float32(0.5147), np.float32(0.4683), np.float32(0.4759), np.float32(0.5069), np.float32(0.4974), np.float32(0.5519), np.float32(0.6005), np.float32(0.5175), np.float32(0.0), np.float32(0.0), np.float32(0.364), np.float32(0.3161), np.float32(0.417), np.float32(0.3946), np.float32(0.4683), np.float32(0.3214), np.float32(0.6574), np.float32(0.6208), np.float32(0.2191), np.float32(0.2498), np.float32(0.002), np.float32(0.088), np.float32(0.3363), np.float32(0.6324), np.float32(0.0), np.float32(0.0), np.float32(0.393), np.float32(0.4728), np.float32(0.5917), np.float32(0.6234)] 
+2026-11-09 07:25:07.110480: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 07:25:07.447728: lr: 0.000579 
+2026-11-09 07:25:07.447986: This epoch took 264.178923 s
+ 
+2026-11-09 07:25:07.448089: 
+epoch:  455 
+2026-11-09 07:29:30.204891: train loss : -0.8382 
+2026-11-09 07:29:38.412001: validation loss: -0.5034 
+2026-11-09 07:29:38.412997: Average global foreground Dice: [np.float32(0.9216), np.float32(0.5191), np.float32(0.5368), np.float32(0.5441), np.float32(0.455), np.float32(0.6032), np.float32(0.516), np.float32(0.6668), np.float32(0.4875), np.float32(0.47), np.float32(0.4457), np.float32(0.5075), np.float32(0.576), np.float32(0.5078), np.float32(0.0019), np.float32(0.0), np.float32(0.3969), np.float32(0.321), np.float32(0.4074), np.float32(0.396), np.float32(0.4122), np.float32(0.3361), np.float32(0.696), np.float32(0.6469), np.float32(0.2099), np.float32(0.2566), np.float32(0.0223), np.float32(0.1336), np.float32(0.3954), np.float32(0.6537), np.float32(0.0), np.float32(0.0), np.float32(0.3298), np.float32(0.4419), np.float32(0.5915), np.float32(0.6188)] 
+2026-11-09 07:29:38.413394: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 07:29:38.798226: lr: 0.000578 
+2026-11-09 07:29:38.798494: This epoch took 271.350327 s
+ 
+2026-11-09 07:29:38.798587: 
+epoch:  456 
+2026-11-09 07:33:47.174712: train loss : -0.8349 
+2026-11-09 07:33:55.332337: validation loss: -0.5156 
+2026-11-09 07:33:55.333450: Average global foreground Dice: [np.float32(0.9227), np.float32(0.5496), np.float32(0.5785), np.float32(0.5685), np.float32(0.4623), np.float32(0.626), np.float32(0.5318), np.float32(0.629), np.float32(0.5295), np.float32(0.5193), np.float32(0.4674), np.float32(0.5155), np.float32(0.5671), np.float32(0.4807), np.float32(0.0113), np.float32(0.0), np.float32(0.4112), np.float32(0.3322), np.float32(0.4515), np.float32(0.4368), np.float32(0.403), np.float32(0.412), np.float32(0.6872), np.float32(0.646), np.float32(0.2386), np.float32(0.3134), np.float32(0.0311), np.float32(0.1884), np.float32(0.3637), np.float32(0.6222), np.float32(0.0), np.float32(0.0), np.float32(0.3723), np.float32(0.4615), np.float32(0.6029), np.float32(0.6395)] 
+2026-11-09 07:33:55.333667: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 07:33:55.667899: lr: 0.000577 
+2026-11-09 07:33:55.668252: This epoch took 256.869598 s
+ 
+2026-11-09 07:33:55.668476: 
+epoch:  457 
+2026-11-09 07:38:04.137000: train loss : -0.8236 
+2026-11-09 07:38:12.285980: validation loss: -0.4711 
+2026-11-09 07:38:12.286971: Average global foreground Dice: [np.float32(0.9268), np.float32(0.4944), np.float32(0.5096), np.float32(0.5199), np.float32(0.4311), np.float32(0.5633), np.float32(0.4639), np.float32(0.641), np.float32(0.418), np.float32(0.5385), np.float32(0.4239), np.float32(0.4935), np.float32(0.5758), np.float32(0.5103), np.float32(0.0), np.float32(0.0), np.float32(0.3802), np.float32(0.3347), np.float32(0.3888), np.float32(0.4031), np.float32(0.4275), np.float32(0.3471), np.float32(0.6862), np.float32(0.6221), np.float32(0.1841), np.float32(0.2351), np.float32(0.0053), np.float32(0.1708), np.float32(0.0021), np.float32(0.6922), np.float32(0.0), np.float32(0.0), np.float32(0.3965), np.float32(0.4694), np.float32(0.5644), np.float32(0.5909)] 
+2026-11-09 07:38:12.287403: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 07:38:12.621742: lr: 0.000576 
+2026-11-09 07:38:12.621986: This epoch took 256.953349 s
+ 
+2026-11-09 07:38:12.622081: 
+epoch:  458 
+2026-11-09 07:42:19.080247: train loss : -0.8388 
+2026-11-09 07:42:27.236156: validation loss: -0.5268 
+2026-11-09 07:42:27.237054: Average global foreground Dice: [np.float32(0.9186), np.float32(0.5462), np.float32(0.5911), np.float32(0.5853), np.float32(0.5234), np.float32(0.6492), np.float32(0.5592), np.float32(0.6844), np.float32(0.4308), np.float32(0.5528), np.float32(0.496), np.float32(0.5413), np.float32(0.6055), np.float32(0.5234), np.float32(0.0583), np.float32(0.0), np.float32(0.4407), np.float32(0.3476), np.float32(0.4538), np.float32(0.4187), np.float32(0.355), np.float32(0.4428), np.float32(0.7784), np.float32(0.7262), np.float32(0.2387), np.float32(0.3043), np.float32(0.0525), np.float32(0.0783), np.float32(0.022), np.float32(0.6671), np.float32(0.0), np.float32(0.0), np.float32(0.45), np.float32(0.5082), np.float32(0.6338), np.float32(0.6688)] 
+2026-11-09 07:42:27.237233: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 07:42:27.566831: lr: 0.000575 
+2026-11-09 07:42:27.567313: This epoch took 254.945166 s
+ 
+2026-11-09 07:42:27.567461: 
+epoch:  459 
+2026-11-09 07:46:36.235313: train loss : -0.8415 
+2026-11-09 07:46:44.398527: validation loss: -0.5010 
+2026-11-09 07:46:44.399760: Average global foreground Dice: [np.float32(0.925), np.float32(0.5043), np.float32(0.5505), np.float32(0.5523), np.float32(0.4759), np.float32(0.6114), np.float32(0.5093), np.float32(0.5637), np.float32(0.4601), np.float32(0.5692), np.float32(0.4661), np.float32(0.5152), np.float32(0.5794), np.float32(0.516), np.float32(0.0809), np.float32(0.0), np.float32(0.3948), np.float32(0.3359), np.float32(0.4012), np.float32(0.3929), np.float32(0.3894), np.float32(0.3507), np.float32(0.7244), np.float32(0.6916), np.float32(0.2073), np.float32(0.2445), np.float32(0.0215), np.float32(0.0608), np.float32(0.1881), np.float32(0.6473), np.float32(0.0), np.float32(0.0), np.float32(0.3589), np.float32(0.4637), np.float32(0.6063), np.float32(0.6313)] 
+2026-11-09 07:46:44.400359: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 07:46:44.733250: lr: 0.000574 
+2026-11-09 07:46:44.733579: This epoch took 257.166045 s
+ 
+2026-11-09 07:46:44.733808: 
+epoch:  460 
+2026-11-09 07:51:14.868588: train loss : -0.8367 
+2026-11-09 07:51:23.031061: validation loss: -0.4574 
+2026-11-09 07:51:23.032141: Average global foreground Dice: [np.float32(0.9166), np.float32(0.4377), np.float32(0.4806), np.float32(0.4675), np.float32(0.373), np.float32(0.5287), np.float32(0.437), np.float32(0.3996), np.float32(0.5054), np.float32(0.4894), np.float32(0.4052), np.float32(0.4274), np.float32(0.5333), np.float32(0.4346), np.float32(0.0), np.float32(0.0), np.float32(0.3124), np.float32(0.2567), np.float32(0.3558), np.float32(0.3567), np.float32(0.4123), np.float32(0.3112), np.float32(0.6477), np.float32(0.606), np.float32(0.1648), np.float32(0.2037), np.float32(0.0066), np.float32(0.0157), np.float32(0.3322), np.float32(0.6631), np.float32(0.0), np.float32(0.0), np.float32(0.3269), np.float32(0.3855), np.float32(0.5131), np.float32(0.5561)] 
+2026-11-09 07:51:23.032566: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 07:51:23.387151: lr: 0.000573 
+2026-11-09 07:51:23.387618: This epoch took 278.653711 s
+ 
+2026-11-09 07:51:23.388000: 
+epoch:  461 
+2026-11-09 07:55:28.483066: train loss : -0.8378 
+2026-11-09 07:55:36.654835: validation loss: -0.4650 
+2026-11-09 07:55:36.655795: Average global foreground Dice: [np.float32(0.9168), np.float32(0.4695), np.float32(0.4914), np.float32(0.5129), np.float32(0.4077), np.float32(0.5623), np.float32(0.4687), np.float32(0.5465), np.float32(0.3988), np.float32(0.5626), np.float32(0.4353), np.float32(0.4546), np.float32(0.5472), np.float32(0.4355), np.float32(0.0), np.float32(0.0), np.float32(0.3485), np.float32(0.2818), np.float32(0.3909), np.float32(0.3892), np.float32(0.3837), np.float32(0.3393), np.float32(0.6354), np.float32(0.6055), np.float32(0.1985), np.float32(0.2185), np.float32(0.0213), np.float32(0.0176), np.float32(0.3894), np.float32(0.6338), np.float32(0.0), np.float32(0.0), np.float32(0.3349), np.float32(0.4396), np.float32(0.5453), np.float32(0.5732)] 
+2026-11-09 07:55:36.655959: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 07:55:36.986053: lr: 0.000572 
+2026-11-09 07:55:36.986389: This epoch took 253.598152 s
+ 
+2026-11-09 07:55:36.986578: 
+epoch:  462 
+2026-11-09 08:00:01.709687: train loss : -0.8420 
+2026-11-09 08:00:09.854361: validation loss: -0.4937 
+2026-11-09 08:00:09.855339: Average global foreground Dice: [np.float32(0.9148), np.float32(0.4848), np.float32(0.5388), np.float32(0.5438), np.float32(0.4267), np.float32(0.6059), np.float32(0.4948), np.float32(0.5995), np.float32(0.5134), np.float32(0.5533), np.float32(0.4575), np.float32(0.5195), np.float32(0.5531), np.float32(0.4661), np.float32(0.0007), np.float32(0.0), np.float32(0.3644), np.float32(0.3093), np.float32(0.432), np.float32(0.4218), np.float32(0.4184), np.float32(0.3591), np.float32(0.6924), np.float32(0.6607), np.float32(0.2544), np.float32(0.2474), np.float32(0.0649), np.float32(0.1948), np.float32(0.3923), np.float32(0.6417), np.float32(0.0), np.float32(0.0), np.float32(0.4178), np.float32(0.4704), np.float32(0.5749), np.float32(0.625)] 
+2026-11-09 08:00:09.855499: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:00:10.186604: lr: 0.000571 
+2026-11-09 08:00:10.187018: This epoch took 273.200368 s
+ 
+2026-11-09 08:00:10.187136: 
+epoch:  463 
+2026-11-09 08:04:24.047412: train loss : -0.8409 
+2026-11-09 08:04:32.233357: validation loss: -0.4664 
+2026-11-09 08:04:32.234324: Average global foreground Dice: [np.float32(0.9209), np.float32(0.4533), np.float32(0.4779), np.float32(0.4893), np.float32(0.3141), np.float32(0.5473), np.float32(0.4358), np.float32(0.617), np.float32(0.4686), np.float32(0.5641), np.float32(0.4226), np.float32(0.4662), np.float32(0.5575), np.float32(0.4237), np.float32(0.0006), np.float32(0.0), np.float32(0.2542), np.float32(0.2197), np.float32(0.3382), np.float32(0.3394), np.float32(0.407), np.float32(0.2815), np.float32(0.6115), np.float32(0.5904), np.float32(0.1539), np.float32(0.1712), np.float32(0.0172), np.float32(0.0), np.float32(0.3194), np.float32(0.6656), np.float32(0.0), np.float32(0.0), np.float32(0.3037), np.float32(0.4091), np.float32(0.5152), np.float32(0.5552)] 
+2026-11-09 08:04:32.234724: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:04:32.573431: lr: 0.00057 
+2026-11-09 08:04:32.573896: This epoch took 262.386703 s
+ 
+2026-11-09 08:04:32.574188: 
+epoch:  464 
+2026-11-09 08:09:16.984252: train loss : -0.8378 
+2026-11-09 08:09:25.131872: validation loss: -0.5073 
+2026-11-09 08:09:25.132807: Average global foreground Dice: [np.float32(0.928), np.float32(0.5371), np.float32(0.5812), np.float32(0.5887), np.float32(0.5061), np.float32(0.6497), np.float32(0.5414), np.float32(0.5857), np.float32(0.4913), np.float32(0.5164), np.float32(0.4901), np.float32(0.5624), np.float32(0.5712), np.float32(0.4782), np.float32(0.0), np.float32(0.0), np.float32(0.4472), np.float32(0.3667), np.float32(0.4798), np.float32(0.4499), np.float32(0.4426), np.float32(0.4413), np.float32(0.7417), np.float32(0.711), np.float32(0.2798), np.float32(0.2814), np.float32(0.0994), np.float32(0.1997), np.float32(0.3114), np.float32(0.6518), np.float32(0.0), np.float32(0.0), np.float32(0.4779), np.float32(0.5509), np.float32(0.6294), np.float32(0.6649)] 
+2026-11-09 08:09:25.133003: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:09:25.462080: lr: 0.00057 
+2026-11-09 08:09:25.462323: This epoch took 292.887920 s
+ 
+2026-11-09 08:09:25.462409: 
+epoch:  465 
+2026-11-09 08:13:46.523853: train loss : -0.8394 
+2026-11-09 08:13:54.695560: validation loss: -0.6166 
+2026-11-09 08:13:54.696778: Average global foreground Dice: [np.float32(0.9248), np.float32(0.6282), np.float32(0.7445), np.float32(0.7556), np.float32(0.6416), np.float32(0.8076), np.float32(0.6937), np.float32(0.3543), np.float32(0.554), np.float32(0.51), np.float32(0.6231), np.float32(0.6727), np.float32(0.6445), np.float32(0.6025), np.float32(0.0009), np.float32(0.0), np.float32(0.6251), np.float32(0.4989), np.float32(0.6435), np.float32(0.5638), np.float32(0.5282), np.float32(0.5364), np.float32(0.8623), np.float32(0.8496), np.float32(0.4204), np.float32(0.4327), np.float32(0.1214), np.float32(0.549), np.float32(0.351), np.float32(0.6454), np.float32(0.0), np.float32(0.0), np.float32(0.56), np.float32(0.6333), np.float32(0.7923), np.float32(0.8224)] 
+2026-11-09 08:13:54.697015: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:13:55.031600: lr: 0.000569 
+2026-11-09 08:13:55.031842: This epoch took 269.569368 s
+ 
+2026-11-09 08:13:55.031962: 
+epoch:  466 
+2026-11-09 08:17:53.484255: train loss : -0.8361 
+2026-11-09 08:18:01.627413: validation loss: -0.5712 
+2026-11-09 08:18:01.628594: Average global foreground Dice: [np.float32(0.9092), np.float32(0.5531), np.float32(0.7053), np.float32(0.7317), np.float32(0.633), np.float32(0.776), np.float32(0.6809), np.float32(0.5013), np.float32(0.4732), np.float32(0.325), np.float32(0.5804), np.float32(0.6134), np.float32(0.5839), np.float32(0.4847), np.float32(0.0), np.float32(0.0), np.float32(0.6752), np.float32(0.4891), np.float32(0.658), np.float32(0.532), np.float32(0.4943), np.float32(0.5968), np.float32(0.7652), np.float32(0.7405), np.float32(0.5118), np.float32(0.387), np.float32(0.2603), np.float32(0.5635), np.float32(0.3717), np.float32(0.6916), np.float32(0.0), np.float32(0.0), np.float32(0.5031), np.float32(0.6637), np.float32(0.7612), np.float32(0.7719)] 
+2026-11-09 08:18:01.628836: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:18:01.967562: lr: 0.000568 
+2026-11-09 08:18:01.967793: This epoch took 246.935767 s
+ 
+2026-11-09 08:18:01.968012: 
+epoch:  467 
+2026-11-09 08:22:18.825581: train loss : -0.8413 
+2026-11-09 08:22:26.973787: validation loss: -0.6329 
+2026-11-09 08:22:26.974769: Average global foreground Dice: [np.float32(0.9243), np.float32(0.6251), np.float32(0.7951), np.float32(0.807), np.float32(0.7417), np.float32(0.8561), np.float32(0.7578), np.float32(0.6111), np.float32(0.5331), np.float32(0.324), np.float32(0.6582), np.float32(0.6643), np.float32(0.622), np.float32(0.5324), np.float32(0.0), np.float32(0.0), np.float32(0.7536), np.float32(0.5799), np.float32(0.7278), np.float32(0.6443), np.float32(0.4982), np.float32(0.5968), np.float32(0.8848), np.float32(0.876), np.float32(0.5236), np.float32(0.4573), np.float32(0.2861), np.float32(0.4212), np.float32(0.3889), np.float32(0.6922), np.float32(0.0), np.float32(0.0), np.float32(0.6342), np.float32(0.6734), np.float32(0.8487), np.float32(0.8589)] 
+2026-11-09 08:22:26.974949: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:22:27.302386: lr: 0.000567 
+2026-11-09 08:22:27.302660: This epoch took 265.334551 s
+ 
+2026-11-09 08:22:27.302863: 
+epoch:  468 
+2026-11-09 08:26:52.368895: train loss : -0.8390 
+2026-11-09 08:27:00.542264: validation loss: -0.5696 
+2026-11-09 08:27:00.543283: Average global foreground Dice: [np.float32(0.9089), np.float32(0.5512), np.float32(0.7309), np.float32(0.7665), np.float32(0.6859), np.float32(0.802), np.float32(0.7362), np.float32(0.6181), np.float32(0.3322), np.float32(0.3746), np.float32(0.5659), np.float32(0.5612), np.float32(0.6267), np.float32(0.3995), np.float32(0.0), np.float32(0.0), np.float32(0.7367), np.float32(0.562), np.float32(0.7273), np.float32(0.6293), np.float32(0.4696), np.float32(0.6496), np.float32(0.7974), np.float32(0.7999), np.float32(0.5667), np.float32(0.2769), np.float32(0.3052), np.float32(0.3606), np.float32(0.3809), np.float32(0.646), np.float32(0.0), np.float32(0.0), np.float32(0.6348), np.float32(0.6527), np.float32(0.8027), np.float32(0.8187)] 
+2026-11-09 08:27:00.543662: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:27:00.877004: lr: 0.000566 
+2026-11-09 08:27:00.877263: This epoch took 273.574131 s
+ 
+2026-11-09 08:27:00.877354: 
+epoch:  469 
+2026-11-09 08:31:22.522322: train loss : -0.8398 
+2026-11-09 08:31:30.700996: validation loss: -0.5931 
+2026-11-09 08:31:30.701925: Average global foreground Dice: [np.float32(0.9214), np.float32(0.6051), np.float32(0.7381), np.float32(0.7421), np.float32(0.6424), np.float32(0.7897), np.float32(0.6838), np.float32(0.5324), np.float32(0.2705), np.float32(0.4714), np.float32(0.5953), np.float32(0.6405), np.float32(0.6107), np.float32(0.5052), np.float32(0.0), np.float32(0.0), np.float32(0.69), np.float32(0.5254), np.float32(0.6877), np.float32(0.6078), np.float32(0.497), np.float32(0.556), np.float32(0.7761), np.float32(0.7772), np.float32(0.5171), np.float32(0.4833), np.float32(0.2558), np.float32(0.2466), np.float32(0.3745), np.float32(0.6998), np.float32(0.0), np.float32(0.0), np.float32(0.5601), np.float32(0.6585), np.float32(0.7789), np.float32(0.7985)] 
+2026-11-09 08:31:30.702110: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:31:31.059756: lr: 0.000565 
+2026-11-09 08:31:31.059979: This epoch took 270.182559 s
+ 
+2026-11-09 08:31:31.060066: 
+epoch:  470 
+2026-11-09 08:35:33.384573: train loss : -0.8455 
+2026-11-09 08:35:41.544412: validation loss: -0.5596 
+2026-11-09 08:35:41.545358: Average global foreground Dice: [np.float32(0.9247), np.float32(0.56), np.float32(0.7396), np.float32(0.7436), np.float32(0.6624), np.float32(0.7905), np.float32(0.7041), np.float32(0.5822), np.float32(0.3569), np.float32(0.3827), np.float32(0.5255), np.float32(0.5789), np.float32(0.5533), np.float32(0.4221), np.float32(0.0011), np.float32(0.0), np.float32(0.7236), np.float32(0.5795), np.float32(0.7287), np.float32(0.6281), np.float32(0.4266), np.float32(0.6048), np.float32(0.8059), np.float32(0.7994), np.float32(0.5495), np.float32(0.4355), np.float32(0.2592), np.float32(0.2899), np.float32(0.3426), np.float32(0.6179), np.float32(0.0), np.float32(0.0), np.float32(0.5974), np.float32(0.6759), np.float32(0.7859), np.float32(0.807)] 
+2026-11-09 08:35:41.545530: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:35:41.891039: lr: 0.000564 
+2026-11-09 08:35:41.891501: This epoch took 250.831371 s
+ 
+2026-11-09 08:35:41.891875: 
+epoch:  471 
+2026-11-09 08:40:03.145394: train loss : -0.8461 
+2026-11-09 08:40:11.315431: validation loss: -0.6173 
+2026-11-09 08:40:11.316427: Average global foreground Dice: [np.float32(0.9259), np.float32(0.6179), np.float32(0.7494), np.float32(0.7436), np.float32(0.6389), np.float32(0.8066), np.float32(0.6855), np.float32(0.4742), np.float32(0.1476), np.float32(0.4579), np.float32(0.621), np.float32(0.6762), np.float32(0.6467), np.float32(0.5925), np.float32(0.0), np.float32(0.0), np.float32(0.6982), np.float32(0.5707), np.float32(0.6988), np.float32(0.6237), np.float32(0.5234), np.float32(0.555), np.float32(0.8374), np.float32(0.8323), np.float32(0.4621), np.float32(0.4761), np.float32(0.2199), np.float32(0.1295), np.float32(0.3386), np.float32(0.7069), np.float32(0.0), np.float32(0.0), np.float32(0.5885), np.float32(0.6637), np.float32(0.784), np.float32(0.8069)] 
+2026-11-09 08:40:11.316680: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:40:11.642687: lr: 0.000563 
+2026-11-09 08:40:11.643024: This epoch took 269.750937 s
+ 
+2026-11-09 08:40:11.643191: 
+epoch:  472 
+2026-11-09 08:44:09.426756: train loss : -0.8382 
+2026-11-09 08:44:17.560990: validation loss: -0.5802 
+2026-11-09 08:44:17.561886: Average global foreground Dice: [np.float32(0.9235), np.float32(0.6092), np.float32(0.7268), np.float32(0.7442), np.float32(0.6522), np.float32(0.8002), np.float32(0.6934), np.float32(0.6618), np.float32(0.5042), np.float32(0.4453), np.float32(0.6202), np.float32(0.6607), np.float32(0.6549), np.float32(0.5579), np.float32(0.0175), np.float32(0.0), np.float32(0.7104), np.float32(0.5683), np.float32(0.7145), np.float32(0.6062), np.float32(0.472), np.float32(0.6092), np.float32(0.7863), np.float32(0.7757), np.float32(0.4947), np.float32(0.4631), np.float32(0.1574), np.float32(0.2043), np.float32(0.2289), np.float32(0.6652), np.float32(0.0), np.float32(0.0), np.float32(0.6258), np.float32(0.6629), np.float32(0.7802), np.float32(0.805)] 
+2026-11-09 08:44:17.562245: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:44:17.888367: lr: 0.000562 
+2026-11-09 08:44:17.888637: This epoch took 246.245364 s
+ 
+2026-11-09 08:44:17.888851: 
+epoch:  473 
+2026-11-09 08:48:19.672880: train loss : -0.8440 
+2026-11-09 08:48:27.808113: validation loss: -0.5958 
+2026-11-09 08:48:27.809258: Average global foreground Dice: [np.float32(0.9244), np.float32(0.5949), np.float32(0.7464), np.float32(0.7495), np.float32(0.6251), np.float32(0.8006), np.float32(0.6994), np.float32(0.5382), np.float32(0.299), np.float32(0.4374), np.float32(0.5959), np.float32(0.6255), np.float32(0.6126), np.float32(0.4767), np.float32(0.052), np.float32(0.0), np.float32(0.709), np.float32(0.5373), np.float32(0.7116), np.float32(0.6424), np.float32(0.5384), np.float32(0.5877), np.float32(0.8046), np.float32(0.8013), np.float32(0.5415), np.float32(0.4413), np.float32(0.2689), np.float32(0.1619), np.float32(0.3906), np.float32(0.6914), np.float32(0.0), np.float32(0.0), np.float32(0.5369), np.float32(0.6723), np.float32(0.7799), np.float32(0.8035)] 
+2026-11-09 08:48:27.809457: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:48:28.141346: lr: 0.000561 
+2026-11-09 08:48:28.141582: This epoch took 250.252630 s
+ 
+2026-11-09 08:48:28.141658: 
+epoch:  474 
+2026-11-09 08:52:44.957446: train loss : -0.8418 
+2026-11-09 08:52:53.090962: validation loss: -0.5873 
+2026-11-09 08:52:53.091983: Average global foreground Dice: [np.float32(0.918), np.float32(0.5837), np.float32(0.7533), np.float32(0.745), np.float32(0.6573), np.float32(0.8013), np.float32(0.7007), np.float32(0.5196), np.float32(0.4236), np.float32(0.3363), np.float32(0.6188), np.float32(0.644), np.float32(0.611), np.float32(0.476), np.float32(0.0), np.float32(0.0), np.float32(0.7297), np.float32(0.5532), np.float32(0.7234), np.float32(0.6125), np.float32(0.5169), np.float32(0.5837), np.float32(0.7691), np.float32(0.7637), np.float32(0.5106), np.float32(0.4257), np.float32(0.2448), np.float32(0.5471), np.float32(0.3586), np.float32(0.7051), np.float32(0.0), np.float32(0.0), np.float32(0.5975), np.float32(0.6792), np.float32(0.7775), np.float32(0.8053)] 
+2026-11-09 08:52:53.092147: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:52:53.419427: lr: 0.00056 
+2026-11-09 08:52:53.419651: This epoch took 265.277940 s
+ 
+2026-11-09 08:52:53.419734: 
+epoch:  475 
+2026-11-09 08:56:56.990338: train loss : -0.8465 
+2026-11-09 08:57:05.154480: validation loss: -0.6062 
+2026-11-09 08:57:05.155511: Average global foreground Dice: [np.float32(0.9179), np.float32(0.5862), np.float32(0.7723), np.float32(0.7965), np.float32(0.7246), np.float32(0.8413), np.float32(0.772), np.float32(0.6049), np.float32(0.4111), np.float32(0.3407), np.float32(0.5924), np.float32(0.6628), np.float32(0.5509), np.float32(0.5632), np.float32(0.0125), np.float32(0.0), np.float32(0.7389), np.float32(0.5651), np.float32(0.7368), np.float32(0.6101), np.float32(0.5133), np.float32(0.6157), np.float32(0.8626), np.float32(0.8383), np.float32(0.5205), np.float32(0.3982), np.float32(0.2944), np.float32(0.5509), np.float32(0.4141), np.float32(0.7048), np.float32(0.0), np.float32(0.0), np.float32(0.6542), np.float32(0.6711), np.float32(0.832), np.float32(0.8524)] 
+2026-11-09 08:57:05.155701: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 08:57:05.489894: lr: 0.000559 
+2026-11-09 08:57:05.490412: This epoch took 252.070611 s
+ 
+2026-11-09 08:57:05.490784: 
+epoch:  476 
+2026-11-09 09:01:20.658343: train loss : -0.8420 
+2026-11-09 09:01:28.818732: validation loss: -0.4800 
+2026-11-09 09:01:28.819961: Average global foreground Dice: [np.float32(0.913), np.float32(0.4972), np.float32(0.5296), np.float32(0.5097), np.float32(0.4371), np.float32(0.5774), np.float32(0.4852), np.float32(0.6229), np.float32(0.452), np.float32(0.4447), np.float32(0.4189), np.float32(0.4821), np.float32(0.5657), np.float32(0.4761), np.float32(0.0), np.float32(0.0), np.float32(0.4131), np.float32(0.3402), np.float32(0.4438), np.float32(0.408), np.float32(0.3705), np.float32(0.3118), np.float32(0.6431), np.float32(0.5992), np.float32(0.2316), np.float32(0.3134), np.float32(0.072), np.float32(0.1337), np.float32(0.2065), np.float32(0.6441), np.float32(0.0), np.float32(0.0), np.float32(0.4156), np.float32(0.5012), np.float32(0.5626), np.float32(0.5952)] 
+2026-11-09 09:01:28.820493: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:01:29.155615: lr: 0.000558 
+2026-11-09 09:01:29.156013: This epoch took 263.664957 s
+ 
+2026-11-09 09:01:29.156241: 
+epoch:  477 
+2026-11-09 09:05:34.154699: train loss : -0.8426 
+2026-11-09 09:05:42.330465: validation loss: -0.5857 
+2026-11-09 09:05:42.331633: Average global foreground Dice: [np.float32(0.9253), np.float32(0.618), np.float32(0.738), np.float32(0.7395), np.float32(0.6479), np.float32(0.7752), np.float32(0.6913), np.float32(0.5888), np.float32(0.2249), np.float32(0.3463), np.float32(0.5728), np.float32(0.6416), np.float32(0.631), np.float32(0.5654), np.float32(0.0028), np.float32(0.0), np.float32(0.6598), np.float32(0.5229), np.float32(0.651), np.float32(0.569), np.float32(0.5262), np.float32(0.5144), np.float32(0.7842), np.float32(0.7777), np.float32(0.4687), np.float32(0.3887), np.float32(0.2237), np.float32(0.5166), np.float32(0.3433), np.float32(0.6945), np.float32(0.0), np.float32(0.0), np.float32(0.543), np.float32(0.6088), np.float32(0.7719), np.float32(0.7902)] 
+2026-11-09 09:05:42.332087: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:05:42.666735: lr: 0.000557 
+2026-11-09 09:05:42.666960: This epoch took 253.510550 s
+ 
+2026-11-09 09:05:42.667035: 
+epoch:  478 
+2026-11-09 09:09:57.498211: train loss : -0.8446 
+2026-11-09 09:10:05.678826: validation loss: -0.5943 
+2026-11-09 09:10:05.679855: Average global foreground Dice: [np.float32(0.9267), np.float32(0.6078), np.float32(0.7558), np.float32(0.7624), np.float32(0.6747), np.float32(0.8077), np.float32(0.7132), np.float32(0.5775), np.float32(0.2751), np.float32(0.5117), np.float32(0.6109), np.float32(0.6645), np.float32(0.5833), np.float32(0.4843), np.float32(0.041), np.float32(0.0), np.float32(0.7154), np.float32(0.5436), np.float32(0.7125), np.float32(0.6455), np.float32(0.5341), np.float32(0.5782), np.float32(0.8334), np.float32(0.8394), np.float32(0.5288), np.float32(0.4021), np.float32(0.1368), np.float32(0.0657), np.float32(0.3692), np.float32(0.7039), np.float32(0.0), np.float32(0.0), np.float32(0.5501), np.float32(0.6445), np.float32(0.7949), np.float32(0.8166)] 
+2026-11-09 09:10:05.680023: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:10:06.011616: lr: 0.000556 
+2026-11-09 09:10:06.012089: This epoch took 263.345003 s
+ 
+2026-11-09 09:10:06.012394: 
+epoch:  479 
+2026-11-09 09:14:43.152190: train loss : -0.8336 
+2026-11-09 09:14:51.350784: validation loss: -0.6101 
+2026-11-09 09:14:51.351876: Average global foreground Dice: [np.float32(0.9284), np.float32(0.6241), np.float32(0.779), np.float32(0.78), np.float32(0.6796), np.float32(0.8192), np.float32(0.731), np.float32(0.6183), np.float32(0.3085), np.float32(0.4825), np.float32(0.6357), np.float32(0.6589), np.float32(0.5963), np.float32(0.5023), np.float32(0.0299), np.float32(0.0), np.float32(0.7374), np.float32(0.5739), np.float32(0.7256), np.float32(0.637), np.float32(0.539), np.float32(0.6051), np.float32(0.8347), np.float32(0.8453), np.float32(0.5234), np.float32(0.4837), np.float32(0.2363), np.float32(0.7269), np.float32(0.3302), np.float32(0.7285), np.float32(0.0), np.float32(0.0), np.float32(0.5936), np.float32(0.6789), np.float32(0.8124), np.float32(0.8301)] 
+2026-11-09 09:14:51.352286: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:14:51.763232: lr: 0.000555 
+2026-11-09 09:14:51.763558: This epoch took 285.750946 s
+ 
+2026-11-09 09:14:51.763653: 
+epoch:  480 
+2026-11-09 09:18:57.090482: train loss : -0.8440 
+2026-11-09 09:19:05.262759: validation loss: -0.6234 
+2026-11-09 09:19:05.264008: Average global foreground Dice: [np.float32(0.9245), np.float32(0.6381), np.float32(0.7975), np.float32(0.8174), np.float32(0.7357), np.float32(0.8534), np.float32(0.7636), np.float32(0.6551), np.float32(0.42), np.float32(0.4179), np.float32(0.6424), np.float32(0.7017), np.float32(0.6139), np.float32(0.5801), np.float32(0.0206), np.float32(0.0), np.float32(0.7657), np.float32(0.607), np.float32(0.7378), np.float32(0.6287), np.float32(0.509), np.float32(0.5736), np.float32(0.8508), np.float32(0.8572), np.float32(0.4936), np.float32(0.4957), np.float32(0.2306), np.float32(0.6886), np.float32(0.3811), np.float32(0.6834), np.float32(0.0), np.float32(0.0), np.float32(0.6378), np.float32(0.6985), np.float32(0.8414), np.float32(0.8609)] 
+2026-11-09 09:19:05.264588: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:19:05.669896: lr: 0.000554 
+2026-11-09 09:19:05.670328: This epoch took 253.906609 s
+ 
+2026-11-09 09:19:05.670446: 
+epoch:  481 
+2026-11-09 09:23:18.711298: train loss : -0.8451 
+2026-11-09 09:23:26.926258: validation loss: -0.6283 
+2026-11-09 09:23:26.927428: Average global foreground Dice: [np.float32(0.927), np.float32(0.6541), np.float32(0.7889), np.float32(0.8029), np.float32(0.7285), np.float32(0.8462), np.float32(0.7498), np.float32(0.6222), np.float32(0.33), np.float32(0.3853), np.float32(0.622), np.float32(0.6933), np.float32(0.6495), np.float32(0.6167), np.float32(0.0266), np.float32(0.0), np.float32(0.7632), np.float32(0.6073), np.float32(0.7512), np.float32(0.6607), np.float32(0.4819), np.float32(0.5609), np.float32(0.8578), np.float32(0.8436), np.float32(0.4919), np.float32(0.5119), np.float32(0.2363), np.float32(0.7617), np.float32(0.4185), np.float32(0.695), np.float32(0.0), np.float32(0.0), np.float32(0.6383), np.float32(0.6895), np.float32(0.841), np.float32(0.8567)] 
+2026-11-09 09:23:26.927794: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:23:27.326569: lr: 0.000553 
+2026-11-09 09:23:27.326915: This epoch took 261.656399 s
+ 
+2026-11-09 09:23:27.327129: 
+epoch:  482 
+2026-11-09 09:28:00.686285: train loss : -0.8401 
+2026-11-09 09:28:08.916318: validation loss: -0.5902 
+2026-11-09 09:28:08.917321: Average global foreground Dice: [np.float32(0.9147), np.float32(0.5452), np.float32(0.7289), np.float32(0.7402), np.float32(0.6258), np.float32(0.7838), np.float32(0.6958), np.float32(0.6107), np.float32(0.2306), np.float32(0.478), np.float32(0.5905), np.float32(0.6329), np.float32(0.5838), np.float32(0.4632), np.float32(0.0405), np.float32(0.0), np.float32(0.6929), np.float32(0.5299), np.float32(0.6983), np.float32(0.622), np.float32(0.5118), np.float32(0.5927), np.float32(0.8069), np.float32(0.7929), np.float32(0.5128), np.float32(0.366), np.float32(0.235), np.float32(0.6973), np.float32(0.4572), np.float32(0.6834), np.float32(0.0), np.float32(0.0), np.float32(0.556), np.float32(0.6363), np.float32(0.7655), np.float32(0.7869)] 
+2026-11-09 09:28:08.917463: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:28:09.259110: lr: 0.000552 
+2026-11-09 09:28:09.259404: This epoch took 281.932188 s
+ 
+2026-11-09 09:28:09.259790: 
+epoch:  483 
+2026-11-09 09:32:11.849375: train loss : -0.8430 
+2026-11-09 09:32:20.026980: validation loss: -0.5465 
+2026-11-09 09:32:20.027974: Average global foreground Dice: [np.float32(0.9225), np.float32(0.5734), np.float32(0.6821), np.float32(0.6974), np.float32(0.5767), np.float32(0.739), np.float32(0.6444), np.float32(0.6702), np.float32(0.2616), np.float32(0.4654), np.float32(0.5518), np.float32(0.5949), np.float32(0.5908), np.float32(0.5249), np.float32(0.0077), np.float32(0.0), np.float32(0.5676), np.float32(0.4396), np.float32(0.5992), np.float32(0.5518), np.float32(0.4493), np.float32(0.4892), np.float32(0.7692), np.float32(0.7481), np.float32(0.3948), np.float32(0.4296), np.float32(0.1292), np.float32(0.0), np.float32(0.1596), np.float32(0.6749), np.float32(0.0), np.float32(0.0), np.float32(0.406), np.float32(0.5752), np.float32(0.7179), np.float32(0.7432)] 
+2026-11-09 09:32:20.028236: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:32:20.376453: lr: 0.000551 
+2026-11-09 09:32:20.376889: This epoch took 251.116826 s
+ 
+2026-11-09 09:32:20.377165: 
+epoch:  484 
+2026-11-09 09:36:21.235812: train loss : -0.8504 
+2026-11-09 09:36:29.461392: validation loss: -0.6205 
+2026-11-09 09:36:29.462494: Average global foreground Dice: [np.float32(0.9245), np.float32(0.6361), np.float32(0.7776), np.float32(0.8), np.float32(0.7194), np.float32(0.8412), np.float32(0.7398), np.float32(0.6194), np.float32(0.3543), np.float32(0.3496), np.float32(0.6476), np.float32(0.712), np.float32(0.6262), np.float32(0.6227), np.float32(0.0), np.float32(0.0), np.float32(0.7484), np.float32(0.6178), np.float32(0.7228), np.float32(0.6164), np.float32(0.5251), np.float32(0.6095), np.float32(0.8406), np.float32(0.8311), np.float32(0.4684), np.float32(0.4765), np.float32(0.1808), np.float32(0.4038), np.float32(0.3494), np.float32(0.6975), np.float32(0.0), np.float32(0.0), np.float32(0.6142), np.float32(0.6137), np.float32(0.819), np.float32(0.8422)] 
+2026-11-09 09:36:29.462815: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:36:29.842347: lr: 0.00055 
+2026-11-09 09:36:29.842883: This epoch took 249.465618 s
+ 
+2026-11-09 09:36:29.843231: 
+epoch:  485 
+2026-11-09 09:40:54.454116: train loss : -0.8443 
+2026-11-09 09:41:02.672896: validation loss: -0.5779 
+2026-11-09 09:41:02.674543: Average global foreground Dice: [np.float32(0.9137), np.float32(0.5858), np.float32(0.7017), np.float32(0.7169), np.float32(0.6007), np.float32(0.7663), np.float32(0.6714), np.float32(0.5085), np.float32(0.3484), np.float32(0.4154), np.float32(0.602), np.float32(0.6404), np.float32(0.5923), np.float32(0.5395), np.float32(0.0024), np.float32(0.0), np.float32(0.622), np.float32(0.4724), np.float32(0.6247), np.float32(0.5573), np.float32(0.4909), np.float32(0.5311), np.float32(0.7821), np.float32(0.7776), np.float32(0.421), np.float32(0.4156), np.float32(0.1916), np.float32(0.1818), np.float32(0.3153), np.float32(0.683), np.float32(0.0), np.float32(0.0), np.float32(0.4258), np.float32(0.594), np.float32(0.7531), np.float32(0.7735)] 
+2026-11-09 09:41:02.674982: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:41:03.041825: lr: 0.000549 
+2026-11-09 09:41:03.042197: This epoch took 273.198748 s
+ 
+2026-11-09 09:41:03.042406: 
+epoch:  486 
+2026-11-09 09:45:39.543898: train loss : -0.8462 
+2026-11-09 09:45:47.744874: validation loss: -0.6102 
+2026-11-09 09:45:47.746041: Average global foreground Dice: [np.float32(0.9221), np.float32(0.6254), np.float32(0.7784), np.float32(0.7925), np.float32(0.697), np.float32(0.8247), np.float32(0.7316), np.float32(0.6048), np.float32(0.4561), np.float32(0.3799), np.float32(0.6334), np.float32(0.6655), np.float32(0.6288), np.float32(0.5674), np.float32(0.0248), np.float32(0.0), np.float32(0.7314), np.float32(0.6066), np.float32(0.7397), np.float32(0.6413), np.float32(0.5388), np.float32(0.5861), np.float32(0.8213), np.float32(0.8166), np.float32(0.5034), np.float32(0.5086), np.float32(0.2471), np.float32(0.0), np.float32(0.3866), np.float32(0.6517), np.float32(0.0), np.float32(0.0), np.float32(0.6044), np.float32(0.6156), np.float32(0.8139), np.float32(0.842)] 
+2026-11-09 09:45:47.746235: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:45:48.143703: lr: 0.000548 
+2026-11-09 09:45:48.143969: This epoch took 285.101365 s
+ 
+2026-11-09 09:45:48.144046: 
+epoch:  487 
+2026-11-09 09:50:20.576332: train loss : -0.8500 
+2026-11-09 09:50:28.779327: validation loss: -0.6020 
+2026-11-09 09:50:28.780628: Average global foreground Dice: [np.float32(0.9223), np.float32(0.6325), np.float32(0.7693), np.float32(0.7772), np.float32(0.6831), np.float32(0.8234), np.float32(0.726), np.float32(0.5828), np.float32(0.5395), np.float32(0.3795), np.float32(0.6408), np.float32(0.6957), np.float32(0.6001), np.float32(0.5473), np.float32(0.0659), np.float32(0.0), np.float32(0.7221), np.float32(0.6032), np.float32(0.7202), np.float32(0.6248), np.float32(0.5602), np.float32(0.5705), np.float32(0.8142), np.float32(0.8141), np.float32(0.4972), np.float32(0.4583), np.float32(0.2316), np.float32(0.286), np.float32(0.3822), np.float32(0.724), np.float32(0.0), np.float32(0.0), np.float32(0.6097), np.float32(0.6325), np.float32(0.8024), np.float32(0.8314)] 
+2026-11-09 09:50:28.781249: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:50:29.155519: lr: 0.000547 
+2026-11-09 09:50:29.155803: This epoch took 281.011702 s
+ 
+2026-11-09 09:50:29.155888: 
+epoch:  488 
+2026-11-09 09:55:14.628520: train loss : -0.8396 
+2026-11-09 09:55:22.866733: validation loss: -0.6101 
+2026-11-09 09:55:22.867733: Average global foreground Dice: [np.float32(0.9201), np.float32(0.6142), np.float32(0.7521), np.float32(0.7706), np.float32(0.663), np.float32(0.8115), np.float32(0.7194), np.float32(0.5309), np.float32(0.4458), np.float32(0.4121), np.float32(0.6347), np.float32(0.6824), np.float32(0.6006), np.float32(0.5617), np.float32(0.1385), np.float32(0.0), np.float32(0.6642), np.float32(0.5178), np.float32(0.6755), np.float32(0.5762), np.float32(0.5436), np.float32(0.582), np.float32(0.8261), np.float32(0.8297), np.float32(0.4433), np.float32(0.4723), np.float32(0.2158), np.float32(0.2823), np.float32(0.3288), np.float32(0.7237), np.float32(0.0), np.float32(0.0), np.float32(0.5496), np.float32(0.6165), np.float32(0.7994), np.float32(0.8282)] 
+2026-11-09 09:55:22.868181: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:55:23.252074: lr: 0.000546 
+2026-11-09 09:55:23.252643: This epoch took 294.096691 s
+ 
+2026-11-09 09:55:23.252924: 
+epoch:  489 
+2026-11-09 09:59:50.880937: train loss : -0.8474 
+2026-11-09 09:59:59.113630: validation loss: -0.6001 
+2026-11-09 09:59:59.114802: Average global foreground Dice: [np.float32(0.9255), np.float32(0.6426), np.float32(0.7483), np.float32(0.767), np.float32(0.672), np.float32(0.8109), np.float32(0.7148), np.float32(0.5027), np.float32(0.2761), np.float32(0.4723), np.float32(0.6361), np.float32(0.6972), np.float32(0.5911), np.float32(0.5388), np.float32(0.0463), np.float32(0.0), np.float32(0.7089), np.float32(0.5543), np.float32(0.6965), np.float32(0.6057), np.float32(0.5006), np.float32(0.5937), np.float32(0.8245), np.float32(0.8181), np.float32(0.472), np.float32(0.479), np.float32(0.19), np.float32(0.5235), np.float32(0.3465), np.float32(0.7155), np.float32(0.0), np.float32(0.0), np.float32(0.5501), np.float32(0.632), np.float32(0.7904), np.float32(0.8122)] 
+2026-11-09 09:59:59.115043: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 09:59:59.480098: lr: 0.000546 
+2026-11-09 09:59:59.480630: This epoch took 276.227578 s
+ 
+2026-11-09 09:59:59.480934: 
+epoch:  490 
+2026-11-09 10:04:31.212245: train loss : -0.8452 
+2026-11-09 10:04:39.435452: validation loss: -0.5623 
+2026-11-09 10:04:39.436797: Average global foreground Dice: [np.float32(0.9153), np.float32(0.5777), np.float32(0.6779), np.float32(0.6842), np.float32(0.5797), np.float32(0.7371), np.float32(0.6336), np.float32(0.5196), np.float32(0.4232), np.float32(0.4489), np.float32(0.5452), np.float32(0.6148), np.float32(0.5655), np.float32(0.53), np.float32(0.0074), np.float32(0.0), np.float32(0.6084), np.float32(0.4796), np.float32(0.6079), np.float32(0.5535), np.float32(0.4214), np.float32(0.5123), np.float32(0.7784), np.float32(0.7519), np.float32(0.4145), np.float32(0.4175), np.float32(0.1774), np.float32(0.4832), np.float32(0.3909), np.float32(0.6912), np.float32(0.0), np.float32(0.0), np.float32(0.5169), np.float32(0.6002), np.float32(0.7143), np.float32(0.7461)] 
+2026-11-09 10:04:39.437351: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 10:04:39.799919: lr: 0.000545 
+2026-11-09 10:04:39.800274: This epoch took 280.319127 s
+ 
+2026-11-09 10:04:39.800589: 
+epoch:  491 
+2026-11-09 10:09:15.363768: train loss : -0.8461 
+2026-11-09 10:09:23.598692: validation loss: -0.5607 
+2026-11-09 10:09:23.599715: Average global foreground Dice: [np.float32(0.9271), np.float32(0.599), np.float32(0.6753), np.float32(0.6869), np.float32(0.5913), np.float32(0.7443), np.float32(0.6253), np.float32(0.5971), np.float32(0.4093), np.float32(0.4895), np.float32(0.5686), np.float32(0.6441), np.float32(0.6076), np.float32(0.5674), np.float32(0.0), np.float32(0.0), np.float32(0.5801), np.float32(0.4655), np.float32(0.5884), np.float32(0.5295), np.float32(0.5034), np.float32(0.5028), np.float32(0.7532), np.float32(0.7279), np.float32(0.3744), np.float32(0.398), np.float32(0.1025), np.float32(0.33), np.float32(0.3107), np.float32(0.7082), np.float32(0.0), np.float32(0.0), np.float32(0.5143), np.float32(0.5603), np.float32(0.7059), np.float32(0.7389)] 
+2026-11-09 10:09:23.599881: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 10:09:23.954718: lr: 0.000544 
+2026-11-09 10:09:23.955032: This epoch took 284.154301 s
+ 
+2026-11-09 10:09:23.955133: 
+epoch:  492 
+2026-11-09 10:13:50.270344: train loss : -0.8455 
+2026-11-09 10:13:58.533108: validation loss: -0.6086 
+2026-11-09 10:13:58.534323: Average global foreground Dice: [np.float32(0.9286), np.float32(0.6288), np.float32(0.73), np.float32(0.7289), np.float32(0.6451), np.float32(0.7875), np.float32(0.6772), np.float32(0.6015), np.float32(0.3945), np.float32(0.4677), np.float32(0.632), np.float32(0.6705), np.float32(0.6101), np.float32(0.5767), np.float32(0.0), np.float32(0.0), np.float32(0.6339), np.float32(0.5077), np.float32(0.6341), np.float32(0.559), np.float32(0.5529), np.float32(0.5537), np.float32(0.7752), np.float32(0.7664), np.float32(0.4262), np.float32(0.4335), np.float32(0.108), np.float32(0.287), np.float32(0.3978), np.float32(0.7478), np.float32(0.0), np.float32(0.0), np.float32(0.5469), np.float32(0.5995), np.float32(0.7673), np.float32(0.7884)] 
+2026-11-09 10:13:58.534483: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 10:13:58.890308: lr: 0.000543 
+2026-11-09 10:13:58.890676: This epoch took 274.935441 s
+ 
+2026-11-09 10:13:58.890766: 
+epoch:  493 
+2026-11-09 10:18:35.420922: train loss : -0.8432 
+2026-11-09 10:18:43.612436: validation loss: -0.5106 
+2026-11-09 10:18:43.613506: Average global foreground Dice: [np.float32(0.9274), np.float32(0.5313), np.float32(0.587), np.float32(0.5891), np.float32(0.4844), np.float32(0.6516), np.float32(0.5439), np.float32(0.5497), np.float32(0.3847), np.float32(0.5099), np.float32(0.5031), np.float32(0.5515), np.float32(0.5267), np.float32(0.4059), np.float32(0.0), np.float32(0.0), np.float32(0.4571), np.float32(0.3557), np.float32(0.4819), np.float32(0.4486), np.float32(0.4602), np.float32(0.4188), np.float32(0.692), np.float32(0.68), np.float32(0.2903), np.float32(0.3053), np.float32(0.0858), np.float32(0.1353), np.float32(0.3536), np.float32(0.7013), np.float32(0.0), np.float32(0.0), np.float32(0.4031), np.float32(0.5006), np.float32(0.6338), np.float32(0.6575)] 
+2026-11-09 10:18:43.613695: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 10:18:43.966579: lr: 0.000542 
+2026-11-09 10:18:43.967179: This epoch took 285.076353 s
+ 
+2026-11-09 10:18:43.967596: 
+epoch:  494 
+2026-11-09 10:23:02.523718: train loss : -0.8508 
+2026-11-09 10:23:10.750599: validation loss: -0.5526 
+2026-11-09 10:23:10.752342: Average global foreground Dice: [np.float32(0.9232), np.float32(0.5626), np.float32(0.6601), np.float32(0.6525), np.float32(0.5754), np.float32(0.7276), np.float32(0.6151), np.float32(0.581), np.float32(0.0), np.float32(0.4279), np.float32(0.5584), np.float32(0.604), np.float32(0.5609), np.float32(0.5036), np.float32(0.0), np.float32(0.0), np.float32(0.5764), np.float32(0.4049), np.float32(0.5838), np.float32(0.5281), np.float32(0.4633), np.float32(0.4909), np.float32(0.7697), np.float32(0.7647), np.float32(0.37), np.float32(0.3355), np.float32(0.1259), np.float32(0.1076), np.float32(0.3269), np.float32(0.7212), np.float32(0.0), np.float32(0.0), np.float32(0.5161), np.float32(0.5534), np.float32(0.7102), np.float32(0.739)] 
+2026-11-09 10:23:10.752785: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 10:23:11.145845: lr: 0.000541 
+2026-11-09 10:23:11.146275: This epoch took 267.178579 s
+ 
+2026-11-09 10:23:11.146561: 
+epoch:  495 
+2026-11-09 10:27:38.499546: train loss : -0.8458 
+2026-11-09 10:27:46.692769: validation loss: -0.5305 
+2026-11-09 10:27:46.693952: Average global foreground Dice: [np.float32(0.9156), np.float32(0.5603), np.float32(0.6691), np.float32(0.6601), np.float32(0.5745), np.float32(0.7164), np.float32(0.6138), np.float32(0.6357), np.float32(0.3747), np.float32(0.4105), np.float32(0.5331), np.float32(0.5743), np.float32(0.5983), np.float32(0.5222), np.float32(0.061), np.float32(0.0), np.float32(0.5769), np.float32(0.4556), np.float32(0.5679), np.float32(0.4342), np.float32(0.3093), np.float32(0.4064), np.float32(0.7254), np.float32(0.6965), np.float32(0.338), np.float32(0.3438), np.float32(0.1196), np.float32(0.0), np.float32(0.4135), np.float32(0.6549), np.float32(0.0), np.float32(0.0), np.float32(0.5145), np.float32(0.5336), np.float32(0.6852), np.float32(0.7196)] 
+2026-11-09 10:27:46.694291: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 10:27:47.055943: lr: 0.00054 
+2026-11-09 10:27:47.056355: This epoch took 275.909588 s
+ 
+2026-11-09 10:27:47.056624: 
+epoch:  496 
+2026-11-09 10:32:06.124642: train loss : -0.8442 
+2026-11-09 10:32:14.373096: validation loss: -0.5622 
+2026-11-09 10:32:14.374060: Average global foreground Dice: [np.float32(0.9128), np.float32(0.5501), np.float32(0.678), np.float32(0.6885), np.float32(0.5646), np.float32(0.7439), np.float32(0.639), np.float32(0.471), np.float32(0.2397), np.float32(0.3993), np.float32(0.5702), np.float32(0.6027), np.float32(0.5961), np.float32(0.4683), np.float32(0.0), np.float32(0.0), np.float32(0.5567), np.float32(0.434), np.float32(0.5862), np.float32(0.5394), np.float32(0.521), np.float32(0.4999), np.float32(0.6853), np.float32(0.6795), np.float32(0.4041), np.float32(0.3139), np.float32(0.1567), np.float32(0.1822), np.float32(0.4309), np.float32(0.7116), np.float32(0.0), np.float32(0.0), np.float32(0.4595), np.float32(0.5246), np.float32(0.701), np.float32(0.7308)] 
+2026-11-09 10:32:14.374311: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 10:32:14.720403: lr: 0.000539 
+2026-11-09 10:32:14.720903: This epoch took 267.664114 s
+ 
+2026-11-09 10:32:14.721251: 
+epoch:  497 
+2026-11-09 10:36:29.919172: train loss : -0.8518 
+2026-11-09 10:36:38.112258: validation loss: -0.5118 
+2026-11-09 10:36:38.113350: Average global foreground Dice: [np.float32(0.9253), np.float32(0.4889), np.float32(0.5517), np.float32(0.5495), np.float32(0.4068), np.float32(0.6248), np.float32(0.5175), np.float32(0.5391), np.float32(0.3019), np.float32(0.4833), np.float32(0.4736), np.float32(0.5377), np.float32(0.5757), np.float32(0.4946), np.float32(0.0), np.float32(0.0), np.float32(0.3813), np.float32(0.3115), np.float32(0.436), np.float32(0.4104), np.float32(0.4255), np.float32(0.3617), np.float32(0.6407), np.float32(0.6366), np.float32(0.2311), np.float32(0.2818), np.float32(0.0491), np.float32(0.0), np.float32(0.3456), np.float32(0.6726), np.float32(0.0), np.float32(0.0), np.float32(0.3914), np.float32(0.4785), np.float32(0.5765), np.float32(0.6275)] 
+2026-11-09 10:36:38.113754: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 10:36:38.514231: lr: 0.000538 
+2026-11-09 10:36:38.514640: This epoch took 263.793140 s
+ 
+2026-11-09 10:36:38.514969: 
+epoch:  498 
+2026-11-09 10:41:15.467832: train loss : -0.8473 
+2026-11-09 10:41:23.693772: validation loss: -0.5483 
+2026-11-09 10:41:23.695029: Average global foreground Dice: [np.float32(0.9061), np.float32(0.5757), np.float32(0.681), np.float32(0.6794), np.float32(0.5692), np.float32(0.7262), np.float32(0.628), np.float32(0.6018), np.float32(0.418), np.float32(0.409), np.float32(0.5411), np.float32(0.5832), np.float32(0.5644), np.float32(0.4541), np.float32(0.0), np.float32(0.0), np.float32(0.6326), np.float32(0.507), np.float32(0.6397), np.float32(0.5737), np.float32(0.5242), np.float32(0.5487), np.float32(0.7653), np.float32(0.7466), np.float32(0.4709), np.float32(0.3797), np.float32(0.1856), np.float32(0.219), np.float32(0.3648), np.float32(0.6706), np.float32(0.0), np.float32(0.0), np.float32(0.5413), np.float32(0.6225), np.float32(0.7098), np.float32(0.7333)] 
+2026-11-09 10:41:23.695436: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 10:41:24.076888: lr: 0.000537 
+2026-11-09 10:41:24.077580: This epoch took 285.562478 s
+ 
+2026-11-09 10:41:24.077797: 
+epoch:  499 
+2026-11-09 10:46:13.686145: train loss : -0.8376 
+2026-11-09 10:46:21.894598: validation loss: -0.5902 
+2026-11-09 10:46:21.895514: Average global foreground Dice: [np.float32(0.9105), np.float32(0.5954), np.float32(0.7429), np.float32(0.7813), np.float32(0.6794), np.float32(0.8201), np.float32(0.7201), np.float32(0.6124), np.float32(0.1148), np.float32(0.3246), np.float32(0.5687), np.float32(0.6351), np.float32(0.5321), np.float32(0.0737), np.float32(0.0007), np.float32(0.0), np.float32(0.6835), np.float32(0.5726), np.float32(0.6899), np.float32(0.6019), np.float32(0.586), np.float32(0.6123), np.float32(0.7993), np.float32(0.7905), np.float32(0.4952), np.float32(0.4159), np.float32(0.2509), np.float32(0.3804), np.float32(0.4316), np.float32(0.6991), np.float32(0.0), np.float32(0.0), np.float32(0.612), np.float32(0.6587), np.float32(0.786), np.float32(0.8163)] 
+2026-11-09 10:46:21.895774: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 10:46:22.260434: lr: 0.000536 
+2026-11-09 10:46:22.260737: saving scheduled checkpoint file... 
+2026-11-09 10:46:22.514726: saving checkpoint... 
+2026-11-09 10:46:23.757253: done, saving took 1.50 seconds 
+2026-11-09 10:46:23.764854: done 
+2026-11-09 10:46:23.765024: This epoch took 299.687120 s
+ 
+2026-11-09 10:46:23.765105: 
+epoch:  500 
+2026-11-09 10:50:45.662414: train loss : -0.8411 
+2026-11-09 10:50:53.882910: validation loss: -0.5812 
+2026-11-09 10:50:53.884039: Average global foreground Dice: [np.float32(0.9091), np.float32(0.5977), np.float32(0.7348), np.float32(0.7451), np.float32(0.6205), np.float32(0.7899), np.float32(0.6769), np.float32(0.5576), np.float32(0.3555), np.float32(0.4144), np.float32(0.5823), np.float32(0.638), np.float32(0.5621), np.float32(0.4551), np.float32(0.0037), np.float32(0.0), np.float32(0.6642), np.float32(0.5147), np.float32(0.6706), np.float32(0.5798), np.float32(0.5548), np.float32(0.5892), np.float32(0.8002), np.float32(0.775), np.float32(0.4729), np.float32(0.401), np.float32(0.19), np.float32(0.1402), np.float32(0.3701), np.float32(0.7033), np.float32(0.0), np.float32(0.0), np.float32(0.5649), np.float32(0.683), np.float32(0.764), np.float32(0.7872)] 
+2026-11-09 10:50:53.884240: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 10:50:54.254920: lr: 0.000535 
+2026-11-09 10:50:54.255321: This epoch took 270.490162 s
+ 
+2026-11-09 10:50:54.255651: 
+epoch:  501 
+2026-11-09 10:55:36.536074: train loss : -0.8396 
+2026-11-09 10:55:44.753400: validation loss: -0.6295 
+2026-11-09 10:55:44.754458: Average global foreground Dice: [np.float32(0.9214), np.float32(0.6753), np.float32(0.7805), np.float32(0.7899), np.float32(0.7099), np.float32(0.8357), np.float32(0.7315), np.float32(0.5354), np.float32(0.0985), np.float32(0.4373), np.float32(0.6555), np.float32(0.6936), np.float32(0.6564), np.float32(0.5232), np.float32(0.0), np.float32(0.0), np.float32(0.7484), np.float32(0.6108), np.float32(0.7291), np.float32(0.6388), np.float32(0.5724), np.float32(0.5924), np.float32(0.821), np.float32(0.8242), np.float32(0.4717), np.float32(0.4374), np.float32(0.1849), np.float32(0.4471), np.float32(0.4096), np.float32(0.7224), np.float32(0.0), np.float32(0.0), np.float32(0.6), np.float32(0.6644), np.float32(0.8176), np.float32(0.8376)] 
+2026-11-09 10:55:44.754786: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 10:55:45.170328: lr: 0.000534 
+2026-11-09 10:55:45.170720: This epoch took 290.914949 s
+ 
+2026-11-09 10:55:45.170978: 
+epoch:  502 
+2026-11-09 11:00:23.261986: train loss : -0.8495 
+2026-11-09 11:00:31.525795: validation loss: -0.5962 
+2026-11-09 11:00:31.526848: Average global foreground Dice: [np.float32(0.9215), np.float32(0.6028), np.float32(0.7679), np.float32(0.7713), np.float32(0.6713), np.float32(0.8205), np.float32(0.7243), np.float32(0.6371), np.float32(0.4365), np.float32(0.3771), np.float32(0.6236), np.float32(0.6652), np.float32(0.6139), np.float32(0.467), np.float32(0.0), np.float32(0.0), np.float32(0.7345), np.float32(0.6019), np.float32(0.723), np.float32(0.6063), np.float32(0.5169), np.float32(0.6041), np.float32(0.7951), np.float32(0.7995), np.float32(0.5128), np.float32(0.4066), np.float32(0.1761), np.float32(0.5203), np.float32(0.4105), np.float32(0.6943), np.float32(0.0), np.float32(0.0), np.float32(0.6404), np.float32(0.6464), np.float32(0.7922), np.float32(0.8227)] 
+2026-11-09 11:00:31.527268: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:00:31.905416: lr: 0.000533 
+2026-11-09 11:00:31.905992: This epoch took 286.734785 s
+ 
+2026-11-09 11:00:31.906301: 
+epoch:  503 
+2026-11-09 11:04:53.440373: train loss : -0.8439 
+2026-11-09 11:05:01.656226: validation loss: -0.6536 
+2026-11-09 11:05:01.657543: Average global foreground Dice: [np.float32(0.9124), np.float32(0.6462), np.float32(0.81), np.float32(0.8197), np.float32(0.7613), np.float32(0.8648), np.float32(0.7885), np.float32(0.6257), np.float32(0.3699), np.float32(0.3185), np.float32(0.6891), np.float32(0.7134), np.float32(0.6385), np.float32(0.5966), np.float32(0.0), np.float32(0.0), np.float32(0.7714), np.float32(0.6397), np.float32(0.7639), np.float32(0.5796), np.float32(0.5711), np.float32(0.668), np.float32(0.9055), np.float32(0.9044), np.float32(0.5167), np.float32(0.457), np.float32(0.2692), np.float32(0.0881), np.float32(0.3871), np.float32(0.6386), np.float32(0.0), np.float32(0.0), np.float32(0.6681), np.float32(0.6946), np.float32(0.8649), np.float32(0.8741)] 
+2026-11-09 11:05:01.657810: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:05:02.102149: lr: 0.000532 
+2026-11-09 11:05:02.102437: This epoch took 270.195820 s
+ 
+2026-11-09 11:05:02.102520: 
+epoch:  504 
+2026-11-09 11:09:39.238261: train loss : -0.8477 
+2026-11-09 11:09:47.453070: validation loss: -0.5904 
+2026-11-09 11:09:47.454237: Average global foreground Dice: [np.float32(0.9163), np.float32(0.6056), np.float32(0.7559), np.float32(0.7732), np.float32(0.6702), np.float32(0.8142), np.float32(0.7205), np.float32(0.4738), np.float32(0.3694), np.float32(0.3745), np.float32(0.6343), np.float32(0.6602), np.float32(0.63), np.float32(0.4745), np.float32(0.0008), np.float32(0.0), np.float32(0.7311), np.float32(0.5891), np.float32(0.7214), np.float32(0.6191), np.float32(0.5343), np.float32(0.6151), np.float32(0.8432), np.float32(0.8373), np.float32(0.5252), np.float32(0.4013), np.float32(0.2412), np.float32(0.178), np.float32(0.3697), np.float32(0.6998), np.float32(0.0), np.float32(0.0), np.float32(0.6122), np.float32(0.6475), np.float32(0.8024), np.float32(0.8219)] 
+2026-11-09 11:09:47.454526: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:09:47.826315: lr: 0.000531 
+2026-11-09 11:09:47.826596: This epoch took 285.723935 s
+ 
+2026-11-09 11:09:47.826666: 
+epoch:  505 
+2026-11-09 11:13:59.680611: train loss : -0.8446 
+2026-11-09 11:14:07.878394: validation loss: -0.6116 
+2026-11-09 11:14:07.879564: Average global foreground Dice: [np.float32(0.9201), np.float32(0.6259), np.float32(0.7378), np.float32(0.7687), np.float32(0.664), np.float32(0.8102), np.float32(0.7025), np.float32(0.6122), np.float32(0.45), np.float32(0.3883), np.float32(0.6465), np.float32(0.6945), np.float32(0.6235), np.float32(0.5125), np.float32(0.0034), np.float32(0.0), np.float32(0.723), np.float32(0.5728), np.float32(0.7106), np.float32(0.599), np.float32(0.5388), np.float32(0.6171), np.float32(0.7966), np.float32(0.793), np.float32(0.4695), np.float32(0.4682), np.float32(0.2506), np.float32(0.3019), np.float32(0.3811), np.float32(0.7156), np.float32(0.0), np.float32(0.0), np.float32(0.6127), np.float32(0.6583), np.float32(0.792), np.float32(0.814)] 
+2026-11-09 11:14:07.880112: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:14:08.287842: lr: 0.00053 
+2026-11-09 11:14:08.288337: This epoch took 260.461596 s
+ 
+2026-11-09 11:14:08.288472: 
+epoch:  506 
+2026-11-09 11:18:45.875576: train loss : -0.8440 
+2026-11-09 11:18:54.098048: validation loss: -0.5982 
+2026-11-09 11:18:54.099173: Average global foreground Dice: [np.float32(0.9275), np.float32(0.6085), np.float32(0.7584), np.float32(0.7907), np.float32(0.7053), np.float32(0.8176), np.float32(0.7378), np.float32(0.6836), np.float32(0.3146), np.float32(0.4187), np.float32(0.6098), np.float32(0.6389), np.float32(0.6072), np.float32(0.4486), np.float32(0.0), np.float32(0.0), np.float32(0.7492), np.float32(0.5854), np.float32(0.7362), np.float32(0.6324), np.float32(0.5007), np.float32(0.6637), np.float32(0.8046), np.float32(0.8027), np.float32(0.508), np.float32(0.4438), np.float32(0.2977), np.float32(0.1877), np.float32(0.3934), np.float32(0.6738), np.float32(0.0), np.float32(0.0), np.float32(0.6418), np.float32(0.6487), np.float32(0.8073), np.float32(0.8289)] 
+2026-11-09 11:18:54.099564: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:18:54.445965: lr: 0.000529 
+2026-11-09 11:18:54.446269: This epoch took 286.157547 s
+ 
+2026-11-09 11:18:54.446374: 
+epoch:  507 
+2026-11-09 11:23:15.571990: train loss : -0.8469 
+2026-11-09 11:23:23.841178: validation loss: -0.5726 
+2026-11-09 11:23:23.842452: Average global foreground Dice: [np.float32(0.9274), np.float32(0.5926), np.float32(0.7219), np.float32(0.7394), np.float32(0.6527), np.float32(0.7832), np.float32(0.6949), np.float32(0.66), np.float32(0.3097), np.float32(0.3152), np.float32(0.5959), np.float32(0.6246), np.float32(0.5995), np.float32(0.4544), np.float32(0.0022), np.float32(0.0), np.float32(0.7215), np.float32(0.5574), np.float32(0.7219), np.float32(0.6332), np.float32(0.5046), np.float32(0.6424), np.float32(0.7978), np.float32(0.7884), np.float32(0.5063), np.float32(0.4529), np.float32(0.2807), np.float32(0.069), np.float32(0.3502), np.float32(0.6832), np.float32(0.0), np.float32(0.0), np.float32(0.5812), np.float32(0.6675), np.float32(0.7699), np.float32(0.7922)] 
+2026-11-09 11:23:23.843028: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:23:24.205280: lr: 0.000528 
+2026-11-09 11:23:24.205796: This epoch took 269.759355 s
+ 
+2026-11-09 11:23:24.205982: 
+epoch:  508 
+2026-11-09 11:27:47.522285: train loss : -0.8444 
+2026-11-09 11:27:55.714600: validation loss: -0.5922 
+2026-11-09 11:27:55.715907: Average global foreground Dice: [np.float32(0.9176), np.float32(0.6042), np.float32(0.7073), np.float32(0.7382), np.float32(0.6418), np.float32(0.7863), np.float32(0.682), np.float32(0.5851), np.float32(0.3432), np.float32(0.4337), np.float32(0.6041), np.float32(0.6386), np.float32(0.5705), np.float32(0.4544), np.float32(0.0), np.float32(0.0), np.float32(0.7137), np.float32(0.5465), np.float32(0.7094), np.float32(0.6076), np.float32(0.5578), np.float32(0.6242), np.float32(0.7647), np.float32(0.7584), np.float32(0.4908), np.float32(0.4406), np.float32(0.2471), np.float32(0.2558), np.float32(0.4529), np.float32(0.6937), np.float32(0.0), np.float32(0.0), np.float32(0.5868), np.float32(0.6519), np.float32(0.764), np.float32(0.7915)] 
+2026-11-09 11:27:55.716341: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:27:56.130943: lr: 0.000527 
+2026-11-09 11:27:56.131269: This epoch took 271.925187 s
+ 
+2026-11-09 11:27:56.131357: 
+epoch:  509 
+2026-11-09 11:33:03.355823: train loss : -0.8433 
+2026-11-09 11:33:11.637637: validation loss: -0.6053 
+2026-11-09 11:33:11.638901: Average global foreground Dice: [np.float32(0.9186), np.float32(0.575), np.float32(0.7348), np.float32(0.7617), np.float32(0.6315), np.float32(0.7944), np.float32(0.6987), np.float32(0.5357), np.float32(0.3579), np.float32(0.3999), np.float32(0.6133), np.float32(0.6441), np.float32(0.5869), np.float32(0.4782), np.float32(0.0), np.float32(0.0), np.float32(0.706), np.float32(0.5512), np.float32(0.7131), np.float32(0.6158), np.float32(0.5512), np.float32(0.646), np.float32(0.7952), np.float32(0.7891), np.float32(0.5016), np.float32(0.3761), np.float32(0.2787), np.float32(0.2053), np.float32(0.4983), np.float32(0.708), np.float32(0.0), np.float32(0.0), np.float32(0.5647), np.float32(0.6565), np.float32(0.7794), np.float32(0.798)] 
+2026-11-09 11:33:11.639376: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:33:12.064052: lr: 0.000526 
+2026-11-09 11:33:12.064315: This epoch took 315.932905 s
+ 
+2026-11-09 11:33:12.064399: 
+epoch:  510 
+2026-11-09 11:37:26.314848: train loss : -0.8548 
+2026-11-09 11:37:34.562901: validation loss: -0.5993 
+2026-11-09 11:37:34.564040: Average global foreground Dice: [np.float32(0.9273), np.float32(0.6233), np.float32(0.7486), np.float32(0.7577), np.float32(0.6621), np.float32(0.7964), np.float32(0.6941), np.float32(0.5857), np.float32(0.4344), np.float32(0.49), np.float32(0.6225), np.float32(0.6586), np.float32(0.5854), np.float32(0.4472), np.float32(0.0), np.float32(0.0), np.float32(0.7262), np.float32(0.5776), np.float32(0.7105), np.float32(0.6306), np.float32(0.5473), np.float32(0.6467), np.float32(0.7944), np.float32(0.7878), np.float32(0.5201), np.float32(0.4356), np.float32(0.31), np.float32(0.3665), np.float32(0.4097), np.float32(0.6628), np.float32(0.0), np.float32(0.0), np.float32(0.5441), np.float32(0.6612), np.float32(0.7848), np.float32(0.7999)] 
+2026-11-09 11:37:34.564245: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:37:34.945732: lr: 0.000525 
+2026-11-09 11:37:34.946113: This epoch took 262.881650 s
+ 
+2026-11-09 11:37:34.946394: 
+epoch:  511 
+2026-11-09 11:42:17.295601: train loss : -0.8447 
+2026-11-09 11:42:25.500472: validation loss: -0.6314 
+2026-11-09 11:42:25.501787: Average global foreground Dice: [np.float32(0.9291), np.float32(0.6404), np.float32(0.7973), np.float32(0.8067), np.float32(0.7355), np.float32(0.8493), np.float32(0.765), np.float32(0.6955), np.float32(0.434), np.float32(0.373), np.float32(0.593), np.float32(0.681), np.float32(0.666), np.float32(0.5689), np.float32(0.0), np.float32(0.0), np.float32(0.7667), np.float32(0.599), np.float32(0.7617), np.float32(0.6517), np.float32(0.5376), np.float32(0.6982), np.float32(0.8768), np.float32(0.8695), np.float32(0.5062), np.float32(0.476), np.float32(0.3248), np.float32(0.5533), np.float32(0.3063), np.float32(0.6777), np.float32(0.0), np.float32(0.0), np.float32(0.6315), np.float32(0.6725), np.float32(0.8419), np.float32(0.8627)] 
+2026-11-09 11:42:25.502057: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:42:25.842824: lr: 0.000524 
+2026-11-09 11:42:25.843341: This epoch took 290.896856 s
+ 
+2026-11-09 11:42:25.843563: 
+epoch:  512 
+2026-11-09 11:46:44.355436: train loss : -0.8492 
+2026-11-09 11:46:52.560596: validation loss: -0.5751 
+2026-11-09 11:46:52.561565: Average global foreground Dice: [np.float32(0.9239), np.float32(0.6106), np.float32(0.7369), np.float32(0.7555), np.float32(0.6729), np.float32(0.7975), np.float32(0.7117), np.float32(0.648), np.float32(0.467), np.float32(0.344), np.float32(0.5524), np.float32(0.6372), np.float32(0.6184), np.float32(0.4475), np.float32(0.0008), np.float32(0.0), np.float32(0.7332), np.float32(0.5679), np.float32(0.7269), np.float32(0.6541), np.float32(0.515), np.float32(0.6691), np.float32(0.7941), np.float32(0.7785), np.float32(0.5346), np.float32(0.4421), np.float32(0.2725), np.float32(0.3597), np.float32(0.3181), np.float32(0.67), np.float32(0.0), np.float32(0.0), np.float32(0.57), np.float32(0.6464), np.float32(0.7873), np.float32(0.8093)] 
+2026-11-09 11:46:52.561742: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:46:52.920172: lr: 0.000523 
+2026-11-09 11:46:52.920663: This epoch took 267.077010 s
+ 
+2026-11-09 11:46:52.920998: 
+epoch:  513 
+2026-11-09 11:50:46.734355: train loss : -0.8585 
+2026-11-09 11:50:54.951117: validation loss: -0.6187 
+2026-11-09 11:50:54.952691: Average global foreground Dice: [np.float32(0.9261), np.float32(0.641), np.float32(0.806), np.float32(0.8242), np.float32(0.7476), np.float32(0.8572), np.float32(0.7812), np.float32(0.5755), np.float32(0.4003), np.float32(0.3013), np.float32(0.6222), np.float32(0.6769), np.float32(0.6164), np.float32(0.5112), np.float32(0.0006), np.float32(0.0), np.float32(0.7596), np.float32(0.6003), np.float32(0.7603), np.float32(0.6332), np.float32(0.5552), np.float32(0.6933), np.float32(0.8717), np.float32(0.8737), np.float32(0.5273), np.float32(0.3953), np.float32(0.2195), np.float32(0.4696), np.float32(0.325), np.float32(0.697), np.float32(0.0), np.float32(0.0), np.float32(0.6263), np.float32(0.6827), np.float32(0.8507), np.float32(0.866)] 
+2026-11-09 11:50:54.952902: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:50:55.337594: lr: 0.000522 
+2026-11-09 11:50:55.337947: This epoch took 242.416671 s
+ 
+2026-11-09 11:50:55.338082: 
+epoch:  514 
+2026-11-09 11:55:23.448569: train loss : -0.8513 
+2026-11-09 11:55:31.689959: validation loss: -0.5563 
+2026-11-09 11:55:31.690920: Average global foreground Dice: [np.float32(0.9226), np.float32(0.5536), np.float32(0.7081), np.float32(0.709), np.float32(0.6269), np.float32(0.7636), np.float32(0.6642), np.float32(0.6856), np.float32(0.4564), np.float32(0.3531), np.float32(0.5534), np.float32(0.6265), np.float32(0.5938), np.float32(0.4574), np.float32(0.0), np.float32(0.0), np.float32(0.7055), np.float32(0.5536), np.float32(0.7054), np.float32(0.5957), np.float32(0.5007), np.float32(0.6433), np.float32(0.7511), np.float32(0.7381), np.float32(0.4964), np.float32(0.3808), np.float32(0.1522), np.float32(0.5664), np.float32(0.345), np.float32(0.7033), np.float32(0.0), np.float32(0.0), np.float32(0.6096), np.float32(0.646), np.float32(0.7387), np.float32(0.7682)] 
+2026-11-09 11:55:31.691110: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 11:55:32.034680: lr: 0.000521 
+2026-11-09 11:55:32.034971: This epoch took 276.696814 s
+ 
+2026-11-09 11:55:32.035173: 
+epoch:  515 
+2026-11-09 11:59:53.497065: train loss : -0.8504 
+2026-11-09 12:00:01.723119: validation loss: -0.5830 
+2026-11-09 12:00:01.724466: Average global foreground Dice: [np.float32(0.9295), np.float32(0.6138), np.float32(0.759), np.float32(0.7701), np.float32(0.6982), np.float32(0.8193), np.float32(0.7194), np.float32(0.6506), np.float32(0.3789), np.float32(0.3078), np.float32(0.5842), np.float32(0.6479), np.float32(0.6099), np.float32(0.4586), np.float32(0.0), np.float32(0.0), np.float32(0.7383), np.float32(0.5886), np.float32(0.7332), np.float32(0.6261), np.float32(0.5446), np.float32(0.6836), np.float32(0.8178), np.float32(0.8069), np.float32(0.5113), np.float32(0.4063), np.float32(0.2332), np.float32(0.6015), np.float32(0.3472), np.float32(0.6667), np.float32(0.0), np.float32(0.0), np.float32(0.6208), np.float32(0.6023), np.float32(0.8009), np.float32(0.8255)] 
+2026-11-09 12:00:01.725096: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:00:02.108687: lr: 0.00052 
+2026-11-09 12:00:02.108988: This epoch took 270.073742 s
+ 
+2026-11-09 12:00:02.109078: 
+epoch:  516 
+2026-11-09 12:04:33.002048: train loss : -0.8548 
+2026-11-09 12:04:41.199418: validation loss: -0.5764 
+2026-11-09 12:04:41.200471: Average global foreground Dice: [np.float32(0.9212), np.float32(0.5908), np.float32(0.7319), np.float32(0.7371), np.float32(0.6441), np.float32(0.7932), np.float32(0.6832), np.float32(0.6352), np.float32(0.3481), np.float32(0.3905), np.float32(0.5989), np.float32(0.6607), np.float32(0.5603), np.float32(0.4402), np.float32(0.0031), np.float32(0.0), np.float32(0.7018), np.float32(0.5423), np.float32(0.7017), np.float32(0.5966), np.float32(0.558), np.float32(0.6678), np.float32(0.7584), np.float32(0.744), np.float32(0.5025), np.float32(0.3947), np.float32(0.2553), np.float32(0.5952), np.float32(0.403), np.float32(0.6971), np.float32(0.0), np.float32(0.0), np.float32(0.5791), np.float32(0.6483), np.float32(0.7663), np.float32(0.7914)] 
+2026-11-09 12:04:41.200908: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:04:41.553600: lr: 0.000519 
+2026-11-09 12:04:41.553857: This epoch took 279.444721 s
+ 
+2026-11-09 12:04:41.553938: 
+epoch:  517 
+2026-11-09 12:09:20.476426: train loss : -0.8497 
+2026-11-09 12:09:28.718214: validation loss: -0.6187 
+2026-11-09 12:09:28.719505: Average global foreground Dice: [np.float32(0.9218), np.float32(0.5986), np.float32(0.7857), np.float32(0.791), np.float32(0.7157), np.float32(0.8412), np.float32(0.7544), np.float32(0.6049), np.float32(0.3706), np.float32(0.4225), np.float32(0.6077), np.float32(0.7085), np.float32(0.6193), np.float32(0.5481), np.float32(0.0576), np.float32(0.0), np.float32(0.745), np.float32(0.6071), np.float32(0.7544), np.float32(0.6394), np.float32(0.5792), np.float32(0.6806), np.float32(0.8371), np.float32(0.8294), np.float32(0.4935), np.float32(0.4873), np.float32(0.2168), np.float32(0.7528), np.float32(0.4183), np.float32(0.681), np.float32(0.0), np.float32(0.0), np.float32(0.6362), np.float32(0.6281), np.float32(0.8164), np.float32(0.8448)] 
+2026-11-09 12:09:28.720232: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:09:29.096941: lr: 0.000518 
+2026-11-09 12:09:29.097261: This epoch took 287.543236 s
+ 
+2026-11-09 12:09:29.097387: 
+epoch:  518 
+2026-11-09 12:13:29.030447: train loss : -0.8552 
+2026-11-09 12:13:37.273318: validation loss: -0.5932 
+2026-11-09 12:13:37.274385: Average global foreground Dice: [np.float32(0.9216), np.float32(0.6335), np.float32(0.7717), np.float32(0.7883), np.float32(0.7102), np.float32(0.826), np.float32(0.7337), np.float32(0.5855), np.float32(0.4479), np.float32(0.4064), np.float32(0.5897), np.float32(0.6614), np.float32(0.6203), np.float32(0.5152), np.float32(0.0006), np.float32(0.0), np.float32(0.7391), np.float32(0.5981), np.float32(0.7418), np.float32(0.6295), np.float32(0.5269), np.float32(0.6685), np.float32(0.8288), np.float32(0.8153), np.float32(0.4974), np.float32(0.434), np.float32(0.2289), np.float32(0.7739), np.float32(0.2973), np.float32(0.6654), np.float32(0.0), np.float32(0.0), np.float32(0.6226), np.float32(0.6558), np.float32(0.8115), np.float32(0.8354)] 
+2026-11-09 12:13:37.274617: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:13:37.666416: lr: 0.000518 
+2026-11-09 12:13:37.666782: This epoch took 248.569332 s
+ 
+2026-11-09 12:13:37.666906: 
+epoch:  519 
+2026-11-09 12:17:53.185464: train loss : -0.8558 
+2026-11-09 12:18:01.402231: validation loss: -0.5930 
+2026-11-09 12:18:01.403651: Average global foreground Dice: [np.float32(0.9225), np.float32(0.6042), np.float32(0.7465), np.float32(0.751), np.float32(0.648), np.float32(0.8039), np.float32(0.702), np.float32(0.5348), np.float32(0.3997), np.float32(0.4718), np.float32(0.616), np.float32(0.6693), np.float32(0.6183), np.float32(0.525), np.float32(0.0), np.float32(0.0), np.float32(0.7071), np.float32(0.5586), np.float32(0.7167), np.float32(0.6014), np.float32(0.5801), np.float32(0.6517), np.float32(0.8019), np.float32(0.7976), np.float32(0.4632), np.float32(0.4095), np.float32(0.258), np.float32(0.6315), np.float32(0.3311), np.float32(0.7015), np.float32(0.0), np.float32(0.0), np.float32(0.5968), np.float32(0.6491), np.float32(0.7777), np.float32(0.8088)] 
+2026-11-09 12:18:01.403996: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:18:01.866560: lr: 0.000517 
+2026-11-09 12:18:01.867125: This epoch took 264.200118 s
+ 
+2026-11-09 12:18:01.867482: 
+epoch:  520 
+2026-11-09 12:22:10.212808: train loss : -0.8537 
+2026-11-09 12:22:18.408329: validation loss: -0.5872 
+2026-11-09 12:22:18.409666: Average global foreground Dice: [np.float32(0.9281), np.float32(0.6164), np.float32(0.7234), np.float32(0.728), np.float32(0.6187), np.float32(0.7727), np.float32(0.6582), np.float32(0.5968), np.float32(0.4188), np.float32(0.4303), np.float32(0.5876), np.float32(0.6484), np.float32(0.6469), np.float32(0.5587), np.float32(0.0208), np.float32(0.0), np.float32(0.6791), np.float32(0.5535), np.float32(0.6919), np.float32(0.6116), np.float32(0.5389), np.float32(0.6343), np.float32(0.8074), np.float32(0.7926), np.float32(0.4647), np.float32(0.4349), np.float32(0.2258), np.float32(0.4889), np.float32(0.3359), np.float32(0.6838), np.float32(0.0), np.float32(0.0), np.float32(0.591), np.float32(0.6517), np.float32(0.7605), np.float32(0.7864)] 
+2026-11-09 12:22:18.410172: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:22:18.774868: lr: 0.000516 
+2026-11-09 12:22:18.775359: This epoch took 256.907631 s
+ 
+2026-11-09 12:22:18.775673: 
+epoch:  521 
+2026-11-09 12:26:48.304473: train loss : -0.8520 
+2026-11-09 12:26:56.527361: validation loss: -0.5396 
+2026-11-09 12:26:56.529281: Average global foreground Dice: [np.float32(0.9224), np.float32(0.5477), np.float32(0.7139), np.float32(0.7089), np.float32(0.597), np.float32(0.7639), np.float32(0.6527), np.float32(0.654), np.float32(0.3479), np.float32(0.353), np.float32(0.5307), np.float32(0.5884), np.float32(0.6031), np.float32(0.4489), np.float32(0.0), np.float32(0.0), np.float32(0.6445), np.float32(0.524), np.float32(0.6829), np.float32(0.5894), np.float32(0.5303), np.float32(0.6251), np.float32(0.7058), np.float32(0.7212), np.float32(0.475), np.float32(0.3936), np.float32(0.2109), np.float32(0.5953), np.float32(0.3275), np.float32(0.6589), np.float32(0.0), np.float32(0.0), np.float32(0.5968), np.float32(0.6238), np.float32(0.7044), np.float32(0.7588)] 
+2026-11-09 12:26:56.529750: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:26:56.921756: lr: 0.000515 
+2026-11-09 12:26:56.922395: This epoch took 278.146532 s
+ 
+2026-11-09 12:26:56.922734: 
+epoch:  522 
+2026-11-09 12:31:18.757086: train loss : -0.8537 
+2026-11-09 12:31:27.020244: validation loss: -0.5636 
+2026-11-09 12:31:27.021571: Average global foreground Dice: [np.float32(0.9213), np.float32(0.5831), np.float32(0.7385), np.float32(0.7369), np.float32(0.633), np.float32(0.7836), np.float32(0.6848), np.float32(0.6279), np.float32(0.3869), np.float32(0.4312), np.float32(0.5828), np.float32(0.6326), np.float32(0.5976), np.float32(0.4234), np.float32(0.0307), np.float32(0.0), np.float32(0.6928), np.float32(0.5481), np.float32(0.7076), np.float32(0.621), np.float32(0.5188), np.float32(0.6426), np.float32(0.7546), np.float32(0.7586), np.float32(0.5573), np.float32(0.4229), np.float32(0.2799), np.float32(0.6793), np.float32(0.2879), np.float32(0.6745), np.float32(0.0), np.float32(0.0), np.float32(0.5945), np.float32(0.638), np.float32(0.7592), np.float32(0.7866)] 
+2026-11-09 12:31:27.022095: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:31:27.392197: lr: 0.000514 
+2026-11-09 12:31:27.392646: This epoch took 270.469648 s
+ 
+2026-11-09 12:31:27.392780: 
+epoch:  523 
+2026-11-09 12:35:48.325483: train loss : -0.8451 
+2026-11-09 12:35:56.561893: validation loss: -0.6102 
+2026-11-09 12:35:56.563639: Average global foreground Dice: [np.float32(0.9261), np.float32(0.6407), np.float32(0.7716), np.float32(0.7723), np.float32(0.6962), np.float32(0.8237), np.float32(0.726), np.float32(0.6786), np.float32(0.2848), np.float32(0.3816), np.float32(0.6176), np.float32(0.6848), np.float32(0.6356), np.float32(0.5132), np.float32(0.0022), np.float32(0.0), np.float32(0.745), np.float32(0.6128), np.float32(0.7418), np.float32(0.6333), np.float32(0.5873), np.float32(0.6676), np.float32(0.8175), np.float32(0.8125), np.float32(0.5145), np.float32(0.4599), np.float32(0.323), np.float32(0.5148), np.float32(0.3647), np.float32(0.73), np.float32(0.0), np.float32(0.0), np.float32(0.6117), np.float32(0.6654), np.float32(0.8018), np.float32(0.8298)] 
+2026-11-09 12:35:56.564141: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:35:56.925163: lr: 0.000513 
+2026-11-09 12:35:56.925654: This epoch took 269.532815 s
+ 
+2026-11-09 12:35:56.925870: 
+epoch:  524 
+2026-11-09 12:40:19.949434: train loss : -0.8508 
+2026-11-09 12:40:28.162722: validation loss: -0.5880 
+2026-11-09 12:40:28.164091: Average global foreground Dice: [np.float32(0.9266), np.float32(0.5998), np.float32(0.7552), np.float32(0.7543), np.float32(0.6603), np.float32(0.8092), np.float32(0.7156), np.float32(0.6656), np.float32(0.3708), np.float32(0.4304), np.float32(0.6154), np.float32(0.675), np.float32(0.6093), np.float32(0.4629), np.float32(0.0694), np.float32(0.0), np.float32(0.7184), np.float32(0.5829), np.float32(0.7294), np.float32(0.608), np.float32(0.5564), np.float32(0.6505), np.float32(0.8), np.float32(0.7958), np.float32(0.4849), np.float32(0.4458), np.float32(0.263), np.float32(0.2636), np.float32(0.3653), np.float32(0.7335), np.float32(0.0), np.float32(0.0), np.float32(0.6316), np.float32(0.6671), np.float32(0.7849), np.float32(0.8137)] 
+2026-11-09 12:40:28.164604: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:40:28.548672: lr: 0.000512 
+2026-11-09 12:40:28.548974: This epoch took 271.623006 s
+ 
+2026-11-09 12:40:28.549080: 
+epoch:  525 
+2026-11-09 12:44:47.101261: train loss : -0.8476 
+2026-11-09 12:44:55.364332: validation loss: -0.5370 
+2026-11-09 12:44:55.365629: Average global foreground Dice: [np.float32(0.9262), np.float32(0.5882), np.float32(0.6569), np.float32(0.6612), np.float32(0.5838), np.float32(0.718), np.float32(0.6148), np.float32(0.6767), np.float32(0.3537), np.float32(0.4892), np.float32(0.5353), np.float32(0.5867), np.float32(0.6037), np.float32(0.5257), np.float32(0.0353), np.float32(0.0), np.float32(0.5716), np.float32(0.4652), np.float32(0.5854), np.float32(0.5332), np.float32(0.4316), np.float32(0.4704), np.float32(0.7601), np.float32(0.748), np.float32(0.3564), np.float32(0.3826), np.float32(0.1517), np.float32(0.3528), np.float32(0.152), np.float32(0.6614), np.float32(0.0), np.float32(0.0), np.float32(0.5225), np.float32(0.5856), np.float32(0.7008), np.float32(0.7302)] 
+2026-11-09 12:44:55.366141: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:44:55.741714: lr: 0.000511 
+2026-11-09 12:44:55.742061: This epoch took 267.192909 s
+ 
+2026-11-09 12:44:55.742163: 
+epoch:  526 
+2026-11-09 12:49:40.469281: train loss : -0.8478 
+2026-11-09 12:49:48.719660: validation loss: -0.5714 
+2026-11-09 12:49:48.720877: Average global foreground Dice: [np.float32(0.9271), np.float32(0.5891), np.float32(0.6901), np.float32(0.7005), np.float32(0.6221), np.float32(0.7558), np.float32(0.67), np.float32(0.6342), np.float32(0.3236), np.float32(0.3981), np.float32(0.5637), np.float32(0.6156), np.float32(0.6232), np.float32(0.5196), np.float32(0.0212), np.float32(0.0), np.float32(0.623), np.float32(0.516), np.float32(0.6348), np.float32(0.5582), np.float32(0.4891), np.float32(0.5905), np.float32(0.8105), np.float32(0.793), np.float32(0.407), np.float32(0.3786), np.float32(0.1693), np.float32(0.3395), np.float32(0.2694), np.float32(0.7109), np.float32(0.0), np.float32(0.0), np.float32(0.5266), np.float32(0.6104), np.float32(0.7441), np.float32(0.7719)] 
+2026-11-09 12:49:48.721078: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:49:49.104445: lr: 0.00051 
+2026-11-09 12:49:49.104971: This epoch took 293.362742 s
+ 
+2026-11-09 12:49:49.105338: 
+epoch:  527 
+2026-11-09 12:54:02.445042: train loss : -0.8551 
+2026-11-09 12:54:10.680311: validation loss: -0.5860 
+2026-11-09 12:54:10.681482: Average global foreground Dice: [np.float32(0.9247), np.float32(0.5997), np.float32(0.7345), np.float32(0.7418), np.float32(0.6513), np.float32(0.7944), np.float32(0.696), np.float32(0.6415), np.float32(0.3534), np.float32(0.3439), np.float32(0.5781), np.float32(0.6413), np.float32(0.6245), np.float32(0.5602), np.float32(0.0458), np.float32(0.0), np.float32(0.6676), np.float32(0.5591), np.float32(0.6946), np.float32(0.5882), np.float32(0.4868), np.float32(0.5868), np.float32(0.8065), np.float32(0.7949), np.float32(0.4242), np.float32(0.4465), np.float32(0.237), np.float32(0.5046), np.float32(0.2787), np.float32(0.6925), np.float32(0.0), np.float32(0.0), np.float32(0.5543), np.float32(0.6457), np.float32(0.7625), np.float32(0.7915)] 
+2026-11-09 12:54:10.681910: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:54:11.098431: lr: 0.000509 
+2026-11-09 12:54:11.098749: This epoch took 261.992970 s
+ 
+2026-11-09 12:54:11.099162: 
+epoch:  528 
+2026-11-09 12:58:30.938950: train loss : -0.8530 
+2026-11-09 12:58:39.115700: validation loss: -0.5667 
+2026-11-09 12:58:39.117033: Average global foreground Dice: [np.float32(0.9192), np.float32(0.5508), np.float32(0.68), np.float32(0.6777), np.float32(0.5785), np.float32(0.7458), np.float32(0.6502), np.float32(0.5284), np.float32(0.3287), np.float32(0.4247), np.float32(0.5512), np.float32(0.6292), np.float32(0.6055), np.float32(0.5624), np.float32(0.0), np.float32(0.0), np.float32(0.6012), np.float32(0.5012), np.float32(0.6304), np.float32(0.5478), np.float32(0.4894), np.float32(0.5631), np.float32(0.7422), np.float32(0.737), np.float32(0.3744), np.float32(0.3856), np.float32(0.2515), np.float32(0.5014), np.float32(0.2864), np.float32(0.7144), np.float32(0.0), np.float32(0.0), np.float32(0.5602), np.float32(0.6083), np.float32(0.7084), np.float32(0.7508)] 
+2026-11-09 12:58:39.117267: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 12:58:39.493069: lr: 0.000508 
+2026-11-09 12:58:39.493655: This epoch took 268.394082 s
+ 
+2026-11-09 12:58:39.494013: 
+epoch:  529 
+2026-11-09 13:02:52.847981: train loss : -0.8579 
+2026-11-09 13:03:01.063379: validation loss: -0.4972 
+2026-11-09 13:03:01.064746: Average global foreground Dice: [np.float32(0.9235), np.float32(0.4948), np.float32(0.5581), np.float32(0.5796), np.float32(0.4832), np.float32(0.6506), np.float32(0.5388), np.float32(0.6103), np.float32(0.4575), np.float32(0.3646), np.float32(0.4596), np.float32(0.5705), np.float32(0.595), np.float32(0.5207), np.float32(0.0121), np.float32(0.0), np.float32(0.4398), np.float32(0.3561), np.float32(0.49), np.float32(0.4394), np.float32(0.4164), np.float32(0.4357), np.float32(0.6977), np.float32(0.6591), np.float32(0.2725), np.float32(0.2687), np.float32(0.1308), np.float32(0.0771), np.float32(0.2299), np.float32(0.6542), np.float32(0.0), np.float32(0.0), np.float32(0.4759), np.float32(0.5369), np.float32(0.5947), np.float32(0.641)] 
+2026-11-09 13:03:01.064993: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:03:01.455786: lr: 0.000507 
+2026-11-09 13:03:01.456071: This epoch took 261.961804 s
+ 
+2026-11-09 13:03:01.456328: 
+epoch:  530 
+2026-11-09 13:07:41.703043: train loss : -0.8521 
+2026-11-09 13:07:49.967456: validation loss: -0.6250 
+2026-11-09 13:07:49.969183: Average global foreground Dice: [np.float32(0.9255), np.float32(0.5985), np.float32(0.7571), np.float32(0.7727), np.float32(0.6529), np.float32(0.8325), np.float32(0.7342), np.float32(0.6305), np.float32(0.3896), np.float32(0.3263), np.float32(0.6106), np.float32(0.699), np.float32(0.666), np.float32(0.6054), np.float32(0.0093), np.float32(0.0), np.float32(0.6804), np.float32(0.5491), np.float32(0.696), np.float32(0.5834), np.float32(0.5794), np.float32(0.6365), np.float32(0.8396), np.float32(0.836), np.float32(0.4417), np.float32(0.4118), np.float32(0.2657), np.float32(0.5539), np.float32(0.3366), np.float32(0.7325), np.float32(0.0), np.float32(0.0), np.float32(0.603), np.float32(0.6403), np.float32(0.8028), np.float32(0.8273)] 
+2026-11-09 13:07:49.969581: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:07:50.357849: lr: 0.000506 
+2026-11-09 13:07:50.358112: This epoch took 288.901674 s
+ 
+2026-11-09 13:07:50.358191: 
+epoch:  531 
+2026-11-09 13:12:03.943657: train loss : -0.8534 
+2026-11-09 13:12:12.177162: validation loss: -0.6030 
+2026-11-09 13:12:12.178084: Average global foreground Dice: [np.float32(0.9256), np.float32(0.6215), np.float32(0.735), np.float32(0.7316), np.float32(0.6529), np.float32(0.8005), np.float32(0.6922), np.float32(0.6244), np.float32(0.4123), np.float32(0.3326), np.float32(0.6016), np.float32(0.6697), np.float32(0.6505), np.float32(0.5997), np.float32(0.0), np.float32(0.0), np.float32(0.7113), np.float32(0.582), np.float32(0.7047), np.float32(0.6322), np.float32(0.5762), np.float32(0.6138), np.float32(0.792), np.float32(0.7774), np.float32(0.4489), np.float32(0.4337), np.float32(0.295), np.float32(0.6016), np.float32(0.3889), np.float32(0.7287), np.float32(0.0), np.float32(0.0), np.float32(0.6026), np.float32(0.6234), np.float32(0.7707), np.float32(0.8096)] 
+2026-11-09 13:12:12.178456: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:12:12.545318: lr: 0.000505 
+2026-11-09 13:12:12.545846: This epoch took 262.187599 s
+ 
+2026-11-09 13:12:12.546132: 
+epoch:  532 
+2026-11-09 13:17:01.384121: train loss : -0.8499 
+2026-11-09 13:17:09.624540: validation loss: -0.5697 
+2026-11-09 13:17:09.625544: Average global foreground Dice: [np.float32(0.9259), np.float32(0.6074), np.float32(0.6938), np.float32(0.6932), np.float32(0.6186), np.float32(0.7522), np.float32(0.6548), np.float32(0.6939), np.float32(0.4264), np.float32(0.4196), np.float32(0.5569), np.float32(0.61), np.float32(0.5813), np.float32(0.4682), np.float32(0.0024), np.float32(0.0), np.float32(0.679), np.float32(0.5494), np.float32(0.6649), np.float32(0.6071), np.float32(0.5274), np.float32(0.5918), np.float32(0.7621), np.float32(0.7372), np.float32(0.4392), np.float32(0.467), np.float32(0.2764), np.float32(0.2124), np.float32(0.3467), np.float32(0.6939), np.float32(0.0), np.float32(0.0), np.float32(0.5455), np.float32(0.636), np.float32(0.7351), np.float32(0.7588)] 
+2026-11-09 13:17:09.625970: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:17:09.983905: lr: 0.000504 
+2026-11-09 13:17:09.984244: This epoch took 297.437891 s
+ 
+2026-11-09 13:17:09.984344: 
+epoch:  533 
+2026-11-09 13:21:22.153776: train loss : -0.8524 
+2026-11-09 13:21:30.375370: validation loss: -0.5810 
+2026-11-09 13:21:30.376708: Average global foreground Dice: [np.float32(0.9248), np.float32(0.5882), np.float32(0.7068), np.float32(0.7189), np.float32(0.6191), np.float32(0.771), np.float32(0.6696), np.float32(0.6218), np.float32(0.4474), np.float32(0.3583), np.float32(0.5866), np.float32(0.6825), np.float32(0.6364), np.float32(0.5082), np.float32(0.0), np.float32(0.0), np.float32(0.6657), np.float32(0.5238), np.float32(0.6691), np.float32(0.5871), np.float32(0.4901), np.float32(0.5867), np.float32(0.7659), np.float32(0.7535), np.float32(0.4355), np.float32(0.3914), np.float32(0.2748), np.float32(0.5548), np.float32(0.4064), np.float32(0.6992), np.float32(0.0), np.float32(0.0), np.float32(0.5267), np.float32(0.6335), np.float32(0.7362), np.float32(0.7705)] 
+2026-11-09 13:21:30.377144: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:21:30.726627: lr: 0.000503 
+2026-11-09 13:21:30.727067: This epoch took 260.742655 s
+ 
+2026-11-09 13:21:30.727429: 
+epoch:  534 
+2026-11-09 13:26:31.717321: train loss : -0.8546 
+2026-11-09 13:26:39.970004: validation loss: -0.5311 
+2026-11-09 13:26:39.971180: Average global foreground Dice: [np.float32(0.925), np.float32(0.523), np.float32(0.627), np.float32(0.6565), np.float32(0.5521), np.float32(0.7065), np.float32(0.6117), np.float32(0.5982), np.float32(0.3871), np.float32(0.4121), np.float32(0.5114), np.float32(0.5931), np.float32(0.5773), np.float32(0.4263), np.float32(0.0065), np.float32(0.0), np.float32(0.5736), np.float32(0.4612), np.float32(0.6009), np.float32(0.5305), np.float32(0.4452), np.float32(0.5476), np.float32(0.7248), np.float32(0.7057), np.float32(0.4062), np.float32(0.3689), np.float32(0.2815), np.float32(0.4469), np.float32(0.3937), np.float32(0.6932), np.float32(0.0), np.float32(0.0), np.float32(0.5223), np.float32(0.5904), np.float32(0.6594), np.float32(0.7)] 
+2026-11-09 13:26:39.971579: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:26:40.355503: lr: 0.000502 
+2026-11-09 13:26:40.355722: This epoch took 309.628159 s
+ 
+2026-11-09 13:26:40.355783: 
+epoch:  535 
+2026-11-09 13:30:52.850538: train loss : -0.8518 
+2026-11-09 13:31:01.106261: validation loss: -0.5909 
+2026-11-09 13:31:01.107580: Average global foreground Dice: [np.float32(0.9259), np.float32(0.592), np.float32(0.7035), np.float32(0.7348), np.float32(0.6343), np.float32(0.7803), np.float32(0.6904), np.float32(0.5671), np.float32(0.4397), np.float32(0.5011), np.float32(0.6006), np.float32(0.6427), np.float32(0.6213), np.float32(0.5475), np.float32(0.0551), np.float32(0.0), np.float32(0.6424), np.float32(0.5276), np.float32(0.6569), np.float32(0.5792), np.float32(0.4827), np.float32(0.6154), np.float32(0.7982), np.float32(0.796), np.float32(0.4359), np.float32(0.4629), np.float32(0.2648), np.float32(0.5352), np.float32(0.4214), np.float32(0.6951), np.float32(0.0), np.float32(0.0), np.float32(0.5795), np.float32(0.6143), np.float32(0.7524), np.float32(0.7854)] 
+2026-11-09 13:31:01.107811: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:31:01.608532: lr: 0.000501 
+2026-11-09 13:31:01.608816: This epoch took 261.252982 s
+ 
+2026-11-09 13:31:01.608899: 
+epoch:  536 
+2026-11-09 13:35:32.110299: train loss : -0.8524 
+2026-11-09 13:35:40.332726: validation loss: -0.5690 
+2026-11-09 13:35:40.334153: Average global foreground Dice: [np.float32(0.8986), np.float32(0.5654), np.float32(0.711), np.float32(0.7217), np.float32(0.6223), np.float32(0.767), np.float32(0.6887), np.float32(0.4224), np.float32(0.3684), np.float32(0.2481), np.float32(0.5417), np.float32(0.6389), np.float32(0.5631), np.float32(0.4166), np.float32(0.0), np.float32(0.0), np.float32(0.6656), np.float32(0.5087), np.float32(0.6881), np.float32(0.5934), np.float32(0.4898), np.float32(0.6048), np.float32(0.7527), np.float32(0.7263), np.float32(0.4937), np.float32(0.4825), np.float32(0.2248), np.float32(0.5264), np.float32(0.3), np.float32(0.6848), np.float32(0.0), np.float32(0.0), np.float32(0.5513), np.float32(0.6072), np.float32(0.7458), np.float32(0.7767)] 
+2026-11-09 13:35:40.334400: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:35:40.724242: lr: 0.0005 
+2026-11-09 13:35:40.724659: This epoch took 279.115698 s
+ 
+2026-11-09 13:35:40.724795: 
+epoch:  537 
+2026-11-09 13:40:28.561793: train loss : -0.8474 
+2026-11-09 13:40:36.810205: validation loss: -0.5977 
+2026-11-09 13:40:36.811385: Average global foreground Dice: [np.float32(0.9261), np.float32(0.6023), np.float32(0.7227), np.float32(0.7497), np.float32(0.6681), np.float32(0.7958), np.float32(0.7008), np.float32(0.5166), np.float32(0.3944), np.float32(0.4238), np.float32(0.615), np.float32(0.6794), np.float32(0.6172), np.float32(0.5501), np.float32(0.0052), np.float32(0.0), np.float32(0.6812), np.float32(0.5566), np.float32(0.684), np.float32(0.5948), np.float32(0.5116), np.float32(0.5432), np.float32(0.7892), np.float32(0.7903), np.float32(0.4353), np.float32(0.4569), np.float32(0.3494), np.float32(0.5935), np.float32(0.3602), np.float32(0.6863), np.float32(0.0), np.float32(0.0), np.float32(0.5613), np.float32(0.6348), np.float32(0.7695), np.float32(0.799)] 
+2026-11-09 13:40:36.811598: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:40:37.167775: lr: 0.000499 
+2026-11-09 13:40:37.168169: This epoch took 296.443305 s
+ 
+2026-11-09 13:40:37.168419: 
+epoch:  538 
+2026-11-09 13:45:03.893352: train loss : -0.8528 
+2026-11-09 13:45:12.175458: validation loss: -0.5591 
+2026-11-09 13:45:12.176913: Average global foreground Dice: [np.float32(0.9225), np.float32(0.579), np.float32(0.691), np.float32(0.698), np.float32(0.5997), np.float32(0.7532), np.float32(0.66), np.float32(0.5451), np.float32(0.4215), np.float32(0.403), np.float32(0.5559), np.float32(0.6252), np.float32(0.6127), np.float32(0.5124), np.float32(0.0), np.float32(0.0), np.float32(0.6573), np.float32(0.5452), np.float32(0.6764), np.float32(0.5942), np.float32(0.5055), np.float32(0.6008), np.float32(0.7461), np.float32(0.7429), np.float32(0.4368), np.float32(0.4113), np.float32(0.2916), np.float32(0.1401), np.float32(0.3709), np.float32(0.6961), np.float32(0.0), np.float32(0.0), np.float32(0.5922), np.float32(0.6173), np.float32(0.717), np.float32(0.767)] 
+2026-11-09 13:45:12.177476: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:45:12.567686: lr: 0.000498 
+2026-11-09 13:45:12.568157: This epoch took 275.399524 s
+ 
+2026-11-09 13:45:12.568263: 
+epoch:  539 
+2026-11-09 13:49:45.313359: train loss : -0.8493 
+2026-11-09 13:49:53.562679: validation loss: -0.5648 
+2026-11-09 13:49:53.564085: Average global foreground Dice: [np.float32(0.9075), np.float32(0.5756), np.float32(0.6989), np.float32(0.7293), np.float32(0.6066), np.float32(0.7735), np.float32(0.6837), np.float32(0.5739), np.float32(0.4068), np.float32(0.4269), np.float32(0.5615), np.float32(0.6008), np.float32(0.6228), np.float32(0.5282), np.float32(0.0032), np.float32(0.0), np.float32(0.5822), np.float32(0.4529), np.float32(0.6423), np.float32(0.5497), np.float32(0.5082), np.float32(0.5549), np.float32(0.7507), np.float32(0.7189), np.float32(0.4325), np.float32(0.2772), np.float32(0.2292), np.float32(0.3464), np.float32(0.3612), np.float32(0.6993), np.float32(0.0), np.float32(0.0), np.float32(0.5698), np.float32(0.6161), np.float32(0.734), np.float32(0.776)] 
+2026-11-09 13:49:53.564570: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:49:53.962749: lr: 0.000497 
+2026-11-09 13:49:53.963015: This epoch took 281.394678 s
+ 
+2026-11-09 13:49:53.963413: 
+epoch:  540 
+2026-11-09 13:54:24.008090: train loss : -0.8493 
+2026-11-09 13:54:32.287214: validation loss: -0.5986 
+2026-11-09 13:54:32.288446: Average global foreground Dice: [np.float32(0.9155), np.float32(0.615), np.float32(0.7363), np.float32(0.7485), np.float32(0.6486), np.float32(0.7942), np.float32(0.6894), np.float32(0.5816), np.float32(0.4467), np.float32(0.4321), np.float32(0.6276), np.float32(0.6641), np.float32(0.6288), np.float32(0.5221), np.float32(0.0), np.float32(0.0), np.float32(0.701), np.float32(0.5352), np.float32(0.6919), np.float32(0.6199), np.float32(0.5839), np.float32(0.632), np.float32(0.8359), np.float32(0.8236), np.float32(0.4872), np.float32(0.4407), np.float32(0.2585), np.float32(0.574), np.float32(0.42), np.float32(0.7332), np.float32(0.0), np.float32(0.0), np.float32(0.5591), np.float32(0.6629), np.float32(0.7749), np.float32(0.8011)] 
+2026-11-09 13:54:32.288625: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:54:32.680710: lr: 0.000496 
+2026-11-09 13:54:32.681000: This epoch took 278.717307 s
+ 
+2026-11-09 13:54:32.681125: 
+epoch:  541 
+2026-11-09 13:59:11.509818: train loss : -0.8480 
+2026-11-09 13:59:19.758750: validation loss: -0.5694 
+2026-11-09 13:59:19.760376: Average global foreground Dice: [np.float32(0.9188), np.float32(0.5709), np.float32(0.7155), np.float32(0.7227), np.float32(0.6296), np.float32(0.7695), np.float32(0.6669), np.float32(0.615), np.float32(0.3517), np.float32(0.2889), np.float32(0.5437), np.float32(0.6109), np.float32(0.5618), np.float32(0.4957), np.float32(0.0), np.float32(0.0), np.float32(0.6396), np.float32(0.4985), np.float32(0.6539), np.float32(0.55), np.float32(0.5211), np.float32(0.6226), np.float32(0.8313), np.float32(0.815), np.float32(0.4394), np.float32(0.4121), np.float32(0.1722), np.float32(0.6009), np.float32(0.342), np.float32(0.658), np.float32(0.0), np.float32(0.0), np.float32(0.544), np.float32(0.6315), np.float32(0.7586), np.float32(0.7889)] 
+2026-11-09 13:59:19.760855: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 13:59:20.145125: lr: 0.000495 
+2026-11-09 13:59:20.145427: This epoch took 287.464015 s
+ 
+2026-11-09 13:59:20.145532: 
+epoch:  542 
+2026-11-09 14:03:40.056613: train loss : -0.8519 
+2026-11-09 14:03:48.296511: validation loss: -0.6194 
+2026-11-09 14:03:48.297627: Average global foreground Dice: [np.float32(0.923), np.float32(0.6218), np.float32(0.7496), np.float32(0.7469), np.float32(0.6433), np.float32(0.8081), np.float32(0.697), np.float32(0.5417), np.float32(0.4956), np.float32(0.4545), np.float32(0.6207), np.float32(0.6757), np.float32(0.6478), np.float32(0.6062), np.float32(0.0), np.float32(0.0), np.float32(0.6594), np.float32(0.5245), np.float32(0.6868), np.float32(0.5832), np.float32(0.5464), np.float32(0.5212), np.float32(0.8219), np.float32(0.8137), np.float32(0.4235), np.float32(0.4585), np.float32(0.2293), np.float32(0.5953), np.float32(0.3753), np.float32(0.7388), np.float32(0.0), np.float32(0.0), np.float32(0.5653), np.float32(0.6428), np.float32(0.7761), np.float32(0.8075)] 
+2026-11-09 14:03:48.297938: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:03:48.727439: lr: 0.000494 
+2026-11-09 14:03:48.727749: This epoch took 268.582138 s
+ 
+2026-11-09 14:03:48.727849: 
+epoch:  543 
+2026-11-09 14:08:29.954916: train loss : -0.8502 
+2026-11-09 14:08:38.288537: validation loss: -0.4926 
+2026-11-09 14:08:38.289559: Average global foreground Dice: [np.float32(0.9286), np.float32(0.4855), np.float32(0.5093), np.float32(0.5094), np.float32(0.3637), np.float32(0.5822), np.float32(0.4681), np.float32(0.5728), np.float32(0.359), np.float32(0.5094), np.float32(0.4381), np.float32(0.5051), np.float32(0.6018), np.float32(0.5444), np.float32(0.0), np.float32(0.0), np.float32(0.3111), np.float32(0.2505), np.float32(0.3823), np.float32(0.3597), np.float32(0.4297), np.float32(0.3715), np.float32(0.6508), np.float32(0.6296), np.float32(0.1698), np.float32(0.2197), np.float32(0.0774), np.float32(0.1102), np.float32(0.2651), np.float32(0.6838), np.float32(0.0), np.float32(0.0), np.float32(0.362), np.float32(0.449), np.float32(0.5385), np.float32(0.5799)] 
+2026-11-09 14:08:38.289901: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:08:38.664211: lr: 0.000493 
+2026-11-09 14:08:38.664592: This epoch took 289.936676 s
+ 
+2026-11-09 14:08:38.664694: 
+epoch:  544 
+2026-11-09 14:13:16.441652: train loss : -0.8542 
+2026-11-09 14:13:24.679955: validation loss: -0.4256 
+2026-11-09 14:13:24.681100: Average global foreground Dice: [np.float32(0.9268), np.float32(0.4302), np.float32(0.4404), np.float32(0.4162), np.float32(0.3777), np.float32(0.4903), np.float32(0.4004), np.float32(0.6488), np.float32(0.3201), np.float32(0.4673), np.float32(0.3281), np.float32(0.4129), np.float32(0.5536), np.float32(0.4875), np.float32(0.0), np.float32(0.0), np.float32(0.3047), np.float32(0.2642), np.float32(0.3258), np.float32(0.297), np.float32(0.3176), np.float32(0.251), np.float32(0.6536), np.float32(0.6078), np.float32(0.1373), np.float32(0.2128), np.float32(0.0066), np.float32(0.0883), np.float32(0.2037), np.float32(0.6272), np.float32(0.0), np.float32(0.0), np.float32(0.3497), np.float32(0.4257), np.float32(0.476), np.float32(0.5301)] 
+2026-11-09 14:13:24.681881: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:13:25.088557: lr: 0.000492 
+2026-11-09 14:13:25.089057: This epoch took 286.424211 s
+ 
+2026-11-09 14:13:25.089180: 
+epoch:  545 
+2026-11-09 14:17:41.303851: train loss : -0.8563 
+2026-11-09 14:17:49.510528: validation loss: -0.4868 
+2026-11-09 14:17:49.512148: Average global foreground Dice: [np.float32(0.9274), np.float32(0.4969), np.float32(0.5275), np.float32(0.5099), np.float32(0.4194), np.float32(0.584), np.float32(0.4792), np.float32(0.596), np.float32(0.4222), np.float32(0.4682), np.float32(0.4323), np.float32(0.4944), np.float32(0.6035), np.float32(0.5692), np.float32(0.0), np.float32(0.0), np.float32(0.3689), np.float32(0.3287), np.float32(0.4124), np.float32(0.3787), np.float32(0.3794), np.float32(0.3157), np.float32(0.6555), np.float32(0.6104), np.float32(0.1882), np.float32(0.2617), np.float32(0.0628), np.float32(0.1264), np.float32(0.2581), np.float32(0.6573), np.float32(0.0), np.float32(0.0), np.float32(0.4372), np.float32(0.4861), np.float32(0.5476), np.float32(0.601)] 
+2026-11-09 14:17:49.512785: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:17:49.896685: lr: 0.000491 
+2026-11-09 14:17:49.897079: This epoch took 264.807819 s
+ 
+2026-11-09 14:17:49.897341: 
+epoch:  546 
+2026-11-09 14:22:34.982041: train loss : -0.8452 
+2026-11-09 14:22:43.258176: validation loss: -0.5011 
+2026-11-09 14:22:43.259357: Average global foreground Dice: [np.float32(0.9252), np.float32(0.5272), np.float32(0.5468), np.float32(0.5526), np.float32(0.4714), np.float32(0.6106), np.float32(0.4956), np.float32(0.5617), np.float32(0.2797), np.float32(0.4799), np.float32(0.4433), np.float32(0.5134), np.float32(0.5889), np.float32(0.5181), np.float32(0.0016), np.float32(0.0), np.float32(0.4235), np.float32(0.3482), np.float32(0.4427), np.float32(0.4097), np.float32(0.3723), np.float32(0.2972), np.float32(0.7295), np.float32(0.704), np.float32(0.2083), np.float32(0.2892), np.float32(0.0455), np.float32(0.1222), np.float32(0.2318), np.float32(0.6761), np.float32(0.0), np.float32(0.0), np.float32(0.4149), np.float32(0.504), np.float32(0.5957), np.float32(0.6342)] 
+2026-11-09 14:22:43.260190: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:22:43.632859: lr: 0.00049 
+2026-11-09 14:22:43.633480: This epoch took 293.736022 s
+ 
+2026-11-09 14:22:43.633619: 
+epoch:  547 
+2026-11-09 14:27:18.779450: train loss : -0.8489 
+2026-11-09 14:27:27.020051: validation loss: -0.5007 
+2026-11-09 14:27:27.021340: Average global foreground Dice: [np.float32(0.9299), np.float32(0.526), np.float32(0.5553), np.float32(0.5585), np.float32(0.4462), np.float32(0.6167), np.float32(0.5088), np.float32(0.5243), np.float32(0.3336), np.float32(0.4812), np.float32(0.4553), np.float32(0.5248), np.float32(0.5755), np.float32(0.4812), np.float32(0.0), np.float32(0.0), np.float32(0.4107), np.float32(0.3227), np.float32(0.4481), np.float32(0.4338), np.float32(0.4365), np.float32(0.3836), np.float32(0.6171), np.float32(0.6085), np.float32(0.2199), np.float32(0.2851), np.float32(0.0777), np.float32(0.1155), np.float32(0.2949), np.float32(0.6906), np.float32(0.0), np.float32(0.0), np.float32(0.4177), np.float32(0.476), np.float32(0.5833), np.float32(0.6148)] 
+2026-11-09 14:27:27.021640: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:27:27.387636: lr: 0.000489 
+2026-11-09 14:27:27.387898: This epoch took 283.754205 s
+ 
+2026-11-09 14:27:27.387992: 
+epoch:  548 
+2026-11-09 14:31:35.460893: train loss : -0.8585 
+2026-11-09 14:31:43.689417: validation loss: -0.4753 
+2026-11-09 14:31:43.690593: Average global foreground Dice: [np.float32(0.9229), np.float32(0.4859), np.float32(0.5373), np.float32(0.5329), np.float32(0.43), np.float32(0.6021), np.float32(0.4952), np.float32(0.5569), np.float32(0.309), np.float32(0.4631), np.float32(0.4307), np.float32(0.5131), np.float32(0.5802), np.float32(0.4807), np.float32(0.0025), np.float32(0.0), np.float32(0.3851), np.float32(0.3199), np.float32(0.4367), np.float32(0.3976), np.float32(0.427), np.float32(0.3567), np.float32(0.6135), np.float32(0.6047), np.float32(0.2232), np.float32(0.2674), np.float32(0.1047), np.float32(0.1088), np.float32(0.1952), np.float32(0.6536), np.float32(0.0), np.float32(0.0), np.float32(0.419), np.float32(0.4867), np.float32(0.5614), np.float32(0.6064)] 
+2026-11-09 14:31:43.690811: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:31:44.053647: lr: 0.000488 
+2026-11-09 14:31:44.054191: This epoch took 256.666142 s
+ 
+2026-11-09 14:31:44.054616: 
+epoch:  549 
+2026-11-09 14:35:52.604916: train loss : -0.8554 
+2026-11-09 14:36:00.786227: validation loss: -0.5273 
+2026-11-09 14:36:00.787486: Average global foreground Dice: [np.float32(0.9191), np.float32(0.5152), np.float32(0.5973), np.float32(0.5997), np.float32(0.506), np.float32(0.6636), np.float32(0.5587), np.float32(0.5556), np.float32(0.3629), np.float32(0.4588), np.float32(0.4922), np.float32(0.562), np.float32(0.5991), np.float32(0.4849), np.float32(0.0595), np.float32(0.0), np.float32(0.5081), np.float32(0.434), np.float32(0.5285), np.float32(0.4657), np.float32(0.4946), np.float32(0.4428), np.float32(0.7326), np.float32(0.7219), np.float32(0.3072), np.float32(0.3221), np.float32(0.2509), np.float32(0.3554), np.float32(0.3968), np.float32(0.5454), np.float32(0.0), np.float32(0.0), np.float32(0.4748), np.float32(0.5297), np.float32(0.6323), np.float32(0.68)] 
+2026-11-09 14:36:00.787995: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:36:01.182750: lr: 0.000487 
+2026-11-09 14:36:01.183032: saving scheduled checkpoint file... 
+2026-11-09 14:36:01.368108: saving checkpoint... 
+2026-11-09 14:36:01.961209: done, saving took 0.78 seconds 
+2026-11-09 14:36:01.977508: done 
+2026-11-09 14:36:01.977768: This epoch took 257.922890 s
+ 
+2026-11-09 14:36:01.977849: 
+epoch:  550 
+2026-11-09 14:40:22.566640: train loss : -0.8483 
+2026-11-09 14:40:30.795933: validation loss: -0.5565 
+2026-11-09 14:40:30.797218: Average global foreground Dice: [np.float32(0.918), np.float32(0.5831), np.float32(0.6244), np.float32(0.6199), np.float32(0.5157), np.float32(0.702), np.float32(0.5685), np.float32(0.5626), np.float32(0.0202), np.float32(0.4362), np.float32(0.55), np.float32(0.6125), np.float32(0.6227), np.float32(0.5492), np.float32(0.1241), np.float32(0.0), np.float32(0.5073), np.float32(0.4075), np.float32(0.5293), np.float32(0.5046), np.float32(0.491), np.float32(0.39), np.float32(0.7184), np.float32(0.7179), np.float32(0.2878), np.float32(0.3115), np.float32(0.1227), np.float32(0.2928), np.float32(0.3614), np.float32(0.6914), np.float32(0.0), np.float32(0.0), np.float32(0.4453), np.float32(0.5418), np.float32(0.6684), np.float32(0.7011)] 
+2026-11-09 14:40:30.797426: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:40:31.156271: lr: 0.000486 
+2026-11-09 14:40:31.156735: This epoch took 269.178829 s
+ 
+2026-11-09 14:40:31.157020: 
+epoch:  551 
+2026-11-09 14:44:47.486291: train loss : -0.8535 
+2026-11-09 14:44:55.662555: validation loss: -0.4512 
+2026-11-09 14:44:55.664155: Average global foreground Dice: [np.float32(0.9224), np.float32(0.4673), np.float32(0.5131), np.float32(0.5175), np.float32(0.416), np.float32(0.5708), np.float32(0.4713), np.float32(0.4214), np.float32(0.0), np.float32(0.3762), np.float32(0.4185), np.float32(0.4746), np.float32(0.5511), np.float32(0.4487), np.float32(0.0891), np.float32(0.0), np.float32(0.3563), np.float32(0.298), np.float32(0.3977), np.float32(0.3672), np.float32(0.3693), np.float32(0.289), np.float32(0.6255), np.float32(0.5921), np.float32(0.2069), np.float32(0.2177), np.float32(0.0833), np.float32(0.2159), np.float32(0.3383), np.float32(0.5988), np.float32(0.0), np.float32(0.0), np.float32(0.3596), np.float32(0.3741), np.float32(0.5539), np.float32(0.5792)] 
+2026-11-09 14:44:55.664551: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:44:56.044443: lr: 0.000485 
+2026-11-09 14:44:56.044713: This epoch took 264.887602 s
+ 
+2026-11-09 14:44:56.044797: 
+epoch:  552 
+2026-11-09 14:49:18.241362: train loss : -0.8479 
+2026-11-09 14:49:26.480483: validation loss: -0.4678 
+2026-11-09 14:49:26.481679: Average global foreground Dice: [np.float32(0.9304), np.float32(0.483), np.float32(0.4996), np.float32(0.4841), np.float32(0.4075), np.float32(0.5601), np.float32(0.4635), np.float32(0.5603), np.float32(0.1591), np.float32(0.5218), np.float32(0.4172), np.float32(0.4984), np.float32(0.5789), np.float32(0.5054), np.float32(0.1942), np.float32(0.0), np.float32(0.3538), np.float32(0.3097), np.float32(0.3768), np.float32(0.3691), np.float32(0.4155), np.float32(0.3391), np.float32(0.6187), np.float32(0.5767), np.float32(0.1755), np.float32(0.2308), np.float32(0.0158), np.float32(0.1237), np.float32(0.2476), np.float32(0.6317), np.float32(0.0), np.float32(0.0), np.float32(0.3054), np.float32(0.3034), np.float32(0.5314), np.float32(0.5737)] 
+2026-11-09 14:49:26.482302: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:49:26.848058: lr: 0.000484 
+2026-11-09 14:49:26.848717: This epoch took 270.803862 s
+ 
+2026-11-09 14:49:26.849217: 
+epoch:  553 
+2026-11-09 14:53:41.735995: train loss : -0.8513 
+2026-11-09 14:53:49.984131: validation loss: -0.4852 
+2026-11-09 14:53:49.985313: Average global foreground Dice: [np.float32(0.9308), np.float32(0.4808), np.float32(0.5), np.float32(0.5095), np.float32(0.3951), np.float32(0.5795), np.float32(0.4782), np.float32(0.5991), np.float32(0.2327), np.float32(0.4522), np.float32(0.4331), np.float32(0.4875), np.float32(0.5759), np.float32(0.5015), np.float32(0.012), np.float32(0.0), np.float32(0.3315), np.float32(0.2753), np.float32(0.3748), np.float32(0.3498), np.float32(0.3735), np.float32(0.3088), np.float32(0.6792), np.float32(0.6508), np.float32(0.144), np.float32(0.203), np.float32(0.0252), np.float32(0.0359), np.float32(0.3265), np.float32(0.6446), np.float32(0.0), np.float32(0.0), np.float32(0.3995), np.float32(0.4517), np.float32(0.5483), np.float32(0.5899)] 
+2026-11-09 14:53:49.985487: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:53:50.370631: lr: 0.000484 
+2026-11-09 14:53:50.370970: This epoch took 263.521443 s
+ 
+2026-11-09 14:53:50.371241: 
+epoch:  554 
+2026-11-09 14:58:05.582942: train loss : -0.8583 
+2026-11-09 14:58:13.790837: validation loss: -0.4914 
+2026-11-09 14:58:13.791953: Average global foreground Dice: [np.float32(0.93), np.float32(0.505), np.float32(0.5176), np.float32(0.501), np.float32(0.4162), np.float32(0.5848), np.float32(0.4804), np.float32(0.5927), np.float32(0.4071), np.float32(0.4981), np.float32(0.4459), np.float32(0.5262), np.float32(0.5557), np.float32(0.5288), np.float32(0.0), np.float32(0.0), np.float32(0.371), np.float32(0.333), np.float32(0.418), np.float32(0.3836), np.float32(0.396), np.float32(0.3318), np.float32(0.6608), np.float32(0.6312), np.float32(0.1798), np.float32(0.2648), np.float32(0.0215), np.float32(0.0), np.float32(0.313), np.float32(0.6682), np.float32(0.0), np.float32(0.0), np.float32(0.4152), np.float32(0.4516), np.float32(0.5549), np.float32(0.6133)] 
+2026-11-09 14:58:13.792127: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 14:58:14.199711: lr: 0.000483 
+2026-11-09 14:58:14.200078: This epoch took 263.828738 s
+ 
+2026-11-09 14:58:14.200288: 
+epoch:  555 
+2026-11-09 15:02:37.152476: train loss : -0.8586 
+2026-11-09 15:02:45.408330: validation loss: -0.5457 
+2026-11-09 15:02:45.409657: Average global foreground Dice: [np.float32(0.9291), np.float32(0.5825), np.float32(0.6074), np.float32(0.621), np.float32(0.5274), np.float32(0.6932), np.float32(0.5791), np.float32(0.6376), np.float32(0.2834), np.float32(0.3832), np.float32(0.5304), np.float32(0.6095), np.float32(0.632), np.float32(0.609), np.float32(0.0), np.float32(0.0), np.float32(0.4688), np.float32(0.3923), np.float32(0.5079), np.float32(0.4682), np.float32(0.5086), np.float32(0.4435), np.float32(0.7576), np.float32(0.7313), np.float32(0.2682), np.float32(0.2983), np.float32(0.0907), np.float32(0.065), np.float32(0.2584), np.float32(0.6517), np.float32(0.0), np.float32(0.0), np.float32(0.463), np.float32(0.4595), np.float32(0.6608), np.float32(0.7005)] 
+2026-11-09 15:02:45.409822: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:02:45.778384: lr: 0.000482 
+2026-11-09 15:02:45.778652: This epoch took 271.578264 s
+ 
+2026-11-09 15:02:45.778735: 
+epoch:  556 
+2026-11-09 15:07:45.367971: train loss : -0.8478 
+2026-11-09 15:07:53.614517: validation loss: -0.5158 
+2026-11-09 15:07:53.615517: Average global foreground Dice: [np.float32(0.9308), np.float32(0.5467), np.float32(0.5784), np.float32(0.5821), np.float32(0.493), np.float32(0.6454), np.float32(0.5522), np.float32(0.6068), np.float32(0.3166), np.float32(0.4535), np.float32(0.4887), np.float32(0.5459), np.float32(0.6004), np.float32(0.521), np.float32(0.0067), np.float32(0.0), np.float32(0.4496), np.float32(0.3688), np.float32(0.4649), np.float32(0.4248), np.float32(0.4629), np.float32(0.3934), np.float32(0.6769), np.float32(0.6652), np.float32(0.2506), np.float32(0.2967), np.float32(0.0719), np.float32(0.019), np.float32(0.3197), np.float32(0.6217), np.float32(0.0), np.float32(0.0), np.float32(0.4479), np.float32(0.5039), np.float32(0.6267), np.float32(0.6582)] 
+2026-11-09 15:07:53.615769: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:07:53.971552: lr: 0.000481 
+2026-11-09 15:07:53.971857: This epoch took 308.192802 s
+ 
+2026-11-09 15:07:53.971963: 
+epoch:  557 
+2026-11-09 15:12:01.993656: train loss : -0.8523 
+2026-11-09 15:12:10.146935: validation loss: -0.5406 
+2026-11-09 15:12:10.147901: Average global foreground Dice: [np.float32(0.9262), np.float32(0.5685), np.float32(0.5909), np.float32(0.5983), np.float32(0.4621), np.float32(0.6761), np.float32(0.5549), np.float32(0.4774), np.float32(0.2911), np.float32(0.5567), np.float32(0.5131), np.float32(0.5811), np.float32(0.6242), np.float32(0.5342), np.float32(0.0), np.float32(0.0), np.float32(0.4557), np.float32(0.366), np.float32(0.5134), np.float32(0.4726), np.float32(0.5351), np.float32(0.4429), np.float32(0.6873), np.float32(0.6666), np.float32(0.2928), np.float32(0.3069), np.float32(0.0508), np.float32(0.0738), np.float32(0.3564), np.float32(0.6624), np.float32(0.0), np.float32(0.0), np.float32(0.4226), np.float32(0.4447), np.float32(0.6279), np.float32(0.6691)] 
+2026-11-09 15:12:10.148236: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:12:10.485779: lr: 0.00048 
+2026-11-09 15:12:10.486032: This epoch took 256.513985 s
+ 
+2026-11-09 15:12:10.486113: 
+epoch:  558 
+2026-11-09 15:16:35.871695: train loss : -0.8531 
+2026-11-09 15:16:44.087485: validation loss: -0.5301 
+2026-11-09 15:16:44.088917: Average global foreground Dice: [np.float32(0.9216), np.float32(0.5547), np.float32(0.6011), np.float32(0.5933), np.float32(0.4665), np.float32(0.6633), np.float32(0.5492), np.float32(0.4669), np.float32(0.4124), np.float32(0.4677), np.float32(0.4924), np.float32(0.5491), np.float32(0.5945), np.float32(0.541), np.float32(0.003), np.float32(0.0), np.float32(0.4665), np.float32(0.3784), np.float32(0.5073), np.float32(0.4715), np.float32(0.5319), np.float32(0.4693), np.float32(0.7308), np.float32(0.7183), np.float32(0.2964), np.float32(0.3579), np.float32(0.1558), np.float32(0.2283), np.float32(0.3665), np.float32(0.6518), np.float32(0.0), np.float32(0.0), np.float32(0.4465), np.float32(0.5308), np.float32(0.6432), np.float32(0.6796)] 
+2026-11-09 15:16:44.089289: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:16:44.455030: lr: 0.000479 
+2026-11-09 15:16:44.455883: This epoch took 273.969583 s
+ 
+2026-11-09 15:16:44.456193: 
+epoch:  559 
+2026-11-09 15:20:57.951006: train loss : -0.8553 
+2026-11-09 15:21:06.132983: validation loss: -0.5125 
+2026-11-09 15:21:06.134201: Average global foreground Dice: [np.float32(0.9285), np.float32(0.5253), np.float32(0.5808), np.float32(0.5767), np.float32(0.4492), np.float32(0.6422), np.float32(0.548), np.float32(0.4907), np.float32(0.3842), np.float32(0.4218), np.float32(0.4813), np.float32(0.542), np.float32(0.5895), np.float32(0.5518), np.float32(0.0), np.float32(0.0), np.float32(0.4274), np.float32(0.3453), np.float32(0.4804), np.float32(0.4381), np.float32(0.4997), np.float32(0.436), np.float32(0.7062), np.float32(0.6924), np.float32(0.2878), np.float32(0.2894), np.float32(0.1226), np.float32(0.0613), np.float32(0.3524), np.float32(0.6422), np.float32(0.0), np.float32(0.0), np.float32(0.4279), np.float32(0.4858), np.float32(0.6217), np.float32(0.6608)] 
+2026-11-09 15:21:06.134507: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:21:06.472983: lr: 0.000478 
+2026-11-09 15:21:06.473368: This epoch took 262.016962 s
+ 
+2026-11-09 15:21:06.473575: 
+epoch:  560 
+2026-11-09 15:25:09.146114: train loss : -0.8494 
+2026-11-09 15:25:17.322309: validation loss: -0.6083 
+2026-11-09 15:25:17.323340: Average global foreground Dice: [np.float32(0.9239), np.float32(0.6272), np.float32(0.7542), np.float32(0.761), np.float32(0.6714), np.float32(0.8124), np.float32(0.7183), np.float32(0.5114), np.float32(0.1551), np.float32(0.5076), np.float32(0.5971), np.float32(0.642), np.float32(0.6252), np.float32(0.5367), np.float32(0.0106), np.float32(0.0), np.float32(0.7307), np.float32(0.5747), np.float32(0.7325), np.float32(0.6188), np.float32(0.5788), np.float32(0.6313), np.float32(0.8198), np.float32(0.8205), np.float32(0.4817), np.float32(0.4531), np.float32(0.2428), np.float32(0.3992), np.float32(0.4164), np.float32(0.6993), np.float32(0.0), np.float32(0.0), np.float32(0.6033), np.float32(0.6219), np.float32(0.7996), np.float32(0.8242)] 
+2026-11-09 15:25:17.323581: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:25:17.683178: lr: 0.000477 
+2026-11-09 15:25:17.683543: This epoch took 251.209890 s
+ 
+2026-11-09 15:25:17.683652: 
+epoch:  561 
+2026-11-09 15:29:33.483458: train loss : -0.8567 
+2026-11-09 15:29:41.693482: validation loss: -0.5953 
+2026-11-09 15:29:41.694461: Average global foreground Dice: [np.float32(0.9199), np.float32(0.5996), np.float32(0.7551), np.float32(0.7583), np.float32(0.6606), np.float32(0.8014), np.float32(0.7064), np.float32(0.4577), np.float32(0.2335), np.float32(0.5111), np.float32(0.5943), np.float32(0.6437), np.float32(0.614), np.float32(0.4917), np.float32(0.0649), np.float32(0.0), np.float32(0.7175), np.float32(0.5514), np.float32(0.7213), np.float32(0.6117), np.float32(0.5936), np.float32(0.6219), np.float32(0.8023), np.float32(0.7963), np.float32(0.5089), np.float32(0.4349), np.float32(0.3355), np.float32(0.2462), np.float32(0.4267), np.float32(0.6826), np.float32(0.0), np.float32(0.0), np.float32(0.5276), np.float32(0.6406), np.float32(0.7899), np.float32(0.81)] 
+2026-11-09 15:29:41.694778: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:29:42.039494: lr: 0.000476 
+2026-11-09 15:29:42.040038: This epoch took 264.356280 s
+ 
+2026-11-09 15:29:42.040226: 
+epoch:  562 
+2026-11-09 15:34:21.359163: train loss : -0.8433 
+2026-11-09 15:34:29.630752: validation loss: -0.5838 
+2026-11-09 15:34:29.631676: Average global foreground Dice: [np.float32(0.9189), np.float32(0.5754), np.float32(0.7463), np.float32(0.7395), np.float32(0.6351), np.float32(0.7913), np.float32(0.6978), np.float32(0.5424), np.float32(0.4369), np.float32(0.4133), np.float32(0.6102), np.float32(0.6528), np.float32(0.5766), np.float32(0.4785), np.float32(0.0), np.float32(0.0), np.float32(0.7129), np.float32(0.5695), np.float32(0.7121), np.float32(0.6185), np.float32(0.5809), np.float32(0.6505), np.float32(0.815), np.float32(0.8171), np.float32(0.5192), np.float32(0.4098), np.float32(0.2175), np.float32(0.0761), np.float32(0.4824), np.float32(0.7109), np.float32(0.0), np.float32(0.0), np.float32(0.5998), np.float32(0.6439), np.float32(0.7731), np.float32(0.8023)] 
+2026-11-09 15:34:29.631997: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:34:29.970658: lr: 0.000475 
+2026-11-09 15:34:29.971075: This epoch took 287.930774 s
+ 
+2026-11-09 15:34:29.971280: 
+epoch:  563 
+2026-11-09 15:39:08.459998: train loss : -0.8506 
+2026-11-09 15:39:16.667919: validation loss: -0.5958 
+2026-11-09 15:39:16.669156: Average global foreground Dice: [np.float32(0.9233), np.float32(0.6144), np.float32(0.7566), np.float32(0.7693), np.float32(0.6932), np.float32(0.8135), np.float32(0.7281), np.float32(0.4588), np.float32(0.4132), np.float32(0.3804), np.float32(0.5957), np.float32(0.6329), np.float32(0.6234), np.float32(0.4848), np.float32(0.0), np.float32(0.0), np.float32(0.7348), np.float32(0.5792), np.float32(0.7346), np.float32(0.6382), np.float32(0.5705), np.float32(0.6712), np.float32(0.8152), np.float32(0.8256), np.float32(0.5259), np.float32(0.4654), np.float32(0.2758), np.float32(0.0127), np.float32(0.4415), np.float32(0.672), np.float32(0.0), np.float32(0.0), np.float32(0.6111), np.float32(0.678), np.float32(0.8079), np.float32(0.8288)] 
+2026-11-09 15:39:16.669601: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:39:17.017316: lr: 0.000474 
+2026-11-09 15:39:17.017692: This epoch took 287.046342 s
+ 
+2026-11-09 15:39:17.017990: 
+epoch:  564 
+2026-11-09 15:43:16.266083: train loss : -0.8589 
+2026-11-09 15:43:24.471446: validation loss: -0.6034 
+2026-11-09 15:43:24.472472: Average global foreground Dice: [np.float32(0.9238), np.float32(0.6209), np.float32(0.7569), np.float32(0.7608), np.float32(0.674), np.float32(0.8067), np.float32(0.7132), np.float32(0.635), np.float32(0.3695), np.float32(0.4556), np.float32(0.594), np.float32(0.6543), np.float32(0.622), np.float32(0.4952), np.float32(0.0007), np.float32(0.0), np.float32(0.735), np.float32(0.5775), np.float32(0.7175), np.float32(0.6418), np.float32(0.5575), np.float32(0.6124), np.float32(0.8094), np.float32(0.8053), np.float32(0.5028), np.float32(0.4711), np.float32(0.354), np.float32(0.43), np.float32(0.3921), np.float32(0.6888), np.float32(0.0), np.float32(0.0), np.float32(0.6059), np.float32(0.6785), np.float32(0.7961), np.float32(0.8167)] 
+2026-11-09 15:43:24.472756: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:43:24.812783: lr: 0.000473 
+2026-11-09 15:43:24.813077: This epoch took 247.794852 s
+ 
+2026-11-09 15:43:24.813321: 
+epoch:  565 
+2026-11-09 15:47:38.853898: train loss : -0.8558 
+2026-11-09 15:47:47.020555: validation loss: -0.6120 
+2026-11-09 15:47:47.021518: Average global foreground Dice: [np.float32(0.9246), np.float32(0.6121), np.float32(0.749), np.float32(0.7616), np.float32(0.6611), np.float32(0.7975), np.float32(0.694), np.float32(0.4336), np.float32(0.4143), np.float32(0.4366), np.float32(0.6067), np.float32(0.652), np.float32(0.6387), np.float32(0.5223), np.float32(0.0), np.float32(0.0), np.float32(0.7213), np.float32(0.5549), np.float32(0.7203), np.float32(0.6351), np.float32(0.5842), np.float32(0.6218), np.float32(0.8253), np.float32(0.831), np.float32(0.4958), np.float32(0.4575), np.float32(0.3851), np.float32(0.3758), np.float32(0.4898), np.float32(0.7015), np.float32(0.0), np.float32(0.0), np.float32(0.5856), np.float32(0.6879), np.float32(0.7972), np.float32(0.8111)] 
+2026-11-09 15:47:47.021691: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:47:47.360839: lr: 0.000472 
+2026-11-09 15:47:47.361280: This epoch took 262.547705 s
+ 
+2026-11-09 15:47:47.361612: 
+epoch:  566 
+2026-11-09 15:52:25.099957: train loss : -0.8533 
+2026-11-09 15:52:33.268472: validation loss: -0.6150 
+2026-11-09 15:52:33.269482: Average global foreground Dice: [np.float32(0.9232), np.float32(0.6158), np.float32(0.7526), np.float32(0.7589), np.float32(0.6607), np.float32(0.8055), np.float32(0.7068), np.float32(0.5391), np.float32(0.4025), np.float32(0.4735), np.float32(0.6053), np.float32(0.6693), np.float32(0.6449), np.float32(0.5519), np.float32(0.0), np.float32(0.0), np.float32(0.7217), np.float32(0.5743), np.float32(0.7213), np.float32(0.6168), np.float32(0.5516), np.float32(0.6202), np.float32(0.8102), np.float32(0.8069), np.float32(0.4956), np.float32(0.4698), np.float32(0.2838), np.float32(0.6394), np.float32(0.3972), np.float32(0.7172), np.float32(0.0), np.float32(0.0), np.float32(0.6239), np.float32(0.6725), np.float32(0.7935), np.float32(0.8197)] 
+2026-11-09 15:52:33.269738: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:52:33.611759: lr: 0.000471 
+2026-11-09 15:52:33.612024: This epoch took 286.250209 s
+ 
+2026-11-09 15:52:33.612120: 
+epoch:  567 
+2026-11-09 15:56:56.093220: train loss : -0.8541 
+2026-11-09 15:57:04.280598: validation loss: -0.6508 
+2026-11-09 15:57:04.281566: Average global foreground Dice: [np.float32(0.926), np.float32(0.6537), np.float32(0.8017), np.float32(0.801), np.float32(0.7279), np.float32(0.853), np.float32(0.7658), np.float32(0.5399), np.float32(0.2553), np.float32(0.4752), np.float32(0.6595), np.float32(0.6976), np.float32(0.6646), np.float32(0.6255), np.float32(0.0), np.float32(0.0), np.float32(0.7583), np.float32(0.616), np.float32(0.752), np.float32(0.6371), np.float32(0.5685), np.float32(0.6312), np.float32(0.8599), np.float32(0.857), np.float32(0.4821), np.float32(0.4573), np.float32(0.2917), np.float32(0.5104), np.float32(0.3916), np.float32(0.7364), np.float32(0.0), np.float32(0.0), np.float32(0.6576), np.float32(0.6744), np.float32(0.8395), np.float32(0.8628)] 
+2026-11-09 15:57:04.281828: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 15:57:04.623063: lr: 0.00047 
+2026-11-09 15:57:04.623553: This epoch took 271.011368 s
+ 
+2026-11-09 15:57:04.623889: 
+epoch:  568 
+2026-11-09 16:01:08.835588: train loss : -0.8612 
+2026-11-09 16:01:16.981938: validation loss: -0.6422 
+2026-11-09 16:01:16.982938: Average global foreground Dice: [np.float32(0.9253), np.float32(0.6306), np.float32(0.7917), np.float32(0.802), np.float32(0.7221), np.float32(0.8412), np.float32(0.7718), np.float32(0.5481), np.float32(0.3604), np.float32(0.3106), np.float32(0.6447), np.float32(0.714), np.float32(0.6517), np.float32(0.5452), np.float32(0.0), np.float32(0.0), np.float32(0.7662), np.float32(0.621), np.float32(0.7651), np.float32(0.6413), np.float32(0.5746), np.float32(0.6798), np.float32(0.8467), np.float32(0.8515), np.float32(0.5198), np.float32(0.4112), np.float32(0.3252), np.float32(0.6011), np.float32(0.4266), np.float32(0.7312), np.float32(0.0), np.float32(0.0), np.float32(0.6711), np.float32(0.6908), np.float32(0.839), np.float32(0.8579)] 
+2026-11-09 16:01:16.983199: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:01:17.322409: lr: 0.000469 
+2026-11-09 16:01:17.322688: This epoch took 252.698582 s
+ 
+2026-11-09 16:01:17.322912: 
+epoch:  569 
+2026-11-09 16:05:37.011396: train loss : -0.8511 
+2026-11-09 16:05:45.160732: validation loss: -0.5993 
+2026-11-09 16:05:45.161748: Average global foreground Dice: [np.float32(0.9163), np.float32(0.5908), np.float32(0.7703), np.float32(0.7797), np.float32(0.6764), np.float32(0.8243), np.float32(0.7499), np.float32(0.5056), np.float32(0.373), np.float32(0.3707), np.float32(0.6235), np.float32(0.647), np.float32(0.6111), np.float32(0.4583), np.float32(0.0), np.float32(0.0), np.float32(0.7415), np.float32(0.5801), np.float32(0.7537), np.float32(0.6528), np.float32(0.5649), np.float32(0.6969), np.float32(0.8009), np.float32(0.8169), np.float32(0.5157), np.float32(0.4117), np.float32(0.3421), np.float32(0.5863), np.float32(0.4042), np.float32(0.6988), np.float32(0.0), np.float32(0.0), np.float32(0.6204), np.float32(0.6785), np.float32(0.8107), np.float32(0.8283)] 
+2026-11-09 16:05:45.161972: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:05:45.516312: lr: 0.000468 
+2026-11-09 16:05:45.516546: This epoch took 268.193522 s
+ 
+2026-11-09 16:05:45.516625: 
+epoch:  570 
+2026-11-09 16:10:05.657326: train loss : -0.8582 
+2026-11-09 16:10:13.811361: validation loss: -0.6147 
+2026-11-09 16:10:13.812462: Average global foreground Dice: [np.float32(0.9238), np.float32(0.6229), np.float32(0.7587), np.float32(0.7731), np.float32(0.6802), np.float32(0.8092), np.float32(0.7255), np.float32(0.5715), np.float32(0.3871), np.float32(0.4343), np.float32(0.6222), np.float32(0.6619), np.float32(0.6407), np.float32(0.5207), np.float32(0.0), np.float32(0.0), np.float32(0.7422), np.float32(0.5916), np.float32(0.7337), np.float32(0.6333), np.float32(0.5693), np.float32(0.6465), np.float32(0.8017), np.float32(0.8123), np.float32(0.506), np.float32(0.4693), np.float32(0.319), np.float32(0.6527), np.float32(0.4042), np.float32(0.7156), np.float32(0.0), np.float32(0.0), np.float32(0.6149), np.float32(0.6761), np.float32(0.8019), np.float32(0.8241)] 
+2026-11-09 16:10:13.812635: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:10:14.158178: lr: 0.000467 
+2026-11-09 16:10:14.158541: This epoch took 268.641860 s
+ 
+2026-11-09 16:10:14.158751: 
+epoch:  571 
+2026-11-09 16:14:05.058770: train loss : -0.8656 
+2026-11-09 16:14:13.211730: validation loss: -0.5949 
+2026-11-09 16:14:13.212592: Average global foreground Dice: [np.float32(0.9203), np.float32(0.5932), np.float32(0.7738), np.float32(0.773), np.float32(0.6939), np.float32(0.8168), np.float32(0.7419), np.float32(0.5818), np.float32(0.394), np.float32(0.3842), np.float32(0.5961), np.float32(0.6343), np.float32(0.5892), np.float32(0.4801), np.float32(0.0), np.float32(0.0), np.float32(0.7373), np.float32(0.585), np.float32(0.7401), np.float32(0.6199), np.float32(0.5614), np.float32(0.6843), np.float32(0.8174), np.float32(0.8226), np.float32(0.517), np.float32(0.4067), np.float32(0.3516), np.float32(0.6915), np.float32(0.4319), np.float32(0.6674), np.float32(0.0), np.float32(0.0), np.float32(0.6417), np.float32(0.6555), np.float32(0.8084), np.float32(0.8376)] 
+2026-11-09 16:14:13.212735: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:14:13.564348: lr: 0.000466 
+2026-11-09 16:14:13.564724: This epoch took 239.405881 s
+ 
+2026-11-09 16:14:13.564831: 
+epoch:  572 
+2026-11-09 16:18:34.548213: train loss : -0.8518 
+2026-11-09 16:18:42.704134: validation loss: -0.6038 
+2026-11-09 16:18:42.705319: Average global foreground Dice: [np.float32(0.9239), np.float32(0.6209), np.float32(0.7559), np.float32(0.7502), np.float32(0.6784), np.float32(0.8026), np.float32(0.7117), np.float32(0.6597), np.float32(0.4156), np.float32(0.4382), np.float32(0.6282), np.float32(0.6765), np.float32(0.6208), np.float32(0.5403), np.float32(0.0), np.float32(0.0), np.float32(0.7247), np.float32(0.579), np.float32(0.7305), np.float32(0.6297), np.float32(0.5845), np.float32(0.6502), np.float32(0.8351), np.float32(0.8346), np.float32(0.47), np.float32(0.4571), np.float32(0.2799), np.float32(0.6246), np.float32(0.3963), np.float32(0.7064), np.float32(0.0), np.float32(0.0), np.float32(0.5947), np.float32(0.6761), np.float32(0.7863), np.float32(0.8158)] 
+2026-11-09 16:18:42.705645: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:18:43.078362: lr: 0.000465 
+2026-11-09 16:18:43.078615: This epoch took 269.513724 s
+ 
+2026-11-09 16:18:43.078697: 
+epoch:  573 
+2026-11-09 16:23:07.183947: train loss : -0.8552 
+2026-11-09 16:23:15.370370: validation loss: -0.6522 
+2026-11-09 16:23:15.371348: Average global foreground Dice: [np.float32(0.9247), np.float32(0.6389), np.float32(0.791), np.float32(0.81), np.float32(0.7447), np.float32(0.8542), np.float32(0.7772), np.float32(0.689), np.float32(0.4481), np.float32(0.4172), np.float32(0.6458), np.float32(0.684), np.float32(0.6499), np.float32(0.5905), np.float32(0.0006), np.float32(0.0), np.float32(0.7663), np.float32(0.6138), np.float32(0.7595), np.float32(0.6129), np.float32(0.6114), np.float32(0.6948), np.float32(0.9033), np.float32(0.9061), np.float32(0.4878), np.float32(0.4646), np.float32(0.3012), np.float32(0.5248), np.float32(0.4433), np.float32(0.7041), np.float32(0.0), np.float32(0.0), np.float32(0.6619), np.float32(0.7074), np.float32(0.8542), np.float32(0.8729)] 
+2026-11-09 16:23:15.371539: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:23:15.708941: lr: 0.000464 
+2026-11-09 16:23:15.709373: This epoch took 272.630618 s
+ 
+2026-11-09 16:23:15.709543: 
+epoch:  574 
+2026-11-09 16:27:45.137722: train loss : -0.8560 
+2026-11-09 16:27:53.309969: validation loss: -0.6452 
+2026-11-09 16:27:53.311015: Average global foreground Dice: [np.float32(0.9209), np.float32(0.6452), np.float32(0.7881), np.float32(0.8043), np.float32(0.7446), np.float32(0.8401), np.float32(0.7686), np.float32(0.5982), np.float32(0.2944), np.float32(0.4186), np.float32(0.6414), np.float32(0.6763), np.float32(0.6412), np.float32(0.5168), np.float32(0.0), np.float32(0.0), np.float32(0.7617), np.float32(0.5847), np.float32(0.7548), np.float32(0.6136), np.float32(0.5653), np.float32(0.6775), np.float32(0.8941), np.float32(0.8906), np.float32(0.5061), np.float32(0.4427), np.float32(0.3769), np.float32(0.5487), np.float32(0.3421), np.float32(0.6561), np.float32(0.0), np.float32(0.0), np.float32(0.619), np.float32(0.6984), np.float32(0.8468), np.float32(0.8604)] 
+2026-11-09 16:27:53.311171: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:27:53.665219: lr: 0.000463 
+2026-11-09 16:27:53.665558: This epoch took 277.955932 s
+ 
+2026-11-09 16:27:53.665771: 
+epoch:  575 
+2026-11-09 16:32:30.097466: train loss : -0.8543 
+2026-11-09 16:32:38.273079: validation loss: -0.6325 
+2026-11-09 16:32:38.274559: Average global foreground Dice: [np.float32(0.9262), np.float32(0.6549), np.float32(0.7808), np.float32(0.7841), np.float32(0.7222), np.float32(0.8232), np.float32(0.7387), np.float32(0.6163), np.float32(0.3556), np.float32(0.4509), np.float32(0.639), np.float32(0.6731), np.float32(0.639), np.float32(0.5339), np.float32(0.0035), np.float32(0.0), np.float32(0.7521), np.float32(0.5922), np.float32(0.7382), np.float32(0.6558), np.float32(0.563), np.float32(0.6796), np.float32(0.8896), np.float32(0.8789), np.float32(0.4702), np.float32(0.4469), np.float32(0.3454), np.float32(0.3433), np.float32(0.3778), np.float32(0.6649), np.float32(0.0), np.float32(0.0), np.float32(0.5928), np.float32(0.6714), np.float32(0.8348), np.float32(0.8475)] 
+2026-11-09 16:32:38.275061: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:32:38.613343: lr: 0.000462 
+2026-11-09 16:32:38.613584: This epoch took 284.947449 s
+ 
+2026-11-09 16:32:38.613838: 
+epoch:  576 
+2026-11-09 16:36:58.745432: train loss : -0.8519 
+2026-11-09 16:37:06.909065: validation loss: -0.6317 
+2026-11-09 16:37:06.910058: Average global foreground Dice: [np.float32(0.9243), np.float32(0.6242), np.float32(0.7776), np.float32(0.7756), np.float32(0.7062), np.float32(0.829), np.float32(0.7459), np.float32(0.6172), np.float32(0.1645), np.float32(0.4081), np.float32(0.6342), np.float32(0.6816), np.float32(0.5993), np.float32(0.5253), np.float32(0.0), np.float32(0.0), np.float32(0.7384), np.float32(0.6038), np.float32(0.7425), np.float32(0.6331), np.float32(0.6086), np.float32(0.6807), np.float32(0.8659), np.float32(0.8711), np.float32(0.4902), np.float32(0.4509), np.float32(0.3719), np.float32(0.3994), np.float32(0.3873), np.float32(0.7024), np.float32(0.0), np.float32(0.0), np.float32(0.6512), np.float32(0.6875), np.float32(0.8234), np.float32(0.8473)] 
+2026-11-09 16:37:06.910462: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:37:07.263825: lr: 0.000461 
+2026-11-09 16:37:07.264089: This epoch took 268.650145 s
+ 
+2026-11-09 16:37:07.264182: 
+epoch:  577 
+2026-11-09 16:41:34.899205: train loss : -0.8540 
+2026-11-09 16:41:43.083543: validation loss: -0.6710 
+2026-11-09 16:41:43.084491: Average global foreground Dice: [np.float32(0.9296), np.float32(0.7022), np.float32(0.826), np.float32(0.8524), np.float32(0.7942), np.float32(0.8871), np.float32(0.796), np.float32(0.634), np.float32(0.1932), np.float32(0.4425), np.float32(0.7028), np.float32(0.7383), np.float32(0.6976), np.float32(0.6541), np.float32(0.0), np.float32(0.0), np.float32(0.7843), np.float32(0.6264), np.float32(0.764), np.float32(0.6208), np.float32(0.6264), np.float32(0.6678), np.float32(0.9254), np.float32(0.9177), np.float32(0.4953), np.float32(0.4782), np.float32(0.3062), np.float32(0.523), np.float32(0.3576), np.float32(0.6917), np.float32(0.0), np.float32(0.0), np.float32(0.6366), np.float32(0.7065), np.float32(0.8878), np.float32(0.8933)] 
+2026-11-09 16:41:43.084653: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:41:43.422906: lr: 0.00046 
+2026-11-09 16:41:43.423218: This epoch took 276.158973 s
+ 
+2026-11-09 16:41:43.423345: 
+epoch:  578 
+2026-11-09 16:46:03.659611: train loss : -0.8517 
+2026-11-09 16:46:11.805470: validation loss: -0.6795 
+2026-11-09 16:46:11.806519: Average global foreground Dice: [np.float32(0.9233), np.float32(0.6488), np.float32(0.81), np.float32(0.8292), np.float32(0.7747), np.float32(0.8766), np.float32(0.8026), np.float32(0.6106), np.float32(0.3458), np.float32(0.4576), np.float32(0.6546), np.float32(0.718), np.float32(0.6846), np.float32(0.6285), np.float32(0.0115), np.float32(0.0), np.float32(0.7682), np.float32(0.5932), np.float32(0.7658), np.float32(0.6281), np.float32(0.6189), np.float32(0.6257), np.float32(0.9075), np.float32(0.8975), np.float32(0.4882), np.float32(0.4412), np.float32(0.4317), np.float32(0.5253), np.float32(0.3733), np.float32(0.6851), np.float32(0.0), np.float32(0.0), np.float32(0.6406), np.float32(0.6999), np.float32(0.866), np.float32(0.8798)] 
+2026-11-09 16:46:11.806772: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:46:12.160418: lr: 0.000459 
+2026-11-09 16:46:12.160700: This epoch took 268.737281 s
+ 
+2026-11-09 16:46:12.160792: 
+epoch:  579 
+2026-11-09 16:51:01.056585: train loss : -0.8517 
+2026-11-09 16:51:09.203234: validation loss: -0.6743 
+2026-11-09 16:51:09.204289: Average global foreground Dice: [np.float32(0.9263), np.float32(0.6662), np.float32(0.8206), np.float32(0.8366), np.float32(0.7956), np.float32(0.881), np.float32(0.8089), np.float32(0.6317), np.float32(0.3663), np.float32(0.464), np.float32(0.7008), np.float32(0.7409), np.float32(0.6906), np.float32(0.5952), np.float32(0.0344), np.float32(0.0), np.float32(0.7822), np.float32(0.6195), np.float32(0.7707), np.float32(0.6543), np.float32(0.582), np.float32(0.6944), np.float32(0.9213), np.float32(0.9193), np.float32(0.5), np.float32(0.4667), np.float32(0.3751), np.float32(0.4961), np.float32(0.4192), np.float32(0.6967), np.float32(0.0), np.float32(0.0), np.float32(0.6299), np.float32(0.6795), np.float32(0.8847), np.float32(0.8916)] 
+2026-11-09 16:51:09.204962: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:51:09.549057: lr: 0.000458 
+2026-11-09 16:51:09.933280: saving checkpoint... 
+2026-11-09 16:51:10.483017: done, saving took 0.93 seconds 
+2026-11-09 16:51:10.492126: This epoch took 298.331264 s
+ 
+2026-11-09 16:51:10.492360: 
+epoch:  580 
+2026-11-09 16:55:16.873964: train loss : -0.8528 
+2026-11-09 16:55:25.037213: validation loss: -0.6553 
+2026-11-09 16:55:25.038235: Average global foreground Dice: [np.float32(0.9288), np.float32(0.68), np.float32(0.8096), np.float32(0.8318), np.float32(0.7606), np.float32(0.8717), np.float32(0.788), np.float32(0.5374), np.float32(0.395), np.float32(0.4068), np.float32(0.6772), np.float32(0.72), np.float32(0.6753), np.float32(0.6267), np.float32(0.0), np.float32(0.0), np.float32(0.7621), np.float32(0.6275), np.float32(0.7558), np.float32(0.6658), np.float32(0.5663), np.float32(0.6606), np.float32(0.8943), np.float32(0.8877), np.float32(0.4939), np.float32(0.4945), np.float32(0.3633), np.float32(0.3645), np.float32(0.363), np.float32(0.7086), np.float32(0.0), np.float32(0.0), np.float32(0.6178), np.float32(0.6854), np.float32(0.8633), np.float32(0.8754)] 
+2026-11-09 16:55:25.038472: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 16:55:25.389133: lr: 0.000457 
+2026-11-09 16:55:25.730125: saving checkpoint... 
+2026-11-09 16:55:28.517241: done, saving took 3.13 seconds 
+2026-11-09 16:55:28.520957: This epoch took 258.028520 s
+ 
+2026-11-09 16:55:28.521276: 
+epoch:  581 
+2026-11-09 16:59:53.880360: train loss : -0.8542 
+2026-11-09 17:00:02.039030: validation loss: -0.6504 
+2026-11-09 17:00:02.040098: Average global foreground Dice: [np.float32(0.9246), np.float32(0.6302), np.float32(0.7959), np.float32(0.8154), np.float32(0.7515), np.float32(0.8569), np.float32(0.7886), np.float32(0.5808), np.float32(0.3734), np.float32(0.4343), np.float32(0.6336), np.float32(0.6921), np.float32(0.6617), np.float32(0.5299), np.float32(0.0), np.float32(0.0), np.float32(0.7563), np.float32(0.5857), np.float32(0.7638), np.float32(0.6624), np.float32(0.5902), np.float32(0.7168), np.float32(0.8925), np.float32(0.8833), np.float32(0.5125), np.float32(0.4917), np.float32(0.3067), np.float32(0.2312), np.float32(0.4117), np.float32(0.6173), np.float32(0.0), np.float32(0.0), np.float32(0.6319), np.float32(0.7004), np.float32(0.8542), np.float32(0.8656)] 
+2026-11-09 17:00:02.040271: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:00:02.389834: lr: 0.000456 
+2026-11-09 17:00:02.731314: saving checkpoint... 
+2026-11-09 17:00:03.321818: done, saving took 0.93 seconds 
+2026-11-09 17:00:03.330838: This epoch took 274.809231 s
+ 
+2026-11-09 17:00:03.331010: 
+epoch:  582 
+2026-11-09 17:04:15.989200: train loss : -0.8557 
+2026-11-09 17:04:24.163070: validation loss: -0.6464 
+2026-11-09 17:04:24.164093: Average global foreground Dice: [np.float32(0.9258), np.float32(0.6457), np.float32(0.787), np.float32(0.7979), np.float32(0.7234), np.float32(0.8476), np.float32(0.759), np.float32(0.5329), np.float32(0.3306), np.float32(0.3559), np.float32(0.6614), np.float32(0.7053), np.float32(0.6518), np.float32(0.5726), np.float32(0.0), np.float32(0.0), np.float32(0.7526), np.float32(0.6025), np.float32(0.746), np.float32(0.646), np.float32(0.5716), np.float32(0.6812), np.float32(0.8738), np.float32(0.878), np.float32(0.4524), np.float32(0.4807), np.float32(0.3142), np.float32(0.0054), np.float32(0.4412), np.float32(0.6936), np.float32(0.0), np.float32(0.0), np.float32(0.6291), np.float32(0.6892), np.float32(0.8359), np.float32(0.8556)] 
+2026-11-09 17:04:24.164337: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:04:24.499277: lr: 0.000455 
+2026-11-09 17:04:24.499736: This epoch took 261.168646 s
+ 
+2026-11-09 17:04:24.500048: 
+epoch:  583 
+2026-11-09 17:08:49.730597: train loss : -0.8565 
+2026-11-09 17:08:57.866123: validation loss: -0.6232 
+2026-11-09 17:08:57.867116: Average global foreground Dice: [np.float32(0.9233), np.float32(0.6034), np.float32(0.7652), np.float32(0.7873), np.float32(0.7075), np.float32(0.8265), np.float32(0.7466), np.float32(0.5826), np.float32(0.3851), np.float32(0.5016), np.float32(0.6151), np.float32(0.6816), np.float32(0.6022), np.float32(0.509), np.float32(0.0), np.float32(0.0), np.float32(0.7434), np.float32(0.5745), np.float32(0.7469), np.float32(0.6577), np.float32(0.5275), np.float32(0.6565), np.float32(0.884), np.float32(0.8817), np.float32(0.4934), np.float32(0.4491), np.float32(0.3643), np.float32(0.2032), np.float32(0.3992), np.float32(0.6239), np.float32(0.0), np.float32(0.0), np.float32(0.652), np.float32(0.6812), np.float32(0.8276), np.float32(0.8498)] 
+2026-11-09 17:08:57.867554: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:08:58.204059: lr: 0.000454 
+2026-11-09 17:08:58.204395: This epoch took 273.704072 s
+ 
+2026-11-09 17:08:58.204628: 
+epoch:  584 
+2026-11-09 17:13:10.385386: train loss : -0.8574 
+2026-11-09 17:13:18.515175: validation loss: -0.6466 
+2026-11-09 17:13:18.516043: Average global foreground Dice: [np.float32(0.9269), np.float32(0.6391), np.float32(0.7927), np.float32(0.8033), np.float32(0.7253), np.float32(0.8504), np.float32(0.7639), np.float32(0.6228), np.float32(0.3619), np.float32(0.486), np.float32(0.637), np.float32(0.6882), np.float32(0.6486), np.float32(0.5716), np.float32(0.0086), np.float32(0.0), np.float32(0.7472), np.float32(0.6014), np.float32(0.7541), np.float32(0.6389), np.float32(0.5681), np.float32(0.6545), np.float32(0.9087), np.float32(0.9046), np.float32(0.4792), np.float32(0.5059), np.float32(0.3694), np.float32(0.2607), np.float32(0.4109), np.float32(0.6957), np.float32(0.0), np.float32(0.0), np.float32(0.6243), np.float32(0.6935), np.float32(0.8491), np.float32(0.8658)] 
+2026-11-09 17:13:18.516335: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:13:18.852155: lr: 0.000453 
+2026-11-09 17:13:19.179640: saving checkpoint... 
+2026-11-09 17:13:19.741534: done, saving took 0.89 seconds 
+2026-11-09 17:13:19.752269: This epoch took 261.547557 s
+ 
+2026-11-09 17:13:19.752537: 
+epoch:  585 
+2026-11-09 17:17:26.105381: train loss : -0.8639 
+2026-11-09 17:17:34.275992: validation loss: -0.6475 
+2026-11-09 17:17:34.277200: Average global foreground Dice: [np.float32(0.9255), np.float32(0.6535), np.float32(0.8137), np.float32(0.8267), np.float32(0.7702), np.float32(0.8663), np.float32(0.7945), np.float32(0.5188), np.float32(0.3691), np.float32(0.3777), np.float32(0.6384), np.float32(0.6851), np.float32(0.6633), np.float32(0.5269), np.float32(0.0), np.float32(0.0), np.float32(0.7743), np.float32(0.6172), np.float32(0.7618), np.float32(0.6529), np.float32(0.5827), np.float32(0.704), np.float32(0.8945), np.float32(0.8899), np.float32(0.5132), np.float32(0.4572), np.float32(0.357), np.float32(0.3804), np.float32(0.4166), np.float32(0.6723), np.float32(0.0), np.float32(0.0), np.float32(0.6009), np.float32(0.6956), np.float32(0.8621), np.float32(0.8732)] 
+2026-11-09 17:17:34.277424: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:17:34.614289: lr: 0.000452 
+2026-11-09 17:17:34.891411: saving checkpoint... 
+2026-11-09 17:17:36.486413: done, saving took 1.87 seconds 
+2026-11-09 17:17:36.492791: This epoch took 256.740075 s
+ 
+2026-11-09 17:17:36.492898: 
+epoch:  586 
+2026-11-09 17:21:49.331964: train loss : -0.8580 
+2026-11-09 17:21:57.496885: validation loss: -0.5904 
+2026-11-09 17:21:57.498228: Average global foreground Dice: [np.float32(0.9223), np.float32(0.6012), np.float32(0.741), np.float32(0.7511), np.float32(0.6506), np.float32(0.7967), np.float32(0.6874), np.float32(0.5994), np.float32(0.3189), np.float32(0.4404), np.float32(0.5599), np.float32(0.6225), np.float32(0.5854), np.float32(0.5043), np.float32(0.0), np.float32(0.0), np.float32(0.688), np.float32(0.5373), np.float32(0.6807), np.float32(0.5464), np.float32(0.5389), np.float32(0.629), np.float32(0.7934), np.float32(0.7889), np.float32(0.4873), np.float32(0.4403), np.float32(0.3828), np.float32(0.0678), np.float32(0.3872), np.float32(0.7092), np.float32(0.0), np.float32(0.0), np.float32(0.5446), np.float32(0.6174), np.float32(0.7791), np.float32(0.8007)] 
+2026-11-09 17:21:57.498949: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:21:57.832788: lr: 0.000451 
+2026-11-09 17:21:57.833052: This epoch took 261.340082 s
+ 
+2026-11-09 17:21:57.833150: 
+epoch:  587 
+2026-11-09 17:26:17.794545: train loss : -0.8569 
+2026-11-09 17:26:25.984878: validation loss: -0.6163 
+2026-11-09 17:26:25.987114: Average global foreground Dice: [np.float32(0.9256), np.float32(0.611), np.float32(0.7644), np.float32(0.7739), np.float32(0.6966), np.float32(0.8279), np.float32(0.7276), np.float32(0.6311), np.float32(0.132), np.float32(0.4658), np.float32(0.603), np.float32(0.6677), np.float32(0.6122), np.float32(0.5501), np.float32(0.0), np.float32(0.0), np.float32(0.7305), np.float32(0.5916), np.float32(0.7445), np.float32(0.6227), np.float32(0.4997), np.float32(0.5844), np.float32(0.8519), np.float32(0.8473), np.float32(0.4667), np.float32(0.53), np.float32(0.3928), np.float32(0.2985), np.float32(0.3422), np.float32(0.7294), np.float32(0.0), np.float32(0.0), np.float32(0.6122), np.float32(0.6492), np.float32(0.8091), np.float32(0.8392)] 
+2026-11-09 17:26:25.987576: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:26:26.325536: lr: 0.00045 
+2026-11-09 17:26:26.325923: This epoch took 268.492709 s
+ 
+2026-11-09 17:26:26.326292: 
+epoch:  588 
+2026-11-09 17:30:25.211055: train loss : -0.8560 
+2026-11-09 17:30:33.349522: validation loss: -0.6302 
+2026-11-09 17:30:33.350715: Average global foreground Dice: [np.float32(0.9261), np.float32(0.6635), np.float32(0.7866), np.float32(0.7933), np.float32(0.7258), np.float32(0.8409), np.float32(0.7477), np.float32(0.6764), np.float32(0.2828), np.float32(0.4201), np.float32(0.614), np.float32(0.668), np.float32(0.6359), np.float32(0.5807), np.float32(0.0), np.float32(0.0), np.float32(0.7429), np.float32(0.5916), np.float32(0.7366), np.float32(0.6001), np.float32(0.567), np.float32(0.6487), np.float32(0.8391), np.float32(0.8355), np.float32(0.4797), np.float32(0.5237), np.float32(0.3984), np.float32(0.0), np.float32(0.3719), np.float32(0.6947), np.float32(0.0), np.float32(0.0), np.float32(0.5873), np.float32(0.6454), np.float32(0.8333), np.float32(0.8517)] 
+2026-11-09 17:30:33.351029: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:30:33.699263: lr: 0.000449 
+2026-11-09 17:30:33.699632: This epoch took 247.372834 s
+ 
+2026-11-09 17:30:33.699914: 
+epoch:  589 
+2026-11-09 17:34:40.567806: train loss : -0.8533 
+2026-11-09 17:34:48.710384: validation loss: -0.6532 
+2026-11-09 17:34:48.711409: Average global foreground Dice: [np.float32(0.9301), np.float32(0.6736), np.float32(0.7886), np.float32(0.8002), np.float32(0.7308), np.float32(0.8509), np.float32(0.7567), np.float32(0.708), np.float32(0.3257), np.float32(0.4618), np.float32(0.6475), np.float32(0.6958), np.float32(0.6614), np.float32(0.5615), np.float32(0.0), np.float32(0.0), np.float32(0.7543), np.float32(0.6059), np.float32(0.741), np.float32(0.6252), np.float32(0.5935), np.float32(0.6821), np.float32(0.8658), np.float32(0.8621), np.float32(0.484), np.float32(0.443), np.float32(0.3918), np.float32(0.2939), np.float32(0.3853), np.float32(0.6986), np.float32(0.0), np.float32(0.0), np.float32(0.5848), np.float32(0.651), np.float32(0.8399), np.float32(0.8567)] 
+2026-11-09 17:34:48.711642: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:34:49.063572: lr: 0.000448 
+2026-11-09 17:34:49.063959: This epoch took 255.363841 s
+ 
+2026-11-09 17:34:49.064224: 
+epoch:  590 
+2026-11-09 17:38:32.617127: train loss : -0.8569 
+2026-11-09 17:38:40.767995: validation loss: -0.6489 
+2026-11-09 17:38:40.769221: Average global foreground Dice: [np.float32(0.9284), np.float32(0.6782), np.float32(0.7981), np.float32(0.8045), np.float32(0.7311), np.float32(0.8545), np.float32(0.7545), np.float32(0.7061), np.float32(0.3688), np.float32(0.4652), np.float32(0.67), np.float32(0.6914), np.float32(0.6846), np.float32(0.6054), np.float32(0.0042), np.float32(0.0), np.float32(0.7581), np.float32(0.6068), np.float32(0.7411), np.float32(0.6326), np.float32(0.5765), np.float32(0.6357), np.float32(0.8768), np.float32(0.8674), np.float32(0.4814), np.float32(0.4707), np.float32(0.3628), np.float32(0.1472), np.float32(0.4088), np.float32(0.6904), np.float32(0.0), np.float32(0.0), np.float32(0.6026), np.float32(0.6536), np.float32(0.842), np.float32(0.8565)] 
+2026-11-09 17:38:40.769440: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:38:41.131025: lr: 0.000447 
+2026-11-09 17:38:41.131285: This epoch took 232.066976 s
+ 
+2026-11-09 17:38:41.131378: 
+epoch:  591 
+2026-11-09 17:43:05.674214: train loss : -0.8527 
+2026-11-09 17:43:13.821787: validation loss: -0.5940 
+2026-11-09 17:43:13.822730: Average global foreground Dice: [np.float32(0.9248), np.float32(0.5951), np.float32(0.7224), np.float32(0.722), np.float32(0.6427), np.float32(0.7793), np.float32(0.6725), np.float32(0.7237), np.float32(0.3242), np.float32(0.4511), np.float32(0.5871), np.float32(0.6371), np.float32(0.6021), np.float32(0.4988), np.float32(0.0), np.float32(0.0), np.float32(0.7103), np.float32(0.5524), np.float32(0.7012), np.float32(0.6042), np.float32(0.528), np.float32(0.6064), np.float32(0.8122), np.float32(0.8028), np.float32(0.4783), np.float32(0.4529), np.float32(0.3632), np.float32(0.0957), np.float32(0.4373), np.float32(0.6768), np.float32(0.0), np.float32(0.0), np.float32(0.5722), np.float32(0.6302), np.float32(0.7684), np.float32(0.7945)] 
+2026-11-09 17:43:13.823194: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:43:14.192387: lr: 0.000446 
+2026-11-09 17:43:14.192646: This epoch took 273.061205 s
+ 
+2026-11-09 17:43:14.192742: 
+epoch:  592 
+2026-11-09 17:47:12.048873: train loss : -0.8616 
+2026-11-09 17:47:20.250088: validation loss: -0.5950 
+2026-11-09 17:47:20.251272: Average global foreground Dice: [np.float32(0.9244), np.float32(0.6139), np.float32(0.7335), np.float32(0.7387), np.float32(0.6592), np.float32(0.7937), np.float32(0.6862), np.float32(0.634), np.float32(0.4026), np.float32(0.4287), np.float32(0.5961), np.float32(0.6399), np.float32(0.6327), np.float32(0.5255), np.float32(0.0027), np.float32(0.0), np.float32(0.7203), np.float32(0.5674), np.float32(0.7131), np.float32(0.6326), np.float32(0.5695), np.float32(0.6354), np.float32(0.8158), np.float32(0.8064), np.float32(0.4607), np.float32(0.4759), np.float32(0.3251), np.float32(0.2485), np.float32(0.3554), np.float32(0.6679), np.float32(0.0), np.float32(0.0), np.float32(0.5972), np.float32(0.6676), np.float32(0.7778), np.float32(0.8035)] 
+2026-11-09 17:47:20.252014: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:47:20.593338: lr: 0.000445 
+2026-11-09 17:47:20.593657: This epoch took 246.400851 s
+ 
+2026-11-09 17:47:20.593890: 
+epoch:  593 
+2026-11-09 17:51:32.283087: train loss : -0.8603 
+2026-11-09 17:51:40.448634: validation loss: -0.6200 
+2026-11-09 17:51:40.449732: Average global foreground Dice: [np.float32(0.9229), np.float32(0.6317), np.float32(0.7659), np.float32(0.7705), np.float32(0.6941), np.float32(0.8279), np.float32(0.7257), np.float32(0.537), np.float32(0.3191), np.float32(0.4492), np.float32(0.6319), np.float32(0.6773), np.float32(0.6482), np.float32(0.5667), np.float32(0.0), np.float32(0.0), np.float32(0.7358), np.float32(0.5905), np.float32(0.7329), np.float32(0.6135), np.float32(0.5421), np.float32(0.626), np.float32(0.8228), np.float32(0.8185), np.float32(0.4918), np.float32(0.4471), np.float32(0.3284), np.float32(0.3609), np.float32(0.4339), np.float32(0.7092), np.float32(0.0), np.float32(0.0), np.float32(0.6231), np.float32(0.6431), np.float32(0.8063), np.float32(0.8317)] 
+2026-11-09 17:51:40.450093: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:51:40.800518: lr: 0.000444 
+2026-11-09 17:51:40.800836: This epoch took 260.206841 s
+ 
+2026-11-09 17:51:40.800935: 
+epoch:  594 
+2026-11-09 17:55:55.775009: train loss : -0.8517 
+2026-11-09 17:56:03.919853: validation loss: -0.6116 
+2026-11-09 17:56:03.920803: Average global foreground Dice: [np.float32(0.9244), np.float32(0.5892), np.float32(0.7618), np.float32(0.7645), np.float32(0.68), np.float32(0.8133), np.float32(0.7295), np.float32(0.6294), np.float32(0.3281), np.float32(0.4977), np.float32(0.6342), np.float32(0.6651), np.float32(0.5946), np.float32(0.4845), np.float32(0.0138), np.float32(0.0), np.float32(0.7318), np.float32(0.573), np.float32(0.7301), np.float32(0.6391), np.float32(0.5718), np.float32(0.6582), np.float32(0.8202), np.float32(0.8175), np.float32(0.508), np.float32(0.4045), np.float32(0.303), np.float32(0.0), np.float32(0.3042), np.float32(0.6895), np.float32(0.0), np.float32(0.0), np.float32(0.6543), np.float32(0.6869), np.float32(0.7979), np.float32(0.8171)] 
+2026-11-09 17:56:03.921168: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 17:56:04.270830: lr: 0.000443 
+2026-11-09 17:56:04.271099: This epoch took 263.470097 s
+ 
+2026-11-09 17:56:04.271284: 
+epoch:  595 
+2026-11-09 18:00:25.933938: train loss : -0.8560 
+2026-11-09 18:00:34.114183: validation loss: -0.6357 
+2026-11-09 18:00:34.115282: Average global foreground Dice: [np.float32(0.93), np.float32(0.6672), np.float32(0.7815), np.float32(0.7927), np.float32(0.6988), np.float32(0.8359), np.float32(0.7393), np.float32(0.639), np.float32(0.3549), np.float32(0.4635), np.float32(0.653), np.float32(0.687), np.float32(0.6405), np.float32(0.5567), np.float32(0.0437), np.float32(0.0), np.float32(0.7464), np.float32(0.5666), np.float32(0.732), np.float32(0.645), np.float32(0.6063), np.float32(0.6705), np.float32(0.8645), np.float32(0.8594), np.float32(0.4793), np.float32(0.4574), np.float32(0.3122), np.float32(0.1211), np.float32(0.3143), np.float32(0.7177), np.float32(0.0), np.float32(0.0), np.float32(0.6013), np.float32(0.673), np.float32(0.8275), np.float32(0.8432)] 
+2026-11-09 18:00:34.115486: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 18:00:34.458676: lr: 0.000442 
+2026-11-09 18:00:34.458920: This epoch took 270.187537 s
+ 
+2026-11-09 18:00:34.459013: 
+epoch:  596 
+2026-11-09 18:05:02.338683: train loss : -0.8560 
+2026-11-09 18:05:10.507182: validation loss: -0.6409 
+2026-11-09 18:05:10.508325: Average global foreground Dice: [np.float32(0.9255), np.float32(0.6545), np.float32(0.7767), np.float32(0.7822), np.float32(0.7085), np.float32(0.8353), np.float32(0.7373), np.float32(0.6632), np.float32(0.4303), np.float32(0.4234), np.float32(0.6294), np.float32(0.6773), np.float32(0.6581), np.float32(0.6018), np.float32(0.0027), np.float32(0.0), np.float32(0.7481), np.float32(0.5982), np.float32(0.7327), np.float32(0.655), np.float32(0.6119), np.float32(0.6648), np.float32(0.8422), np.float32(0.8382), np.float32(0.4773), np.float32(0.4707), np.float32(0.3577), np.float32(0.2874), np.float32(0.277), np.float32(0.7065), np.float32(0.0), np.float32(0.0), np.float32(0.6614), np.float32(0.6921), np.float32(0.8209), np.float32(0.8415)] 
+2026-11-09 18:05:10.508678: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 18:05:10.853891: lr: 0.000441 
+2026-11-09 18:05:10.854139: This epoch took 276.394980 s
+ 
+2026-11-09 18:05:10.854223: 
+epoch:  597 
+2026-11-09 18:09:16.122668: train loss : -0.8593 
+2026-11-09 18:09:24.270916: validation loss: -0.6357 
+2026-11-09 18:09:24.272174: Average global foreground Dice: [np.float32(0.9272), np.float32(0.6616), np.float32(0.7735), np.float32(0.7902), np.float32(0.7053), np.float32(0.8419), np.float32(0.7347), np.float32(0.6373), np.float32(0.3745), np.float32(0.4324), np.float32(0.6232), np.float32(0.6884), np.float32(0.637), np.float32(0.5982), np.float32(0.0), np.float32(0.0), np.float32(0.7394), np.float32(0.5929), np.float32(0.7211), np.float32(0.597), np.float32(0.5775), np.float32(0.6505), np.float32(0.8753), np.float32(0.8659), np.float32(0.4917), np.float32(0.4594), np.float32(0.3555), np.float32(0.0664), np.float32(0.2918), np.float32(0.675), np.float32(0.0), np.float32(0.0), np.float32(0.646), np.float32(0.6778), np.float32(0.8323), np.float32(0.8515)] 
+2026-11-09 18:09:24.272429: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 18:09:24.616662: lr: 0.00044 
+2026-11-09 18:09:24.617165: This epoch took 253.762881 s
+ 
+2026-11-09 18:09:24.617390: 
+epoch:  598 
+2026-11-09 18:13:47.780024: train loss : -0.8539 
+2026-11-09 18:13:55.917133: validation loss: -0.6329 
+2026-11-09 18:13:55.918080: Average global foreground Dice: [np.float32(0.9211), np.float32(0.6398), np.float32(0.7703), np.float32(0.7754), np.float32(0.6898), np.float32(0.826), np.float32(0.7173), np.float32(0.6758), np.float32(0.412), np.float32(0.4593), np.float32(0.6497), np.float32(0.6943), np.float32(0.6564), np.float32(0.5593), np.float32(0.0), np.float32(0.0), np.float32(0.7257), np.float32(0.5823), np.float32(0.7246), np.float32(0.604), np.float32(0.6001), np.float32(0.6663), np.float32(0.8795), np.float32(0.877), np.float32(0.482), np.float32(0.433), np.float32(0.3432), np.float32(0.0828), np.float32(0.3609), np.float32(0.6872), np.float32(0.0), np.float32(0.0), np.float32(0.6055), np.float32(0.6604), np.float32(0.8162), np.float32(0.8392)] 
+2026-11-09 18:13:55.918295: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 18:13:56.258440: lr: 0.000439 
+2026-11-09 18:13:56.258695: This epoch took 271.641223 s
+ 
+2026-11-09 18:13:56.258954: 
+epoch:  599 
+2026-11-09 18:18:27.518435: train loss : -0.8594 
+2026-11-09 18:18:35.690448: validation loss: -0.6463 
+2026-11-09 18:18:35.691604: Average global foreground Dice: [np.float32(0.9282), np.float32(0.6585), np.float32(0.7749), np.float32(0.7837), np.float32(0.6938), np.float32(0.8329), np.float32(0.723), np.float32(0.6912), np.float32(0.373), np.float32(0.4446), np.float32(0.6438), np.float32(0.7112), np.float32(0.6763), np.float32(0.6005), np.float32(0.0), np.float32(0.0), np.float32(0.7353), np.float32(0.5749), np.float32(0.7221), np.float32(0.6422), np.float32(0.5823), np.float32(0.6556), np.float32(0.8659), np.float32(0.8581), np.float32(0.4863), np.float32(0.48), np.float32(0.325), np.float32(0.4014), np.float32(0.4029), np.float32(0.6942), np.float32(0.0), np.float32(0.0), np.float32(0.6016), np.float32(0.6736), np.float32(0.8182), np.float32(0.838)] 
+2026-11-09 18:18:35.692058: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 18:18:36.036049: lr: 0.000438 
+2026-11-09 18:18:36.036301: saving scheduled checkpoint file... 
+2026-11-09 18:18:36.416164: saving checkpoint... 
+2026-11-09 18:18:36.991082: done, saving took 0.95 seconds 
+2026-11-09 18:18:36.993317: done 
+2026-11-09 18:18:36.993528: This epoch took 280.734496 s
+ 
+2026-11-09 18:18:36.993596: 
+epoch:  600 
+2026-11-09 18:22:47.518610: train loss : -0.8558 
+2026-11-09 18:22:55.699058: validation loss: -0.5965 
+2026-11-09 18:22:55.700001: Average global foreground Dice: [np.float32(0.9198), np.float32(0.5816), np.float32(0.7704), np.float32(0.7749), np.float32(0.6652), np.float32(0.8181), np.float32(0.7312), np.float32(0.6334), np.float32(0.4022), np.float32(0.4601), np.float32(0.6207), np.float32(0.6483), np.float32(0.606), np.float32(0.4777), np.float32(0.0), np.float32(0.0), np.float32(0.7202), np.float32(0.5543), np.float32(0.7377), np.float32(0.6009), np.float32(0.5943), np.float32(0.6904), np.float32(0.8294), np.float32(0.8342), np.float32(0.5222), np.float32(0.4079), np.float32(0.4085), np.float32(0.4321), np.float32(0.3041), np.float32(0.6514), np.float32(0.0), np.float32(0.0), np.float32(0.5392), np.float32(0.6403), np.float32(0.8018), np.float32(0.8219)] 
+2026-11-09 18:22:55.700466: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 18:22:56.049636: lr: 0.000437 
+2026-11-09 18:22:56.050066: This epoch took 259.056270 s
+ 
+2026-11-09 18:22:56.050367: 
+epoch:  601