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 8380d11ca8d1f99abeb36b63401da334cb6e6fa8..95cb2e329b3b9b609fea94960d4b97940a25d774 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(-0.5361358), np.float32(-0.59351784), np.float32(-0.5750568), np.float32(-0.59919035), np.float32(-0.58974326), np.float32(-0.5874589), np.float32(-0.5757423), np.float32(-0.43263996), np.float32(-0.59110546), np.float32(-0.3358089), np.float32(-0.35929358), np.float32(-0.5706676), np.float32(-0.5997636), np.float32(-0.58234006), np.float32(-0.49691373), np.float32(-0.5288896), np.float32(-0.49695468), np.float32(-0.51084316), np.float32(-0.48421985), np.float32(-0.5306862), np.float32(-0.5269299), np.float32(-0.48491994), np.float32(-0.46047428), np.float32(-0.43524587), np.float32(-0.565427), np.float32(-0.6103861), np.float32(-0.62313676), np.float32(-0.57096714), np.float32(-0.4766863), np.float32(-0.46849683), np.float32(-0.5955407), np.float32(-0.5608841), np.float32(-0.56826586), np.float32(-0.5609152), np.float32(-0.5959503), np.float32(-0.64753264), np.float32(-0.5694277), np.float32(-0.6168984), np.float32(-0.57259417), np.float32(-0.5675787), np.float32(-0.6034859), np.float32(-0.62074775), np.float32(-0.6027094), np.float32(-0.6225922), np.float32(-0.59570414), np.float32(-0.589707), np.float32(-0.58554536), np.float32(-0.59729433), np.float32(-0.55394495), np.float32(-0.5938789), np.float32(-0.5920611), np.float32(-0.5881391), np.float32(-0.60250247), np.float32(-0.58279866), np.float32(-0.5870523), np.float32(-0.57276213), np.float32(-0.5908261), np.float32(-0.48126763), np.float32(-0.5278641), np.float32(-0.5110498)]",
     "all_val_losses_tr_mode": "[]",
     "also_val_in_tr_mode": "False",
-    "amp_grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7efb50930ad0>",
+    "amp_grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7fb6739e0ad0>",
     "base_num_features": "30",
     "basic_generator_patch_size": "[205 205 205]",
     "batch_dice": "False",
     "batch_size": "2",
-    "best_MA_tr_loss_for_patience": "-0.6829764",
-    "best_epoch_based_on_MA_tr_loss": "140",
-    "best_val_eval_criterion_MA": "0.3936047",
+    "best_MA_tr_loss_for_patience": "-0.7926375",
+    "best_epoch_based_on_MA_tr_loss": "296",
+    "best_val_eval_criterion_MA": "0.524023",
     "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 0x7efb50b09fd0>",
-    "dl_val": "<nnunet_mednext.training.dataloading.dataset_loading.DataLoader3D object at 0x7efb509411d0>",
+    "dl_tr": "<nnunet_mednext.training.dataloading.dataset_loading.DataLoader3D object at 0x7fb673b5dfd0>",
+    "dl_val": "<nnunet_mednext.training.dataloading.dataset_loading.DataLoader3D object at 0x7fb6739d91d0>",
     "do_dummy_2D_aug": "False",
     "ds_loss_weights": "[0.51612903 0.25806452 0.12903226 0.06451613 0.03225806 0.        ]",
-    "epoch": "150",
+    "epoch": "300",
     "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_06_34_55.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_8_18_31_24.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: 0.0008639269723028191\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: 0.0007254178469372199\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 0x7efb50b0b620>",
+    "tr_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7fb673b5f620>",
     "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 0x7efb50941f90>",
+    "val_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7fb6739d9f90>",
     "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 2be27dcb710c3854eb67ac0ac0fbb0b0fefc5578..38bc3d5ab27f9ba9323d56161cb4ed8f01f89636 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
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 0cb418523f050b2425b58a2d001b2905698a34f4..d7fbcf16a3d2b014c076d7348a3b0e5a0d7b72ea 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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+oid sha256:885cb5dad08c5023bf4f818bcb25f4a5bd1613c662a66de086720f85e43c3aa0
+size 742506681
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
index 58b3c5b1e8e456473779589d04517a2784a96b9f..a7e859e38a29c2f6db0cd200e97023edfc6e47cf 100644
--- 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
@@ -1,3 +1,3 @@
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-oid sha256:8af23ca9066b61b525ef239a8d824318039dee4f4a0ba0fecdefa10bdfc3749f
-size 759873
+oid sha256:029091a210992e281bd58336f8f5c465c69be6f8896ee82f381be0c07fac671b
+size 843074
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_8_18_31_24.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_8_18_31_24.txt
new file mode 100644
index 0000000000000000000000000000000000000000..39e12dca5357d02d5f4aaa0c8c58769f85cf9d7d
--- /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_8_18_31_24.txt
@@ -0,0 +1,1890 @@
+Starting... 
+2026-11-08 18:31:24.327109: Creating new 5-fold cross-validation split... 
+2026-11-08 18:31:24.327883: Desired fold for training: 0 
+2026-11-08 18:31:24.327942: This split has 20 training and 5 validation cases. 
+2026-11-08 18:31:24.964929: 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')]) 
+2026-11-08 18:31:24.965340: 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')]) 
+2026-11-08 18:31:26.757729: 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 
+2026-11-08 18:31:27.410569: lr: 0.000725 
+2026-11-08 18:31:45.979354: Unable to plot network architecture: 
+2026-11-08 18:31:45.980905: No module named 'hiddenlayer' 
+2026-11-08 18:31:45.981136: 
+printing the network instead:
+ 
+2026-11-08 18:31:45.981249: 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))
+  )
+) 
+2026-11-08 18:31:45.999493: 
+ 
+2026-11-08 18:31:46.002209: 
+epoch:  300 
+2026-11-08 18:35:44.242789: train loss : -0.7998 
+2026-11-08 18:35:52.376195: validation loss: -0.4719 
+2026-11-08 18:35:52.377470: Average global foreground Dice: [np.float32(0.872), np.float32(0.5292), np.float32(0.5123), np.float32(0.5398), np.float32(0.4434), np.float32(0.5587), np.float32(0.4321), np.float32(0.5488), np.float32(0.3676), np.float32(0.3901), np.float32(0.4664), np.float32(0.5005), np.float32(0.5158), np.float32(0.47), np.float32(0.0), np.float32(0.0), np.float32(0.4466), np.float32(0.3846), np.float32(0.4756), np.float32(0.4503), np.float32(0.436), np.float32(0.4306), np.float32(0.6246), np.float32(0.5644), np.float32(0.2438), np.float32(0.2883), np.float32(0.0303), np.float32(0.0), np.float32(0.2396), np.float32(0.6089), np.float32(0.0), np.float32(0.0), np.float32(0.4348), np.float32(0.4969), np.float32(0.5663), np.float32(0.604)] 
+2026-11-08 18:35:52.377885: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 18:35:52.791823: lr: 0.000724 
+2026-11-08 18:35:52.792150: This epoch took 246.789566 s
+ 
+2026-11-08 18:35:52.792310: 
+epoch:  301 
+2026-11-08 18:39:17.337755: train loss : -0.8077 
+2026-11-08 18:39:25.430110: validation loss: -0.5112 
+2026-11-08 18:39:25.431277: Average global foreground Dice: [np.float32(0.9052), np.float32(0.5346), np.float32(0.5884), np.float32(0.5678), np.float32(0.4478), np.float32(0.6337), np.float32(0.5355), np.float32(0.7676), np.float32(0.246), np.float32(0.5336), np.float32(0.472), np.float32(0.5065), np.float32(0.5517), np.float32(0.4605), np.float32(0.0), np.float32(0.0), np.float32(0.4598), np.float32(0.4066), np.float32(0.5063), np.float32(0.4949), np.float32(0.5093), np.float32(0.5138), np.float32(0.6959), np.float32(0.6701), np.float32(0.3393), np.float32(0.2428), np.float32(0.0325), np.float32(0.0), np.float32(0.3582), np.float32(0.6813), np.float32(0.0), np.float32(0.0), np.float32(0.4092), np.float32(0.5059), np.float32(0.5995), np.float32(0.6439)] 
+2026-11-08 18:39:25.431630: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 18:39:25.792727: lr: 0.000724 
+2026-11-08 18:39:25.793159: This epoch took 213.000758 s
+ 
+2026-11-08 18:39:25.793421: 
+epoch:  302 
+2026-11-08 18:43:29.972877: train loss : -0.7982 
+2026-11-08 18:43:38.061782: validation loss: -0.5747 
+2026-11-08 18:43:38.062826: Average global foreground Dice: [np.float32(0.9221), np.float32(0.6121), np.float32(0.6927), np.float32(0.6958), np.float32(0.6183), np.float32(0.7653), np.float32(0.6596), np.float32(0.3857), np.float32(0.3195), np.float32(0.5181), np.float32(0.5674), np.float32(0.6155), np.float32(0.6318), np.float32(0.5464), np.float32(0.0), np.float32(0.0), np.float32(0.624), np.float32(0.4986), np.float32(0.651), np.float32(0.582), np.float32(0.5892), np.float32(0.5097), np.float32(0.8017), np.float32(0.8054), np.float32(0.3892), np.float32(0.4219), np.float32(0.0), np.float32(0.0), np.float32(0.2919), np.float32(0.6661), np.float32(0.0), np.float32(0.0), np.float32(0.4841), np.float32(0.5823), np.float32(0.7485), np.float32(0.7797)] 
+2026-11-08 18:43:38.063028: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 18:43:38.394420: lr: 0.000723 
+2026-11-08 18:43:38.394873: This epoch took 252.601190 s
+ 
+2026-11-08 18:43:38.395137: 
+epoch:  303 
+2026-11-08 18:47:46.218858: train loss : -0.7938 
+2026-11-08 18:47:54.408196: validation loss: -0.5042 
+2026-11-08 18:47:54.409378: Average global foreground Dice: [np.float32(0.9088), np.float32(0.5301), np.float32(0.5421), np.float32(0.5423), np.float32(0.4578), np.float32(0.6094), np.float32(0.5035), np.float32(0.556), np.float32(0.1762), np.float32(0.36), np.float32(0.438), np.float32(0.521), np.float32(0.5742), np.float32(0.504), np.float32(0.0), np.float32(0.0), np.float32(0.3971), np.float32(0.3471), np.float32(0.4421), np.float32(0.4328), np.float32(0.4586), np.float32(0.384), np.float32(0.6929), np.float32(0.665), np.float32(0.2213), np.float32(0.2699), np.float32(0.0072), np.float32(0.0), np.float32(0.3864), np.float32(0.63), np.float32(0.0), np.float32(0.0), np.float32(0.411), np.float32(0.464), np.float32(0.5967), np.float32(0.6389)] 
+2026-11-08 18:47:54.409687: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 18:47:54.733821: lr: 0.000722 
+2026-11-08 18:47:54.734061: This epoch took 256.338828 s
+ 
+2026-11-08 18:47:54.734146: 
+epoch:  304 
+2026-11-08 18:51:53.070042: train loss : -0.8014 
+2026-11-08 18:52:01.172846: validation loss: -0.5159 
+2026-11-08 18:52:01.173774: Average global foreground Dice: [np.float32(0.9315), np.float32(0.5775), np.float32(0.5441), np.float32(0.5968), np.float32(0.5166), np.float32(0.6473), np.float32(0.5502), np.float32(0.6639), np.float32(0.3249), np.float32(0.2597), np.float32(0.4938), np.float32(0.4432), np.float32(0.5999), np.float32(0.4975), np.float32(0.0), np.float32(0.0), np.float32(0.4813), np.float32(0.4182), np.float32(0.4826), np.float32(0.4751), np.float32(0.4705), np.float32(0.4684), np.float32(0.7118), np.float32(0.6836), np.float32(0.2361), np.float32(0.3197), np.float32(0.0), np.float32(0.0), np.float32(0.3365), np.float32(0.6148), np.float32(0.0), np.float32(0.0), np.float32(0.4525), np.float32(0.5425), np.float32(0.6461), np.float32(0.68)] 
+2026-11-08 18:52:01.174126: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 18:52:01.507477: lr: 0.000721 
+2026-11-08 18:52:01.507900: This epoch took 246.773692 s
+ 
+2026-11-08 18:52:01.508109: 
+epoch:  305 
+2026-11-08 18:56:01.611797: train loss : -0.8042 
+2026-11-08 18:56:09.695859: validation loss: -0.4745 
+2026-11-08 18:56:09.696815: Average global foreground Dice: [np.float32(0.9029), np.float32(0.4891), np.float32(0.4758), np.float32(0.478), np.float32(0.3915), np.float32(0.553), np.float32(0.4537), np.float32(0.15), np.float32(0.2562), np.float32(0.5102), np.float32(0.4035), np.float32(0.4831), np.float32(0.5657), np.float32(0.4919), np.float32(0.0), np.float32(0.0), np.float32(0.3226), np.float32(0.3097), np.float32(0.3822), np.float32(0.3618), np.float32(0.4482), np.float32(0.3549), np.float32(0.6016), np.float32(0.5614), np.float32(0.1642), np.float32(0.2343), np.float32(0.0087), np.float32(0.0), np.float32(0.3739), np.float32(0.7013), np.float32(0.0), np.float32(0.0), np.float32(0.3706), np.float32(0.4346), np.float32(0.5291), np.float32(0.5794)] 
+2026-11-08 18:56:09.697000: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 18:56:10.014084: lr: 0.00072 
+2026-11-08 18:56:10.014302: This epoch took 248.505919 s
+ 
+2026-11-08 18:56:10.014540: 
+epoch:  306 
+2026-11-08 19:00:00.156902: train loss : -0.7774 
+2026-11-08 19:00:08.250353: validation loss: -0.4513 
+2026-11-08 19:00:08.251373: Average global foreground Dice: [np.float32(0.9203), np.float32(0.4287), np.float32(0.5408), np.float32(0.4916), np.float32(0.4198), np.float32(0.5725), np.float32(0.495), np.float32(0.39), np.float32(0.0079), np.float32(0.2639), np.float32(0.4335), np.float32(0.4916), np.float32(0.5676), np.float32(0.4718), np.float32(0.0), np.float32(0.0), np.float32(0.4931), np.float32(0.4412), np.float32(0.521), np.float32(0.4416), np.float32(0.348), np.float32(0.3555), np.float32(0.6508), np.float32(0.6311), np.float32(0.4457), np.float32(0.2338), np.float32(0.2576), np.float32(0.0), np.float32(0.3045), np.float32(0.5524), np.float32(0.0), np.float32(0.0), np.float32(0.4027), np.float32(0.4084), np.float32(0.5243), np.float32(0.5901)] 
+2026-11-08 19:00:08.251666: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:00:08.598956: lr: 0.000719 
+2026-11-08 19:00:08.599635: This epoch took 238.584932 s
+ 
+2026-11-08 19:00:08.599958: 
+epoch:  307 
+2026-11-08 19:04:09.528030: train loss : -0.7675 
+2026-11-08 19:04:17.606400: validation loss: -0.6073 
+2026-11-08 19:04:17.607333: Average global foreground Dice: [np.float32(0.8997), np.float32(0.6244), np.float32(0.7842), np.float32(0.7395), np.float32(0.7012), np.float32(0.7975), np.float32(0.7234), np.float32(0.55), np.float32(0.0291), np.float32(0.3542), np.float32(0.5265), np.float32(0.6417), np.float32(0.5993), np.float32(0.4386), np.float32(0.0), np.float32(0.0), np.float32(0.7489), np.float32(0.576), np.float32(0.7262), np.float32(0.6105), np.float32(0.52), np.float32(0.6973), np.float32(0.8837), np.float32(0.8618), np.float32(0.5311), np.float32(0.3792), np.float32(0.0532), np.float32(0.0), np.float32(0.4354), np.float32(0.6268), np.float32(0.0), np.float32(0.0), np.float32(0.5895), np.float32(0.6489), np.float32(0.8238), np.float32(0.8383)] 
+2026-11-08 19:04:17.607644: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:04:17.945559: lr: 0.000718 
+2026-11-08 19:04:17.945835: This epoch took 249.345689 s
+ 
+2026-11-08 19:04:17.946075: 
+epoch:  308 
+2026-11-08 19:08:43.833176: train loss : -0.7710 
+2026-11-08 19:08:51.963994: validation loss: -0.5942 
+2026-11-08 19:08:51.964906: Average global foreground Dice: [np.float32(0.904), np.float32(0.6225), np.float32(0.7963), np.float32(0.7761), np.float32(0.6935), np.float32(0.8294), np.float32(0.7196), np.float32(0.5381), np.float32(0.1573), np.float32(0.3782), np.float32(0.5458), np.float32(0.6343), np.float32(0.5911), np.float32(0.5358), np.float32(0.0), np.float32(0.0), np.float32(0.6641), np.float32(0.4764), np.float32(0.6477), np.float32(0.5661), np.float32(0.5393), np.float32(0.6542), np.float32(0.8815), np.float32(0.8732), np.float32(0.4865), np.float32(0.4658), np.float32(0.1709), np.float32(0.0), np.float32(0.2117), np.float32(0.5516), np.float32(0.0), np.float32(0.0), np.float32(0.6198), np.float32(0.6213), np.float32(0.8284), np.float32(0.8485)] 
+2026-11-08 19:08:51.965437: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:08:52.289291: lr: 0.000717 
+2026-11-08 19:08:52.289545: This epoch took 274.343392 s
+ 
+2026-11-08 19:08:52.289617: 
+epoch:  309 
+2026-11-08 19:12:48.124366: train loss : -0.7965 
+2026-11-08 19:12:56.189170: validation loss: -0.5709 
+2026-11-08 19:12:56.190302: Average global foreground Dice: [np.float32(0.9195), np.float32(0.5776), np.float32(0.6771), np.float32(0.6701), np.float32(0.5872), np.float32(0.7225), np.float32(0.6235), np.float32(0.4927), np.float32(0.3213), np.float32(0.5215), np.float32(0.5568), np.float32(0.624), np.float32(0.6079), np.float32(0.5573), np.float32(0.0), np.float32(0.0), np.float32(0.5639), np.float32(0.4506), np.float32(0.5672), np.float32(0.5269), np.float32(0.4986), np.float32(0.4822), np.float32(0.8076), np.float32(0.7958), np.float32(0.3346), np.float32(0.4515), np.float32(0.1096), np.float32(0.0), np.float32(0.1816), np.float32(0.5312), np.float32(0.0), np.float32(0.0), np.float32(0.4661), np.float32(0.5428), np.float32(0.7256), np.float32(0.7625)] 
+2026-11-08 19:12:56.190794: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:12:56.512538: lr: 0.000716 
+2026-11-08 19:12:56.512813: This epoch took 244.223135 s
+ 
+2026-11-08 19:12:56.512899: 
+epoch:  310 
+2026-11-08 19:16:55.232770: train loss : -0.8001 
+2026-11-08 19:17:03.340826: validation loss: -0.5838 
+2026-11-08 19:17:03.342109: Average global foreground Dice: [np.float32(0.9103), np.float32(0.6328), np.float32(0.7435), np.float32(0.7552), np.float32(0.6698), np.float32(0.8017), np.float32(0.6988), np.float32(0.5782), np.float32(0.3271), np.float32(0.3425), np.float32(0.5869), np.float32(0.614), np.float32(0.6148), np.float32(0.5059), np.float32(0.0), np.float32(0.0), np.float32(0.7266), np.float32(0.5878), np.float32(0.7042), np.float32(0.6189), np.float32(0.4835), np.float32(0.5779), np.float32(0.8569), np.float32(0.8498), np.float32(0.4814), np.float32(0.5378), np.float32(0.0985), np.float32(0.0), np.float32(0.3633), np.float32(0.6668), np.float32(0.0), np.float32(0.0), np.float32(0.6008), np.float32(0.6341), np.float32(0.7947), np.float32(0.8254)] 
+2026-11-08 19:17:03.342345: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:17:03.677371: lr: 0.000715 
+2026-11-08 19:17:03.677617: This epoch took 247.164651 s
+ 
+2026-11-08 19:17:03.677699: 
+epoch:  311 
+2026-11-08 19:21:05.246756: train loss : -0.7976 
+2026-11-08 19:21:13.405212: validation loss: -0.4923 
+2026-11-08 19:21:13.406483: Average global foreground Dice: [np.float32(0.9162), np.float32(0.5125), np.float32(0.6077), np.float32(0.5475), np.float32(0.4626), np.float32(0.572), np.float32(0.4849), np.float32(0.4821), np.float32(0.1123), np.float32(0.3811), np.float32(0.4218), np.float32(0.5061), np.float32(0.5792), np.float32(0.4158), np.float32(0.0), np.float32(0.0), np.float32(0.4763), np.float32(0.368), np.float32(0.5158), np.float32(0.5031), np.float32(0.4806), np.float32(0.4809), np.float32(0.7642), np.float32(0.7562), np.float32(0.3771), np.float32(0.3777), np.float32(0.1302), np.float32(0.0), np.float32(0.2398), np.float32(0.6425), np.float32(0.0), np.float32(0.0), np.float32(0.4836), np.float32(0.5246), np.float32(0.6317), np.float32(0.6784)] 
+2026-11-08 19:21:13.407048: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:21:13.746673: lr: 0.000714 
+2026-11-08 19:21:13.746952: This epoch took 250.069198 s
+ 
+2026-11-08 19:21:13.747042: 
+epoch:  312 
+2026-11-08 19:25:03.037922: train loss : -0.8108 
+2026-11-08 19:25:11.168913: validation loss: -0.5715 
+2026-11-08 19:25:11.169930: Average global foreground Dice: [np.float32(0.922), np.float32(0.5891), np.float32(0.721), np.float32(0.7084), np.float32(0.6318), np.float32(0.7592), np.float32(0.6707), np.float32(0.5564), np.float32(0.1954), np.float32(0.3755), np.float32(0.5449), np.float32(0.5911), np.float32(0.6158), np.float32(0.5206), np.float32(0.0), np.float32(0.0), np.float32(0.6654), np.float32(0.5479), np.float32(0.6674), np.float32(0.5976), np.float32(0.5656), np.float32(0.5786), np.float32(0.7661), np.float32(0.7702), np.float32(0.4728), np.float32(0.4098), np.float32(0.1449), np.float32(0.0), np.float32(0.3824), np.float32(0.694), np.float32(0.0), np.float32(0.0), np.float32(0.5685), np.float32(0.5501), np.float32(0.759), np.float32(0.7903)] 
+2026-11-08 19:25:11.170113: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:25:11.536582: lr: 0.000713 
+2026-11-08 19:25:11.536938: This epoch took 237.789829 s
+ 
+2026-11-08 19:25:11.537175: 
+epoch:  313 
+2026-11-08 19:29:35.178737: train loss : -0.8036 
+2026-11-08 19:29:43.358232: validation loss: -0.5447 
+2026-11-08 19:29:43.359418: Average global foreground Dice: [np.float32(0.9142), np.float32(0.5683), np.float32(0.6655), np.float32(0.616), np.float32(0.5454), np.float32(0.6902), np.float32(0.6159), np.float32(0.565), np.float32(0.1968), np.float32(0.4302), np.float32(0.4704), np.float32(0.5708), np.float32(0.5605), np.float32(0.5813), np.float32(0.0), np.float32(0.0), np.float32(0.5232), np.float32(0.4585), np.float32(0.5578), np.float32(0.5087), np.float32(0.5222), np.float32(0.4675), np.float32(0.7817), np.float32(0.7648), np.float32(0.3702), np.float32(0.3881), np.float32(0.1713), np.float32(0.0), np.float32(0.3703), np.float32(0.7086), np.float32(0.0), np.float32(0.0), np.float32(0.4392), np.float32(0.4951), np.float32(0.6924), np.float32(0.7382)] 
+2026-11-08 19:29:43.359911: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:29:43.708709: lr: 0.000712 
+2026-11-08 19:29:43.708957: This epoch took 272.171658 s
+ 
+2026-11-08 19:29:43.709037: 
+epoch:  314 
+2026-11-08 19:33:56.085723: train loss : -0.8002 
+2026-11-08 19:34:04.200809: validation loss: -0.4191 
+2026-11-08 19:34:04.202032: Average global foreground Dice: [np.float32(0.9024), np.float32(0.4244), np.float32(0.4302), np.float32(0.4034), np.float32(0.3183), np.float32(0.4837), np.float32(0.3829), np.float32(0.4614), np.float32(0.1817), np.float32(0.3909), np.float32(0.3344), np.float32(0.4745), np.float32(0.5398), np.float32(0.4732), np.float32(0.0), np.float32(0.0), np.float32(0.255), np.float32(0.2627), np.float32(0.3053), np.float32(0.3028), np.float32(0.361), np.float32(0.2387), np.float32(0.6102), np.float32(0.5439), np.float32(0.1304), np.float32(0.2482), np.float32(0.0343), np.float32(0.0), np.float32(0.173), np.float32(0.5635), np.float32(0.0), np.float32(0.0), np.float32(0.32), np.float32(0.3096), np.float32(0.4346), np.float32(0.4968)] 
+2026-11-08 19:34:04.202227: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:34:04.533359: lr: 0.000711 
+2026-11-08 19:34:04.533731: This epoch took 260.824274 s
+ 
+2026-11-08 19:34:04.533906: 
+epoch:  315 
+2026-11-08 19:38:05.795880: train loss : -0.8073 
+2026-11-08 19:38:13.883506: validation loss: -0.5245 
+2026-11-08 19:38:13.885457: Average global foreground Dice: [np.float32(0.9092), np.float32(0.5537), np.float32(0.6178), np.float32(0.6219), np.float32(0.5162), np.float32(0.6827), np.float32(0.5937), np.float32(0.483), np.float32(0.3592), np.float32(0.4386), np.float32(0.4813), np.float32(0.5763), np.float32(0.6077), np.float32(0.558), np.float32(0.0), np.float32(0.0), np.float32(0.4574), np.float32(0.3852), np.float32(0.5125), np.float32(0.4703), np.float32(0.4406), np.float32(0.3754), np.float32(0.7236), np.float32(0.6952), np.float32(0.3058), np.float32(0.3229), np.float32(0.0827), np.float32(0.0), np.float32(0.2235), np.float32(0.6129), np.float32(0.0), np.float32(0.0), np.float32(0.4582), np.float32(0.5134), np.float32(0.6563), np.float32(0.7013)] 
+2026-11-08 19:38:13.885867: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:38:14.244194: lr: 0.00071 
+2026-11-08 19:38:14.244887: This epoch took 249.710894 s
+ 
+2026-11-08 19:38:14.245100: 
+epoch:  316 
+2026-11-08 19:42:33.179911: train loss : -0.7874 
+2026-11-08 19:42:41.308463: validation loss: -0.5221 
+2026-11-08 19:42:41.309706: Average global foreground Dice: [np.float32(0.9002), np.float32(0.5307), np.float32(0.5784), np.float32(0.5589), np.float32(0.5124), np.float32(0.6609), np.float32(0.5848), np.float32(0.6411), np.float32(0.2236), np.float32(0.4131), np.float32(0.4472), np.float32(0.5728), np.float32(0.6197), np.float32(0.5568), np.float32(0.0), np.float32(0.0), np.float32(0.4576), np.float32(0.4269), np.float32(0.4984), np.float32(0.4362), np.float32(0.3593), np.float32(0.3219), np.float32(0.7928), np.float32(0.7482), np.float32(0.2595), np.float32(0.3041), np.float32(0.0381), np.float32(0.0), np.float32(0.0584), np.float32(0.567), np.float32(0.0), np.float32(0.0), np.float32(0.4873), np.float32(0.5039), np.float32(0.6262), np.float32(0.6966)] 
+2026-11-08 19:42:41.310171: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:42:41.642301: lr: 0.00071 
+2026-11-08 19:42:41.642593: This epoch took 267.397356 s
+ 
+2026-11-08 19:42:41.642785: 
+epoch:  317 
+2026-11-08 19:47:04.631555: train loss : -0.7994 
+2026-11-08 19:47:12.781024: validation loss: -0.6511 
+2026-11-08 19:47:12.781981: Average global foreground Dice: [np.float32(0.9155), np.float32(0.6816), np.float32(0.8172), np.float32(0.8427), np.float32(0.817), np.float32(0.9005), np.float32(0.8312), np.float32(0.6735), np.float32(0.306), np.float32(0.5028), np.float32(0.6793), np.float32(0.7165), np.float32(0.6743), np.float32(0.5644), np.float32(0.0), np.float32(0.0), np.float32(0.7854), np.float32(0.6109), np.float32(0.7725), np.float32(0.6419), np.float32(0.5205), np.float32(0.6659), np.float32(0.9027), np.float32(0.8986), np.float32(0.5204), np.float32(0.458), np.float32(0.1895), np.float32(0.0), np.float32(0.2258), np.float32(0.6189), np.float32(0.0), np.float32(0.0), np.float32(0.6167), np.float32(0.667), np.float32(0.8997), np.float32(0.8985)] 
+2026-11-08 19:47:12.782296: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:47:13.125643: lr: 0.000709 
+2026-11-08 19:47:13.125907: This epoch took 271.483020 s
+ 
+2026-11-08 19:47:13.126002: 
+epoch:  318 
+2026-11-08 19:50:57.675050: train loss : -0.8124 
+2026-11-08 19:51:05.771650: validation loss: -0.6520 
+2026-11-08 19:51:05.772699: Average global foreground Dice: [np.float32(0.9226), np.float32(0.6583), np.float32(0.8212), np.float32(0.836), np.float32(0.8045), np.float32(0.8946), np.float32(0.8376), np.float32(0.7016), np.float32(0.3317), np.float32(0.3974), np.float32(0.6363), np.float32(0.6876), np.float32(0.6406), np.float32(0.5303), np.float32(0.0), np.float32(0.0), np.float32(0.7841), np.float32(0.5887), np.float32(0.7865), np.float32(0.6273), np.float32(0.5646), np.float32(0.6974), np.float32(0.9053), np.float32(0.9125), np.float32(0.5483), np.float32(0.3443), np.float32(0.2368), np.float32(0.0), np.float32(0.3295), np.float32(0.6272), np.float32(0.0), np.float32(0.0), np.float32(0.6507), np.float32(0.6203), np.float32(0.8956), np.float32(0.8946)] 
+2026-11-08 19:51:05.772882: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:51:06.164747: lr: 0.000708 
+2026-11-08 19:51:06.165035: This epoch took 233.038970 s
+ 
+2026-11-08 19:51:06.165130: 
+epoch:  319 
+2026-11-08 19:55:12.248830: train loss : -0.8085 
+2026-11-08 19:55:20.323827: validation loss: -0.2408 
+2026-11-08 19:55:20.324847: Average global foreground Dice: [np.float32(0.9112), np.float32(0.1966), np.float32(0.1579), np.float32(0.1719), np.float32(0.0882), np.float32(0.1005), np.float32(0.0746), np.float32(0.3331), np.float32(0.0015), np.float32(0.4245), np.float32(0.1646), np.float32(0.1679), np.float32(0.3358), np.float32(0.1403), np.float32(0.0), np.float32(0.0), np.float32(0.0688), np.float32(0.0616), np.float32(0.1432), np.float32(0.1564), np.float32(0.224), np.float32(0.184), np.float32(0.2038), np.float32(0.1948), np.float32(0.0705), np.float32(0.0807), np.float32(0.0246), np.float32(0.0), np.float32(0.153), np.float32(0.306), np.float32(0.0), np.float32(0.0), np.float32(0.0838), np.float32(0.0639), np.float32(0.1779), np.float32(0.1332)] 
+2026-11-08 19:55:20.325216: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:55:20.661634: lr: 0.000707 
+2026-11-08 19:55:20.662092: This epoch took 254.496894 s
+ 
+2026-11-08 19:55:20.662465: 
+epoch:  320 
+2026-11-08 19:59:42.295021: train loss : -0.7987 
+2026-11-08 19:59:50.449194: validation loss: -0.5933 
+2026-11-08 19:59:50.450198: Average global foreground Dice: [np.float32(0.9213), np.float32(0.6351), np.float32(0.7641), np.float32(0.706), np.float32(0.6719), np.float32(0.7777), np.float32(0.7011), np.float32(0.4041), np.float32(0.0067), np.float32(0.3642), np.float32(0.5583), np.float32(0.6778), np.float32(0.5866), np.float32(0.5299), np.float32(0.0), np.float32(0.0), np.float32(0.7142), np.float32(0.5381), np.float32(0.7262), np.float32(0.6397), np.float32(0.5485), np.float32(0.6113), np.float32(0.8784), np.float32(0.8586), np.float32(0.4889), np.float32(0.5005), np.float32(0.1874), np.float32(0.0), np.float32(0.2586), np.float32(0.6731), np.float32(0.0), np.float32(0.0), np.float32(0.6058), np.float32(0.6368), np.float32(0.7926), np.float32(0.7794)] 
+2026-11-08 19:59:50.450937: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 19:59:50.782911: lr: 0.000706 
+2026-11-08 19:59:50.783140: This epoch took 270.120416 s
+ 
+2026-11-08 19:59:50.783226: 
+epoch:  321 
+2026-11-08 20:03:45.602587: train loss : -0.7889 
+2026-11-08 20:03:53.678844: validation loss: -0.5750 
+2026-11-08 20:03:53.679869: Average global foreground Dice: [np.float32(0.9293), np.float32(0.6092), np.float32(0.751), np.float32(0.7618), np.float32(0.6778), np.float32(0.8074), np.float32(0.6738), np.float32(0.5911), np.float32(0.0815), np.float32(0.3445), np.float32(0.5458), np.float32(0.6172), np.float32(0.5662), np.float32(0.5037), np.float32(0.0), np.float32(0.0), np.float32(0.7255), np.float32(0.549), np.float32(0.7153), np.float32(0.5904), np.float32(0.534), np.float32(0.6297), np.float32(0.8209), np.float32(0.8119), np.float32(0.5554), np.float32(0.4445), np.float32(0.0908), np.float32(0.0), np.float32(0.3424), np.float32(0.5368), np.float32(0.0), np.float32(0.0), np.float32(0.5756), np.float32(0.6154), np.float32(0.7914), np.float32(0.8125)] 
+2026-11-08 20:03:53.680098: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:03:54.013834: lr: 0.000705 
+2026-11-08 20:03:54.014053: This epoch took 243.230771 s
+ 
+2026-11-08 20:03:54.014135: 
+epoch:  322 
+2026-11-08 20:07:58.603065: train loss : -0.8149 
+2026-11-08 20:08:06.702519: validation loss: -0.5962 
+2026-11-08 20:08:06.703500: Average global foreground Dice: [np.float32(0.9283), np.float32(0.6266), np.float32(0.7584), np.float32(0.7999), np.float32(0.7025), np.float32(0.8326), np.float32(0.7322), np.float32(0.5439), np.float32(0.1012), np.float32(0.3263), np.float32(0.5778), np.float32(0.6388), np.float32(0.588), np.float32(0.514), np.float32(0.0), np.float32(0.0), np.float32(0.7433), np.float32(0.5955), np.float32(0.7434), np.float32(0.6187), np.float32(0.566), np.float32(0.6711), np.float32(0.8336), np.float32(0.8422), np.float32(0.5472), np.float32(0.4201), np.float32(0.2852), np.float32(0.0), np.float32(0.331), np.float32(0.6358), np.float32(0.0), np.float32(0.0), np.float32(0.6305), np.float32(0.5855), np.float32(0.8257), np.float32(0.8367)] 
+2026-11-08 20:08:06.703718: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:08:07.168561: lr: 0.000704 
+2026-11-08 20:08:07.168837: This epoch took 253.154428 s
+ 
+2026-11-08 20:08:07.168934: 
+epoch:  323 
+2026-11-08 20:12:10.643173: train loss : -0.7986 
+2026-11-08 20:12:18.724477: validation loss: -0.5469 
+2026-11-08 20:12:18.725359: Average global foreground Dice: [np.float32(0.9256), np.float32(0.5728), np.float32(0.7223), np.float32(0.6961), np.float32(0.6607), np.float32(0.7548), np.float32(0.6673), np.float32(0.5602), np.float32(0.0742), np.float32(0.3417), np.float32(0.483), np.float32(0.6368), np.float32(0.5835), np.float32(0.4911), np.float32(0.0), np.float32(0.0), np.float32(0.7028), np.float32(0.546), np.float32(0.7048), np.float32(0.5763), np.float32(0.4566), np.float32(0.5115), np.float32(0.8197), np.float32(0.8047), np.float32(0.5041), np.float32(0.4765), np.float32(0.1165), np.float32(0.0), np.float32(0.3938), np.float32(0.608), np.float32(0.0), np.float32(0.0), np.float32(0.4145), np.float32(0.4318), np.float32(0.7366), np.float32(0.7627)] 
+2026-11-08 20:12:18.725936: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:12:19.058465: lr: 0.000703 
+2026-11-08 20:12:19.058734: This epoch took 251.889727 s
+ 
+2026-11-08 20:12:19.058828: 
+epoch:  324 
+2026-11-08 20:16:20.647829: train loss : -0.7940 
+2026-11-08 20:16:28.774866: validation loss: -0.5435 
+2026-11-08 20:16:28.775968: Average global foreground Dice: [np.float32(0.9125), np.float32(0.5524), np.float32(0.6519), np.float32(0.621), np.float32(0.5217), np.float32(0.6924), np.float32(0.6068), np.float32(0.7441), np.float32(0.3727), np.float32(0.4341), np.float32(0.4673), np.float32(0.5688), np.float32(0.5801), np.float32(0.463), np.float32(0.0), np.float32(0.0), np.float32(0.5516), np.float32(0.4385), np.float32(0.5925), np.float32(0.5135), np.float32(0.5128), np.float32(0.5411), np.float32(0.792), np.float32(0.7864), np.float32(0.3982), np.float32(0.3557), np.float32(0.1708), np.float32(0.0), np.float32(0.3475), np.float32(0.6795), np.float32(0.0), np.float32(0.0), np.float32(0.4964), np.float32(0.5254), np.float32(0.6928), np.float32(0.7348)] 
+2026-11-08 20:16:28.776175: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:16:29.106406: lr: 0.000702 
+2026-11-08 20:16:29.106774: This epoch took 250.047883 s
+ 
+2026-11-08 20:16:29.107042: 
+epoch:  325 
+2026-11-08 20:20:45.054725: train loss : -0.7963 
+2026-11-08 20:20:53.142443: validation loss: -0.5579 
+2026-11-08 20:20:53.143474: Average global foreground Dice: [np.float32(0.9149), np.float32(0.6188), np.float32(0.7168), np.float32(0.6912), np.float32(0.6427), np.float32(0.7307), np.float32(0.664), np.float32(0.6047), np.float32(0.2813), np.float32(0.3738), np.float32(0.4933), np.float32(0.4987), np.float32(0.5661), np.float32(0.4895), np.float32(0.0), np.float32(0.0), np.float32(0.72), np.float32(0.584), np.float32(0.7198), np.float32(0.6107), np.float32(0.5346), np.float32(0.5893), np.float32(0.8408), np.float32(0.825), np.float32(0.4604), np.float32(0.4504), np.float32(0.2191), np.float32(0.0), np.float32(0.186), np.float32(0.6798), np.float32(0.0), np.float32(0.0), np.float32(0.5873), np.float32(0.5521), np.float32(0.7656), np.float32(0.797)] 
+2026-11-08 20:20:53.144230: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:20:53.467781: lr: 0.000701 
+2026-11-08 20:20:53.468019: This epoch took 264.360756 s
+ 
+2026-11-08 20:20:53.468096: 
+epoch:  326 
+2026-11-08 20:24:31.158379: train loss : -0.8066 
+2026-11-08 20:24:39.222664: validation loss: -0.5964 
+2026-11-08 20:24:39.223572: Average global foreground Dice: [np.float32(0.9225), np.float32(0.66), np.float32(0.7176), np.float32(0.7432), np.float32(0.6423), np.float32(0.7884), np.float32(0.6842), np.float32(0.6555), np.float32(0.2086), np.float32(0.4731), np.float32(0.6306), np.float32(0.6817), np.float32(0.6253), np.float32(0.5496), np.float32(0.0), np.float32(0.0), np.float32(0.6942), np.float32(0.5719), np.float32(0.6909), np.float32(0.611), np.float32(0.5412), np.float32(0.6111), np.float32(0.851), np.float32(0.8348), np.float32(0.4481), np.float32(0.4626), np.float32(0.2494), np.float32(0.0), np.float32(0.3123), np.float32(0.7229), np.float32(0.0), np.float32(0.0), np.float32(0.5928), np.float32(0.6165), np.float32(0.7798), np.float32(0.7971)] 
+2026-11-08 20:24:39.223734: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:24:39.669062: lr: 0.0007 
+2026-11-08 20:24:39.669325: This epoch took 226.201001 s
+ 
+2026-11-08 20:24:39.669421: 
+epoch:  327 
+2026-11-08 20:28:47.305721: train loss : -0.8113 
+2026-11-08 20:28:55.370894: validation loss: -0.5609 
+2026-11-08 20:28:55.371995: Average global foreground Dice: [np.float32(0.9168), np.float32(0.5951), np.float32(0.6995), np.float32(0.7295), np.float32(0.6506), np.float32(0.781), np.float32(0.6767), np.float32(0.6671), np.float32(0.2234), np.float32(0.4506), np.float32(0.536), np.float32(0.6452), np.float32(0.5454), np.float32(0.4369), np.float32(0.0), np.float32(0.0), np.float32(0.7111), np.float32(0.556), np.float32(0.6981), np.float32(0.607), np.float32(0.478), np.float32(0.5921), np.float32(0.8065), np.float32(0.7952), np.float32(0.5318), np.float32(0.3326), np.float32(0.2549), np.float32(0.0), np.float32(0.1232), np.float32(0.6727), np.float32(0.0), np.float32(0.0), np.float32(0.5232), np.float32(0.6483), np.float32(0.7666), np.float32(0.772)] 
+2026-11-08 20:28:55.372212: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:28:55.704043: lr: 0.000699 
+2026-11-08 20:28:55.704530: This epoch took 256.035038 s
+ 
+2026-11-08 20:28:55.704815: 
+epoch:  328 
+2026-11-08 20:33:27.309046: train loss : -0.7978 
+2026-11-08 20:33:35.407062: validation loss: -0.5952 
+2026-11-08 20:33:35.407969: Average global foreground Dice: [np.float32(0.9203), np.float32(0.6289), np.float32(0.7176), np.float32(0.7484), np.float32(0.6276), np.float32(0.7986), np.float32(0.6986), np.float32(0.6865), np.float32(0.1717), np.float32(0.3374), np.float32(0.5916), np.float32(0.6793), np.float32(0.6034), np.float32(0.5146), np.float32(0.0), np.float32(0.0), np.float32(0.7176), np.float32(0.5771), np.float32(0.7119), np.float32(0.6075), np.float32(0.5615), np.float32(0.6435), np.float32(0.8288), np.float32(0.814), np.float32(0.486), np.float32(0.4068), np.float32(0.3013), np.float32(0.0), np.float32(0.3392), np.float32(0.7038), np.float32(0.0), np.float32(0.0), np.float32(0.5193), np.float32(0.6803), np.float32(0.7828), np.float32(0.7965)] 
+2026-11-08 20:33:35.408161: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:33:35.774539: lr: 0.000698 
+2026-11-08 20:33:35.774800: This epoch took 280.069867 s
+ 
+2026-11-08 20:33:35.775000: 
+epoch:  329 
+2026-11-08 20:37:38.409011: train loss : -0.8016 
+2026-11-08 20:37:46.536486: validation loss: -0.6020 
+2026-11-08 20:37:46.537607: Average global foreground Dice: [np.float32(0.9178), np.float32(0.6526), np.float32(0.7451), np.float32(0.7849), np.float32(0.7115), np.float32(0.8326), np.float32(0.7465), np.float32(0.7686), np.float32(0.1368), np.float32(0.4957), np.float32(0.5976), np.float32(0.6524), np.float32(0.593), np.float32(0.5197), np.float32(0.0), np.float32(0.0), np.float32(0.7488), np.float32(0.5791), np.float32(0.7343), np.float32(0.6479), np.float32(0.5481), np.float32(0.614), np.float32(0.8187), np.float32(0.8269), np.float32(0.5506), np.float32(0.4391), np.float32(0.2753), np.float32(0.0), np.float32(0.4049), np.float32(0.7164), np.float32(0.0), np.float32(0.0), np.float32(0.6174), np.float32(0.6386), np.float32(0.8118), np.float32(0.8271)] 
+2026-11-08 20:37:46.537943: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:37:46.871337: lr: 0.000697 
+2026-11-08 20:37:46.871817: This epoch took 251.096723 s
+ 
+2026-11-08 20:37:46.872029: 
+epoch:  330 
+2026-11-08 20:41:50.162770: train loss : -0.7980 
+2026-11-08 20:41:58.263889: validation loss: -0.5701 
+2026-11-08 20:41:58.265329: Average global foreground Dice: [np.float32(0.9204), np.float32(0.6253), np.float32(0.7062), np.float32(0.7131), np.float32(0.6384), np.float32(0.8003), np.float32(0.6804), np.float32(0.4989), np.float32(0.2056), np.float32(0.3757), np.float32(0.5026), np.float32(0.59), np.float32(0.605), np.float32(0.5236), np.float32(0.0), np.float32(0.0), np.float32(0.6096), np.float32(0.5179), np.float32(0.6202), np.float32(0.5319), np.float32(0.4796), np.float32(0.5611), np.float32(0.7997), np.float32(0.8093), np.float32(0.3631), np.float32(0.2726), np.float32(0.0935), np.float32(0.0), np.float32(0.1289), np.float32(0.7334), np.float32(0.0), np.float32(0.0), np.float32(0.4191), np.float32(0.6177), np.float32(0.7479), np.float32(0.792)] 
+2026-11-08 20:41:58.265661: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:41:58.611384: lr: 0.000696 
+2026-11-08 20:41:58.611851: This epoch took 251.739743 s
+ 
+2026-11-08 20:41:58.612191: 
+epoch:  331 
+2026-11-08 20:46:25.581387: train loss : -0.8079 
+2026-11-08 20:46:33.718070: validation loss: -0.5466 
+2026-11-08 20:46:33.718988: Average global foreground Dice: [np.float32(0.9228), np.float32(0.5885), np.float32(0.6855), np.float32(0.701), np.float32(0.5764), np.float32(0.7547), np.float32(0.6354), np.float32(0.6058), np.float32(0.0919), np.float32(0.4453), np.float32(0.528), np.float32(0.5884), np.float32(0.5539), np.float32(0.4781), np.float32(0.0), np.float32(0.0), np.float32(0.6396), np.float32(0.5032), np.float32(0.663), np.float32(0.5718), np.float32(0.4705), np.float32(0.5125), np.float32(0.7478), np.float32(0.7409), np.float32(0.4509), np.float32(0.4204), np.float32(0.2438), np.float32(0.0), np.float32(0.2798), np.float32(0.6525), np.float32(0.0), np.float32(0.0), np.float32(0.5867), np.float32(0.6026), np.float32(0.7227), np.float32(0.7516)] 
+2026-11-08 20:46:33.719571: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:46:34.047689: lr: 0.000696 
+2026-11-08 20:46:34.048108: This epoch took 275.435569 s
+ 
+2026-11-08 20:46:34.048422: 
+epoch:  332 
+2026-11-08 20:50:35.246358: train loss : -0.8080 
+2026-11-08 20:50:43.336998: validation loss: -0.5508 
+2026-11-08 20:50:43.338037: Average global foreground Dice: [np.float32(0.925), np.float32(0.5917), np.float32(0.6471), np.float32(0.6648), np.float32(0.5635), np.float32(0.734), np.float32(0.6079), np.float32(0.5205), np.float32(0.1947), np.float32(0.3694), np.float32(0.4806), np.float32(0.5814), np.float32(0.5483), np.float32(0.5257), np.float32(0.0), np.float32(0.0), np.float32(0.5929), np.float32(0.4629), np.float32(0.6136), np.float32(0.5588), np.float32(0.4493), np.float32(0.5431), np.float32(0.7422), np.float32(0.7316), np.float32(0.3791), np.float32(0.4079), np.float32(0.0097), np.float32(0.0), np.float32(0.3587), np.float32(0.6758), np.float32(0.0), np.float32(0.0), np.float32(0.5488), np.float32(0.5974), np.float32(0.7004), np.float32(0.7366)] 
+2026-11-08 20:50:43.338475: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:50:43.676474: lr: 0.000695 
+2026-11-08 20:50:43.676800: This epoch took 249.628133 s
+ 
+2026-11-08 20:50:43.676891: 
+epoch:  333 
+2026-11-08 20:54:53.658220: train loss : -0.8057 
+2026-11-08 20:55:01.734160: validation loss: -0.6043 
+2026-11-08 20:55:01.735325: Average global foreground Dice: [np.float32(0.9229), np.float32(0.6336), np.float32(0.7644), np.float32(0.757), np.float32(0.6825), np.float32(0.829), np.float32(0.7288), np.float32(0.4035), np.float32(0.3037), np.float32(0.3631), np.float32(0.5996), np.float32(0.6359), np.float32(0.6259), np.float32(0.5788), np.float32(0.0), np.float32(0.0), np.float32(0.7278), np.float32(0.5694), np.float32(0.739), np.float32(0.5841), np.float32(0.5177), np.float32(0.6689), np.float32(0.8349), np.float32(0.8251), np.float32(0.4927), np.float32(0.4462), np.float32(0.2317), np.float32(0.0), np.float32(0.3723), np.float32(0.7349), np.float32(0.0), np.float32(0.0), np.float32(0.6275), np.float32(0.6506), np.float32(0.8009), np.float32(0.8236)] 
+2026-11-08 20:55:01.735591: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:55:02.073526: lr: 0.000694 
+2026-11-08 20:55:02.073778: This epoch took 258.396818 s
+ 
+2026-11-08 20:55:02.073875: 
+epoch:  334 
+2026-11-08 20:59:04.467031: train loss : -0.8090 
+2026-11-08 20:59:12.560250: validation loss: -0.6014 
+2026-11-08 20:59:12.561217: Average global foreground Dice: [np.float32(0.9275), np.float32(0.6484), np.float32(0.7514), np.float32(0.7621), np.float32(0.6711), np.float32(0.8281), np.float32(0.7119), np.float32(0.5413), np.float32(0.3856), np.float32(0.3981), np.float32(0.5953), np.float32(0.6516), np.float32(0.6262), np.float32(0.5791), np.float32(0.0), np.float32(0.0), np.float32(0.7261), np.float32(0.5698), np.float32(0.725), np.float32(0.6216), np.float32(0.5209), np.float32(0.6127), np.float32(0.7959), np.float32(0.7824), np.float32(0.494), np.float32(0.5013), np.float32(0.1181), np.float32(0.0), np.float32(0.4127), np.float32(0.7093), np.float32(0.0), np.float32(0.0), np.float32(0.6122), np.float32(0.6529), np.float32(0.7945), np.float32(0.811)] 
+2026-11-08 20:59:12.561498: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 20:59:12.888222: lr: 0.000693 
+2026-11-08 20:59:12.888689: This epoch took 250.814745 s
+ 
+2026-11-08 20:59:12.888817: 
+epoch:  335 
+2026-11-08 21:03:11.380672: train loss : -0.7914 
+2026-11-08 21:03:19.458335: validation loss: -0.5145 
+2026-11-08 21:03:19.459239: Average global foreground Dice: [np.float32(0.9246), np.float32(0.529), np.float32(0.6047), np.float32(0.5511), np.float32(0.5285), np.float32(0.6328), np.float32(0.5391), np.float32(0.0176), np.float32(0.0204), np.float32(0.4328), np.float32(0.4052), np.float32(0.5194), np.float32(0.577), np.float32(0.4961), np.float32(0.0), np.float32(0.0), np.float32(0.5141), np.float32(0.4211), np.float32(0.5176), np.float32(0.5142), np.float32(0.5262), np.float32(0.5838), np.float32(0.7174), np.float32(0.7073), np.float32(0.3449), np.float32(0.363), np.float32(0.1629), np.float32(0.0), np.float32(0.2302), np.float32(0.6367), np.float32(0.0), np.float32(0.0), np.float32(0.4019), np.float32(0.4451), np.float32(0.642), np.float32(0.6922)] 
+2026-11-08 21:03:19.459563: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:03:19.809373: lr: 0.000692 
+2026-11-08 21:03:19.809628: This epoch took 246.920740 s
+ 
+2026-11-08 21:03:19.809884: 
+epoch:  336 
+2026-11-08 21:07:28.581381: train loss : -0.8106 
+2026-11-08 21:07:36.727054: validation loss: -0.4601 
+2026-11-08 21:07:36.728227: Average global foreground Dice: [np.float32(0.925), np.float32(0.4729), np.float32(0.501), np.float32(0.497), np.float32(0.4228), np.float32(0.5749), np.float32(0.4714), np.float32(0.3989), np.float32(0.3737), np.float32(0.4219), np.float32(0.3943), np.float32(0.5205), np.float32(0.5578), np.float32(0.4994), np.float32(0.0), np.float32(0.0), np.float32(0.3843), np.float32(0.3182), np.float32(0.4014), np.float32(0.3957), np.float32(0.3882), np.float32(0.3663), np.float32(0.6333), np.float32(0.5852), np.float32(0.1905), np.float32(0.271), np.float32(0.018), np.float32(0.0), np.float32(0.2133), np.float32(0.6284), np.float32(0.0), np.float32(0.0), np.float32(0.4242), np.float32(0.4828), np.float32(0.5373), np.float32(0.5871)] 
+2026-11-08 21:07:36.728591: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:07:37.064619: lr: 0.000691 
+2026-11-08 21:07:37.064842: This epoch took 257.254857 s
+ 
+2026-11-08 21:07:37.064917: 
+epoch:  337 
+2026-11-08 21:11:42.747325: train loss : -0.8008 
+2026-11-08 21:11:50.850737: validation loss: -0.5419 
+2026-11-08 21:11:50.851851: Average global foreground Dice: [np.float32(0.9188), np.float32(0.546), np.float32(0.6533), np.float32(0.6796), np.float32(0.577), np.float32(0.727), np.float32(0.636), np.float32(0.4875), np.float32(0.4139), np.float32(0.4109), np.float32(0.5343), np.float32(0.6096), np.float32(0.6311), np.float32(0.4504), np.float32(0.0), np.float32(0.0), np.float32(0.6068), np.float32(0.5032), np.float32(0.6311), np.float32(0.5716), np.float32(0.4759), np.float32(0.5122), np.float32(0.6952), np.float32(0.676), np.float32(0.4025), np.float32(0.4215), np.float32(0.0468), np.float32(0.0), np.float32(0.3781), np.float32(0.654), np.float32(0.0), np.float32(0.0), np.float32(0.5775), np.float32(0.5667), np.float32(0.6888), np.float32(0.7242)] 
+2026-11-08 21:11:50.852456: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:11:51.182975: lr: 0.00069 
+2026-11-08 21:11:51.183215: This epoch took 254.118239 s
+ 
+2026-11-08 21:11:51.183304: 
+epoch:  338 
+2026-11-08 21:15:56.124753: train loss : -0.8131 
+2026-11-08 21:16:04.228264: validation loss: -0.5635 
+2026-11-08 21:16:04.229299: Average global foreground Dice: [np.float32(0.9191), np.float32(0.5527), np.float32(0.6355), np.float32(0.6546), np.float32(0.5172), np.float32(0.7123), np.float32(0.6043), np.float32(0.4408), np.float32(0.3856), np.float32(0.489), np.float32(0.5439), np.float32(0.6039), np.float32(0.6348), np.float32(0.4958), np.float32(0.0), np.float32(0.0), np.float32(0.5263), np.float32(0.4226), np.float32(0.5533), np.float32(0.5148), np.float32(0.5312), np.float32(0.5219), np.float32(0.7016), np.float32(0.6647), np.float32(0.3044), np.float32(0.3386), np.float32(0.0688), np.float32(0.0), np.float32(0.3685), np.float32(0.7133), np.float32(0.0), np.float32(0.0), np.float32(0.5016), np.float32(0.546), np.float32(0.6734), np.float32(0.7045)] 
+2026-11-08 21:16:04.229502: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:16:04.569129: lr: 0.000689 
+2026-11-08 21:16:04.569370: This epoch took 253.385977 s
+ 
+2026-11-08 21:16:04.569448: 
+epoch:  339 
+2026-11-08 21:20:41.254562: train loss : -0.8127 
+2026-11-08 21:20:49.420247: validation loss: -0.5604 
+2026-11-08 21:20:49.421274: Average global foreground Dice: [np.float32(0.9288), np.float32(0.5749), np.float32(0.6569), np.float32(0.6942), np.float32(0.598), np.float32(0.7427), np.float32(0.6454), np.float32(0.4817), np.float32(0.162), np.float32(0.4701), np.float32(0.5516), np.float32(0.6075), np.float32(0.6662), np.float32(0.5256), np.float32(0.0), np.float32(0.0), np.float32(0.5866), np.float32(0.4831), np.float32(0.5952), np.float32(0.5317), np.float32(0.5003), np.float32(0.5091), np.float32(0.7772), np.float32(0.7573), np.float32(0.3171), np.float32(0.3898), np.float32(0.0177), np.float32(0.0), np.float32(0.3026), np.float32(0.6806), np.float32(0.0), np.float32(0.0), np.float32(0.5575), np.float32(0.5853), np.float32(0.7061), np.float32(0.7329)] 
+2026-11-08 21:20:49.421577: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:20:49.762935: lr: 0.000688 
+2026-11-08 21:20:49.763178: This epoch took 285.193570 s
+ 
+2026-11-08 21:20:49.763279: 
+epoch:  340 
+2026-11-08 21:24:37.669219: train loss : -0.8150 
+2026-11-08 21:24:45.751140: validation loss: -0.5169 
+2026-11-08 21:24:45.752088: Average global foreground Dice: [np.float32(0.9148), np.float32(0.523), np.float32(0.6096), np.float32(0.6225), np.float32(0.5487), np.float32(0.6728), np.float32(0.5842), np.float32(0.4906), np.float32(0.3326), np.float32(0.3331), np.float32(0.4793), np.float32(0.5496), np.float32(0.5901), np.float32(0.4514), np.float32(0.0), np.float32(0.0), np.float32(0.5494), np.float32(0.445), np.float32(0.5441), np.float32(0.5046), np.float32(0.4106), np.float32(0.4316), np.float32(0.7518), np.float32(0.7172), np.float32(0.3584), np.float32(0.3547), np.float32(0.0178), np.float32(0.0), np.float32(0.1733), np.float32(0.6711), np.float32(0.0), np.float32(0.0), np.float32(0.4769), np.float32(0.5281), np.float32(0.6701), np.float32(0.6994)] 
+2026-11-08 21:24:45.752758: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:24:46.084367: lr: 0.000687 
+2026-11-08 21:24:46.084638: This epoch took 236.321106 s
+ 
+2026-11-08 21:24:46.084774: 
+epoch:  341 
+2026-11-08 21:28:53.838012: train loss : -0.8128 
+2026-11-08 21:29:01.940280: validation loss: -0.5262 
+2026-11-08 21:29:01.941308: Average global foreground Dice: [np.float32(0.9279), np.float32(0.5348), np.float32(0.6076), np.float32(0.6384), np.float32(0.5455), np.float32(0.6908), np.float32(0.5762), np.float32(0.537), np.float32(0.3374), np.float32(0.341), np.float32(0.4857), np.float32(0.5574), np.float32(0.6005), np.float32(0.4571), np.float32(0.0), np.float32(0.0), np.float32(0.5356), np.float32(0.4272), np.float32(0.5416), np.float32(0.5058), np.float32(0.4635), np.float32(0.5057), np.float32(0.7426), np.float32(0.7188), np.float32(0.3071), np.float32(0.3034), np.float32(0.0529), np.float32(0.0), np.float32(0.1679), np.float32(0.6847), np.float32(0.0), np.float32(0.0), np.float32(0.4208), np.float32(0.5161), np.float32(0.6673), np.float32(0.6951)] 
+2026-11-08 21:29:01.941471: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:29:02.279083: lr: 0.000686 
+2026-11-08 21:29:02.279338: This epoch took 256.194483 s
+ 
+2026-11-08 21:29:02.279446: 
+epoch:  342 
+2026-11-08 21:33:01.060543: train loss : -0.7903 
+2026-11-08 21:33:09.140677: validation loss: -0.3878 
+2026-11-08 21:33:09.141654: Average global foreground Dice: [np.float32(0.9138), np.float32(0.4435), np.float32(0.4288), np.float32(0.3657), np.float32(0.3636), np.float32(0.3895), np.float32(0.3755), np.float32(0.3174), np.float32(0.4515), np.float32(0.3272), np.float32(0.2921), np.float32(0.3679), np.float32(0.455), np.float32(0.3505), np.float32(0.0), np.float32(0.0), np.float32(0.3273), np.float32(0.2857), np.float32(0.3454), np.float32(0.362), np.float32(0.3388), np.float32(0.2794), np.float32(0.4572), np.float32(0.4323), np.float32(0.2029), np.float32(0.2314), np.float32(0.0072), np.float32(0.0), np.float32(0.0596), np.float32(0.4251), np.float32(0.0), np.float32(0.0), np.float32(0.3886), np.float32(0.3453), np.float32(0.3797), np.float32(0.4204)] 
+2026-11-08 21:33:09.142016: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:33:09.474525: lr: 0.000685 
+2026-11-08 21:33:09.474820: This epoch took 247.195296 s
+ 
+2026-11-08 21:33:09.474990: 
+epoch:  343 
+2026-11-08 21:37:19.096908: train loss : -0.8082 
+2026-11-08 21:37:27.197313: validation loss: -0.3881 
+2026-11-08 21:37:27.198415: Average global foreground Dice: [np.float32(0.9221), np.float32(0.3876), np.float32(0.3953), np.float32(0.4227), np.float32(0.2992), np.float32(0.4725), np.float32(0.3896), np.float32(0.5016), np.float32(0.1425), np.float32(0.4904), np.float32(0.3444), np.float32(0.3986), np.float32(0.5103), np.float32(0.4013), np.float32(0.0), np.float32(0.0), np.float32(0.25), np.float32(0.2101), np.float32(0.3158), np.float32(0.3011), np.float32(0.3398), np.float32(0.2278), np.float32(0.5081), np.float32(0.4732), np.float32(0.1521), np.float32(0.1535), np.float32(0.0429), np.float32(0.0), np.float32(0.214), np.float32(0.5379), np.float32(0.0), np.float32(0.0), np.float32(0.3322), np.float32(0.3681), np.float32(0.449), np.float32(0.4912)] 
+2026-11-08 21:37:27.198870: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:37:27.526468: lr: 0.000684 
+2026-11-08 21:37:27.526927: This epoch took 258.051847 s
+ 
+2026-11-08 21:37:27.527104: 
+epoch:  344 
+2026-11-08 21:41:10.270939: train loss : -0.8157 
+2026-11-08 21:41:18.360058: validation loss: -0.4465 
+2026-11-08 21:41:18.361460: Average global foreground Dice: [np.float32(0.905), np.float32(0.4526), np.float32(0.4522), np.float32(0.4866), np.float32(0.3654), np.float32(0.5253), np.float32(0.4353), np.float32(0.3196), np.float32(0.1957), np.float32(0.3802), np.float32(0.3907), np.float32(0.4678), np.float32(0.517), np.float32(0.4164), np.float32(0.0), np.float32(0.0), np.float32(0.3233), np.float32(0.2552), np.float32(0.3671), np.float32(0.3667), np.float32(0.341), np.float32(0.2742), np.float32(0.6027), np.float32(0.5752), np.float32(0.167), np.float32(0.2077), np.float32(0.0042), np.float32(0.0), np.float32(0.0592), np.float32(0.1958), np.float32(0.0), np.float32(0.0), np.float32(0.35), np.float32(0.3361), np.float32(0.5164), np.float32(0.5383)] 
+2026-11-08 21:41:18.361787: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:41:18.696842: lr: 0.000683 
+2026-11-08 21:41:18.697073: This epoch took 231.169885 s
+ 
+2026-11-08 21:41:18.697155: 
+epoch:  345 
+2026-11-08 21:45:26.193174: train loss : -0.8086 
+2026-11-08 21:45:34.258427: validation loss: -0.4600 
+2026-11-08 21:45:34.259496: Average global foreground Dice: [np.float32(0.9218), np.float32(0.5015), np.float32(0.5092), np.float32(0.5301), np.float32(0.4318), np.float32(0.5821), np.float32(0.4774), np.float32(0.4229), np.float32(0.2512), np.float32(0.5206), np.float32(0.4277), np.float32(0.5166), np.float32(0.4972), np.float32(0.398), np.float32(0.0), np.float32(0.0), np.float32(0.3708), np.float32(0.3189), np.float32(0.4061), np.float32(0.4018), np.float32(0.3828), np.float32(0.2987), np.float32(0.6925), np.float32(0.652), np.float32(0.1839), np.float32(0.2776), np.float32(0.0082), np.float32(0.0), np.float32(0.1156), np.float32(0.4913), np.float32(0.0), np.float32(0.0), np.float32(0.3661), np.float32(0.4469), np.float32(0.5694), np.float32(0.5976)] 
+2026-11-08 21:45:34.259850: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:45:34.593432: lr: 0.000682 
+2026-11-08 21:45:34.593702: This epoch took 255.896366 s
+ 
+2026-11-08 21:45:34.593892: 
+epoch:  346 
+2026-11-08 21:49:46.643366: train loss : -0.8040 
+2026-11-08 21:49:54.757005: validation loss: -0.4585 
+2026-11-08 21:49:54.757924: Average global foreground Dice: [np.float32(0.9064), np.float32(0.4856), np.float32(0.5077), np.float32(0.5151), np.float32(0.428), np.float32(0.5639), np.float32(0.463), np.float32(0.5931), np.float32(0.1519), np.float32(0.4701), np.float32(0.4034), np.float32(0.4956), np.float32(0.4676), np.float32(0.3798), np.float32(0.0), np.float32(0.0), np.float32(0.3989), np.float32(0.3232), np.float32(0.4167), np.float32(0.4223), np.float32(0.381), np.float32(0.3741), np.float32(0.6512), np.float32(0.605), np.float32(0.1909), np.float32(0.2993), np.float32(0.0048), np.float32(0.0), np.float32(0.1679), np.float32(0.5397), np.float32(0.0), np.float32(0.0), np.float32(0.431), np.float32(0.4369), np.float32(0.5515), np.float32(0.5755)] 
+2026-11-08 21:49:54.758131: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:49:55.142523: lr: 0.000681 
+2026-11-08 21:49:55.142731: This epoch took 260.548762 s
+ 
+2026-11-08 21:49:55.142803: 
+epoch:  347 
+2026-11-08 21:54:04.397498: train loss : -0.8134 
+2026-11-08 21:54:12.480071: validation loss: -0.4575 
+2026-11-08 21:54:12.481020: Average global foreground Dice: [np.float32(0.9215), np.float32(0.4815), np.float32(0.4919), np.float32(0.5011), np.float32(0.408), np.float32(0.5583), np.float32(0.4666), np.float32(0.58), np.float32(0.2018), np.float32(0.5022), np.float32(0.4253), np.float32(0.5132), np.float32(0.5645), np.float32(0.4948), np.float32(0.0), np.float32(0.0), np.float32(0.354), np.float32(0.3269), np.float32(0.3905), np.float32(0.3776), np.float32(0.3138), np.float32(0.2922), np.float32(0.668), np.float32(0.636), np.float32(0.1782), np.float32(0.2575), np.float32(0.0136), np.float32(0.0), np.float32(0.1746), np.float32(0.6708), np.float32(0.0), np.float32(0.0), np.float32(0.3989), np.float32(0.4443), np.float32(0.5459), np.float32(0.5889)] 
+2026-11-08 21:54:12.481415: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:54:12.815221: lr: 0.00068 
+2026-11-08 21:54:12.815608: This epoch took 257.672729 s
+ 
+2026-11-08 21:54:12.815846: 
+epoch:  348 
+2026-11-08 21:58:00.303796: train loss : -0.8233 
+2026-11-08 21:58:08.403137: validation loss: -0.4906 
+2026-11-08 21:58:08.404171: Average global foreground Dice: [np.float32(0.92), np.float32(0.512), np.float32(0.5087), np.float32(0.5383), np.float32(0.421), np.float32(0.5862), np.float32(0.5124), np.float32(0.6587), np.float32(0.276), np.float32(0.4841), np.float32(0.47), np.float32(0.5213), np.float32(0.5585), np.float32(0.4985), np.float32(0.0), np.float32(0.0), np.float32(0.3615), np.float32(0.3137), np.float32(0.421), np.float32(0.4037), np.float32(0.384), np.float32(0.3827), np.float32(0.6676), np.float32(0.6286), np.float32(0.1987), np.float32(0.2332), np.float32(0.0492), np.float32(0.0), np.float32(0.1856), np.float32(0.6104), np.float32(0.0), np.float32(0.0), np.float32(0.4067), np.float32(0.4401), np.float32(0.5774), np.float32(0.6107)] 
+2026-11-08 21:58:08.404476: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 21:58:08.744248: lr: 0.00068 
+2026-11-08 21:58:08.744501: This epoch took 235.928549 s
+ 
+2026-11-08 21:58:08.744608: 
+epoch:  349 
+2026-11-08 22:01:52.085613: train loss : -0.8157 
+2026-11-08 22:02:00.175811: validation loss: -0.4642 
+2026-11-08 22:02:00.177291: Average global foreground Dice: [np.float32(0.9231), np.float32(0.4877), np.float32(0.4865), np.float32(0.5127), np.float32(0.4061), np.float32(0.5619), np.float32(0.4649), np.float32(0.5846), np.float32(0.1878), np.float32(0.5321), np.float32(0.4274), np.float32(0.4996), np.float32(0.5463), np.float32(0.4733), np.float32(0.0), np.float32(0.0), np.float32(0.3505), np.float32(0.2903), np.float32(0.3835), np.float32(0.3783), np.float32(0.3475), np.float32(0.3091), np.float32(0.6423), np.float32(0.6149), np.float32(0.1896), np.float32(0.2221), np.float32(0.0005), np.float32(0.0), np.float32(0.1805), np.float32(0.5707), np.float32(0.0), np.float32(0.0), np.float32(0.3949), np.float32(0.4422), np.float32(0.5596), np.float32(0.5865)] 
+2026-11-08 22:02:00.177649: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:02:00.517229: lr: 0.000679 
+2026-11-08 22:02:00.517562: saving scheduled checkpoint file... 
+2026-11-08 22:02:00.921605: saving checkpoint... 
+2026-11-08 22:02:01.510216: done, saving took 0.99 seconds 
+2026-11-08 22:02:01.512658: done 
+2026-11-08 22:02:01.512882: This epoch took 232.768084 s
+ 
+2026-11-08 22:02:01.513058: 
+epoch:  350 
+2026-11-08 22:06:02.825085: train loss : -0.8123 
+2026-11-08 22:06:10.934136: validation loss: -0.4864 
+2026-11-08 22:06:10.935238: Average global foreground Dice: [np.float32(0.917), np.float32(0.5017), np.float32(0.5183), np.float32(0.5255), np.float32(0.4463), np.float32(0.5871), np.float32(0.4785), np.float32(0.5198), np.float32(0.2534), np.float32(0.4266), np.float32(0.4443), np.float32(0.5555), np.float32(0.5623), np.float32(0.4793), np.float32(0.0), np.float32(0.0), np.float32(0.4024), np.float32(0.359), np.float32(0.4117), np.float32(0.4183), np.float32(0.3761), np.float32(0.3516), np.float32(0.6369), np.float32(0.5871), np.float32(0.1935), np.float32(0.2567), np.float32(0.0271), np.float32(0.0), np.float32(0.1921), np.float32(0.6359), np.float32(0.0), np.float32(0.0), np.float32(0.4485), np.float32(0.4923), np.float32(0.5639), np.float32(0.6047)] 
+2026-11-08 22:06:10.935585: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:06:11.278596: lr: 0.000678 
+2026-11-08 22:06:11.278813: This epoch took 249.765680 s
+ 
+2026-11-08 22:06:11.278893: 
+epoch:  351 
+2026-11-08 22:10:03.008396: train loss : -0.8149 
+2026-11-08 22:10:11.076434: validation loss: -0.5364 
+2026-11-08 22:10:11.077735: Average global foreground Dice: [np.float32(0.9245), np.float32(0.5448), np.float32(0.5831), np.float32(0.5869), np.float32(0.4927), np.float32(0.6563), np.float32(0.5627), np.float32(0.5476), np.float32(0.3412), np.float32(0.4904), np.float32(0.5177), np.float32(0.5974), np.float32(0.6381), np.float32(0.6281), np.float32(0.0), np.float32(0.0), np.float32(0.4254), np.float32(0.3894), np.float32(0.4751), np.float32(0.4289), np.float32(0.4578), np.float32(0.3414), np.float32(0.771), np.float32(0.7551), np.float32(0.2249), np.float32(0.328), np.float32(0.0184), np.float32(0.0), np.float32(0.1511), np.float32(0.735), np.float32(0.0), np.float32(0.0), np.float32(0.5027), np.float32(0.4843), np.float32(0.6457), np.float32(0.6915)] 
+2026-11-08 22:10:11.078096: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:10:11.410052: lr: 0.000677 
+2026-11-08 22:10:11.410256: This epoch took 240.131303 s
+ 
+2026-11-08 22:10:11.410335: 
+epoch:  352 
+2026-11-08 22:14:21.006665: train loss : -0.8107 
+2026-11-08 22:14:29.066998: validation loss: -0.5062 
+2026-11-08 22:14:29.067971: Average global foreground Dice: [np.float32(0.908), np.float32(0.5585), np.float32(0.5345), np.float32(0.5532), np.float32(0.4711), np.float32(0.6291), np.float32(0.5161), np.float32(0.5295), np.float32(0.3773), np.float32(0.3862), np.float32(0.487), np.float32(0.5588), np.float32(0.5939), np.float32(0.563), np.float32(0.0), np.float32(0.0), np.float32(0.4157), np.float32(0.3946), np.float32(0.4357), np.float32(0.4151), np.float32(0.4568), np.float32(0.3276), np.float32(0.6375), np.float32(0.6251), np.float32(0.2057), np.float32(0.2771), np.float32(0.0), np.float32(0.0), np.float32(0.2586), np.float32(0.5082), np.float32(0.0), np.float32(0.0), np.float32(0.4507), np.float32(0.3841), np.float32(0.6005), np.float32(0.6389)] 
+2026-11-08 22:14:29.068179: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:14:29.452872: lr: 0.000676 
+2026-11-08 22:14:29.453074: This epoch took 258.042681 s
+ 
+2026-11-08 22:14:29.453150: 
+epoch:  353 
+2026-11-08 22:18:15.097834: train loss : -0.8185 
+2026-11-08 22:18:23.200555: validation loss: -0.4766 
+2026-11-08 22:18:23.201561: Average global foreground Dice: [np.float32(0.9296), np.float32(0.4932), np.float32(0.4948), np.float32(0.5204), np.float32(0.3979), np.float32(0.5802), np.float32(0.4851), np.float32(0.5313), np.float32(0.3325), np.float32(0.45), np.float32(0.4279), np.float32(0.5128), np.float32(0.5783), np.float32(0.5123), np.float32(0.0), np.float32(0.0), np.float32(0.3233), np.float32(0.304), np.float32(0.3829), np.float32(0.3531), np.float32(0.4515), np.float32(0.3398), np.float32(0.6535), np.float32(0.6249), np.float32(0.1948), np.float32(0.2175), np.float32(0.0392), np.float32(0.0), np.float32(0.2147), np.float32(0.6821), np.float32(0.0), np.float32(0.0), np.float32(0.4008), np.float32(0.4151), np.float32(0.5501), np.float32(0.5909)] 
+2026-11-08 22:18:23.201743: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:18:23.538062: lr: 0.000675 
+2026-11-08 22:18:23.538433: This epoch took 234.085231 s
+ 
+2026-11-08 22:18:23.538656: 
+epoch:  354 
+2026-11-08 22:22:38.554202: train loss : -0.8198 
+2026-11-08 22:22:46.651516: validation loss: -0.5367 
+2026-11-08 22:22:46.652539: Average global foreground Dice: [np.float32(0.9267), np.float32(0.5685), np.float32(0.6034), np.float32(0.6183), np.float32(0.5244), np.float32(0.684), np.float32(0.5758), np.float32(0.4944), np.float32(0.1629), np.float32(0.3693), np.float32(0.4899), np.float32(0.5727), np.float32(0.6027), np.float32(0.5527), np.float32(0.0), np.float32(0.0), np.float32(0.4938), np.float32(0.4383), np.float32(0.5055), np.float32(0.4677), np.float32(0.4464), np.float32(0.3786), np.float32(0.7254), np.float32(0.7152), np.float32(0.276), np.float32(0.3475), np.float32(0.0877), np.float32(0.0), np.float32(0.3488), np.float32(0.6588), np.float32(0.0), np.float32(0.0), np.float32(0.5277), np.float32(0.5497), np.float32(0.6574), np.float32(0.6915)] 
+2026-11-08 22:22:46.653103: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:22:46.998327: lr: 0.000674 
+2026-11-08 22:22:46.998562: This epoch took 263.459826 s
+ 
+2026-11-08 22:22:46.998635: 
+epoch:  355 
+2026-11-08 22:26:18.323799: train loss : -0.8233 
+2026-11-08 22:26:26.421307: validation loss: -0.5946 
+2026-11-08 22:26:26.422508: Average global foreground Dice: [np.float32(0.9194), np.float32(0.6285), np.float32(0.7432), np.float32(0.7745), np.float32(0.6895), np.float32(0.8272), np.float32(0.7264), np.float32(0.5838), np.float32(0.2163), np.float32(0.4017), np.float32(0.6189), np.float32(0.663), np.float32(0.602), np.float32(0.5294), np.float32(0.0), np.float32(0.0), np.float32(0.7305), np.float32(0.5805), np.float32(0.7218), np.float32(0.6071), np.float32(0.5407), np.float32(0.6448), np.float32(0.7928), np.float32(0.7775), np.float32(0.5013), np.float32(0.5392), np.float32(0.2125), np.float32(0.0), np.float32(0.3336), np.float32(0.6645), np.float32(0.0), np.float32(0.0), np.float32(0.6087), np.float32(0.5825), np.float32(0.7925), np.float32(0.8154)] 
+2026-11-08 22:26:26.422693: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:26:26.767084: lr: 0.000673 
+2026-11-08 22:26:26.767352: This epoch took 219.768664 s
+ 
+2026-11-08 22:26:26.767454: 
+epoch:  356 
+2026-11-08 22:30:50.672188: train loss : -0.8160 
+2026-11-08 22:30:58.796663: validation loss: -0.5533 
+2026-11-08 22:30:58.797624: Average global foreground Dice: [np.float32(0.915), np.float32(0.577), np.float32(0.6867), np.float32(0.7023), np.float32(0.6115), np.float32(0.7536), np.float32(0.6542), np.float32(0.5911), np.float32(0.4115), np.float32(0.2688), np.float32(0.5284), np.float32(0.6213), np.float32(0.5114), np.float32(0.4552), np.float32(0.0), np.float32(0.0108), np.float32(0.6529), np.float32(0.529), np.float32(0.6511), np.float32(0.5552), np.float32(0.4969), np.float32(0.6023), np.float32(0.774), np.float32(0.7654), np.float32(0.4913), np.float32(0.4936), np.float32(0.1941), np.float32(0.0), np.float32(0.2687), np.float32(0.597), np.float32(0.0), np.float32(0.0), np.float32(0.5556), np.float32(0.589), np.float32(0.7336), np.float32(0.7663)] 
+2026-11-08 22:30:58.797793: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:30:59.133149: lr: 0.000672 
+2026-11-08 22:30:59.133409: This epoch took 272.365883 s
+ 
+2026-11-08 22:30:59.133653: 
+epoch:  357 
+2026-11-08 22:35:03.836634: train loss : -0.8153 
+2026-11-08 22:35:11.970335: validation loss: -0.5870 
+2026-11-08 22:35:11.971496: Average global foreground Dice: [np.float32(0.9144), np.float32(0.5894), np.float32(0.7457), np.float32(0.7404), np.float32(0.6423), np.float32(0.805), np.float32(0.7083), np.float32(0.4417), np.float32(0.3454), np.float32(0.3715), np.float32(0.5585), np.float32(0.6408), np.float32(0.5698), np.float32(0.5119), np.float32(0.0), np.float32(0.0), np.float32(0.6665), np.float32(0.5101), np.float32(0.6642), np.float32(0.5577), np.float32(0.522), np.float32(0.6262), np.float32(0.8344), np.float32(0.8221), np.float32(0.5322), np.float32(0.4937), np.float32(0.2036), np.float32(0.0), np.float32(0.3675), np.float32(0.6771), np.float32(0.0), np.float32(0.0), np.float32(0.4609), np.float32(0.5643), np.float32(0.7885), np.float32(0.8112)] 
+2026-11-08 22:35:11.971891: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:35:12.304655: lr: 0.000671 
+2026-11-08 22:35:12.304972: This epoch took 253.171225 s
+ 
+2026-11-08 22:35:12.305185: 
+epoch:  358 
+2026-11-08 22:39:18.143479: train loss : -0.8135 
+2026-11-08 22:39:26.227751: validation loss: -0.4878 
+2026-11-08 22:39:26.228853: Average global foreground Dice: [np.float32(0.9009), np.float32(0.4858), np.float32(0.54), np.float32(0.5762), np.float32(0.433), np.float32(0.6301), np.float32(0.4889), np.float32(0.4968), np.float32(0.3228), np.float32(0.5541), np.float32(0.4446), np.float32(0.523), np.float32(0.6034), np.float32(0.4949), np.float32(0.0), np.float32(0.0), np.float32(0.3432), np.float32(0.3028), np.float32(0.4098), np.float32(0.3797), np.float32(0.4344), np.float32(0.2996), np.float32(0.6895), np.float32(0.6376), np.float32(0.2839), np.float32(0.2404), np.float32(0.0989), np.float32(0.0), np.float32(0.1176), np.float32(0.5587), np.float32(0.0), np.float32(0.0), np.float32(0.421), np.float32(0.4445), np.float32(0.5809), np.float32(0.6117)] 
+2026-11-08 22:39:26.229374: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:39:26.596941: lr: 0.00067 
+2026-11-08 22:39:26.597186: This epoch took 254.291843 s
+ 
+2026-11-08 22:39:26.597257: 
+epoch:  359 
+2026-11-08 22:43:41.698597: train loss : -0.8091 
+2026-11-08 22:43:49.800246: validation loss: -0.5117 
+2026-11-08 22:43:49.801437: Average global foreground Dice: [np.float32(0.9193), np.float32(0.496), np.float32(0.5778), np.float32(0.5893), np.float32(0.4773), np.float32(0.6509), np.float32(0.574), np.float32(0.6052), np.float32(0.3309), np.float32(0.4089), np.float32(0.4457), np.float32(0.5205), np.float32(0.5591), np.float32(0.5029), np.float32(0.0), np.float32(0.0), np.float32(0.4898), np.float32(0.4008), np.float32(0.5294), np.float32(0.4789), np.float32(0.4466), np.float32(0.4105), np.float32(0.6383), np.float32(0.6151), np.float32(0.3188), np.float32(0.2463), np.float32(0.0088), np.float32(0.0), np.float32(0.2196), np.float32(0.587), np.float32(0.0), np.float32(0.0), np.float32(0.4957), np.float32(0.4938), np.float32(0.6256), np.float32(0.6685)] 
+2026-11-08 22:43:49.801739: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:43:50.148545: lr: 0.000669 
+2026-11-08 22:43:50.149033: This epoch took 263.551719 s
+ 
+2026-11-08 22:43:50.149395: 
+epoch:  360 
+2026-11-08 22:47:58.917369: train loss : -0.8108 
+2026-11-08 22:48:07.034262: validation loss: -0.3223 
+2026-11-08 22:48:07.035374: Average global foreground Dice: [np.float32(0.9253), np.float32(0.3432), np.float32(0.3221), np.float32(0.3153), np.float32(0.2468), np.float32(0.3319), np.float32(0.2803), np.float32(0.4041), np.float32(0.2114), np.float32(0.2142), np.float32(0.2472), np.float32(0.3273), np.float32(0.4354), np.float32(0.2877), np.float32(0.0), np.float32(0.0), np.float32(0.206), np.float32(0.1736), np.float32(0.2229), np.float32(0.186), np.float32(0.1525), np.float32(0.0441), np.float32(0.4435), np.float32(0.3951), np.float32(0.1284), np.float32(0.1484), np.float32(0.0), np.float32(0.0), np.float32(0.11), np.float32(0.3911), np.float32(0.0), np.float32(0.0), np.float32(0.24), np.float32(0.2659), np.float32(0.3566), np.float32(0.3816)] 
+2026-11-08 22:48:07.035802: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:48:07.380113: lr: 0.000668 
+2026-11-08 22:48:07.380355: This epoch took 257.230370 s
+ 
+2026-11-08 22:48:07.380613: 
+epoch:  361 
+2026-11-08 22:52:25.793006: train loss : -0.8155 
+2026-11-08 22:52:33.888883: validation loss: -0.5822 
+2026-11-08 22:52:33.890060: Average global foreground Dice: [np.float32(0.9309), np.float32(0.6099), np.float32(0.7339), np.float32(0.7719), np.float32(0.6876), np.float32(0.796), np.float32(0.7089), np.float32(0.6602), np.float32(0.3836), np.float32(0.4204), np.float32(0.6099), np.float32(0.6345), np.float32(0.5841), np.float32(0.4377), np.float32(0.0), np.float32(0.0), np.float32(0.7193), np.float32(0.5346), np.float32(0.6961), np.float32(0.6085), np.float32(0.5021), np.float32(0.601), np.float32(0.812), np.float32(0.8029), np.float32(0.5114), np.float32(0.4514), np.float32(0.1761), np.float32(0.0), np.float32(0.3252), np.float32(0.6428), np.float32(0.0), np.float32(0.0), np.float32(0.5901), np.float32(0.6313), np.float32(0.8009), np.float32(0.8062)] 
+2026-11-08 22:52:33.890426: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:52:34.235399: lr: 0.000667 
+2026-11-08 22:52:34.235628: This epoch took 266.854914 s
+ 
+2026-11-08 22:52:34.235706: 
+epoch:  362 
+2026-11-08 22:56:21.978266: train loss : -0.8181 
+2026-11-08 22:56:30.047742: validation loss: -0.5605 
+2026-11-08 22:56:30.048766: Average global foreground Dice: [np.float32(0.9262), np.float32(0.5833), np.float32(0.6857), np.float32(0.7079), np.float32(0.5952), np.float32(0.7517), np.float32(0.6453), np.float32(0.7), np.float32(0.2302), np.float32(0.3683), np.float32(0.5552), np.float32(0.611), np.float32(0.5803), np.float32(0.4961), np.float32(0.0), np.float32(0.0), np.float32(0.6085), np.float32(0.4698), np.float32(0.603), np.float32(0.5512), np.float32(0.4667), np.float32(0.5437), np.float32(0.7391), np.float32(0.7259), np.float32(0.4015), np.float32(0.3345), np.float32(0.0974), np.float32(0.0182), np.float32(0.2707), np.float32(0.6913), np.float32(0.0), np.float32(0.0), np.float32(0.5343), np.float32(0.5584), np.float32(0.7386), np.float32(0.7527)] 
+2026-11-08 22:56:30.048931: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 22:56:30.403532: lr: 0.000666 
+2026-11-08 22:56:30.403867: This epoch took 236.168093 s
+ 
+2026-11-08 22:56:30.404004: 
+epoch:  363 
+2026-11-08 23:00:37.930616: train loss : -0.8054 
+2026-11-08 23:00:46.067070: validation loss: -0.4708 
+2026-11-08 23:00:46.068098: Average global foreground Dice: [np.float32(0.9137), np.float32(0.5135), np.float32(0.5082), np.float32(0.5203), np.float32(0.3943), np.float32(0.5768), np.float32(0.4739), np.float32(0.631), np.float32(0.2218), np.float32(0.4159), np.float32(0.4258), np.float32(0.4739), np.float32(0.5524), np.float32(0.4637), np.float32(0.0), np.float32(0.0), np.float32(0.3456), np.float32(0.2947), np.float32(0.3762), np.float32(0.3626), np.float32(0.4252), np.float32(0.2944), np.float32(0.6401), np.float32(0.6098), np.float32(0.1681), np.float32(0.2336), np.float32(0.0536), np.float32(0.0323), np.float32(0.2637), np.float32(0.5568), np.float32(0.0), np.float32(0.0), np.float32(0.4214), np.float32(0.373), np.float32(0.547), np.float32(0.5896)] 
+2026-11-08 23:00:46.068282: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:00:46.404380: lr: 0.000665 
+2026-11-08 23:00:46.404643: This epoch took 256.000540 s
+ 
+2026-11-08 23:00:46.404829: 
+epoch:  364 
+2026-11-08 23:05:03.525478: train loss : -0.8174 
+2026-11-08 23:05:11.693845: validation loss: -0.5341 
+2026-11-08 23:05:11.694912: Average global foreground Dice: [np.float32(0.9129), np.float32(0.568), np.float32(0.5921), np.float32(0.6168), np.float32(0.4728), np.float32(0.6666), np.float32(0.5391), np.float32(0.6267), np.float32(0.3304), np.float32(0.5231), np.float32(0.5085), np.float32(0.5611), np.float32(0.5064), np.float32(0.4157), np.float32(0.0), np.float32(0.0), np.float32(0.4862), np.float32(0.3862), np.float32(0.4954), np.float32(0.4899), np.float32(0.5), np.float32(0.3751), np.float32(0.7218), np.float32(0.6932), np.float32(0.2464), np.float32(0.3202), np.float32(0.0694), np.float32(0.0211), np.float32(0.2739), np.float32(0.6597), np.float32(0.0), np.float32(0.0), np.float32(0.4571), np.float32(0.4423), np.float32(0.6562), np.float32(0.6715)] 
+2026-11-08 23:05:11.695440: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:05:12.047280: lr: 0.000665 
+2026-11-08 23:05:12.047703: This epoch took 265.642786 s
+ 
+2026-11-08 23:05:12.048039: 
+epoch:  365 
+2026-11-08 23:09:10.895156: train loss : -0.8215 
+2026-11-08 23:09:19.028962: validation loss: -0.4914 
+2026-11-08 23:09:19.030089: Average global foreground Dice: [np.float32(0.9201), np.float32(0.5152), np.float32(0.5488), np.float32(0.5592), np.float32(0.4755), np.float32(0.619), np.float32(0.5186), np.float32(0.6203), np.float32(0.3058), np.float32(0.4449), np.float32(0.4256), np.float32(0.5226), np.float32(0.5592), np.float32(0.4874), np.float32(0.0), np.float32(0.0), np.float32(0.4272), np.float32(0.3763), np.float32(0.4528), np.float32(0.4284), np.float32(0.3883), np.float32(0.3203), np.float32(0.7138), np.float32(0.6786), np.float32(0.2505), np.float32(0.3078), np.float32(0.0778), np.float32(0.0), np.float32(0.1858), np.float32(0.609), np.float32(0.0), np.float32(0.0), np.float32(0.4909), np.float32(0.4779), np.float32(0.5961), np.float32(0.6453)] 
+2026-11-08 23:09:19.030512: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:09:19.362500: lr: 0.000664 
+2026-11-08 23:09:19.362927: This epoch took 247.314647 s
+ 
+2026-11-08 23:09:19.363189: 
+epoch:  366 
+2026-11-08 23:13:46.543855: train loss : -0.8161 
+2026-11-08 23:13:54.683488: validation loss: -0.4550 
+2026-11-08 23:13:54.684285: Average global foreground Dice: [np.float32(0.9236), np.float32(0.4716), np.float32(0.5169), np.float32(0.4737), np.float32(0.3801), np.float32(0.5531), np.float32(0.461), np.float32(0.5237), np.float32(0.3051), np.float32(0.4765), np.float32(0.4022), np.float32(0.4415), np.float32(0.5301), np.float32(0.4386), np.float32(0.0), np.float32(0.0), np.float32(0.3375), np.float32(0.3181), np.float32(0.4095), np.float32(0.4013), np.float32(0.476), np.float32(0.3365), np.float32(0.6285), np.float32(0.5771), np.float32(0.2628), np.float32(0.2773), np.float32(0.0745), np.float32(0.0), np.float32(0.2406), np.float32(0.5878), np.float32(0.0), np.float32(0.0), np.float32(0.3711), np.float32(0.3502), np.float32(0.5081), np.float32(0.5776)] 
+2026-11-08 23:13:54.684444: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:13:55.050162: lr: 0.000663 
+2026-11-08 23:13:55.050385: This epoch took 275.687000 s
+ 
+2026-11-08 23:13:55.050601: 
+epoch:  367 
+2026-11-08 23:18:06.110959: train loss : -0.8195 
+2026-11-08 23:18:14.263678: validation loss: -0.4633 
+2026-11-08 23:18:14.264832: Average global foreground Dice: [np.float32(0.9318), np.float32(0.4951), np.float32(0.5368), np.float32(0.4945), np.float32(0.3959), np.float32(0.5696), np.float32(0.4727), np.float32(0.5824), np.float32(0.2977), np.float32(0.3338), np.float32(0.405), np.float32(0.4813), np.float32(0.5515), np.float32(0.496), np.float32(0.0), np.float32(0.0), np.float32(0.3439), np.float32(0.3057), np.float32(0.3926), np.float32(0.3761), np.float32(0.4155), np.float32(0.4091), np.float32(0.6775), np.float32(0.6477), np.float32(0.2082), np.float32(0.2659), np.float32(0.044), np.float32(0.0736), np.float32(0.159), np.float32(0.5686), np.float32(0.0), np.float32(0.0), np.float32(0.316), np.float32(0.2864), np.float32(0.5441), np.float32(0.5998)] 
+2026-11-08 23:18:14.265535: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:18:14.610178: lr: 0.000662 
+2026-11-08 23:18:14.610624: This epoch took 259.559941 s
+ 
+2026-11-08 23:18:14.610930: 
+epoch:  368 
+2026-11-08 23:22:23.888024: train loss : -0.8147 
+2026-11-08 23:22:31.959521: validation loss: -0.4170 
+2026-11-08 23:22:31.960575: Average global foreground Dice: [np.float32(0.9309), np.float32(0.4474), np.float32(0.4345), np.float32(0.4224), np.float32(0.3371), np.float32(0.5025), np.float32(0.403), np.float32(0.517), np.float32(0.4197), np.float32(0.4507), np.float32(0.3398), np.float32(0.4279), np.float32(0.4989), np.float32(0.4), np.float32(0.0), np.float32(0.0), np.float32(0.2912), np.float32(0.2604), np.float32(0.325), np.float32(0.312), np.float32(0.3846), np.float32(0.3097), np.float32(0.5846), np.float32(0.5312), np.float32(0.1142), np.float32(0.1826), np.float32(0.0004), np.float32(0.0085), np.float32(0.1017), np.float32(0.4878), np.float32(0.0), np.float32(0.0), np.float32(0.3467), np.float32(0.3462), np.float32(0.4608), np.float32(0.4944)] 
+2026-11-08 23:22:31.960791: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:22:32.304480: lr: 0.000661 
+2026-11-08 23:22:32.304903: This epoch took 257.693754 s
+ 
+2026-11-08 23:22:32.305055: 
+epoch:  369 
+2026-11-08 23:26:36.440016: train loss : -0.8132 
+2026-11-08 23:26:44.526367: validation loss: -0.5931 
+2026-11-08 23:26:44.527401: Average global foreground Dice: [np.float32(0.9222), np.float32(0.6251), np.float32(0.7332), np.float32(0.76), np.float32(0.6427), np.float32(0.8088), np.float32(0.6992), np.float32(0.6634), np.float32(0.3242), np.float32(0.3611), np.float32(0.5786), np.float32(0.631), np.float32(0.5681), np.float32(0.4886), np.float32(0.0), np.float32(0.0), np.float32(0.7066), np.float32(0.5676), np.float32(0.694), np.float32(0.6103), np.float32(0.5735), np.float32(0.6792), np.float32(0.77), np.float32(0.75), np.float32(0.5396), np.float32(0.5054), np.float32(0.2219), np.float32(0.0072), np.float32(0.4338), np.float32(0.687), np.float32(0.0), np.float32(0.0), np.float32(0.5946), np.float32(0.6313), np.float32(0.7812), np.float32(0.8007)] 
+2026-11-08 23:26:44.527569: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:26:44.871440: lr: 0.00066 
+2026-11-08 23:26:44.871919: This epoch took 252.566790 s
+ 
+2026-11-08 23:26:44.872529: 
+epoch:  370 
+2026-11-08 23:30:44.741993: train loss : -0.8210 
+2026-11-08 23:30:52.820966: validation loss: -0.5931 
+2026-11-08 23:30:52.822022: Average global foreground Dice: [np.float32(0.9315), np.float32(0.6765), np.float32(0.7626), np.float32(0.8009), np.float32(0.7), np.float32(0.8328), np.float32(0.7294), np.float32(0.6008), np.float32(0.2944), np.float32(0.3821), np.float32(0.639), np.float32(0.636), np.float32(0.617), np.float32(0.5487), np.float32(0.0198), np.float32(0.0), np.float32(0.7485), np.float32(0.5961), np.float32(0.7254), np.float32(0.6166), np.float32(0.5761), np.float32(0.6564), np.float32(0.8438), np.float32(0.8284), np.float32(0.5427), np.float32(0.4557), np.float32(0.1246), np.float32(0.0104), np.float32(0.3329), np.float32(0.651), np.float32(0.0), np.float32(0.0), np.float32(0.6242), np.float32(0.6373), np.float32(0.8248), np.float32(0.8326)] 
+2026-11-08 23:30:52.822451: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:30:53.158667: lr: 0.000659 
+2026-11-08 23:30:53.159052: This epoch took 248.286402 s
+ 
+2026-11-08 23:30:53.159391: 
+epoch:  371 
+2026-11-08 23:34:32.897178: train loss : -0.8222 
+2026-11-08 23:34:41.010911: validation loss: -0.5609 
+2026-11-08 23:34:41.011872: Average global foreground Dice: [np.float32(0.9208), np.float32(0.566), np.float32(0.7167), np.float32(0.7155), np.float32(0.6052), np.float32(0.7603), np.float32(0.6783), np.float32(0.5802), np.float32(0.3356), np.float32(0.3407), np.float32(0.5418), np.float32(0.5928), np.float32(0.5478), np.float32(0.4694), np.float32(0.0), np.float32(0.0), np.float32(0.6266), np.float32(0.5417), np.float32(0.6425), np.float32(0.5715), np.float32(0.5664), np.float32(0.6434), np.float32(0.7395), np.float32(0.7317), np.float32(0.4454), np.float32(0.3835), np.float32(0.1894), np.float32(0.0281), np.float32(0.2339), np.float32(0.6499), np.float32(0.0), np.float32(0.0), np.float32(0.623), np.float32(0.5688), np.float32(0.7111), np.float32(0.7562)] 
+2026-11-08 23:34:41.012034: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:34:41.355819: lr: 0.000658 
+2026-11-08 23:34:41.356127: This epoch took 228.196512 s
+ 
+2026-11-08 23:34:41.356230: 
+epoch:  372 
+2026-11-08 23:38:48.853955: train loss : -0.8219 
+2026-11-08 23:38:56.977326: validation loss: -0.5520 
+2026-11-08 23:38:56.978372: Average global foreground Dice: [np.float32(0.9144), np.float32(0.5706), np.float32(0.6671), np.float32(0.6884), np.float32(0.5675), np.float32(0.7339), np.float32(0.6289), np.float32(0.4937), np.float32(0.4233), np.float32(0.3749), np.float32(0.5383), np.float32(0.5794), np.float32(0.5736), np.float32(0.5634), np.float32(0.0), np.float32(0.0), np.float32(0.5846), np.float32(0.5042), np.float32(0.6007), np.float32(0.5189), np.float32(0.5235), np.float32(0.5888), np.float32(0.7023), np.float32(0.6848), np.float32(0.407), np.float32(0.3414), np.float32(0.1247), np.float32(0.157), np.float32(0.1465), np.float32(0.6209), np.float32(0.0), np.float32(0.0), np.float32(0.5632), np.float32(0.4988), np.float32(0.6785), np.float32(0.7238)] 
+2026-11-08 23:38:56.978663: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:38:57.317942: lr: 0.000657 
+2026-11-08 23:38:57.318220: This epoch took 255.961918 s
+ 
+2026-11-08 23:38:57.318450: 
+epoch:  373 
+2026-11-08 23:43:05.424359: train loss : -0.8264 
+2026-11-08 23:43:13.560143: validation loss: -0.5917 
+2026-11-08 23:43:13.561357: Average global foreground Dice: [np.float32(0.919), np.float32(0.6183), np.float32(0.7464), np.float32(0.7612), np.float32(0.6558), np.float32(0.7983), np.float32(0.709), np.float32(0.5543), np.float32(0.4014), np.float32(0.3198), np.float32(0.5775), np.float32(0.6244), np.float32(0.6162), np.float32(0.554), np.float32(0.0), np.float32(0.0), np.float32(0.6687), np.float32(0.5799), np.float32(0.6749), np.float32(0.5974), np.float32(0.5964), np.float32(0.6609), np.float32(0.7863), np.float32(0.7767), np.float32(0.4218), np.float32(0.434), np.float32(0.1347), np.float32(0.0323), np.float32(0.2355), np.float32(0.6774), np.float32(0.0), np.float32(0.0), np.float32(0.606), np.float32(0.5573), np.float32(0.7679), np.float32(0.7956)] 
+2026-11-08 23:43:13.561901: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:43:13.912002: lr: 0.000656 
+2026-11-08 23:43:13.912250: This epoch took 256.593704 s
+ 
+2026-11-08 23:43:13.912335: 
+epoch:  374 
+2026-11-08 23:47:19.237250: train loss : -0.8170 
+2026-11-08 23:47:27.378643: validation loss: -0.5074 
+2026-11-08 23:47:27.379748: Average global foreground Dice: [np.float32(0.9145), np.float32(0.5136), np.float32(0.6174), np.float32(0.5965), np.float32(0.5142), np.float32(0.6607), np.float32(0.5662), np.float32(0.5497), np.float32(0.091), np.float32(0.4594), np.float32(0.4713), np.float32(0.5166), np.float32(0.489), np.float32(0.3749), np.float32(0.0), np.float32(0.0), np.float32(0.5595), np.float32(0.486), np.float32(0.5811), np.float32(0.5266), np.float32(0.5156), np.float32(0.5623), np.float32(0.689), np.float32(0.6322), np.float32(0.3916), np.float32(0.3931), np.float32(0.1484), np.float32(0.1969), np.float32(0.1596), np.float32(0.6409), np.float32(0.0), np.float32(0.0), np.float32(0.4949), np.float32(0.4807), np.float32(0.6158), np.float32(0.6579)] 
+2026-11-08 23:47:27.380143: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:47:27.706908: lr: 0.000655 
+2026-11-08 23:47:27.707223: This epoch took 253.794834 s
+ 
+2026-11-08 23:47:27.707420: 
+epoch:  375 
+2026-11-08 23:51:33.646398: train loss : -0.8145 
+2026-11-08 23:51:41.741930: validation loss: -0.5534 
+2026-11-08 23:51:41.743119: Average global foreground Dice: [np.float32(0.9172), np.float32(0.5776), np.float32(0.6503), np.float32(0.6653), np.float32(0.5498), np.float32(0.7321), np.float32(0.5903), np.float32(0.2397), np.float32(0.1271), np.float32(0.3522), np.float32(0.5281), np.float32(0.6143), np.float32(0.5912), np.float32(0.5219), np.float32(0.0036), np.float32(0.0), np.float32(0.5588), np.float32(0.4671), np.float32(0.588), np.float32(0.5313), np.float32(0.5161), np.float32(0.48), np.float32(0.7347), np.float32(0.7022), np.float32(0.3459), np.float32(0.4262), np.float32(0.0142), np.float32(0.0111), np.float32(0.1786), np.float32(0.7227), np.float32(0.0), np.float32(0.0), np.float32(0.5249), np.float32(0.561), np.float32(0.6815), np.float32(0.7216)] 
+2026-11-08 23:51:41.743474: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:51:42.067762: lr: 0.000654 
+2026-11-08 23:51:42.068045: This epoch took 254.360498 s
+ 
+2026-11-08 23:51:42.068210: 
+epoch:  376 
+2026-11-08 23:56:13.973347: train loss : -0.8206 
+2026-11-08 23:56:22.131270: validation loss: -0.5590 
+2026-11-08 23:56:22.132160: Average global foreground Dice: [np.float32(0.9091), np.float32(0.5904), np.float32(0.7006), np.float32(0.7022), np.float32(0.5875), np.float32(0.7569), np.float32(0.6494), np.float32(0.5282), np.float32(0.4433), np.float32(0.4307), np.float32(0.5604), np.float32(0.6095), np.float32(0.616), np.float32(0.526), np.float32(0.0014), np.float32(0.0), np.float32(0.6004), np.float32(0.4971), np.float32(0.6141), np.float32(0.5507), np.float32(0.5206), np.float32(0.5727), np.float32(0.7297), np.float32(0.695), np.float32(0.3514), np.float32(0.3953), np.float32(0.139), np.float32(0.0491), np.float32(0.2412), np.float32(0.6701), np.float32(0.0), np.float32(0.0), np.float32(0.5609), np.float32(0.5635), np.float32(0.7025), np.float32(0.7418)] 
+2026-11-08 23:56:22.132759: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-08 23:56:22.466174: lr: 0.000653 
+2026-11-08 23:56:22.466783: This epoch took 280.398494 s
+ 
+2026-11-08 23:56:22.467041: 
+epoch:  377 
+2026-11-09 00:00:20.459232: train loss : -0.8228 
+2026-11-09 00:00:28.560869: validation loss: -0.5256 
+2026-11-09 00:00:28.561825: Average global foreground Dice: [np.float32(0.9155), np.float32(0.5421), np.float32(0.6304), np.float32(0.6443), np.float32(0.5368), np.float32(0.6921), np.float32(0.5862), np.float32(0.5277), np.float32(0.398), np.float32(0.5331), np.float32(0.4975), np.float32(0.5343), np.float32(0.5557), np.float32(0.4485), np.float32(0.0), np.float32(0.0), np.float32(0.5309), np.float32(0.4402), np.float32(0.5522), np.float32(0.503), np.float32(0.4292), np.float32(0.4989), np.float32(0.7352), np.float32(0.6966), np.float32(0.3747), np.float32(0.3089), np.float32(0.0713), np.float32(0.0604), np.float32(0.2687), np.float32(0.6889), np.float32(0.0), np.float32(0.0), np.float32(0.519), np.float32(0.4901), np.float32(0.6673), np.float32(0.6895)] 
+2026-11-09 00:00:28.562105: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:00:28.899099: lr: 0.000652 
+2026-11-09 00:00:28.899326: This epoch took 246.432028 s
+ 
+2026-11-09 00:00:28.899408: 
+epoch:  378 
+2026-11-09 00:04:40.769384: train loss : -0.8184 
+2026-11-09 00:04:48.859301: validation loss: -0.5040 
+2026-11-09 00:04:48.860231: Average global foreground Dice: [np.float32(0.9233), np.float32(0.5179), np.float32(0.5965), np.float32(0.5885), np.float32(0.4906), np.float32(0.6377), np.float32(0.544), np.float32(0.5885), np.float32(0.4424), np.float32(0.4376), np.float32(0.4608), np.float32(0.5226), np.float32(0.5355), np.float32(0.438), np.float32(0.0051), np.float32(0.0), np.float32(0.4756), np.float32(0.4193), np.float32(0.4985), np.float32(0.4565), np.float32(0.4758), np.float32(0.5313), np.float32(0.6725), np.float32(0.6518), np.float32(0.3432), np.float32(0.3401), np.float32(0.1256), np.float32(0.284), np.float32(0.2892), np.float32(0.5535), np.float32(0.0), np.float32(0.0), np.float32(0.4785), np.float32(0.4877), np.float32(0.6299), np.float32(0.6714)] 
+2026-11-09 00:04:48.860612: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:04:49.179744: lr: 0.000651 
+2026-11-09 00:04:49.179957: This epoch took 260.280493 s
+ 
+2026-11-09 00:04:49.180032: 
+epoch:  379 
+2026-11-09 00:09:08.716618: train loss : -0.8224 
+2026-11-09 00:09:16.875314: validation loss: -0.5780 
+2026-11-09 00:09:16.876341: Average global foreground Dice: [np.float32(0.9275), np.float32(0.5928), np.float32(0.7405), np.float32(0.7128), np.float32(0.6468), np.float32(0.7855), np.float32(0.6902), np.float32(0.59), np.float32(0.4446), np.float32(0.3639), np.float32(0.5435), np.float32(0.62), np.float32(0.5625), np.float32(0.4365), np.float32(0.0), np.float32(0.0), np.float32(0.6666), np.float32(0.5443), np.float32(0.674), np.float32(0.599), np.float32(0.5326), np.float32(0.6146), np.float32(0.8239), np.float32(0.8007), np.float32(0.4632), np.float32(0.4294), np.float32(0.2334), np.float32(0.3232), np.float32(0.2228), np.float32(0.6044), np.float32(0.0), np.float32(0.0), np.float32(0.5655), np.float32(0.6024), np.float32(0.7745), np.float32(0.8061)] 
+2026-11-09 00:09:16.876798: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:09:17.201751: lr: 0.00065 
+2026-11-09 00:09:17.202180: This epoch took 268.022030 s
+ 
+2026-11-09 00:09:17.202500: 
+epoch:  380 
+2026-11-09 00:13:22.555022: train loss : -0.8273 
+2026-11-09 00:13:30.656630: validation loss: -0.4783 
+2026-11-09 00:13:30.657680: Average global foreground Dice: [np.float32(0.9148), np.float32(0.4794), np.float32(0.6222), np.float32(0.5933), np.float32(0.4561), np.float32(0.6559), np.float32(0.5519), np.float32(0.4999), np.float32(0.3767), np.float32(0.4063), np.float32(0.4629), np.float32(0.5329), np.float32(0.4665), np.float32(0.345), np.float32(0.0648), np.float32(0.0), np.float32(0.3769), np.float32(0.3584), np.float32(0.4952), np.float32(0.4387), np.float32(0.4723), np.float32(0.5553), np.float32(0.6192), np.float32(0.6308), np.float32(0.4994), np.float32(0.3315), np.float32(0.1337), np.float32(0.1633), np.float32(0.2298), np.float32(0.5471), np.float32(0.0), np.float32(0.0), np.float32(0.3892), np.float32(0.4319), np.float32(0.6389), np.float32(0.6777)] 
+2026-11-09 00:13:30.658155: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:13:31.027892: lr: 0.000649 
+2026-11-09 00:13:31.028591: This epoch took 253.825753 s
+ 
+2026-11-09 00:13:31.028805: 
+epoch:  381 
+2026-11-09 00:17:29.592562: train loss : -0.8262 
+2026-11-09 00:17:37.666766: validation loss: -0.4757 
+2026-11-09 00:17:37.667678: Average global foreground Dice: [np.float32(0.9114), np.float32(0.4977), np.float32(0.5366), np.float32(0.5274), np.float32(0.4443), np.float32(0.5779), np.float32(0.4728), np.float32(0.562), np.float32(0.4453), np.float32(0.2594), np.float32(0.426), np.float32(0.5046), np.float32(0.5218), np.float32(0.4208), np.float32(0.0), np.float32(0.0), np.float32(0.4102), np.float32(0.3255), np.float32(0.4343), np.float32(0.4337), np.float32(0.4381), np.float32(0.3639), np.float32(0.6848), np.float32(0.6425), np.float32(0.245), np.float32(0.2751), np.float32(0.0287), np.float32(0.1707), np.float32(0.1467), np.float32(0.6055), np.float32(0.0), np.float32(0.0), np.float32(0.3727), np.float32(0.4229), np.float32(0.5674), np.float32(0.5984)] 
+2026-11-09 00:17:37.667840: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:17:37.999959: lr: 0.000648 
+2026-11-09 00:17:38.000446: This epoch took 246.971571 s
+ 
+2026-11-09 00:17:38.000646: 
+epoch:  382 
+2026-11-09 00:21:32.687595: train loss : -0.8291 
+2026-11-09 00:21:40.818309: validation loss: -0.5662 
+2026-11-09 00:21:40.819492: Average global foreground Dice: [np.float32(0.924), np.float32(0.6146), np.float32(0.6884), np.float32(0.6974), np.float32(0.5984), np.float32(0.744), np.float32(0.6376), np.float32(0.5595), np.float32(0.0317), np.float32(0.4267), np.float32(0.5616), np.float32(0.6162), np.float32(0.5917), np.float32(0.522), np.float32(0.0), np.float32(0.0), np.float32(0.5542), np.float32(0.447), np.float32(0.569), np.float32(0.5188), np.float32(0.5199), np.float32(0.5304), np.float32(0.8132), np.float32(0.8053), np.float32(0.3296), np.float32(0.4029), np.float32(0.0973), np.float32(0.2367), np.float32(0.2023), np.float32(0.6541), np.float32(0.0), np.float32(0.0), np.float32(0.4737), np.float32(0.5333), np.float32(0.745), np.float32(0.7693)] 
+2026-11-09 00:21:40.819750: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:21:41.148798: lr: 0.000648 
+2026-11-09 00:21:41.149184: This epoch took 243.148445 s
+ 
+2026-11-09 00:21:41.149398: 
+epoch:  383 
+2026-11-09 00:25:47.449238: train loss : -0.8283 
+2026-11-09 00:25:55.532974: validation loss: -0.4838 
+2026-11-09 00:25:55.534099: Average global foreground Dice: [np.float32(0.9039), np.float32(0.4771), np.float32(0.5555), np.float32(0.5477), np.float32(0.3992), np.float32(0.6044), np.float32(0.5078), np.float32(0.5016), np.float32(0.4674), np.float32(0.5025), np.float32(0.4461), np.float32(0.493), np.float32(0.5473), np.float32(0.4009), np.float32(0.0), np.float32(0.0), np.float32(0.356), np.float32(0.2808), np.float32(0.4381), np.float32(0.4252), np.float32(0.4858), np.float32(0.461), np.float32(0.6598), np.float32(0.6356), np.float32(0.2375), np.float32(0.238), np.float32(0.0768), np.float32(0.162), np.float32(0.2378), np.float32(0.6229), np.float32(0.0), np.float32(0.0), np.float32(0.3906), np.float32(0.4664), np.float32(0.5823), np.float32(0.6226)] 
+2026-11-09 00:25:55.534515: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:25:55.860452: lr: 0.000647 
+2026-11-09 00:25:55.860706: This epoch took 254.711222 s
+ 
+2026-11-09 00:25:55.860981: 
+epoch:  384 
+2026-11-09 00:30:05.174530: train loss : -0.8281 
+2026-11-09 00:30:13.263123: validation loss: -0.4885 
+2026-11-09 00:30:13.264162: Average global foreground Dice: [np.float32(0.9075), np.float32(0.5108), np.float32(0.5431), np.float32(0.5432), np.float32(0.4535), np.float32(0.6006), np.float32(0.5046), np.float32(0.537), np.float32(0.217), np.float32(0.4821), np.float32(0.443), np.float32(0.5069), np.float32(0.5541), np.float32(0.497), np.float32(0.1033), np.float32(0.0), np.float32(0.3824), np.float32(0.3196), np.float32(0.4165), np.float32(0.4172), np.float32(0.4509), np.float32(0.4124), np.float32(0.6891), np.float32(0.6507), np.float32(0.2113), np.float32(0.2912), np.float32(0.0449), np.float32(0.1513), np.float32(0.1647), np.float32(0.5706), np.float32(0.0), np.float32(0.0), np.float32(0.4003), np.float32(0.4137), np.float32(0.5813), np.float32(0.62)] 
+2026-11-09 00:30:13.264331: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:30:13.608314: lr: 0.000646 
+2026-11-09 00:30:13.608587: This epoch took 257.747479 s
+ 
+2026-11-09 00:30:13.608681: 
+epoch:  385 
+2026-11-09 00:34:27.026788: train loss : -0.8319 
+2026-11-09 00:34:35.138028: validation loss: -0.5239 
+2026-11-09 00:34:35.138986: Average global foreground Dice: [np.float32(0.9096), np.float32(0.5631), np.float32(0.5716), np.float32(0.5916), np.float32(0.4963), np.float32(0.644), np.float32(0.5463), np.float32(0.6348), np.float32(0.3007), np.float32(0.328), np.float32(0.5014), np.float32(0.562), np.float32(0.5733), np.float32(0.5681), np.float32(0.0), np.float32(0.0), np.float32(0.4368), np.float32(0.381), np.float32(0.4616), np.float32(0.4381), np.float32(0.4023), np.float32(0.3621), np.float32(0.7641), np.float32(0.7224), np.float32(0.2223), np.float32(0.2976), np.float32(0.0318), np.float32(0.2153), np.float32(0.1677), np.float32(0.6179), np.float32(0.0), np.float32(0.0), np.float32(0.4752), np.float32(0.5144), np.float32(0.6208), np.float32(0.6609)] 
+2026-11-09 00:34:35.139181: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:34:35.485266: lr: 0.000645 
+2026-11-09 00:34:35.485520: This epoch took 261.876772 s
+ 
+2026-11-09 00:34:35.485612: 
+epoch:  386 
+2026-11-09 00:38:44.712996: train loss : -0.8059 
+2026-11-09 00:38:52.827018: validation loss: -0.6148 
+2026-11-09 00:38:52.828115: Average global foreground Dice: [np.float32(0.9078), np.float32(0.662), np.float32(0.7719), np.float32(0.792), np.float32(0.7329), np.float32(0.8429), np.float32(0.735), np.float32(0.5648), np.float32(0.4251), np.float32(0.3296), np.float32(0.6281), np.float32(0.6852), np.float32(0.5933), np.float32(0.5343), np.float32(0.0079), np.float32(0.0), np.float32(0.7273), np.float32(0.6093), np.float32(0.7032), np.float32(0.6093), np.float32(0.5285), np.float32(0.6429), np.float32(0.8649), np.float32(0.8438), np.float32(0.4862), np.float32(0.4381), np.float32(0.1882), np.float32(0.3596), np.float32(0.3744), np.float32(0.6229), np.float32(0.0), np.float32(0.0), np.float32(0.5729), np.float32(0.4963), np.float32(0.8318), np.float32(0.8379)] 
+2026-11-09 00:38:52.828562: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:38:53.156890: lr: 0.000644 
+2026-11-09 00:38:53.157262: This epoch took 257.671585 s
+ 
+2026-11-09 00:38:53.157558: 
+epoch:  387 
+2026-11-09 00:42:49.026658: train loss : -0.8283 
+2026-11-09 00:42:57.158045: validation loss: -0.5013 
+2026-11-09 00:42:57.159074: Average global foreground Dice: [np.float32(0.9079), np.float32(0.5162), np.float32(0.6075), np.float32(0.5945), np.float32(0.5049), np.float32(0.6403), np.float32(0.5421), np.float32(0.6497), np.float32(0.318), np.float32(0.4671), np.float32(0.4382), np.float32(0.5025), np.float32(0.4856), np.float32(0.4597), np.float32(0.0886), np.float32(0.0), np.float32(0.4939), np.float32(0.4013), np.float32(0.5175), np.float32(0.4837), np.float32(0.3956), np.float32(0.4648), np.float32(0.6635), np.float32(0.6409), np.float32(0.339), np.float32(0.3463), np.float32(0.1176), np.float32(0.2645), np.float32(0.308), np.float32(0.564), np.float32(0.0), np.float32(0.0), np.float32(0.4698), np.float32(0.4638), np.float32(0.6097), np.float32(0.6533)] 
+2026-11-09 00:42:57.159539: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:42:57.483178: lr: 0.000643 
+2026-11-09 00:42:57.483711: This epoch took 244.325907 s
+ 
+2026-11-09 00:42:57.483969: 
+epoch:  388 
+2026-11-09 00:47:27.229210: train loss : -0.8265 
+2026-11-09 00:47:35.401314: validation loss: -0.4971 
+2026-11-09 00:47:35.402354: Average global foreground Dice: [np.float32(0.913), np.float32(0.5161), np.float32(0.5414), np.float32(0.5539), np.float32(0.4229), np.float32(0.6039), np.float32(0.5005), np.float32(0.576), np.float32(0.3842), np.float32(0.375), np.float32(0.4371), np.float32(0.5323), np.float32(0.5454), np.float32(0.4836), np.float32(0.0), np.float32(0.0), np.float32(0.3638), np.float32(0.3125), np.float32(0.4126), np.float32(0.4022), np.float32(0.396), np.float32(0.3571), np.float32(0.6464), np.float32(0.6101), np.float32(0.2224), np.float32(0.2581), np.float32(0.0), np.float32(0.0218), np.float32(0.1719), np.float32(0.5999), np.float32(0.0), np.float32(0.0), np.float32(0.3096), np.float32(0.3638), np.float32(0.5706), np.float32(0.6023)] 
+2026-11-09 00:47:35.402698: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:47:35.731766: lr: 0.000642 
+2026-11-09 00:47:35.732033: This epoch took 278.247888 s
+ 
+2026-11-09 00:47:35.732255: 
+epoch:  389 
+2026-11-09 00:51:51.657454: train loss : -0.8180 
+2026-11-09 00:51:59.810249: validation loss: -0.4717 
+2026-11-09 00:51:59.811324: Average global foreground Dice: [np.float32(0.9226), np.float32(0.5274), np.float32(0.5202), np.float32(0.5339), np.float32(0.4203), np.float32(0.5757), np.float32(0.4838), np.float32(0.5849), np.float32(0.3495), np.float32(0.4777), np.float32(0.4098), np.float32(0.5044), np.float32(0.5524), np.float32(0.4703), np.float32(0.0), np.float32(0.0), np.float32(0.374), np.float32(0.3293), np.float32(0.3859), np.float32(0.3846), np.float32(0.3243), np.float32(0.2858), np.float32(0.6823), np.float32(0.6414), np.float32(0.2032), np.float32(0.2573), np.float32(0.0), np.float32(0.0), np.float32(0.1941), np.float32(0.5503), np.float32(0.0), np.float32(0.0), np.float32(0.3645), np.float32(0.3729), np.float32(0.5755), np.float32(0.5979)] 
+2026-11-09 00:51:59.811546: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:52:00.152010: lr: 0.000641 
+2026-11-09 00:52:00.152244: This epoch took 264.419763 s
+ 
+2026-11-09 00:52:00.152351: 
+epoch:  390 
+2026-11-09 00:55:57.249741: train loss : -0.8206 
+2026-11-09 00:56:05.338729: validation loss: -0.6098 
+2026-11-09 00:56:05.339625: Average global foreground Dice: [np.float32(0.9261), np.float32(0.6588), np.float32(0.7547), np.float32(0.7704), np.float32(0.698), np.float32(0.8055), np.float32(0.7238), np.float32(0.7951), np.float32(0.3204), np.float32(0.3456), np.float32(0.6368), np.float32(0.6714), np.float32(0.6351), np.float32(0.5569), np.float32(0.0), np.float32(0.0), np.float32(0.7342), np.float32(0.6017), np.float32(0.7084), np.float32(0.6307), np.float32(0.5329), np.float32(0.6146), np.float32(0.8174), np.float32(0.8234), np.float32(0.4827), np.float32(0.4268), np.float32(0.0402), np.float32(0.0058), np.float32(0.4564), np.float32(0.6668), np.float32(0.0), np.float32(0.0), np.float32(0.6155), np.float32(0.6385), np.float32(0.7961), np.float32(0.8156)] 
+2026-11-09 00:56:05.339969: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 00:56:05.686725: lr: 0.00064 
+2026-11-09 00:56:05.686914: This epoch took 245.534497 s
+ 
+2026-11-09 00:56:05.686987: 
+epoch:  391 
+2026-11-09 01:00:27.228214: train loss : -0.8194 
+2026-11-09 01:00:35.317582: validation loss: -0.6234 
+2026-11-09 01:00:35.318663: Average global foreground Dice: [np.float32(0.9242), np.float32(0.6158), np.float32(0.7652), np.float32(0.7822), np.float32(0.7038), np.float32(0.8291), np.float32(0.7335), np.float32(0.5138), np.float32(0.4322), np.float32(0.3791), np.float32(0.6059), np.float32(0.6711), np.float32(0.5755), np.float32(0.5554), np.float32(0.0279), np.float32(0.0), np.float32(0.7231), np.float32(0.5894), np.float32(0.7054), np.float32(0.6451), np.float32(0.5453), np.float32(0.5818), np.float32(0.8439), np.float32(0.8546), np.float32(0.5174), np.float32(0.4537), np.float32(0.309), np.float32(0.4987), np.float32(0.4127), np.float32(0.6949), np.float32(0.0), np.float32(0.0), np.float32(0.5767), np.float32(0.5462), np.float32(0.8128), np.float32(0.8284)] 
+2026-11-09 01:00:35.319079: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:00:35.688548: lr: 0.000639 
+2026-11-09 01:00:35.688785: This epoch took 270.001744 s
+ 
+2026-11-09 01:00:35.688876: 
+epoch:  392 
+2026-11-09 01:04:23.910050: train loss : -0.8294 
+2026-11-09 01:04:31.984317: validation loss: -0.5854 
+2026-11-09 01:04:31.985387: Average global foreground Dice: [np.float32(0.9216), np.float32(0.6031), np.float32(0.758), np.float32(0.7592), np.float32(0.6596), np.float32(0.7954), np.float32(0.716), np.float32(0.5534), np.float32(0.4486), np.float32(0.2796), np.float32(0.6099), np.float32(0.6338), np.float32(0.5209), np.float32(0.4025), np.float32(0.0), np.float32(0.0), np.float32(0.6983), np.float32(0.5502), np.float32(0.7), np.float32(0.6287), np.float32(0.5632), np.float32(0.6458), np.float32(0.8495), np.float32(0.8476), np.float32(0.5127), np.float32(0.4219), np.float32(0.2729), np.float32(0.0), np.float32(0.3571), np.float32(0.658), np.float32(0.0), np.float32(0.0), np.float32(0.5873), np.float32(0.6007), np.float32(0.7976), np.float32(0.8114)] 
+2026-11-09 01:04:31.985592: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:04:32.316488: lr: 0.000638 
+2026-11-09 01:04:32.316791: This epoch took 236.627846 s
+ 
+2026-11-09 01:04:32.316898: 
+epoch:  393 
+2026-11-09 01:08:28.867954: train loss : -0.8277 
+2026-11-09 01:08:36.945743: validation loss: -0.6228 
+2026-11-09 01:08:36.946776: Average global foreground Dice: [np.float32(0.9222), np.float32(0.6483), np.float32(0.8009), np.float32(0.8452), np.float32(0.7668), np.float32(0.8744), np.float32(0.7933), np.float32(0.7087), np.float32(0.4102), np.float32(0.4058), np.float32(0.6455), np.float32(0.6503), np.float32(0.5639), np.float32(0.4364), np.float32(0.0), np.float32(0.0), np.float32(0.7811), np.float32(0.6432), np.float32(0.7611), np.float32(0.686), np.float32(0.5515), np.float32(0.685), np.float32(0.8692), np.float32(0.8726), np.float32(0.541), np.float32(0.3887), np.float32(0.2678), np.float32(0.0734), np.float32(0.3837), np.float32(0.6751), np.float32(0.0), np.float32(0.0), np.float32(0.669), np.float32(0.612), np.float32(0.8688), np.float32(0.8775)] 
+2026-11-09 01:08:36.947561: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:08:37.289966: lr: 0.000637 
+2026-11-09 01:08:37.290201: This epoch took 244.973227 s
+ 
+2026-11-09 01:08:37.290295: 
+epoch:  394 
+2026-11-09 01:13:09.019139: train loss : -0.8224 
+2026-11-09 01:13:17.133460: validation loss: -0.6411 
+2026-11-09 01:13:17.134468: Average global foreground Dice: [np.float32(0.929), np.float32(0.6912), np.float32(0.8009), np.float32(0.8123), np.float32(0.7493), np.float32(0.8602), np.float32(0.7599), np.float32(0.7132), np.float32(0.4593), np.float32(0.3804), np.float32(0.6879), np.float32(0.7074), np.float32(0.6464), np.float32(0.5736), np.float32(0.0), np.float32(0.0), np.float32(0.7762), np.float32(0.625), np.float32(0.753), np.float32(0.6709), np.float32(0.5823), np.float32(0.6791), np.float32(0.8782), np.float32(0.8815), np.float32(0.4974), np.float32(0.5294), np.float32(0.2206), np.float32(0.4726), np.float32(0.3507), np.float32(0.6945), np.float32(0.0), np.float32(0.0), np.float32(0.6605), np.float32(0.662), np.float32(0.8524), np.float32(0.8667)] 
+2026-11-09 01:13:17.134682: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:13:17.502764: lr: 0.000636 
+2026-11-09 01:13:17.503083: This epoch took 280.212717 s
+ 
+2026-11-09 01:13:17.503266: 
+epoch:  395 
+2026-11-09 01:17:05.432148: train loss : -0.8250 
+2026-11-09 01:17:13.521382: validation loss: -0.5655 
+2026-11-09 01:17:13.522547: Average global foreground Dice: [np.float32(0.9183), np.float32(0.6124), np.float32(0.6963), np.float32(0.7275), np.float32(0.6149), np.float32(0.7708), np.float32(0.6503), np.float32(0.6755), np.float32(0.4226), np.float32(0.4018), np.float32(0.5725), np.float32(0.6309), np.float32(0.6022), np.float32(0.5279), np.float32(0.0), np.float32(0.0), np.float32(0.5552), np.float32(0.4921), np.float32(0.6008), np.float32(0.5254), np.float32(0.5289), np.float32(0.5743), np.float32(0.8174), np.float32(0.801), np.float32(0.4203), np.float32(0.4408), np.float32(0.0133), np.float32(0.6043), np.float32(0.1551), np.float32(0.6094), np.float32(0.0), np.float32(0.0), np.float32(0.5301), np.float32(0.4335), np.float32(0.7666), np.float32(0.7871)] 
+2026-11-09 01:17:13.522979: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:17:13.858423: lr: 0.000635 
+2026-11-09 01:17:13.858908: This epoch took 236.355557 s
+ 
+2026-11-09 01:17:13.859218: 
+epoch:  396 
+2026-11-09 01:21:09.488010: train loss : -0.8172 
+2026-11-09 01:21:17.581673: validation loss: -0.6079 
+2026-11-09 01:21:17.582531: Average global foreground Dice: [np.float32(0.9277), np.float32(0.6508), np.float32(0.7683), np.float32(0.7953), np.float32(0.7057), np.float32(0.8275), np.float32(0.731), np.float32(0.6845), np.float32(0.5184), np.float32(0.3209), np.float32(0.6186), np.float32(0.6367), np.float32(0.5999), np.float32(0.4507), np.float32(0.0), np.float32(0.0), np.float32(0.7394), np.float32(0.5781), np.float32(0.719), np.float32(0.6376), np.float32(0.4838), np.float32(0.659), np.float32(0.8578), np.float32(0.8427), np.float32(0.5481), np.float32(0.5335), np.float32(0.3419), np.float32(0.2643), np.float32(0.3883), np.float32(0.6816), np.float32(0.0), np.float32(0.0), np.float32(0.5969), np.float32(0.6179), np.float32(0.8251), np.float32(0.8325)] 
+2026-11-09 01:21:17.582699: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:21:17.922696: lr: 0.000634 
+2026-11-09 01:21:17.922921: This epoch took 244.063441 s
+ 
+2026-11-09 01:21:17.923000: 
+epoch:  397 
+2026-11-09 01:25:19.182256: train loss : -0.8253 
+2026-11-09 01:25:27.282691: validation loss: -0.6640 
+2026-11-09 01:25:27.283742: Average global foreground Dice: [np.float32(0.9223), np.float32(0.6922), np.float32(0.8049), np.float32(0.8295), np.float32(0.7357), np.float32(0.8727), np.float32(0.7762), np.float32(0.6244), np.float32(0.4612), np.float32(0.354), np.float32(0.6887), np.float32(0.7042), np.float32(0.6273), np.float32(0.5539), np.float32(0.0), np.float32(0.0), np.float32(0.7705), np.float32(0.6471), np.float32(0.7446), np.float32(0.6751), np.float32(0.5635), np.float32(0.6691), np.float32(0.8734), np.float32(0.8735), np.float32(0.5225), np.float32(0.4857), np.float32(0.3261), np.float32(0.2666), np.float32(0.4472), np.float32(0.7138), np.float32(0.0), np.float32(0.0), np.float32(0.5996), np.float32(0.5693), np.float32(0.8666), np.float32(0.8704)] 
+2026-11-09 01:25:27.284281: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:25:27.630634: lr: 0.000633 
+2026-11-09 01:25:27.630959: This epoch took 249.707904 s
+ 
+2026-11-09 01:25:27.631065: 
+epoch:  398 
+2026-11-09 01:28:54.632780: train loss : -0.8188 
+2026-11-09 01:29:02.742991: validation loss: -0.4793 
+2026-11-09 01:29:02.744136: Average global foreground Dice: [np.float32(0.9019), np.float32(0.4744), np.float32(0.5169), np.float32(0.5051), np.float32(0.4081), np.float32(0.5833), np.float32(0.5009), np.float32(0.7529), np.float32(0.5232), np.float32(0.4181), np.float32(0.4187), np.float32(0.4723), np.float32(0.5381), np.float32(0.4678), np.float32(0.0), np.float32(0.0), np.float32(0.3299), np.float32(0.2694), np.float32(0.404), np.float32(0.3769), np.float32(0.3964), np.float32(0.3719), np.float32(0.6683), np.float32(0.6234), np.float32(0.1963), np.float32(0.2747), np.float32(0.0791), np.float32(0.0147), np.float32(0.2993), np.float32(0.6223), np.float32(0.0), np.float32(0.0), np.float32(0.4192), np.float32(0.4181), np.float32(0.5578), np.float32(0.5971)] 
+2026-11-09 01:29:02.744553: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:29:03.085208: lr: 0.000632 
+2026-11-09 01:29:03.085570: This epoch took 215.454448 s
+ 
+2026-11-09 01:29:03.085762: 
+epoch:  399 
+2026-11-09 01:33:05.454135: train loss : -0.8298 
+2026-11-09 01:33:13.580303: validation loss: -0.5214 
+2026-11-09 01:33:13.581534: Average global foreground Dice: [np.float32(0.9056), np.float32(0.5358), np.float32(0.5835), np.float32(0.5904), np.float32(0.4663), np.float32(0.6527), np.float32(0.5725), np.float32(0.6425), np.float32(0.4812), np.float32(0.5245), np.float32(0.4735), np.float32(0.5179), np.float32(0.5622), np.float32(0.4978), np.float32(0.0), np.float32(0.0), np.float32(0.3926), np.float32(0.3249), np.float32(0.4458), np.float32(0.4258), np.float32(0.4017), np.float32(0.4116), np.float32(0.7176), np.float32(0.6923), np.float32(0.2371), np.float32(0.2989), np.float32(0.0961), np.float32(0.1407), np.float32(0.3175), np.float32(0.6461), np.float32(0.0), np.float32(0.0), np.float32(0.3762), np.float32(0.4459), np.float32(0.639), np.float32(0.6695)] 
+2026-11-09 01:33:13.581873: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:33:13.938552: lr: 0.000631 
+2026-11-09 01:33:13.938814: saving scheduled checkpoint file... 
+2026-11-09 01:33:14.182360: saving checkpoint... 
+2026-11-09 01:33:16.165851: done, saving took 2.23 seconds 
+2026-11-09 01:33:16.168361: done 
+2026-11-09 01:33:16.168595: This epoch took 253.082761 s
+ 
+2026-11-09 01:33:16.168664: 
+epoch:  400 
+2026-11-09 01:37:43.067381: train loss : -0.8107 
+2026-11-09 01:37:51.236359: validation loss: -0.6052 
+2026-11-09 01:37:51.237664: Average global foreground Dice: [np.float32(0.924), np.float32(0.6202), np.float32(0.7587), np.float32(0.7866), np.float32(0.7201), np.float32(0.8229), np.float32(0.7491), np.float32(0.7297), np.float32(0.1328), np.float32(0.3902), np.float32(0.576), np.float32(0.5798), np.float32(0.5857), np.float32(0.4689), np.float32(0.0), np.float32(0.0), np.float32(0.7544), np.float32(0.5849), np.float32(0.7474), np.float32(0.6008), np.float32(0.5092), np.float32(0.6447), np.float32(0.8611), np.float32(0.8424), np.float32(0.4979), np.float32(0.4056), np.float32(0.1397), np.float32(0.0), np.float32(0.4064), np.float32(0.6964), np.float32(0.0), np.float32(0.0), np.float32(0.6578), np.float32(0.6404), np.float32(0.8236), np.float32(0.8379)] 
+2026-11-09 01:37:51.237966: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:37:51.583690: lr: 0.00063 
+2026-11-09 01:37:51.584031: This epoch took 275.415310 s
+ 
+2026-11-09 01:37:51.584118: 
+epoch:  401 
+2026-11-09 01:42:15.360737: train loss : -0.8318 
+2026-11-09 01:42:23.593293: validation loss: -0.5725 
+2026-11-09 01:42:23.594563: Average global foreground Dice: [np.float32(0.9105), np.float32(0.585), np.float32(0.7127), np.float32(0.7514), np.float32(0.6449), np.float32(0.7995), np.float32(0.7097), np.float32(0.5281), np.float32(0.432), np.float32(0.5026), np.float32(0.6156), np.float32(0.6166), np.float32(0.5611), np.float32(0.4543), np.float32(0.0), np.float32(0.0), np.float32(0.7251), np.float32(0.572), np.float32(0.7136), np.float32(0.5669), np.float32(0.5513), np.float32(0.6154), np.float32(0.7995), np.float32(0.769), np.float32(0.5265), np.float32(0.35), np.float32(0.1093), np.float32(0.0), np.float32(0.4428), np.float32(0.681), np.float32(0.0), np.float32(0.0), np.float32(0.6138), np.float32(0.6152), np.float32(0.7797), np.float32(0.8038)] 
+2026-11-09 01:42:23.594830: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:42:23.947845: lr: 0.00063 
+2026-11-09 01:42:23.948348: This epoch took 272.364170 s
+ 
+2026-11-09 01:42:23.948689: 
+epoch:  402 
+2026-11-09 01:46:52.645025: train loss : -0.8212 
+2026-11-09 01:47:00.880812: validation loss: -0.5963 
+2026-11-09 01:47:00.882095: Average global foreground Dice: [np.float32(0.9251), np.float32(0.6162), np.float32(0.7759), np.float32(0.8033), np.float32(0.7287), np.float32(0.8385), np.float32(0.7675), np.float32(0.6495), np.float32(0.1946), np.float32(0.4321), np.float32(0.6411), np.float32(0.6365), np.float32(0.5472), np.float32(0.4011), np.float32(0.0), np.float32(0.0), np.float32(0.7649), np.float32(0.5949), np.float32(0.7337), np.float32(0.6478), np.float32(0.4579), np.float32(0.5992), np.float32(0.8472), np.float32(0.8371), np.float32(0.5523), np.float32(0.3595), np.float32(0.2557), np.float32(0.4364), np.float32(0.4028), np.float32(0.6722), np.float32(0.0), np.float32(0.0), np.float32(0.6342), np.float32(0.6476), np.float32(0.8378), np.float32(0.8465)] 
+2026-11-09 01:47:00.882621: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:47:01.250552: lr: 0.000629 
+2026-11-09 01:47:01.251092: This epoch took 277.302141 s
+ 
+2026-11-09 01:47:01.251422: 
+epoch:  403 
+2026-11-09 01:51:32.953376: train loss : -0.8250 
+2026-11-09 01:51:41.185556: validation loss: -0.5747 
+2026-11-09 01:51:41.186928: Average global foreground Dice: [np.float32(0.923), np.float32(0.5854), np.float32(0.7188), np.float32(0.7415), np.float32(0.6277), np.float32(0.7795), np.float32(0.6792), np.float32(0.6313), np.float32(0.3469), np.float32(0.4764), np.float32(0.5913), np.float32(0.6153), np.float32(0.565), np.float32(0.442), np.float32(0.0), np.float32(0.0), np.float32(0.7077), np.float32(0.5489), np.float32(0.6914), np.float32(0.5913), np.float32(0.4661), np.float32(0.518), np.float32(0.7973), np.float32(0.7685), np.float32(0.5144), np.float32(0.4454), np.float32(0.27), np.float32(0.4044), np.float32(0.4102), np.float32(0.6917), np.float32(0.0), np.float32(0.0), np.float32(0.5907), np.float32(0.5998), np.float32(0.7689), np.float32(0.791)] 
+2026-11-09 01:51:41.187697: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:51:41.578422: lr: 0.000628 
+2026-11-09 01:51:41.578721: This epoch took 280.327072 s
+ 
+2026-11-09 01:51:41.578812: 
+epoch:  404 
+2026-11-09 01:56:05.863197: train loss : -0.8276 
+2026-11-09 01:56:14.040441: validation loss: -0.6111 
+2026-11-09 01:56:14.041534: Average global foreground Dice: [np.float32(0.9297), np.float32(0.6452), np.float32(0.7785), np.float32(0.805), np.float32(0.7121), np.float32(0.8455), np.float32(0.7586), np.float32(0.6794), np.float32(0.4312), np.float32(0.4035), np.float32(0.6247), np.float32(0.6453), np.float32(0.6484), np.float32(0.5324), np.float32(0.0), np.float32(0.0), np.float32(0.7621), np.float32(0.6072), np.float32(0.7415), np.float32(0.6383), np.float32(0.5338), np.float32(0.6533), np.float32(0.8438), np.float32(0.8344), np.float32(0.536), np.float32(0.5073), np.float32(0.2027), np.float32(0.2224), np.float32(0.2104), np.float32(0.6968), np.float32(0.0), np.float32(0.0), np.float32(0.6234), np.float32(0.7012), np.float32(0.8394), np.float32(0.8544)] 
+2026-11-09 01:56:14.042304: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 01:56:14.404923: lr: 0.000627 
+2026-11-09 01:56:14.405387: This epoch took 272.826508 s
+ 
+2026-11-09 01:56:14.405688: 
+epoch:  405 
+2026-11-09 02:00:46.843743: train loss : -0.8318 
+2026-11-09 02:00:55.063407: validation loss: -0.5895 
+2026-11-09 02:00:55.064459: Average global foreground Dice: [np.float32(0.93), np.float32(0.6042), np.float32(0.7507), np.float32(0.7592), np.float32(0.6709), np.float32(0.8098), np.float32(0.7088), np.float32(0.7073), np.float32(0.4518), np.float32(0.4093), np.float32(0.5652), np.float32(0.6204), np.float32(0.5711), np.float32(0.4914), np.float32(0.025), np.float32(0.0), np.float32(0.7312), np.float32(0.5604), np.float32(0.7102), np.float32(0.6035), np.float32(0.5518), np.float32(0.603), np.float32(0.8387), np.float32(0.8239), np.float32(0.4951), np.float32(0.4602), np.float32(0.2616), np.float32(0.6829), np.float32(0.4267), np.float32(0.7237), np.float32(0.0), np.float32(0.0), np.float32(0.6637), np.float32(0.6488), np.float32(0.7913), np.float32(0.8157)] 
+2026-11-09 02:00:55.064779: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:00:55.416066: lr: 0.000626 
+2026-11-09 02:00:55.416580: This epoch took 281.010673 s
+ 
+2026-11-09 02:00:55.416850: 
+epoch:  406 
+2026-11-09 02:05:16.176019: train loss : -0.8328 
+2026-11-09 02:05:24.388069: validation loss: -0.5982 
+2026-11-09 02:05:24.389606: Average global foreground Dice: [np.float32(0.9299), np.float32(0.6397), np.float32(0.7534), np.float32(0.7603), np.float32(0.6783), np.float32(0.8044), np.float32(0.7065), np.float32(0.6018), np.float32(0.4408), np.float32(0.488), np.float32(0.58), np.float32(0.6186), np.float32(0.5996), np.float32(0.4976), np.float32(0.0981), np.float32(0.0), np.float32(0.7326), np.float32(0.5839), np.float32(0.7126), np.float32(0.6379), np.float32(0.4998), np.float32(0.5686), np.float32(0.8262), np.float32(0.8088), np.float32(0.4855), np.float32(0.4358), np.float32(0.2483), np.float32(0.6914), np.float32(0.294), np.float32(0.6946), np.float32(0.0), np.float32(0.0), np.float32(0.6199), np.float32(0.6301), np.float32(0.799), np.float32(0.8165)] 
+2026-11-09 02:05:24.390104: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:05:24.780376: lr: 0.000625 
+2026-11-09 02:05:25.296555: saving checkpoint... 
+2026-11-09 02:05:25.966014: done, saving took 1.19 seconds 
+2026-11-09 02:05:25.977915: This epoch took 270.560973 s
+ 
+2026-11-09 02:05:25.978194: 
+epoch:  407 
+2026-11-09 02:09:59.347685: train loss : -0.8385 
+2026-11-09 02:10:07.554173: validation loss: -0.6097 
+2026-11-09 02:10:07.555514: Average global foreground Dice: [np.float32(0.9285), np.float32(0.6111), np.float32(0.7444), np.float32(0.7492), np.float32(0.6426), np.float32(0.8076), np.float32(0.7022), np.float32(0.6832), np.float32(0.5088), np.float32(0.5341), np.float32(0.6155), np.float32(0.6394), np.float32(0.5895), np.float32(0.4954), np.float32(0.0353), np.float32(0.0), np.float32(0.7206), np.float32(0.5833), np.float32(0.7034), np.float32(0.6146), np.float32(0.5658), np.float32(0.6596), np.float32(0.8284), np.float32(0.8103), np.float32(0.5092), np.float32(0.5058), np.float32(0.2352), np.float32(0.7482), np.float32(0.3942), np.float32(0.7315), np.float32(0.0), np.float32(0.0), np.float32(0.6261), np.float32(0.6438), np.float32(0.7846), np.float32(0.813)] 
+2026-11-09 02:10:07.555995: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:10:07.958057: lr: 0.000624 
+2026-11-09 02:10:08.187054: saving checkpoint... 
+2026-11-09 02:10:11.212919: done, saving took 3.25 seconds 
+2026-11-09 02:10:11.220977: This epoch took 285.242681 s
+ 
+2026-11-09 02:10:11.221314: 
+epoch:  408 
+2026-11-09 02:14:24.442356: train loss : -0.8392 
+2026-11-09 02:14:32.658177: validation loss: -0.6048 
+2026-11-09 02:14:32.661204: Average global foreground Dice: [np.float32(0.9289), np.float32(0.6411), np.float32(0.7481), np.float32(0.7651), np.float32(0.6923), np.float32(0.8142), np.float32(0.7198), np.float32(0.684), np.float32(0.4352), np.float32(0.4174), np.float32(0.6103), np.float32(0.6204), np.float32(0.5889), np.float32(0.4871), np.float32(0.0153), np.float32(0.0), np.float32(0.7326), np.float32(0.5743), np.float32(0.7137), np.float32(0.6275), np.float32(0.536), np.float32(0.6333), np.float32(0.8358), np.float32(0.8165), np.float32(0.5171), np.float32(0.4941), np.float32(0.2188), np.float32(0.5796), np.float32(0.3477), np.float32(0.7137), np.float32(0.0), np.float32(0.0), np.float32(0.615), np.float32(0.6334), np.float32(0.8019), np.float32(0.8176)] 
+2026-11-09 02:14:32.661690: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:14:33.019876: lr: 0.000623 
+2026-11-09 02:14:33.365866: saving checkpoint... 
+2026-11-09 02:14:35.207170: done, saving took 2.19 seconds 
+2026-11-09 02:14:35.217507: This epoch took 263.995817 s
+ 
+2026-11-09 02:14:35.219407: 
+epoch:  409 
+2026-11-09 02:19:16.100700: train loss : -0.8297 
+2026-11-09 02:19:24.323596: validation loss: -0.5995 
+2026-11-09 02:19:24.324673: Average global foreground Dice: [np.float32(0.9307), np.float32(0.6085), np.float32(0.7632), np.float32(0.7977), np.float32(0.7308), np.float32(0.8345), np.float32(0.7549), np.float32(0.6225), np.float32(0.4156), np.float32(0.4003), np.float32(0.5972), np.float32(0.6019), np.float32(0.5813), np.float32(0.3945), np.float32(0.0011), np.float32(0.0), np.float32(0.7643), np.float32(0.5926), np.float32(0.7349), np.float32(0.6469), np.float32(0.5136), np.float32(0.6844), np.float32(0.85), np.float32(0.8412), np.float32(0.5705), np.float32(0.4933), np.float32(0.2351), np.float32(0.303), np.float32(0.3845), np.float32(0.7018), np.float32(0.0), np.float32(0.0), np.float32(0.6207), np.float32(0.6743), np.float32(0.8327), np.float32(0.8421)] 
+2026-11-09 02:19:24.324874: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:19:24.728730: lr: 0.000622 
+2026-11-09 02:19:25.005287: saving checkpoint... 
+2026-11-09 02:19:25.686813: done, saving took 0.96 seconds 
+2026-11-09 02:19:25.697046: This epoch took 290.475634 s
+ 
+2026-11-09 02:19:25.697296: 
+epoch:  410 
+2026-11-09 02:23:39.876240: train loss : -0.8347 
+2026-11-09 02:23:48.011598: validation loss: -0.6229 
+2026-11-09 02:23:48.012825: Average global foreground Dice: [np.float32(0.9331), np.float32(0.6317), np.float32(0.794), np.float32(0.8117), np.float32(0.7275), np.float32(0.8526), np.float32(0.7696), np.float32(0.5592), np.float32(0.4861), np.float32(0.3339), np.float32(0.6219), np.float32(0.648), np.float32(0.5948), np.float32(0.4993), np.float32(0.0), np.float32(0.0), np.float32(0.7591), np.float32(0.6), np.float32(0.746), np.float32(0.6251), np.float32(0.5366), np.float32(0.6816), np.float32(0.8712), np.float32(0.873), np.float32(0.5533), np.float32(0.4788), np.float32(0.2383), np.float32(0.5044), np.float32(0.3339), np.float32(0.7331), np.float32(0.0), np.float32(0.0), np.float32(0.6565), np.float32(0.6682), np.float32(0.8501), np.float32(0.8587)] 
+2026-11-09 02:23:48.013004: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:23:48.369844: lr: 0.000621 
+2026-11-09 02:23:48.707096: saving checkpoint... 
+2026-11-09 02:23:51.858328: done, saving took 3.49 seconds 
+2026-11-09 02:23:51.869899: This epoch took 266.172375 s
+ 
+2026-11-09 02:23:51.870154: 
+epoch:  411 
+2026-11-09 02:28:16.013003: train loss : -0.8124 
+2026-11-09 02:28:24.215770: validation loss: -0.6419 
+2026-11-09 02:28:24.217104: Average global foreground Dice: [np.float32(0.9276), np.float32(0.6653), np.float32(0.8031), np.float32(0.831), np.float32(0.7437), np.float32(0.8645), np.float32(0.7883), np.float32(0.5024), np.float32(0.2687), np.float32(0.3934), np.float32(0.6831), np.float32(0.6972), np.float32(0.6233), np.float32(0.5376), np.float32(0.0188), np.float32(0.0), np.float32(0.7679), np.float32(0.6067), np.float32(0.7427), np.float32(0.6124), np.float32(0.6298), np.float32(0.6786), np.float32(0.8918), np.float32(0.8938), np.float32(0.5015), np.float32(0.4998), np.float32(0.2819), np.float32(0.1775), np.float32(0.3124), np.float32(0.7368), np.float32(0.0), np.float32(0.0), np.float32(0.635), np.float32(0.6351), np.float32(0.8721), np.float32(0.8782)] 
+2026-11-09 02:28:24.217927: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:28:24.565935: lr: 0.00062 
+2026-11-09 02:28:24.808882: saving checkpoint... 
+2026-11-09 02:28:27.476798: done, saving took 2.91 seconds 
+2026-11-09 02:28:27.487460: This epoch took 275.617004 s
+ 
+2026-11-09 02:28:27.487914: 
+epoch:  412 
+2026-11-09 02:33:08.273062: train loss : -0.8315 
+2026-11-09 02:33:16.418311: validation loss: -0.6492 
+2026-11-09 02:33:16.419379: Average global foreground Dice: [np.float32(0.9241), np.float32(0.6669), np.float32(0.8265), np.float32(0.8345), np.float32(0.7761), np.float32(0.8728), np.float32(0.7994), np.float32(0.5635), np.float32(0.3188), np.float32(0.4664), np.float32(0.6934), np.float32(0.7213), np.float32(0.629), np.float32(0.5712), np.float32(0.0), np.float32(0.0), np.float32(0.778), np.float32(0.6207), np.float32(0.7543), np.float32(0.6234), np.float32(0.5723), np.float32(0.7092), np.float32(0.9179), np.float32(0.9116), np.float32(0.5126), np.float32(0.4923), np.float32(0.2532), np.float32(0.0154), np.float32(0.4513), np.float32(0.6733), np.float32(0.0), np.float32(0.0), np.float32(0.6591), np.float32(0.6439), np.float32(0.8828), np.float32(0.8906)] 
+2026-11-09 02:33:16.419685: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:33:16.765880: lr: 0.000619 
+2026-11-09 02:33:17.234851: saving checkpoint... 
+2026-11-09 02:33:17.926215: done, saving took 1.16 seconds 
+2026-11-09 02:33:17.937412: This epoch took 290.449212 s
+ 
+2026-11-09 02:33:17.937902: 
+epoch:  413 
+2026-11-09 02:38:04.029731: train loss : -0.8304 
+2026-11-09 02:38:12.217264: validation loss: -0.6514 
+2026-11-09 02:38:12.218405: Average global foreground Dice: [np.float32(0.9288), np.float32(0.7116), np.float32(0.8102), np.float32(0.8282), np.float32(0.7705), np.float32(0.8626), np.float32(0.7656), np.float32(0.6053), np.float32(0.3688), np.float32(0.421), np.float32(0.6983), np.float32(0.7251), np.float32(0.6462), np.float32(0.5999), np.float32(0.097), np.float32(0.021), np.float32(0.768), np.float32(0.616), np.float32(0.7342), np.float32(0.6285), np.float32(0.584), np.float32(0.6661), np.float32(0.91), np.float32(0.9054), np.float32(0.4799), np.float32(0.5586), np.float32(0.1142), np.float32(0.434), np.float32(0.3929), np.float32(0.6841), np.float32(0.0), np.float32(0.0), np.float32(0.6122), np.float32(0.638), np.float32(0.8707), np.float32(0.8765)] 
+2026-11-09 02:38:12.218793: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:38:12.605130: lr: 0.000618 
+2026-11-09 02:38:13.079168: saving checkpoint... 
+2026-11-09 02:38:15.867447: done, saving took 3.26 seconds 
+2026-11-09 02:38:15.879146: This epoch took 297.940995 s
+ 
+2026-11-09 02:38:15.879662: 
+epoch:  414 
+2026-11-09 02:43:00.215498: train loss : -0.8250 
+2026-11-09 02:43:08.423494: validation loss: -0.6118 
+2026-11-09 02:43:08.424624: Average global foreground Dice: [np.float32(0.9288), np.float32(0.6208), np.float32(0.7874), np.float32(0.8064), np.float32(0.7248), np.float32(0.8351), np.float32(0.7622), np.float32(0.5799), np.float32(0.373), np.float32(0.3972), np.float32(0.6452), np.float32(0.67), np.float32(0.5958), np.float32(0.4931), np.float32(0.0479), np.float32(0.0), np.float32(0.7546), np.float32(0.5919), np.float32(0.7398), np.float32(0.6198), np.float32(0.5288), np.float32(0.6669), np.float32(0.8705), np.float32(0.8724), np.float32(0.5196), np.float32(0.4874), np.float32(0.2737), np.float32(0.099), np.float32(0.4922), np.float32(0.6235), np.float32(0.0), np.float32(0.0), np.float32(0.6081), np.float32(0.615), np.float32(0.8429), np.float32(0.8526)] 
+2026-11-09 02:43:08.424904: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:43:08.834831: lr: 0.000617 
+2026-11-09 02:43:09.438952: saving checkpoint... 
+2026-11-09 02:43:12.095768: done, saving took 3.26 seconds 
+2026-11-09 02:43:12.098822: This epoch took 296.218816 s
+ 
+2026-11-09 02:43:12.099037: 
+epoch:  415 
+2026-11-09 02:47:43.517001: train loss : -0.8315 
+2026-11-09 02:47:51.669796: validation loss: -0.6640 
+2026-11-09 02:47:51.670963: Average global foreground Dice: [np.float32(0.9319), np.float32(0.666), np.float32(0.8213), np.float32(0.8412), np.float32(0.7833), np.float32(0.8776), np.float32(0.8002), np.float32(0.6212), np.float32(0.2807), np.float32(0.4435), np.float32(0.7074), np.float32(0.7199), np.float32(0.6809), np.float32(0.6626), np.float32(0.003), np.float32(0.0), np.float32(0.7813), np.float32(0.6143), np.float32(0.7648), np.float32(0.62), np.float32(0.5356), np.float32(0.6793), np.float32(0.9149), np.float32(0.9069), np.float32(0.4826), np.float32(0.5226), np.float32(0.1247), np.float32(0.7075), np.float32(0.3517), np.float32(0.6924), np.float32(0.0), np.float32(0.0), np.float32(0.6739), np.float32(0.6403), np.float32(0.8816), np.float32(0.8928)] 
+2026-11-09 02:47:51.671148: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:47:52.044884: lr: 0.000616 
+2026-11-09 02:47:52.503169: saving checkpoint... 
+2026-11-09 02:47:53.233160: done, saving took 1.19 seconds 
+2026-11-09 02:47:53.243553: This epoch took 281.144430 s
+ 
+2026-11-09 02:47:53.243770: 
+epoch:  416 
+2026-11-09 02:52:37.768309: train loss : -0.8319 
+2026-11-09 02:52:45.957491: validation loss: -0.6711 
+2026-11-09 02:52:45.958771: Average global foreground Dice: [np.float32(0.9213), np.float32(0.6779), np.float32(0.8305), np.float32(0.8567), np.float32(0.8009), np.float32(0.8875), np.float32(0.813), np.float32(0.6956), np.float32(0.3072), np.float32(0.4244), np.float32(0.6989), np.float32(0.7267), np.float32(0.6636), np.float32(0.6202), np.float32(0.0), np.float32(0.0), np.float32(0.7937), np.float32(0.6172), np.float32(0.7749), np.float32(0.6491), np.float32(0.5209), np.float32(0.701), np.float32(0.9187), np.float32(0.9064), np.float32(0.5424), np.float32(0.5441), np.float32(0.3118), np.float32(0.7313), np.float32(0.4329), np.float32(0.685), np.float32(0.0), np.float32(0.0), np.float32(0.6041), np.float32(0.6261), np.float32(0.8954), np.float32(0.8967)] 
+2026-11-09 02:52:45.959091: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:52:46.358557: lr: 0.000615 
+2026-11-09 02:52:46.740157: saving checkpoint... 
+2026-11-09 02:52:47.378614: done, saving took 1.02 seconds 
+2026-11-09 02:52:47.380795: This epoch took 294.136931 s
+ 
+2026-11-09 02:52:47.380967: 
+epoch:  417 
+2026-11-09 02:57:07.945304: train loss : -0.8243 
+2026-11-09 02:57:16.104892: validation loss: -0.5724 
+2026-11-09 02:57:16.105895: Average global foreground Dice: [np.float32(0.9281), np.float32(0.5902), np.float32(0.7391), np.float32(0.7447), np.float32(0.6296), np.float32(0.796), np.float32(0.6818), np.float32(0.5839), np.float32(0.3019), np.float32(0.3159), np.float32(0.6158), np.float32(0.6831), np.float32(0.6242), np.float32(0.5132), np.float32(0.0), np.float32(0.0), np.float32(0.6857), np.float32(0.5351), np.float32(0.6823), np.float32(0.5668), np.float32(0.5212), np.float32(0.571), np.float32(0.7733), np.float32(0.7751), np.float32(0.4912), np.float32(0.5536), np.float32(0.2666), np.float32(0.0058), np.float32(0.3718), np.float32(0.6557), np.float32(0.0), np.float32(0.0), np.float32(0.518), np.float32(0.6101), np.float32(0.7751), np.float32(0.7926)] 
+2026-11-09 02:57:16.106084: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 02:57:16.448504: lr: 0.000614 
+2026-11-09 02:57:16.448836: This epoch took 269.067732 s
+ 
+2026-11-09 02:57:16.448940: 
+epoch:  418 
+2026-11-09 03:01:56.497566: train loss : -0.8293 
+2026-11-09 03:02:04.696965: validation loss: -0.5934 
+2026-11-09 03:02:04.698422: Average global foreground Dice: [np.float32(0.9272), np.float32(0.6192), np.float32(0.7466), np.float32(0.7708), np.float32(0.6713), np.float32(0.8187), np.float32(0.7185), np.float32(0.6667), np.float32(0.4382), np.float32(0.4309), np.float32(0.6277), np.float32(0.6724), np.float32(0.6284), np.float32(0.4994), np.float32(0.0), np.float32(0.0), np.float32(0.7379), np.float32(0.5734), np.float32(0.7194), np.float32(0.6123), np.float32(0.5491), np.float32(0.6356), np.float32(0.8009), np.float32(0.7829), np.float32(0.5693), np.float32(0.4817), np.float32(0.2731), np.float32(0.3257), np.float32(0.2123), np.float32(0.645), np.float32(0.0), np.float32(0.0), np.float32(0.5858), np.float32(0.6482), np.float32(0.8041), np.float32(0.8198)] 
+2026-11-09 03:02:04.699021: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:02:05.050833: lr: 0.000613 
+2026-11-09 03:02:05.051269: This epoch took 288.602242 s
+ 
+2026-11-09 03:02:05.051607: 
+epoch:  419 
+2026-11-09 03:06:18.144269: train loss : -0.8380 
+2026-11-09 03:06:26.335277: validation loss: -0.6037 
+2026-11-09 03:06:26.336754: Average global foreground Dice: [np.float32(0.9254), np.float32(0.6012), np.float32(0.7779), np.float32(0.7854), np.float32(0.7063), np.float32(0.8295), np.float32(0.7551), np.float32(0.6713), np.float32(0.4467), np.float32(0.4925), np.float32(0.642), np.float32(0.7077), np.float32(0.6249), np.float32(0.4859), np.float32(0.014), np.float32(0.0), np.float32(0.7487), np.float32(0.5889), np.float32(0.7358), np.float32(0.6211), np.float32(0.4744), np.float32(0.6668), np.float32(0.8613), np.float32(0.8571), np.float32(0.5348), np.float32(0.4991), np.float32(0.3389), np.float32(0.4283), np.float32(0.3538), np.float32(0.6195), np.float32(0.0), np.float32(0.0), np.float32(0.6366), np.float32(0.6492), np.float32(0.8231), np.float32(0.8397)] 
+2026-11-09 03:06:26.337091: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:06:26.701834: lr: 0.000612 
+2026-11-09 03:06:26.702311: This epoch took 261.650371 s
+ 
+2026-11-09 03:06:26.702573: 
+epoch:  420 
+2026-11-09 03:10:44.399623: train loss : -0.8352 
+2026-11-09 03:10:52.587655: validation loss: -0.6219 
+2026-11-09 03:10:52.589063: Average global foreground Dice: [np.float32(0.9272), np.float32(0.6516), np.float32(0.7648), np.float32(0.8054), np.float32(0.7167), np.float32(0.8521), np.float32(0.7415), np.float32(0.7021), np.float32(0.3804), np.float32(0.4132), np.float32(0.6685), np.float32(0.7037), np.float32(0.6497), np.float32(0.5933), np.float32(0.0), np.float32(0.0), np.float32(0.7481), np.float32(0.5963), np.float32(0.7305), np.float32(0.6265), np.float32(0.5343), np.float32(0.6657), np.float32(0.8138), np.float32(0.8134), np.float32(0.4942), np.float32(0.451), np.float32(0.3024), np.float32(0.4928), np.float32(0.395), np.float32(0.7111), np.float32(0.0), np.float32(0.0), np.float32(0.606), np.float32(0.587), np.float32(0.8334), np.float32(0.8502)] 
+2026-11-09 03:10:52.589373: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:10:52.936393: lr: 0.000612 
+2026-11-09 03:10:52.936682: This epoch took 266.233962 s
+ 
+2026-11-09 03:10:52.936764: 
+epoch:  421 
+2026-11-09 03:15:28.484012: train loss : -0.8259 
+2026-11-09 03:15:36.699086: validation loss: -0.5115 
+2026-11-09 03:15:36.700207: Average global foreground Dice: [np.float32(0.9299), np.float32(0.563), np.float32(0.5929), np.float32(0.5906), np.float32(0.5055), np.float32(0.6512), np.float32(0.5412), np.float32(0.7441), np.float32(0.2651), np.float32(0.4992), np.float32(0.4703), np.float32(0.5698), np.float32(0.5744), np.float32(0.5739), np.float32(0.0025), np.float32(0.0), np.float32(0.4922), np.float32(0.4152), np.float32(0.5028), np.float32(0.4868), np.float32(0.4599), np.float32(0.4104), np.float32(0.6904), np.float32(0.6559), np.float32(0.2776), np.float32(0.3585), np.float32(0.0483), np.float32(0.0311), np.float32(0.195), np.float32(0.6693), np.float32(0.0), np.float32(0.0), np.float32(0.4719), np.float32(0.5285), np.float32(0.6308), np.float32(0.6628)] 
+2026-11-09 03:15:36.700831: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:15:37.046464: lr: 0.000611 
+2026-11-09 03:15:37.046953: This epoch took 284.110134 s
+ 
+2026-11-09 03:15:37.047094: 
+epoch:  422 
+2026-11-09 03:20:16.278553: train loss : -0.8278 
+2026-11-09 03:20:24.479146: validation loss: -0.4695 
+2026-11-09 03:20:24.480539: Average global foreground Dice: [np.float32(0.9212), np.float32(0.4799), np.float32(0.5428), np.float32(0.5485), np.float32(0.4526), np.float32(0.5961), np.float32(0.5082), np.float32(0.702), np.float32(0.4053), np.float32(0.4119), np.float32(0.4278), np.float32(0.4691), np.float32(0.5131), np.float32(0.4119), np.float32(0.0), np.float32(0.0), np.float32(0.4555), np.float32(0.3557), np.float32(0.4654), np.float32(0.462), np.float32(0.4108), np.float32(0.4438), np.float32(0.6392), np.float32(0.6091), np.float32(0.323), np.float32(0.0901), np.float32(0.0735), np.float32(0.1251), np.float32(0.2982), np.float32(0.6118), np.float32(0.0), np.float32(0.0), np.float32(0.4379), np.float32(0.4291), np.float32(0.5749), np.float32(0.6077)] 
+2026-11-09 03:20:24.480976: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:20:24.831424: lr: 0.00061 
+2026-11-09 03:20:24.831965: This epoch took 287.784798 s
+ 
+2026-11-09 03:20:24.832334: 
+epoch:  423 
+2026-11-09 03:24:47.417929: train loss : -0.8271 
+2026-11-09 03:24:55.590055: validation loss: -0.5904 
+2026-11-09 03:24:55.591178: Average global foreground Dice: [np.float32(0.9254), np.float32(0.6295), np.float32(0.7673), np.float32(0.7892), np.float32(0.6887), np.float32(0.8238), np.float32(0.7397), np.float32(0.7601), np.float32(0.3734), np.float32(0.3293), np.float32(0.595), np.float32(0.6368), np.float32(0.5863), np.float32(0.4818), np.float32(0.0006), np.float32(0.0), np.float32(0.7461), np.float32(0.5828), np.float32(0.7449), np.float32(0.6213), np.float32(0.5204), np.float32(0.6274), np.float32(0.8525), np.float32(0.8426), np.float32(0.541), np.float32(0.4965), np.float32(0.2248), np.float32(0.0122), np.float32(0.3506), np.float32(0.6121), np.float32(0.0), np.float32(0.0), np.float32(0.6285), np.float32(0.5978), np.float32(0.819), np.float32(0.8302)] 
+2026-11-09 03:24:55.591373: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:24:55.942905: lr: 0.000609 
+2026-11-09 03:24:55.943413: This epoch took 271.110833 s
+ 
+2026-11-09 03:24:55.943794: 
+epoch:  424 
+2026-11-09 03:29:27.341665: train loss : -0.8342 
+2026-11-09 03:29:35.541414: validation loss: -0.5988 
+2026-11-09 03:29:35.542638: Average global foreground Dice: [np.float32(0.9184), np.float32(0.6248), np.float32(0.7555), np.float32(0.7687), np.float32(0.6707), np.float32(0.811), np.float32(0.7184), np.float32(0.7315), np.float32(0.2669), np.float32(0.3581), np.float32(0.6172), np.float32(0.6764), np.float32(0.59), np.float32(0.5358), np.float32(0.0), np.float32(0.0), np.float32(0.7294), np.float32(0.5746), np.float32(0.7199), np.float32(0.6077), np.float32(0.5373), np.float32(0.6423), np.float32(0.8243), np.float32(0.79), np.float32(0.5116), np.float32(0.4928), np.float32(0.2023), np.float32(0.0224), np.float32(0.3135), np.float32(0.6282), np.float32(0.0), np.float32(0.0), np.float32(0.6011), np.float32(0.6187), np.float32(0.802), np.float32(0.8259)] 
+2026-11-09 03:29:35.542817: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:29:35.896007: lr: 0.000608 
+2026-11-09 03:29:35.896315: This epoch took 279.952249 s
+ 
+2026-11-09 03:29:35.896422: 
+epoch:  425 
+2026-11-09 03:34:11.006089: train loss : -0.8289 
+2026-11-09 03:34:19.137714: validation loss: -0.5923 
+2026-11-09 03:34:19.138893: Average global foreground Dice: [np.float32(0.93), np.float32(0.6229), np.float32(0.7586), np.float32(0.7956), np.float32(0.7009), np.float32(0.8345), np.float32(0.7506), np.float32(0.7109), np.float32(0.3159), np.float32(0.3524), np.float32(0.6072), np.float32(0.6484), np.float32(0.6158), np.float32(0.5305), np.float32(0.0324), np.float32(0.0), np.float32(0.7422), np.float32(0.5844), np.float32(0.7345), np.float32(0.6156), np.float32(0.5356), np.float32(0.6438), np.float32(0.8434), np.float32(0.8427), np.float32(0.5343), np.float32(0.3909), np.float32(0.1644), np.float32(0.0313), np.float32(0.3487), np.float32(0.6727), np.float32(0.0), np.float32(0.0), np.float32(0.5861), np.float32(0.581), np.float32(0.8284), np.float32(0.8442)] 
+2026-11-09 03:34:19.139360: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:34:19.539199: lr: 0.000607 
+2026-11-09 03:34:19.539669: This epoch took 283.643164 s
+ 
+2026-11-09 03:34:19.539930: 
+epoch:  426 
+2026-11-09 03:39:02.030152: train loss : -0.8344 
+2026-11-09 03:39:10.258916: validation loss: -0.5812 
+2026-11-09 03:39:10.260191: Average global foreground Dice: [np.float32(0.9295), np.float32(0.6289), np.float32(0.7422), np.float32(0.7732), np.float32(0.6828), np.float32(0.815), np.float32(0.7233), np.float32(0.6575), np.float32(0.3939), np.float32(0.3052), np.float32(0.6022), np.float32(0.6799), np.float32(0.5528), np.float32(0.4585), np.float32(0.0), np.float32(0.0), np.float32(0.7447), np.float32(0.6049), np.float32(0.7323), np.float32(0.6468), np.float32(0.4753), np.float32(0.6623), np.float32(0.8264), np.float32(0.8083), np.float32(0.5332), np.float32(0.4628), np.float32(0.2121), np.float32(0.1498), np.float32(0.3241), np.float32(0.6584), np.float32(0.0), np.float32(0.0), np.float32(0.578), np.float32(0.621), np.float32(0.8069), np.float32(0.8276)] 
+2026-11-09 03:39:10.260711: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:39:10.619554: lr: 0.000606 
+2026-11-09 03:39:10.619850: This epoch took 291.079801 s
+ 
+2026-11-09 03:39:10.619964: 
+epoch:  427 
+2026-11-09 03:43:33.513023: train loss : -0.8330 
+2026-11-09 03:43:41.746335: validation loss: -0.5996 
+2026-11-09 03:43:41.747577: Average global foreground Dice: [np.float32(0.9266), np.float32(0.63), np.float32(0.7789), np.float32(0.7858), np.float32(0.6973), np.float32(0.8232), np.float32(0.7365), np.float32(0.6765), np.float32(0.3736), np.float32(0.3119), np.float32(0.6007), np.float32(0.6485), np.float32(0.577), np.float32(0.4743), np.float32(0.0), np.float32(0.0), np.float32(0.7423), np.float32(0.5703), np.float32(0.738), np.float32(0.6364), np.float32(0.575), np.float32(0.6798), np.float32(0.8338), np.float32(0.8362), np.float32(0.5548), np.float32(0.4579), np.float32(0.2178), np.float32(0.1422), np.float32(0.3235), np.float32(0.7057), np.float32(0.0), np.float32(0.0), np.float32(0.5923), np.float32(0.6031), np.float32(0.8162), np.float32(0.8338)] 
+2026-11-09 03:43:41.748092: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:43:42.096939: lr: 0.000605 
+2026-11-09 03:43:42.097314: This epoch took 271.477147 s
+ 
+2026-11-09 03:43:42.097668: 
+epoch:  428 
+2026-11-09 03:47:53.090838: train loss : -0.8354 
+2026-11-09 03:48:01.282179: validation loss: -0.6008 
+2026-11-09 03:48:01.283206: Average global foreground Dice: [np.float32(0.9238), np.float32(0.627), np.float32(0.7602), np.float32(0.7796), np.float32(0.6933), np.float32(0.8248), np.float32(0.7286), np.float32(0.7122), np.float32(0.3985), np.float32(0.2885), np.float32(0.5887), np.float32(0.6789), np.float32(0.5979), np.float32(0.5643), np.float32(0.0031), np.float32(0.0), np.float32(0.7414), np.float32(0.5896), np.float32(0.7406), np.float32(0.6199), np.float32(0.5642), np.float32(0.6374), np.float32(0.862), np.float32(0.8536), np.float32(0.5104), np.float32(0.446), np.float32(0.237), np.float32(0.0609), np.float32(0.2973), np.float32(0.6789), np.float32(0.0), np.float32(0.0), np.float32(0.6277), np.float32(0.6594), np.float32(0.8153), np.float32(0.8421)] 
+2026-11-09 03:48:01.283377: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:48:01.663873: lr: 0.000604 
+2026-11-09 03:48:01.664213: This epoch took 259.566422 s
+ 
+2026-11-09 03:48:01.664326: 
+epoch:  429 
+2026-11-09 03:52:09.139490: train loss : -0.8374 
+2026-11-09 03:52:17.299153: validation loss: -0.5928 
+2026-11-09 03:52:17.300394: Average global foreground Dice: [np.float32(0.9241), np.float32(0.6104), np.float32(0.7544), np.float32(0.7661), np.float32(0.6779), np.float32(0.8056), np.float32(0.7141), np.float32(0.651), np.float32(0.2594), np.float32(0.3478), np.float32(0.5736), np.float32(0.6457), np.float32(0.5784), np.float32(0.4793), np.float32(0.0051), np.float32(0.0), np.float32(0.7307), np.float32(0.5239), np.float32(0.7196), np.float32(0.5853), np.float32(0.5427), np.float32(0.5978), np.float32(0.8373), np.float32(0.8332), np.float32(0.5224), np.float32(0.4935), np.float32(0.2764), np.float32(0.0405), np.float32(0.2327), np.float32(0.6589), np.float32(0.0), np.float32(0.0), np.float32(0.5872), np.float32(0.6218), np.float32(0.7979), np.float32(0.8234)] 
+2026-11-09 03:52:17.300663: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:52:17.681543: lr: 0.000603 
+2026-11-09 03:52:17.681947: This epoch took 256.017541 s
+ 
+2026-11-09 03:52:17.682228: 
+epoch:  430 
+2026-11-09 03:56:12.662692: train loss : -0.8420 
+2026-11-09 03:56:20.813624: validation loss: -0.5953 
+2026-11-09 03:56:20.814749: Average global foreground Dice: [np.float32(0.9284), np.float32(0.6256), np.float32(0.725), np.float32(0.7479), np.float32(0.6394), np.float32(0.7922), np.float32(0.684), np.float32(0.648), np.float32(0.4368), np.float32(0.3942), np.float32(0.625), np.float32(0.6802), np.float32(0.5812), np.float32(0.5078), np.float32(0.0041), np.float32(0.0), np.float32(0.7035), np.float32(0.5112), np.float32(0.7056), np.float32(0.6023), np.float32(0.577), np.float32(0.6109), np.float32(0.8124), np.float32(0.7968), np.float32(0.4706), np.float32(0.4414), np.float32(0.284), np.float32(0.335), np.float32(0.2053), np.float32(0.738), np.float32(0.0), np.float32(0.0), np.float32(0.5701), np.float32(0.6123), np.float32(0.7757), np.float32(0.7997)] 
+2026-11-09 03:56:20.815286: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 03:56:21.161935: lr: 0.000602 
+2026-11-09 03:56:21.162196: This epoch took 243.479711 s
+ 
+2026-11-09 03:56:21.162355: 
+epoch:  431 
+2026-11-09 04:00:28.394609: train loss : -0.8346 
+2026-11-09 04:00:36.572972: validation loss: -0.6147 
+2026-11-09 04:00:36.573969: Average global foreground Dice: [np.float32(0.9302), np.float32(0.6167), np.float32(0.7655), np.float32(0.7841), np.float32(0.6801), np.float32(0.8224), np.float32(0.7392), np.float32(0.6371), np.float32(0.2628), np.float32(0.3173), np.float32(0.6223), np.float32(0.6679), np.float32(0.5812), np.float32(0.4225), np.float32(0.0), np.float32(0.0), np.float32(0.7285), np.float32(0.568), np.float32(0.7269), np.float32(0.6057), np.float32(0.556), np.float32(0.6653), np.float32(0.8634), np.float32(0.8662), np.float32(0.5503), np.float32(0.4202), np.float32(0.282), np.float32(0.2176), np.float32(0.4325), np.float32(0.6938), np.float32(0.0), np.float32(0.0), np.float32(0.568), np.float32(0.637), np.float32(0.8145), np.float32(0.8336)] 
+2026-11-09 04:00:36.574574: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:00:36.932351: lr: 0.000601 
+2026-11-09 04:00:36.932620: This epoch took 255.770175 s
+ 
+2026-11-09 04:00:36.932705: 
+epoch:  432 
+2026-11-09 04:04:50.484315: train loss : -0.8331 
+2026-11-09 04:04:58.645667: validation loss: -0.6153 
+2026-11-09 04:04:58.646765: Average global foreground Dice: [np.float32(0.9295), np.float32(0.6321), np.float32(0.7759), np.float32(0.793), np.float32(0.71), np.float32(0.8346), np.float32(0.7516), np.float32(0.6608), np.float32(0.2492), np.float32(0.2673), np.float32(0.6355), np.float32(0.6669), np.float32(0.598), np.float32(0.5085), np.float32(0.0061), np.float32(0.0), np.float32(0.7522), np.float32(0.5752), np.float32(0.7429), np.float32(0.5936), np.float32(0.51), np.float32(0.6652), np.float32(0.827), np.float32(0.8265), np.float32(0.519), np.float32(0.4419), np.float32(0.2443), np.float32(0.0), np.float32(0.2816), np.float32(0.7258), np.float32(0.0), np.float32(0.0), np.float32(0.5769), np.float32(0.6699), np.float32(0.8299), np.float32(0.8436)] 
+2026-11-09 04:04:58.647025: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:04:59.013661: lr: 0.0006 
+2026-11-09 04:04:59.013964: This epoch took 262.081101 s
+ 
+2026-11-09 04:04:59.014052: 
+epoch:  433 
+2026-11-09 04:09:14.271151: train loss : -0.8379 
+2026-11-09 04:09:22.410328: validation loss: -0.5744 
+2026-11-09 04:09:22.411590: Average global foreground Dice: [np.float32(0.9261), np.float32(0.5985), np.float32(0.7207), np.float32(0.7473), np.float32(0.6444), np.float32(0.7938), np.float32(0.7055), np.float32(0.6686), np.float32(0.3089), np.float32(0.4526), np.float32(0.5833), np.float32(0.6321), np.float32(0.5051), np.float32(0.3862), np.float32(0.0006), np.float32(0.0), np.float32(0.7203), np.float32(0.5102), np.float32(0.7012), np.float32(0.5869), np.float32(0.5107), np.float32(0.6225), np.float32(0.7958), np.float32(0.7747), np.float32(0.5205), np.float32(0.3014), np.float32(0.3383), np.float32(0.245), np.float32(0.3772), np.float32(0.7256), np.float32(0.0), np.float32(0.0), np.float32(0.542), np.float32(0.6336), np.float32(0.7785), np.float32(0.7993)] 
+2026-11-09 04:09:22.411960: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:09:22.758382: lr: 0.000599 
+2026-11-09 04:09:22.758695: This epoch took 263.744576 s
+ 
+2026-11-09 04:09:22.758892: 
+epoch:  434 
+2026-11-09 04:14:01.738775: train loss : -0.8345 
+2026-11-09 04:14:09.940447: validation loss: -0.6080 
+2026-11-09 04:14:09.942038: Average global foreground Dice: [np.float32(0.9263), np.float32(0.6257), np.float32(0.7693), np.float32(0.7685), np.float32(0.695), np.float32(0.819), np.float32(0.7254), np.float32(0.6614), np.float32(0.4874), np.float32(0.355), np.float32(0.6015), np.float32(0.6473), np.float32(0.5578), np.float32(0.5457), np.float32(0.0415), np.float32(0.0), np.float32(0.7354), np.float32(0.5325), np.float32(0.7174), np.float32(0.6119), np.float32(0.5254), np.float32(0.6506), np.float32(0.8904), np.float32(0.8753), np.float32(0.4945), np.float32(0.4235), np.float32(0.3262), np.float32(0.2205), np.float32(0.2793), np.float32(0.7076), np.float32(0.0), np.float32(0.0), np.float32(0.5976), np.float32(0.6574), np.float32(0.8145), np.float32(0.8365)] 
+2026-11-09 04:14:09.942476: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:14:10.317970: lr: 0.000598 
+2026-11-09 04:14:10.318440: This epoch took 287.559472 s
+ 
+2026-11-09 04:14:10.318690: 
+epoch:  435 
+2026-11-09 04:18:46.444659: train loss : -0.8358 
+2026-11-09 04:18:54.590915: validation loss: -0.5871 
+2026-11-09 04:18:54.592378: Average global foreground Dice: [np.float32(0.931), np.float32(0.6177), np.float32(0.7558), np.float32(0.7583), np.float32(0.6727), np.float32(0.805), np.float32(0.7072), np.float32(0.6896), np.float32(0.3413), np.float32(0.4659), np.float32(0.6147), np.float32(0.6434), np.float32(0.5327), np.float32(0.439), np.float32(0.0585), np.float32(0.0), np.float32(0.7347), np.float32(0.5712), np.float32(0.716), np.float32(0.6039), np.float32(0.5172), np.float32(0.6394), np.float32(0.8693), np.float32(0.8552), np.float32(0.5077), np.float32(0.4114), np.float32(0.2789), np.float32(0.0811), np.float32(0.3761), np.float32(0.6844), np.float32(0.0), np.float32(0.0), np.float32(0.5856), np.float32(0.6199), np.float32(0.8039), np.float32(0.8203)] 
+2026-11-09 04:18:54.592898: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:18:54.958752: lr: 0.000597 
+2026-11-09 04:18:54.959110: This epoch took 284.640326 s
+ 
+2026-11-09 04:18:54.959227: 
+epoch:  436 
+2026-11-09 04:23:03.550999: train loss : -0.8388 
+2026-11-09 04:23:11.729357: validation loss: -0.6392 
+2026-11-09 04:23:11.730666: Average global foreground Dice: [np.float32(0.9252), np.float32(0.672), np.float32(0.7927), np.float32(0.7951), np.float32(0.7276), np.float32(0.8388), np.float32(0.7441), np.float32(0.7031), np.float32(0.3718), np.float32(0.4808), np.float32(0.6576), np.float32(0.6924), np.float32(0.6259), np.float32(0.5983), np.float32(0.0043), np.float32(0.0), np.float32(0.755), np.float32(0.6008), np.float32(0.7233), np.float32(0.619), np.float32(0.5725), np.float32(0.6777), np.float32(0.8879), np.float32(0.8728), np.float32(0.4857), np.float32(0.469), np.float32(0.2598), np.float32(0.0064), np.float32(0.2822), np.float32(0.701), np.float32(0.0), np.float32(0.0), np.float32(0.6073), np.float32(0.6054), np.float32(0.8437), np.float32(0.8552)] 
+2026-11-09 04:23:11.731190: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:23:12.100047: lr: 0.000596 
+2026-11-09 04:23:12.100307: This epoch took 257.140994 s
+ 
+2026-11-09 04:23:12.100404: 
+epoch:  437 
+2026-11-09 04:26:57.850777: train loss : -0.8394 
+2026-11-09 04:27:06.069438: validation loss: -0.6031 
+2026-11-09 04:27:06.070591: Average global foreground Dice: [np.float32(0.9279), np.float32(0.6392), np.float32(0.7376), np.float32(0.7705), np.float32(0.6884), np.float32(0.8119), np.float32(0.7188), np.float32(0.6301), np.float32(0.4176), np.float32(0.355), np.float32(0.6327), np.float32(0.6829), np.float32(0.5823), np.float32(0.4649), np.float32(0.0008), np.float32(0.0), np.float32(0.7383), np.float32(0.5588), np.float32(0.711), np.float32(0.62), np.float32(0.4752), np.float32(0.645), np.float32(0.8454), np.float32(0.8287), np.float32(0.5102), np.float32(0.3979), np.float32(0.3587), np.float32(0.4659), np.float32(0.3725), np.float32(0.6691), np.float32(0.0), np.float32(0.0), np.float32(0.5784), np.float32(0.6231), np.float32(0.8096), np.float32(0.8252)] 
+2026-11-09 04:27:06.070868: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:27:06.426328: lr: 0.000595 
+2026-11-09 04:27:06.426824: This epoch took 234.326346 s
+ 
+2026-11-09 04:27:06.427032: 
+epoch:  438 
+2026-11-09 04:31:18.231375: train loss : -0.8295 
+2026-11-09 04:31:26.407972: validation loss: -0.6256 
+2026-11-09 04:31:26.408899: Average global foreground Dice: [np.float32(0.9249), np.float32(0.596), np.float32(0.7595), np.float32(0.7528), np.float32(0.6633), np.float32(0.8113), np.float32(0.7036), np.float32(0.5445), np.float32(0.3409), np.float32(0.4758), np.float32(0.6275), np.float32(0.6928), np.float32(0.6383), np.float32(0.5486), np.float32(0.0918), np.float32(0.0), np.float32(0.7093), np.float32(0.5644), np.float32(0.6966), np.float32(0.5887), np.float32(0.5775), np.float32(0.6115), np.float32(0.8653), np.float32(0.8418), np.float32(0.4962), np.float32(0.4493), np.float32(0.2223), np.float32(0.4801), np.float32(0.3819), np.float32(0.7027), np.float32(0.0), np.float32(0.0), np.float32(0.5656), np.float32(0.6262), np.float32(0.791), np.float32(0.8172)] 
+2026-11-09 04:31:26.409049: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:31:26.753614: lr: 0.000594 
+2026-11-09 04:31:26.754122: This epoch took 260.327003 s
+ 
+2026-11-09 04:31:26.754417: 
+epoch:  439 
+2026-11-09 04:35:40.824277: train loss : -0.8306 
+2026-11-09 04:35:49.014557: validation loss: -0.5012 
+2026-11-09 04:35:49.015701: Average global foreground Dice: [np.float32(0.9295), np.float32(0.5309), np.float32(0.5401), np.float32(0.5347), np.float32(0.4618), np.float32(0.608), np.float32(0.5033), np.float32(0.5785), np.float32(0.5433), np.float32(0.5415), np.float32(0.4404), np.float32(0.5193), np.float32(0.5803), np.float32(0.5479), np.float32(0.0), np.float32(0.0), np.float32(0.4153), np.float32(0.3541), np.float32(0.4252), np.float32(0.407), np.float32(0.4424), np.float32(0.385), np.float32(0.6844), np.float32(0.6365), np.float32(0.2), np.float32(0.2725), np.float32(0.0244), np.float32(0.0062), np.float32(0.1294), np.float32(0.6467), np.float32(0.0), np.float32(0.0), np.float32(0.477), np.float32(0.4651), np.float32(0.5855), np.float32(0.6223)] 
+2026-11-09 04:35:49.015890: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:35:49.384713: lr: 0.000593 
+2026-11-09 04:35:49.385193: This epoch took 262.630530 s
+ 
+2026-11-09 04:35:49.385344: 
+epoch:  440 
+2026-11-09 04:39:47.255687: train loss : -0.8342 
+2026-11-09 04:39:55.448078: validation loss: -0.5393 
+2026-11-09 04:39:55.449632: Average global foreground Dice: [np.float32(0.9325), np.float32(0.5587), np.float32(0.5951), np.float32(0.5967), np.float32(0.4785), np.float32(0.6637), np.float32(0.5535), np.float32(0.5146), np.float32(0.474), np.float32(0.5102), np.float32(0.5032), np.float32(0.5665), np.float32(0.596), np.float32(0.5205), np.float32(0.0), np.float32(0.0), np.float32(0.4283), np.float32(0.3455), np.float32(0.4695), np.float32(0.4436), np.float32(0.5331), np.float32(0.4795), np.float32(0.6927), np.float32(0.6663), np.float32(0.2504), np.float32(0.2819), np.float32(0.0542), np.float32(0.198), np.float32(0.1965), np.float32(0.705), np.float32(0.0), np.float32(0.0), np.float32(0.4619), np.float32(0.5004), np.float32(0.6409), np.float32(0.6681)] 
+2026-11-09 04:39:55.450197: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:39:55.808697: lr: 0.000592 
+2026-11-09 04:39:55.809277: This epoch took 246.423856 s
+ 
+2026-11-09 04:39:55.809690: 
+epoch:  441 
+2026-11-09 04:44:11.599679: train loss : -0.8300 
+2026-11-09 04:44:19.767508: validation loss: -0.4801 
+2026-11-09 04:44:19.769414: Average global foreground Dice: [np.float32(0.9332), np.float32(0.5104), np.float32(0.5212), np.float32(0.511), np.float32(0.4322), np.float32(0.5804), np.float32(0.4836), np.float32(0.588), np.float32(0.3953), np.float32(0.5781), np.float32(0.4207), np.float32(0.508), np.float32(0.5787), np.float32(0.4915), np.float32(0.0), np.float32(0.0), np.float32(0.3931), np.float32(0.3387), np.float32(0.4015), np.float32(0.3999), np.float32(0.4406), np.float32(0.3629), np.float32(0.6437), np.float32(0.5982), np.float32(0.215), np.float32(0.2659), np.float32(0.021), np.float32(0.0404), np.float32(0.211), np.float32(0.6462), np.float32(0.0), np.float32(0.0), np.float32(0.4059), np.float32(0.4594), np.float32(0.5733), np.float32(0.5976)] 
+2026-11-09 04:44:19.769893: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:44:20.161807: lr: 0.000592 
+2026-11-09 04:44:20.162157: This epoch took 264.352225 s
+ 
+2026-11-09 04:44:20.162361: 
+epoch:  442 
+2026-11-09 04:48:06.186424: train loss : -0.8351 
+2026-11-09 04:48:14.381907: validation loss: -0.5493 
+2026-11-09 04:48:14.382990: Average global foreground Dice: [np.float32(0.9345), np.float32(0.6055), np.float32(0.6486), np.float32(0.6695), np.float32(0.5764), np.float32(0.7143), np.float32(0.612), np.float32(0.6985), np.float32(0.3438), np.float32(0.4234), np.float32(0.5556), np.float32(0.6044), np.float32(0.6288), np.float32(0.5481), np.float32(0.0), np.float32(0.0), np.float32(0.5297), np.float32(0.4255), np.float32(0.5296), np.float32(0.5049), np.float32(0.448), np.float32(0.4722), np.float32(0.7843), np.float32(0.7531), np.float32(0.3304), np.float32(0.3494), np.float32(0.0443), np.float32(0.2665), np.float32(0.1639), np.float32(0.6473), np.float32(0.0), np.float32(0.0), np.float32(0.3213), np.float32(0.542), np.float32(0.7106), np.float32(0.7241)] 
+2026-11-09 04:48:14.383241: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:48:14.751550: lr: 0.000591 
+2026-11-09 04:48:14.751876: This epoch took 234.589433 s
+ 
+2026-11-09 04:48:14.752047: 
+epoch:  443 
+2026-11-09 04:52:09.615667: train loss : -0.8417 
+2026-11-09 04:52:17.822064: validation loss: -0.5305 
+2026-11-09 04:52:17.823372: Average global foreground Dice: [np.float32(0.9305), np.float32(0.5512), np.float32(0.6164), np.float32(0.608), np.float32(0.5205), np.float32(0.6698), np.float32(0.577), np.float32(0.6659), np.float32(0.4018), np.float32(0.4428), np.float32(0.4933), np.float32(0.5755), np.float32(0.5936), np.float32(0.5311), np.float32(0.0), np.float32(0.0), np.float32(0.5013), np.float32(0.4233), np.float32(0.5122), np.float32(0.4794), np.float32(0.5004), np.float32(0.4274), np.float32(0.7323), np.float32(0.7004), np.float32(0.3186), np.float32(0.3686), np.float32(0.1007), np.float32(0.3196), np.float32(0.1836), np.float32(0.6928), np.float32(0.0), np.float32(0.0), np.float32(0.486), np.float32(0.5257), np.float32(0.6608), np.float32(0.7007)] 
+2026-11-09 04:52:17.823716: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:52:18.220476: lr: 0.00059 
+2026-11-09 04:52:18.221017: This epoch took 243.468898 s
+ 
+2026-11-09 04:52:18.221412: 
+epoch:  444 
+2026-11-09 04:56:44.733015: train loss : -0.8421 
+2026-11-09 04:56:52.924289: validation loss: -0.6134 
+2026-11-09 04:56:52.925392: Average global foreground Dice: [np.float32(0.9303), np.float32(0.6205), np.float32(0.7346), np.float32(0.7576), np.float32(0.6683), np.float32(0.8127), np.float32(0.7123), np.float32(0.6426), np.float32(0.4326), np.float32(0.4993), np.float32(0.6181), np.float32(0.6914), np.float32(0.6427), np.float32(0.6262), np.float32(0.0297), np.float32(0.0), np.float32(0.6824), np.float32(0.5783), np.float32(0.6785), np.float32(0.5896), np.float32(0.6104), np.float32(0.6336), np.float32(0.8117), np.float32(0.7945), np.float32(0.4285), np.float32(0.4025), np.float32(0.0956), np.float32(0.4682), np.float32(0.3032), np.float32(0.7398), np.float32(0.0), np.float32(0.0), np.float32(0.5991), np.float32(0.6345), np.float32(0.7928), np.float32(0.815)] 
+2026-11-09 04:56:52.925576: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 04:56:53.277070: lr: 0.000589 
+2026-11-09 04:56:53.277628: This epoch took 275.055922 s
+ 
+2026-11-09 04:56:53.277831: 
+epoch:  445 
+2026-11-09 05:01:38.934386: train loss : -0.8232 
+2026-11-09 05:01:47.151727: validation loss: -0.5964 
+2026-11-09 05:01:47.153219: Average global foreground Dice: [np.float32(0.9213), np.float32(0.5541), np.float32(0.7541), np.float32(0.7126), np.float32(0.6432), np.float32(0.7762), np.float32(0.706), np.float32(0.3195), np.float32(0.0), np.float32(0.2416), np.float32(0.5821), np.float32(0.6228), np.float32(0.5766), np.float32(0.5125), np.float32(0.0224), np.float32(0.0), np.float32(0.6636), np.float32(0.5432), np.float32(0.6574), np.float32(0.5656), np.float32(0.5773), np.float32(0.6157), np.float32(0.8516), np.float32(0.8171), np.float32(0.4724), np.float32(0.4047), np.float32(0.2848), np.float32(0.2468), np.float32(0.4784), np.float32(0.6571), np.float32(0.0), np.float32(0.0), np.float32(0.5295), np.float32(0.5388), np.float32(0.7725), np.float32(0.8015)] 
+2026-11-09 05:01:47.153581: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 05:01:47.511153: lr: 0.000588 
+2026-11-09 05:01:47.511544: This epoch took 294.233631 s
+ 
+2026-11-09 05:01:47.511737: 
+epoch:  446 
+2026-11-09 05:06:22.884328: train loss : -0.8315 
+2026-11-09 05:06:31.133904: validation loss: -0.6151 
+2026-11-09 05:06:31.135195: Average global foreground Dice: [np.float32(0.9241), np.float32(0.6359), np.float32(0.7473), np.float32(0.7111), np.float32(0.6136), np.float32(0.7757), np.float32(0.6776), np.float32(0.6149), np.float32(0.5826), np.float32(0.3532), np.float32(0.6038), np.float32(0.6897), np.float32(0.6384), np.float32(0.6269), np.float32(0.0), np.float32(0.0), np.float32(0.6521), np.float32(0.5498), np.float32(0.6552), np.float32(0.5711), np.float32(0.6004), np.float32(0.495), np.float32(0.8786), np.float32(0.8602), np.float32(0.4142), np.float32(0.496), np.float32(0.1464), np.float32(0.475), np.float32(0.3701), np.float32(0.7033), np.float32(0.0), np.float32(0.0), np.float32(0.6005), np.float32(0.6091), np.float32(0.7852), np.float32(0.8134)] 
+2026-11-09 05:06:31.135733: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 05:06:31.486438: lr: 0.000587 
+2026-11-09 05:06:31.486942: This epoch took 283.975104 s
+ 
+2026-11-09 05:06:31.487358: 
+epoch:  447 
+2026-11-09 05:10:53.644234: train loss : -0.8351 
+2026-11-09 05:11:01.850585: validation loss: -0.6356 
+2026-11-09 05:11:01.851692: Average global foreground Dice: [np.float32(0.9253), np.float32(0.6336), np.float32(0.7712), np.float32(0.7854), np.float32(0.699), np.float32(0.8233), np.float32(0.7401), np.float32(0.5938), np.float32(0.5786), np.float32(0.3725), np.float32(0.6388), np.float32(0.6995), np.float32(0.6202), np.float32(0.591), np.float32(0.0008), np.float32(0.0), np.float32(0.6967), np.float32(0.5566), np.float32(0.6992), np.float32(0.5929), np.float32(0.5668), np.float32(0.6489), np.float32(0.8867), np.float32(0.8763), np.float32(0.4835), np.float32(0.3696), np.float32(0.2209), np.float32(0.5532), np.float32(0.4444), np.float32(0.6509), np.float32(0.0), np.float32(0.0), np.float32(0.605), np.float32(0.6626), np.float32(0.8211), np.float32(0.8454)] 
+2026-11-09 05:11:01.852086: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 05:11:02.210697: lr: 0.000586 
+2026-11-09 05:11:02.211251: This epoch took 270.723767 s
+ 
+2026-11-09 05:11:02.211614: 
+epoch:  448 
+2026-11-09 05:14:58.659184: train loss : -0.8384 
+2026-11-09 05:15:06.824991: validation loss: -0.5122 
+2026-11-09 05:15:06.826401: Average global foreground Dice: [np.float32(0.9231), np.float32(0.5286), np.float32(0.6125), np.float32(0.5885), np.float32(0.4726), np.float32(0.6393), np.float32(0.5426), np.float32(0.5594), np.float32(0.5182), np.float32(0.4491), np.float32(0.4778), np.float32(0.5592), np.float32(0.5195), np.float32(0.4548), np.float32(0.0), np.float32(0.0), np.float32(0.4939), np.float32(0.4098), np.float32(0.5066), np.float32(0.465), np.float32(0.4772), np.float32(0.5302), np.float32(0.7247), np.float32(0.6945), np.float32(0.3522), np.float32(0.304), np.float32(0.1163), np.float32(0.2377), np.float32(0.3778), np.float32(0.623), np.float32(0.0), np.float32(0.0), np.float32(0.4109), np.float32(0.501), np.float32(0.6226), np.float32(0.6606)] 
+2026-11-09 05:15:06.826921: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 05:15:07.175508: lr: 0.000585 
+2026-11-09 05:15:07.175823: This epoch took 244.963982 s
+ 
+2026-11-09 05:15:07.175910: 
+epoch:  449 
+2026-11-09 05:19:09.390949: train loss : -0.8368 
+2026-11-09 05:19:17.599387: validation loss: -0.5308 
+2026-11-09 05:19:17.600595: Average global foreground Dice: [np.float32(0.9223), np.float32(0.5537), np.float32(0.617), np.float32(0.5844), np.float32(0.4912), np.float32(0.6562), np.float32(0.5577), np.float32(0.6524), np.float32(0.5431), np.float32(0.5474), np.float32(0.5036), np.float32(0.5743), np.float32(0.587), np.float32(0.5438), np.float32(0.0019), np.float32(0.0), np.float32(0.4736), np.float32(0.3925), np.float32(0.5182), np.float32(0.4755), np.float32(0.482), np.float32(0.4352), np.float32(0.7329), np.float32(0.7156), np.float32(0.3113), np.float32(0.3518), np.float32(0.1192), np.float32(0.0), np.float32(0.156), np.float32(0.6573), np.float32(0.0), np.float32(0.0), np.float32(0.5129), np.float32(0.5181), np.float32(0.6476), np.float32(0.6936)] 
+2026-11-09 05:19:17.601076: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 05:19:17.952959: lr: 0.000584 
+2026-11-09 05:19:17.953488: saving scheduled checkpoint file... 
+2026-11-09 05:19:18.127465: saving checkpoint... 
+2026-11-09 05:19:20.678296: done, saving took 2.72 seconds 
+2026-11-09 05:19:20.687770: done 
+2026-11-09 05:19:20.687979: This epoch took 253.512017 s
+ 
+2026-11-09 05:19:20.688184: 
+epoch:  450 
+2026-11-09 05:23:17.409127: train loss : -0.8370 
+2026-11-09 05:23:25.585481: validation loss: -0.5170 
+2026-11-09 05:23:25.586553: Average global foreground Dice: [np.float32(0.9312), np.float32(0.5599), np.float32(0.6203), np.float32(0.5938), np.float32(0.5225), np.float32(0.6433), np.float32(0.558), np.float32(0.6783), np.float32(0.5615), np.float32(0.3726), np.float32(0.4851), np.float32(0.5294), np.float32(0.5751), np.float32(0.5599), np.float32(0.0), np.float32(0.0), np.float32(0.4754), np.float32(0.4124), np.float32(0.4668), np.float32(0.4507), np.float32(0.4542), np.float32(0.4647), np.float32(0.7024), np.float32(0.6716), np.float32(0.3054), np.float32(0.3291), np.float32(0.1357), np.float32(0.2458), np.float32(0.1606), np.float32(0.6569), np.float32(0.0), np.float32(0.0), np.float32(0.4401), np.float32(0.5086), np.float32(0.6499), np.float32(0.6708)] 
+2026-11-09 05:23:25.586732: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-11-09 05:23:25.932205: lr: 0.000583 
+2026-11-09 05:23:25.932486: This epoch took 245.244097 s
+ 
+2026-11-09 05:23:25.932561: 
+epoch:  451