diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json
index 12ada0c8864006d8ce4b2c73b9324fd647d6aac5..bb5a2a32f0efe2990f8b602893b5a9dcef09e9e7 100644
--- a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/debug.json
@@ -1,28 +1,28 @@
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np.float32(-0.5542636), np.float32(-0.60311216), np.float32(-0.6242764), np.float32(-0.48853743), np.float32(-0.5821557), np.float32(-0.49966392), np.float32(-0.556243), np.float32(-0.5687842), np.float32(-0.6119947), np.float32(-0.58567077), np.float32(-0.5712553), np.float32(-0.5650744), np.float32(-0.5330271), np.float32(-0.5247377), np.float32(-0.5409106), np.float32(-0.56644666), np.float32(-0.6057763), np.float32(-0.58654577), np.float32(-0.59365934), np.float32(-0.5873908), np.float32(-0.5800569), np.float32(-0.59618473), np.float32(-0.580809), np.float32(-0.6193095), np.float32(-0.6041505), np.float32(-0.58360475), np.float32(-0.432224), np.float32(-0.5465583), np.float32(-0.5427347), np.float32(-0.56444055), np.float32(-0.57028586), np.float32(-0.55129266), np.float32(-0.56420475), np.float32(-0.510321), np.float32(-0.36882773), np.float32(-0.5445628), np.float32(-0.6621425), np.float32(-0.5960452), np.float32(-0.4654146), np.float32(-0.46860072), np.float32(-0.56340444), np.float32(-0.5976894), np.float32(-0.5957069), np.float32(-0.55442333), np.float32(-0.5517012), np.float32(-0.5326272), np.float32(-0.5385176), np.float32(-0.47810552), np.float32(-0.45405078), np.float32(-0.530535), np.float32(-0.6400422), np.float32(-0.45627463), np.float32(-0.5757327), np.float32(-0.57046807), np.float32(-0.5561066), np.float32(-0.5493367), np.float32(-0.5787172), np.float32(-0.5607002), np.float32(-0.56044465)]",
     "all_val_losses_tr_mode": "[]",
     "also_val_in_tr_mode": "False",
-    "amp_grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f0241a78980>",
+    "amp_grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7ff589d14ec0>",
     "base_num_features": "30",
     "basic_generator_patch_size": "[205 205 205]",
     "batch_dice": "False",
     "batch_size": "2",
-    "best_MA_tr_loss_for_patience": "-0.6763729",
-    "best_epoch_based_on_MA_tr_loss": "148",
-    "best_val_eval_criterion_MA": "0.37075663",
+    "best_MA_tr_loss_for_patience": "-0.77806145",
+    "best_epoch_based_on_MA_tr_loss": "298",
+    "best_val_eval_criterion_MA": "0.50481176",
     "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 0x7f0241ba5fd0>",
-    "dl_val": "<nnunet_mednext.training.dataloading.dataset_loading.DataLoader3D object at 0x7f0241c34f50>",
+    "dl_tr": "<nnunet_mednext.training.dataloading.dataset_loading.DataLoader3D object at 0x7ff589f1e3c0>",
+    "dl_val": "<nnunet_mednext.training.dataloading.dataset_loading.DataLoader3D object at 0x7ff589d3ce10>",
     "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_B_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_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1', '/marimo/TopBrain2026/nnUNet_preprocessed/Task001_Brain2026', False, 0, True, False, True)",
     "initial_lr": "0.001",
-    "log_file": "/marimo/TopBrain2026/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_10_15_09_20_22.txt",
+    "log_file": "/marimo/TopBrain2026/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_10_16_03_02_01.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_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0",
     "output_folder_base": "/marimo/TopBrain2026/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1",
     "oversample_foreground_percent": "0.33",
@@ -71,7 +71,7 @@
     "save_latest_only": "True",
     "stage": "0",
     "threeD": "True",
-    "tr_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7f0241ba74d0>",
+    "tr_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7ff589f1fa10>",
     "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 0x7f0241c35310>",
+    "val_gen": "<batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter object at 0x7ff589d3dbd0>",
     "was_initialized": "True",
     "weight_decay": "3e-05"
 }
\ No newline at end of file
diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model
index 7b3db630add3c7ea9b97829893d1eaf51d050b49..0ad294658077c2128d7a7c1890bc9a2e8da408a1 100644
--- a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_best.model
@@ -1,3 +1,3 @@
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-oid sha256:fb5e050c7b2086aaa4a830e11c6b6ff65d85b20b91c6902e54cbf266467c1fc6
-size 127087618
+oid sha256:817036bb4f2fe9374d97182121c8c6dbad8c09dba3996d4b0b00b040a941b2a2
+size 127106306
diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model
index 7b62fe210e0e4b72094f26f357d8346b045b2066..858cca6a690bda45db527392a741bfdc7d625183 100644
--- a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model
@@ -1,3 +1,3 @@
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-oid sha256:25fe6d097d8cc7bb2c28d3100ecad18f5e6a974076148ab5d1a474d94cdbe95e
-size 127095408
+oid sha256:79fceae22e377bf81d843b908c205b3f3d5a6134afff26b5494d68b736fc8714
+size 127122352
diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/progress.png b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/progress.png
index 1e38ed1d11cdefaef2eaadfeaa90eadee16d3da0..c913bf3f445176eb6ab9bf75ba113098a75c5257 100644
--- a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/progress.png
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/progress.png
@@ -1,3 +1,3 @@
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-oid sha256:8be1cbd14a4ab3bcbf7d82c7d653d5c650dec2db533aca646f558e85a16295ae
-size 700059
+oid sha256:bf4f176d0daa428d7700c0e7a4b051bcc4cfacacac7cefedd764e78fbffe4ff4
+size 839347
diff --git a/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_10_16_03_02_01.txt b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_10_16_03_02_01.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fa669be548a1f1a8d4b53809d0f7a479ea1345d7
--- /dev/null
+++ b/MedNeXt_Base/fold_0/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/training_log_2026_10_16_03_02_01.txt
@@ -0,0 +1,1634 @@
+Starting... 
+2026-10-16 03:02:01.068069: Creating new 5-fold cross-validation split... 
+2026-10-16 03:02:01.068854: Desired fold for training: 0 
+2026-10-16 03:02:01.068914: This split has 20 training and 5 validation cases. 
+2026-10-16 03:02:01.775336: TRAINING KEYS:
+ odict_keys([np.str_('topcow_ct_001'), np.str_('topcow_ct_002'), np.str_('topcow_ct_003'), np.str_('topcow_ct_004'), np.str_('topcow_ct_005'), np.str_('topcow_ct_006'), np.str_('topcow_ct_007'), np.str_('topcow_ct_008'), np.str_('topcow_ct_011'), np.str_('topcow_ct_012'), np.str_('topcow_ct_013'), np.str_('topcow_ct_014'), np.str_('topcow_ct_016'), np.str_('topcow_ct_017'), np.str_('topcow_ct_020'), np.str_('topcow_ct_021'), np.str_('topcow_ct_023'), np.str_('topcow_ct_025'), np.str_('topcow_ct_026'), np.str_('topcow_ct_027')]) 
+2026-10-16 03:02:01.776405: VALIDATION KEYS:
+ odict_keys([np.str_('topcow_ct_010'), np.str_('topcow_ct_015'), np.str_('topcow_ct_018'), np.str_('topcow_ct_022'), np.str_('topcow_ct_024')]) 
+2026-10-16 03:02:03.312483: loading checkpoint /marimo/TopBrain2026/nnUNet_results/nnUNet/3d_fullres/Task001_Brain2026/nnUNetTrainerV2_MedNeXt_B_kernel3__nnUNetPlansv2.1_trgSp_1x1x1/fold_0/model_latest.model train= True 
+2026-10-16 03:02:03.463135: lr: 0.000725 
+2026-10-16 03:02:06.929291: Unable to plot network architecture: 
+2026-10-16 03:02:06.930043: No module named 'hiddenlayer' 
+2026-10-16 03:02:06.932237: 
+printing the network instead:
+ 
+2026-10-16 03:02:06.932364: MedNeXt(
+  (stem): Conv3d(1, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+  (enc_block_0): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32)
+      (norm): GroupNorm(32, 32, eps=1e-05, affine=True)
+      (conv2): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(64, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32)
+      (norm): GroupNorm(32, 32, eps=1e-05, affine=True)
+      (conv2): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(64, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (down_0): MedNeXtDownBlock(
+    (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=32)
+    (norm): GroupNorm(32, 32, eps=1e-05, affine=True)
+    (conv2): Conv3d(32, 96, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(96, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (enc_block_1): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64)
+      (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+      (conv2): Conv3d(64, 192, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(192, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64)
+      (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+      (conv2): Conv3d(64, 192, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(192, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (down_1): MedNeXtDownBlock(
+    (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=64)
+    (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+    (conv2): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(256, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (enc_block_2): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (down_2): MedNeXtDownBlock(
+    (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=128)
+    (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+    (conv2): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(512, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (enc_block_3): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (down_3): MedNeXtDownBlock(
+    (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=256)
+    (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+    (conv2): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(1024, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (bottleneck): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512)
+      (norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+      (conv2): Conv3d(512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=512)
+      (norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+      (conv2): Conv3d(512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(2048, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (up_3): MedNeXtUpBlock(
+    (conv1): ConvTranspose3d(512, 512, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=512)
+    (norm): GroupNorm(512, 512, eps=1e-05, affine=True)
+    (conv2): Conv3d(512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(2048, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): ConvTranspose3d(512, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (dec_block_3): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=256)
+      (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+      (conv2): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(1024, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (up_2): MedNeXtUpBlock(
+    (conv1): ConvTranspose3d(256, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=256)
+    (norm): GroupNorm(256, 256, eps=1e-05, affine=True)
+    (conv2): Conv3d(256, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(1024, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): ConvTranspose3d(256, 128, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (dec_block_2): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=128)
+      (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+      (conv2): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (up_1): MedNeXtUpBlock(
+    (conv1): ConvTranspose3d(128, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=128)
+    (norm): GroupNorm(128, 128, eps=1e-05, affine=True)
+    (conv2): Conv3d(128, 384, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(384, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): ConvTranspose3d(128, 64, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (dec_block_1): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64)
+      (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+      (conv2): Conv3d(64, 192, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(192, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=64)
+      (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+      (conv2): Conv3d(64, 192, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(192, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (up_0): MedNeXtUpBlock(
+    (conv1): ConvTranspose3d(64, 64, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), groups=64)
+    (norm): GroupNorm(64, 64, eps=1e-05, affine=True)
+    (conv2): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (act): GELU(approximate='none')
+    (conv3): Conv3d(128, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    (res_conv): ConvTranspose3d(64, 32, kernel_size=(1, 1, 1), stride=(2, 2, 2))
+  )
+  (dec_block_0): Sequential(
+    (0): MedNeXtBlock(
+      (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32)
+      (norm): GroupNorm(32, 32, eps=1e-05, affine=True)
+      (conv2): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(64, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+    (1): MedNeXtBlock(
+      (conv1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), groups=32)
+      (norm): GroupNorm(32, 32, eps=1e-05, affine=True)
+      (conv2): Conv3d(32, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+      (act): GELU(approximate='none')
+      (conv3): Conv3d(64, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+    )
+  )
+  (out_0): OutBlock(
+    (conv_out): ConvTranspose3d(32, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+  )
+  (out_1): OutBlock(
+    (conv_out): ConvTranspose3d(64, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+  )
+  (out_2): OutBlock(
+    (conv_out): ConvTranspose3d(128, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+  )
+  (out_3): OutBlock(
+    (conv_out): ConvTranspose3d(256, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+  )
+  (out_4): OutBlock(
+    (conv_out): ConvTranspose3d(512, 37, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+  )
+) 
+2026-10-16 03:02:06.954666: 
+ 
+2026-10-16 03:02:06.956821: 
+epoch:  300 
+2026-10-16 03:06:41.459872: train loss : -0.7879 
+2026-10-16 03:06:46.873315: validation loss: -0.5485 
+2026-10-16 03:06:46.874321: Average global foreground Dice: [np.float32(0.9187), np.float32(0.6133), np.float32(0.7028), np.float32(0.7059), np.float32(0.587), np.float32(0.7326), np.float32(0.6467), np.float32(0.635), np.float32(0.1949), np.float32(0.4721), np.float32(0.5444), np.float32(0.6001), np.float32(0.5973), np.float32(0.4699), np.float32(0.0), np.float32(0.0), np.float32(0.652), np.float32(0.5226), np.float32(0.6509), np.float32(0.6446), np.float32(0.5218), np.float32(0.6384), np.float32(0.6719), np.float32(0.6751), np.float32(0.4857), np.float32(0.3672), np.float32(0.0), np.float32(0.0), np.float32(0.1086), np.float32(0.4672), np.float32(0.0), np.float32(0.0), np.float32(0.4244), np.float32(0.5213), np.float32(0.7144), np.float32(0.7289)] 
+2026-10-16 03:06:46.874494: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:06:47.257824: lr: 0.000724 
+2026-10-16 03:06:47.258074: This epoch took 280.300866 s
+ 
+2026-10-16 03:06:47.258163: 
+epoch:  301 
+2026-10-16 03:11:03.821900: train loss : -0.7782 
+2026-10-16 03:11:09.147716: validation loss: -0.6248 
+2026-10-16 03:11:09.148484: Average global foreground Dice: [np.float32(0.9169), np.float32(0.6828), np.float32(0.7855), np.float32(0.8237), np.float32(0.7507), np.float32(0.849), np.float32(0.7652), np.float32(0.4682), np.float32(0.0), np.float32(0.4758), np.float32(0.5932), np.float32(0.6578), np.float32(0.598), np.float32(0.553), np.float32(0.0), np.float32(0.0), np.float32(0.7731), np.float32(0.6405), np.float32(0.7429), np.float32(0.6754), np.float32(0.5819), np.float32(0.6134), np.float32(0.838), np.float32(0.8313), np.float32(0.489), np.float32(0.4739), np.float32(0.0), np.float32(0.0), np.float32(0.5069), np.float32(0.6257), np.float32(0.0), np.float32(0.0), np.float32(0.581), np.float32(0.6358), np.float32(0.8482), np.float32(0.8548)] 
+2026-10-16 03:11:09.148655: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:11:09.529301: lr: 0.000724 
+2026-10-16 03:11:09.529691: This epoch took 262.271469 s
+ 
+2026-10-16 03:11:09.529886: 
+epoch:  302 
+2026-10-16 03:15:27.942356: train loss : -0.7855 
+2026-10-16 03:15:33.298663: validation loss: -0.5930 
+2026-10-16 03:15:33.299598: Average global foreground Dice: [np.float32(0.9047), np.float32(0.631), np.float32(0.6989), np.float32(0.7476), np.float32(0.6546), np.float32(0.7672), np.float32(0.6412), np.float32(0.4801), np.float32(0.0065), np.float32(0.5041), np.float32(0.5861), np.float32(0.5966), np.float32(0.6061), np.float32(0.5313), np.float32(0.0), np.float32(0.0138), np.float32(0.7119), np.float32(0.5801), np.float32(0.6671), np.float32(0.6394), np.float32(0.4339), np.float32(0.5709), np.float32(0.8588), np.float32(0.8407), np.float32(0.4871), np.float32(0.4358), np.float32(0.0), np.float32(0.0), np.float32(0.5049), np.float32(0.5733), np.float32(0.0), np.float32(0.0), np.float32(0.5872), np.float32(0.6337), np.float32(0.8046), np.float32(0.8159)] 
+2026-10-16 03:15:33.299868: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:15:33.633686: lr: 0.000723 
+2026-10-16 03:15:33.634224: This epoch took 264.104258 s
+ 
+2026-10-16 03:15:33.634564: 
+epoch:  303 
+2026-10-16 03:19:58.100272: train loss : -0.7809 
+2026-10-16 03:20:03.442634: validation loss: -0.5570 
+2026-10-16 03:20:03.443610: Average global foreground Dice: [np.float32(0.8695), np.float32(0.6013), np.float32(0.7339), np.float32(0.7645), np.float32(0.6789), np.float32(0.766), np.float32(0.7325), np.float32(0.0), np.float32(0.0), np.float32(0.4051), np.float32(0.4505), np.float32(0.5513), np.float32(0.6008), np.float32(0.5021), np.float32(0.0), np.float32(0.0), np.float32(0.736), np.float32(0.5887), np.float32(0.6972), np.float32(0.6316), np.float32(0.483), np.float32(0.6223), np.float32(0.7902), np.float32(0.7655), np.float32(0.5186), np.float32(0.3599), np.float32(0.0), np.float32(0.0), np.float32(0.1721), np.float32(0.309), np.float32(0.0), np.float32(0.0), np.float32(0.5421), np.float32(0.5467), np.float32(0.8095), np.float32(0.8231)] 
+2026-10-16 03:20:03.443837: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:20:03.772347: lr: 0.000722 
+2026-10-16 03:20:03.772690: This epoch took 270.137887 s
+ 
+2026-10-16 03:20:03.772858: 
+epoch:  304 
+2026-10-16 03:24:22.888178: train loss : -0.7858 
+2026-10-16 03:24:28.239398: validation loss: -0.5599 
+2026-10-16 03:24:28.240340: Average global foreground Dice: [np.float32(0.9076), np.float32(0.5802), np.float32(0.724), np.float32(0.7492), np.float32(0.6629), np.float32(0.7892), np.float32(0.6803), np.float32(0.6589), np.float32(0.0), np.float32(0.5711), np.float32(0.5442), np.float32(0.5953), np.float32(0.5553), np.float32(0.4365), np.float32(0.0), np.float32(0.0), np.float32(0.7067), np.float32(0.5697), np.float32(0.698), np.float32(0.6207), np.float32(0.5444), np.float32(0.6119), np.float32(0.774), np.float32(0.7545), np.float32(0.5009), np.float32(0.4372), np.float32(0.0), np.float32(0.0), np.float32(0.3296), np.float32(0.5218), np.float32(0.0), np.float32(0.0), np.float32(0.595), np.float32(0.5914), np.float32(0.7754), np.float32(0.7998)] 
+2026-10-16 03:24:28.240494: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:24:28.619925: lr: 0.000721 
+2026-10-16 03:24:28.620241: This epoch took 264.847297 s
+ 
+2026-10-16 03:24:28.620412: 
+epoch:  305 
+2026-10-16 03:28:52.447998: train loss : -0.7897 
+2026-10-16 03:28:57.788657: validation loss: -0.5930 
+2026-10-16 03:28:57.789694: Average global foreground Dice: [np.float32(0.9189), np.float32(0.6325), np.float32(0.7449), np.float32(0.7722), np.float32(0.6642), np.float32(0.8003), np.float32(0.6856), np.float32(0.6495), np.float32(0.0), np.float32(0.6095), np.float32(0.5597), np.float32(0.6319), np.float32(0.6005), np.float32(0.5204), np.float32(0.0), np.float32(0.0), np.float32(0.7219), np.float32(0.5963), np.float32(0.69), np.float32(0.6401), np.float32(0.5252), np.float32(0.5681), np.float32(0.8074), np.float32(0.783), np.float32(0.4725), np.float32(0.4512), np.float32(0.0), np.float32(0.0), np.float32(0.4014), np.float32(0.5886), np.float32(0.0), np.float32(0.0), np.float32(0.5642), np.float32(0.6447), np.float32(0.7953), np.float32(0.8095)] 
+2026-10-16 03:28:57.789907: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:28:58.113026: lr: 0.00072 
+2026-10-16 03:28:58.113287: This epoch took 269.492802 s
+ 
+2026-10-16 03:28:58.113513: 
+epoch:  306 
+2026-10-16 03:33:07.644141: train loss : -0.7892 
+2026-10-16 03:33:12.984409: validation loss: -0.6038 
+2026-10-16 03:33:12.985391: Average global foreground Dice: [np.float32(0.9036), np.float32(0.6345), np.float32(0.7703), np.float32(0.7833), np.float32(0.7067), np.float32(0.8199), np.float32(0.7327), np.float32(0.6055), np.float32(0.0), np.float32(0.457), np.float32(0.5505), np.float32(0.5834), np.float32(0.5943), np.float32(0.4706), np.float32(0.0), np.float32(0.0), np.float32(0.7329), np.float32(0.6265), np.float32(0.7117), np.float32(0.6479), np.float32(0.5707), np.float32(0.6538), np.float32(0.854), np.float32(0.8232), np.float32(0.537), np.float32(0.5479), np.float32(0.0), np.float32(0.0), np.float32(0.4656), np.float32(0.5512), np.float32(0.0), np.float32(0.0), np.float32(0.546), np.float32(0.6517), np.float32(0.822), np.float32(0.8338)] 
+2026-10-16 03:33:12.985589: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:33:13.317792: lr: 0.000719 
+2026-10-16 03:33:13.318057: This epoch took 255.204251 s
+ 
+2026-10-16 03:33:13.318132: 
+epoch:  307 
+2026-10-16 03:37:17.850089: train loss : -0.7856 
+2026-10-16 03:37:23.191604: validation loss: -0.5295 
+2026-10-16 03:37:23.192682: Average global foreground Dice: [np.float32(0.8934), np.float32(0.6238), np.float32(0.7138), np.float32(0.7027), np.float32(0.5832), np.float32(0.7332), np.float32(0.6124), np.float32(0.696), np.float32(0.0055), np.float32(0.5342), np.float32(0.4869), np.float32(0.5213), np.float32(0.5554), np.float32(0.4414), np.float32(0.0), np.float32(0.0), np.float32(0.677), np.float32(0.5249), np.float32(0.6565), np.float32(0.6171), np.float32(0.5188), np.float32(0.5892), np.float32(0.7583), np.float32(0.7331), np.float32(0.4474), np.float32(0.474), np.float32(0.0), np.float32(0.0), np.float32(0.0073), np.float32(0.3989), np.float32(0.0), np.float32(0.0), np.float32(0.5165), np.float32(0.477), np.float32(0.7307), np.float32(0.7551)] 
+2026-10-16 03:37:23.192918: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:37:23.549325: lr: 0.000718 
+2026-10-16 03:37:23.549584: This epoch took 250.231396 s
+ 
+2026-10-16 03:37:23.549681: 
+epoch:  308 
+2026-10-16 03:41:16.112135: train loss : -0.7897 
+2026-10-16 03:41:21.497507: validation loss: -0.5797 
+2026-10-16 03:41:21.498470: Average global foreground Dice: [np.float32(0.91), np.float32(0.6349), np.float32(0.7219), np.float32(0.7562), np.float32(0.6317), np.float32(0.788), np.float32(0.6758), np.float32(0.6032), np.float32(0.1277), np.float32(0.5413), np.float32(0.5963), np.float32(0.6532), np.float32(0.601), np.float32(0.4869), np.float32(0.0), np.float32(0.0), np.float32(0.7111), np.float32(0.5531), np.float32(0.6824), np.float32(0.6439), np.float32(0.5189), np.float32(0.5592), np.float32(0.8206), np.float32(0.8107), np.float32(0.5035), np.float32(0.4284), np.float32(0.0), np.float32(0.0), np.float32(0.2256), np.float32(0.6433), np.float32(0.0), np.float32(0.0), np.float32(0.4981), np.float32(0.543), np.float32(0.788), np.float32(0.8033)] 
+2026-10-16 03:41:21.498658: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:41:21.831182: lr: 0.000717 
+2026-10-16 03:41:21.831644: This epoch took 238.281822 s
+ 
+2026-10-16 03:41:21.831958: 
+epoch:  309 
+2026-10-16 03:45:35.864111: train loss : -0.7842 
+2026-10-16 03:45:41.198687: validation loss: -0.5158 
+2026-10-16 03:45:41.199803: Average global foreground Dice: [np.float32(0.9032), np.float32(0.5397), np.float32(0.659), np.float32(0.6621), np.float32(0.5443), np.float32(0.7102), np.float32(0.6055), np.float32(0.5794), np.float32(0.0897), np.float32(0.5084), np.float32(0.4917), np.float32(0.5591), np.float32(0.4978), np.float32(0.4329), np.float32(0.0), np.float32(0.0), np.float32(0.651), np.float32(0.527), np.float32(0.6389), np.float32(0.5869), np.float32(0.5072), np.float32(0.5394), np.float32(0.6828), np.float32(0.6748), np.float32(0.468), np.float32(0.4421), np.float32(0.0497), np.float32(0.0), np.float32(0.2908), np.float32(0.6097), np.float32(0.0), np.float32(0.0), np.float32(0.4713), np.float32(0.5338), np.float32(0.6732), np.float32(0.7059)] 
+2026-10-16 03:45:41.200005: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:45:41.520246: lr: 0.000716 
+2026-10-16 03:45:41.520608: This epoch took 259.688392 s
+ 
+2026-10-16 03:45:41.521167: 
+epoch:  310 
+2026-10-16 03:49:55.481884: train loss : -0.7923 
+2026-10-16 03:50:00.820529: validation loss: -0.5576 
+2026-10-16 03:50:00.821644: Average global foreground Dice: [np.float32(0.9117), np.float32(0.6038), np.float32(0.6899), np.float32(0.7271), np.float32(0.6347), np.float32(0.7711), np.float32(0.6648), np.float32(0.7334), np.float32(0.1845), np.float32(0.5553), np.float32(0.5709), np.float32(0.5938), np.float32(0.5501), np.float32(0.454), np.float32(0.0), np.float32(0.0), np.float32(0.7036), np.float32(0.5361), np.float32(0.6926), np.float32(0.641), np.float32(0.5327), np.float32(0.5648), np.float32(0.7683), np.float32(0.7562), np.float32(0.4591), np.float32(0.4329), np.float32(0.2424), np.float32(0.0), np.float32(0.3647), np.float32(0.5564), np.float32(0.0), np.float32(0.0), np.float32(0.5502), np.float32(0.514), np.float32(0.7583), np.float32(0.777)] 
+2026-10-16 03:50:00.821860: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:50:01.155264: lr: 0.000715 
+2026-10-16 03:50:01.155528: This epoch took 259.634216 s
+ 
+2026-10-16 03:50:01.155628: 
+epoch:  311 
+2026-10-16 03:54:10.143985: train loss : -0.7747 
+2026-10-16 03:54:15.494238: validation loss: -0.5472 
+2026-10-16 03:54:15.495085: Average global foreground Dice: [np.float32(0.9006), np.float32(0.5715), np.float32(0.6787), np.float32(0.729), np.float32(0.6359), np.float32(0.7662), np.float32(0.6826), np.float32(0.5676), np.float32(0.0349), np.float32(0.4393), np.float32(0.5914), np.float32(0.6319), np.float32(0.5433), np.float32(0.5557), np.float32(0.0), np.float32(0.0), np.float32(0.629), np.float32(0.4391), np.float32(0.6383), np.float32(0.5611), np.float32(0.5079), np.float32(0.517), np.float32(0.8462), np.float32(0.83), np.float32(0.4603), np.float32(0.3654), np.float32(0.0), np.float32(0.0), np.float32(0.2848), np.float32(0.1824), np.float32(0.0), np.float32(0.0), np.float32(0.4538), np.float32(0.4879), np.float32(0.7584), np.float32(0.7925)] 
+2026-10-16 03:54:15.495323: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:54:15.824874: lr: 0.000714 
+2026-10-16 03:54:15.825154: This epoch took 254.669460 s
+ 
+2026-10-16 03:54:15.825765: 
+epoch:  312 
+2026-10-16 03:58:23.679432: train loss : -0.7828 
+2026-10-16 03:58:29.030550: validation loss: -0.5035 
+2026-10-16 03:58:29.031560: Average global foreground Dice: [np.float32(0.9058), np.float32(0.5611), np.float32(0.6153), np.float32(0.5928), np.float32(0.4713), np.float32(0.6669), np.float32(0.5755), np.float32(0.5693), np.float32(0.0076), np.float32(0.556), np.float32(0.4033), np.float32(0.4631), np.float32(0.5144), np.float32(0.4276), np.float32(0.0), np.float32(0.0), np.float32(0.4744), np.float32(0.3614), np.float32(0.5165), np.float32(0.5303), np.float32(0.4566), np.float32(0.5045), np.float32(0.733), np.float32(0.7118), np.float32(0.4204), np.float32(0.391), np.float32(0.0598), np.float32(0.0), np.float32(0.2592), np.float32(0.4757), np.float32(0.0), np.float32(0.0), np.float32(0.407), np.float32(0.4282), np.float32(0.6482), np.float32(0.7154)] 
+2026-10-16 03:58:29.031929: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 03:58:29.391339: lr: 0.000713 
+2026-10-16 03:58:29.391829: This epoch took 253.565863 s
+ 
+2026-10-16 03:58:29.392154: 
+epoch:  313 
+2026-10-16 04:03:10.436024: train loss : -0.7914 
+2026-10-16 04:03:15.806380: validation loss: -0.5490 
+2026-10-16 04:03:15.807278: Average global foreground Dice: [np.float32(0.9096), np.float32(0.6046), np.float32(0.6695), np.float32(0.6869), np.float32(0.5083), np.float32(0.7337), np.float32(0.6182), np.float32(0.5185), np.float32(0.0), np.float32(0.317), np.float32(0.506), np.float32(0.5881), np.float32(0.5568), np.float32(0.4567), np.float32(0.0), np.float32(0.0), np.float32(0.5442), np.float32(0.438), np.float32(0.5611), np.float32(0.5267), np.float32(0.5495), np.float32(0.5608), np.float32(0.801), np.float32(0.7896), np.float32(0.4465), np.float32(0.4017), np.float32(0.1192), np.float32(0.0), np.float32(0.1917), np.float32(0.5582), np.float32(0.0), np.float32(0.0), np.float32(0.4744), np.float32(0.4731), np.float32(0.7532), np.float32(0.7768)] 
+2026-10-16 04:03:15.807504: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:03:16.179900: lr: 0.000712 
+2026-10-16 04:03:16.180210: This epoch took 286.787829 s
+ 
+2026-10-16 04:03:16.180319: 
+epoch:  314 
+2026-10-16 04:07:15.872216: train loss : -0.7886 
+2026-10-16 04:07:21.227839: validation loss: -0.5740 
+2026-10-16 04:07:21.229339: Average global foreground Dice: [np.float32(0.8947), np.float32(0.6083), np.float32(0.7392), np.float32(0.7784), np.float32(0.7138), np.float32(0.8147), np.float32(0.7283), np.float32(0.5216), np.float32(0.0057), np.float32(0.4286), np.float32(0.5784), np.float32(0.6034), np.float32(0.5668), np.float32(0.462), np.float32(0.0), np.float32(0.0), np.float32(0.743), np.float32(0.5778), np.float32(0.7312), np.float32(0.6635), np.float32(0.4906), np.float32(0.5587), np.float32(0.8271), np.float32(0.8012), np.float32(0.4583), np.float32(0.4407), np.float32(0.0815), np.float32(0.0), np.float32(0.2241), np.float32(0.4485), np.float32(0.0), np.float32(0.0), np.float32(0.6076), np.float32(0.5716), np.float32(0.807), np.float32(0.8201)] 
+2026-10-16 04:07:21.230008: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:07:21.803500: lr: 0.000711 
+2026-10-16 04:07:21.803720: This epoch took 245.623269 s
+ 
+2026-10-16 04:07:21.803801: 
+epoch:  315 
+2026-10-16 04:11:43.905098: train loss : -0.7902 
+2026-10-16 04:11:49.250366: validation loss: -0.5195 
+2026-10-16 04:11:49.251419: Average global foreground Dice: [np.float32(0.9083), np.float32(0.5886), np.float32(0.6536), np.float32(0.6797), np.float32(0.5651), np.float32(0.7125), np.float32(0.5907), np.float32(0.6323), np.float32(0.0018), np.float32(0.3466), np.float32(0.4845), np.float32(0.5416), np.float32(0.5416), np.float32(0.4765), np.float32(0.0), np.float32(0.0), np.float32(0.5479), np.float32(0.4189), np.float32(0.5161), np.float32(0.4488), np.float32(0.4235), np.float32(0.5251), np.float32(0.625), np.float32(0.6529), np.float32(0.4029), np.float32(0.4006), np.float32(0.0021), np.float32(0.0), np.float32(0.3381), np.float32(0.5261), np.float32(0.0), np.float32(0.0), np.float32(0.4251), np.float32(0.3662), np.float32(0.694), np.float32(0.7179)] 
+2026-10-16 04:11:49.251768: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:11:49.578041: lr: 0.00071 
+2026-10-16 04:11:49.578513: This epoch took 267.774658 s
+ 
+2026-10-16 04:11:49.578753: 
+epoch:  316 
+2026-10-16 04:15:52.073071: train loss : -0.7950 
+2026-10-16 04:15:57.433335: validation loss: -0.4716 
+2026-10-16 04:15:57.434535: Average global foreground Dice: [np.float32(0.9131), np.float32(0.5705), np.float32(0.5848), np.float32(0.5917), np.float32(0.4093), np.float32(0.619), np.float32(0.4669), np.float32(0.6129), np.float32(0.0), np.float32(0.4576), np.float32(0.4668), np.float32(0.4621), np.float32(0.5235), np.float32(0.4526), np.float32(0.0), np.float32(0.0), np.float32(0.4043), np.float32(0.3229), np.float32(0.3918), np.float32(0.3768), np.float32(0.4157), np.float32(0.3517), np.float32(0.6185), np.float32(0.6541), np.float32(0.2939), np.float32(0.3342), np.float32(0.0372), np.float32(0.0), np.float32(0.2299), np.float32(0.6186), np.float32(0.0), np.float32(0.0), np.float32(0.3145), np.float32(0.2885), np.float32(0.5968), np.float32(0.6243)] 
+2026-10-16 04:15:57.434752: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:15:57.762392: lr: 0.00071 
+2026-10-16 04:15:57.762783: This epoch took 248.183840 s
+ 
+2026-10-16 04:15:57.762900: 
+epoch:  317 
+2026-10-16 04:20:28.649690: train loss : -0.7991 
+2026-10-16 04:20:33.989943: validation loss: -0.4647 
+2026-10-16 04:20:33.990868: Average global foreground Dice: [np.float32(0.9033), np.float32(0.5312), np.float32(0.5087), np.float32(0.5214), np.float32(0.3987), np.float32(0.5469), np.float32(0.4448), np.float32(0.6088), np.float32(0.0), np.float32(0.4509), np.float32(0.4032), np.float32(0.4745), np.float32(0.5294), np.float32(0.5048), np.float32(0.0), np.float32(0.0), np.float32(0.3807), np.float32(0.3591), np.float32(0.3822), np.float32(0.399), np.float32(0.3647), np.float32(0.3885), np.float32(0.718), np.float32(0.693), np.float32(0.2982), np.float32(0.3388), np.float32(0.0876), np.float32(0.0), np.float32(0.088), np.float32(0.6065), np.float32(0.0), np.float32(0.0), np.float32(0.3857), np.float32(0.3601), np.float32(0.5619), np.float32(0.6208)] 
+2026-10-16 04:20:33.991022: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:20:34.317823: lr: 0.000709 
+2026-10-16 04:20:34.318363: This epoch took 276.555407 s
+ 
+2026-10-16 04:20:34.318535: 
+epoch:  318 
+2026-10-16 04:25:04.785147: train loss : -0.7821 
+2026-10-16 04:25:10.120545: validation loss: -0.5613 
+2026-10-16 04:25:10.121634: Average global foreground Dice: [np.float32(0.9034), np.float32(0.5991), np.float32(0.6917), np.float32(0.7311), np.float32(0.6473), np.float32(0.7603), np.float32(0.6755), np.float32(0.6934), np.float32(0.0), np.float32(0.5357), np.float32(0.5691), np.float32(0.6502), np.float32(0.5896), np.float32(0.5088), np.float32(0.0), np.float32(0.0), np.float32(0.689), np.float32(0.5524), np.float32(0.6697), np.float32(0.6073), np.float32(0.5103), np.float32(0.6395), np.float32(0.7866), np.float32(0.7444), np.float32(0.4659), np.float32(0.3155), np.float32(0.153), np.float32(0.0), np.float32(0.308), np.float32(0.5514), np.float32(0.0), np.float32(0.0), np.float32(0.5175), np.float32(0.5473), np.float32(0.744), np.float32(0.7644)] 
+2026-10-16 04:25:10.121850: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:25:10.437975: lr: 0.000708 
+2026-10-16 04:25:10.438381: This epoch took 276.119746 s
+ 
+2026-10-16 04:25:10.438651: 
+epoch:  319 
+2026-10-16 04:29:22.443473: train loss : -0.7895 
+2026-10-16 04:29:27.803836: validation loss: -0.5534 
+2026-10-16 04:29:27.804862: Average global foreground Dice: [np.float32(0.9134), np.float32(0.6036), np.float32(0.7088), np.float32(0.7275), np.float32(0.6386), np.float32(0.7663), np.float32(0.6658), np.float32(0.7156), np.float32(0.0071), np.float32(0.4128), np.float32(0.5568), np.float32(0.5984), np.float32(0.6274), np.float32(0.5609), np.float32(0.0), np.float32(0.0), np.float32(0.6631), np.float32(0.4779), np.float32(0.6444), np.float32(0.5902), np.float32(0.3955), np.float32(0.6103), np.float32(0.7881), np.float32(0.744), np.float32(0.4465), np.float32(0.4032), np.float32(0.1951), np.float32(0.0), np.float32(0.25), np.float32(0.5351), np.float32(0.0), np.float32(0.0), np.float32(0.4807), np.float32(0.5681), np.float32(0.7477), np.float32(0.7652)] 
+2026-10-16 04:29:27.805110: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:29:28.136742: lr: 0.000707 
+2026-10-16 04:29:28.137024: This epoch took 257.698189 s
+ 
+2026-10-16 04:29:28.137120: 
+epoch:  320 
+2026-10-16 04:33:27.768205: train loss : -0.8040 
+2026-10-16 04:33:33.104897: validation loss: -0.5527 
+2026-10-16 04:33:33.105814: Average global foreground Dice: [np.float32(0.9028), np.float32(0.6071), np.float32(0.7173), np.float32(0.7503), np.float32(0.6446), np.float32(0.7873), np.float32(0.6928), np.float32(0.6992), np.float32(0.0152), np.float32(0.4193), np.float32(0.5456), np.float32(0.6066), np.float32(0.5606), np.float32(0.4745), np.float32(0.0), np.float32(0.0071), np.float32(0.6711), np.float32(0.4971), np.float32(0.6641), np.float32(0.6197), np.float32(0.4316), np.float32(0.6042), np.float32(0.7046), np.float32(0.6967), np.float32(0.5093), np.float32(0.4868), np.float32(0.2253), np.float32(0.0), np.float32(0.3786), np.float32(0.5634), np.float32(0.0), np.float32(0.0), np.float32(0.553), np.float32(0.5929), np.float32(0.7386), np.float32(0.764)] 
+2026-10-16 04:33:33.105989: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:33:33.426641: lr: 0.000706 
+2026-10-16 04:33:33.427306: This epoch took 245.290123 s
+ 
+2026-10-16 04:33:33.427514: 
+epoch:  321 
+2026-10-16 04:37:47.637791: train loss : -0.7981 
+2026-10-16 04:37:52.989597: validation loss: -0.5723 
+2026-10-16 04:37:52.990616: Average global foreground Dice: [np.float32(0.918), np.float32(0.6437), np.float32(0.7452), np.float32(0.7659), np.float32(0.6624), np.float32(0.8032), np.float32(0.6822), np.float32(0.8234), np.float32(0.0), np.float32(0.5394), np.float32(0.5438), np.float32(0.614), np.float32(0.595), np.float32(0.5305), np.float32(0.0), np.float32(0.0), np.float32(0.6858), np.float32(0.5448), np.float32(0.6559), np.float32(0.5873), np.float32(0.4548), np.float32(0.595), np.float32(0.7832), np.float32(0.7816), np.float32(0.4561), np.float32(0.4532), np.float32(0.2664), np.float32(0.0), np.float32(0.1204), np.float32(0.4887), np.float32(0.0), np.float32(0.0), np.float32(0.6126), np.float32(0.6003), np.float32(0.7822), np.float32(0.8071)] 
+2026-10-16 04:37:52.991170: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:37:53.316332: lr: 0.000705 
+2026-10-16 04:37:53.316758: This epoch took 259.889172 s
+ 
+2026-10-16 04:37:53.317037: 
+epoch:  322 
+2026-10-16 04:41:56.743570: train loss : -0.8001 
+2026-10-16 04:42:02.111962: validation loss: -0.5683 
+2026-10-16 04:42:02.112961: Average global foreground Dice: [np.float32(0.9113), np.float32(0.6301), np.float32(0.7061), np.float32(0.7158), np.float32(0.6049), np.float32(0.727), np.float32(0.6347), np.float32(0.7062), np.float32(0.0), np.float32(0.4628), np.float32(0.5397), np.float32(0.5888), np.float32(0.5957), np.float32(0.5245), np.float32(0.0), np.float32(0.0015), np.float32(0.6309), np.float32(0.5252), np.float32(0.6172), np.float32(0.5799), np.float32(0.4913), np.float32(0.557), np.float32(0.7147), np.float32(0.7014), np.float32(0.419), np.float32(0.4372), np.float32(0.18), np.float32(0.0), np.float32(0.3588), np.float32(0.6244), np.float32(0.0), np.float32(0.0), np.float32(0.5374), np.float32(0.5364), np.float32(0.7358), np.float32(0.7558)] 
+2026-10-16 04:42:02.113214: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:42:02.477752: lr: 0.000704 
+2026-10-16 04:42:02.478094: This epoch took 249.160836 s
+ 
+2026-10-16 04:42:02.478279: 
+epoch:  323 
+2026-10-16 04:46:01.035229: train loss : -0.7984 
+2026-10-16 04:46:06.384471: validation loss: -0.5746 
+2026-10-16 04:46:06.385828: Average global foreground Dice: [np.float32(0.9108), np.float32(0.6605), np.float32(0.7292), np.float32(0.7474), np.float32(0.6162), np.float32(0.7546), np.float32(0.634), np.float32(0.6449), np.float32(0.0), np.float32(0.2543), np.float32(0.5656), np.float32(0.6227), np.float32(0.6198), np.float32(0.5643), np.float32(0.0), np.float32(0.0), np.float32(0.6622), np.float32(0.5446), np.float32(0.6386), np.float32(0.5769), np.float32(0.5648), np.float32(0.6291), np.float32(0.7859), np.float32(0.7499), np.float32(0.4817), np.float32(0.3525), np.float32(0.1973), np.float32(0.0), np.float32(0.184), np.float32(0.4974), np.float32(0.0), np.float32(0.0), np.float32(0.5742), np.float32(0.5529), np.float32(0.7644), np.float32(0.7704)] 
+2026-10-16 04:46:06.386119: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:46:06.709471: lr: 0.000703 
+2026-10-16 04:46:06.709769: This epoch took 244.231416 s
+ 
+2026-10-16 04:46:06.709981: 
+epoch:  324 
+2026-10-16 04:50:39.862647: train loss : -0.7945 
+2026-10-16 04:50:45.195271: validation loss: -0.5937 
+2026-10-16 04:50:45.196430: Average global foreground Dice: [np.float32(0.8748), np.float32(0.592), np.float32(0.7434), np.float32(0.7948), np.float32(0.6998), np.float32(0.8245), np.float32(0.7327), np.float32(0.6283), np.float32(0.0), np.float32(0.1801), np.float32(0.502), np.float32(0.5988), np.float32(0.5695), np.float32(0.499), np.float32(0.0), np.float32(0.0), np.float32(0.7589), np.float32(0.6156), np.float32(0.7406), np.float32(0.6558), np.float32(0.5514), np.float32(0.6796), np.float32(0.8953), np.float32(0.8559), np.float32(0.5561), np.float32(0.4747), np.float32(0.1678), np.float32(0.0), np.float32(0.4882), np.float32(0.6149), np.float32(0.0), np.float32(0.0), np.float32(0.6369), np.float32(0.651), np.float32(0.8257), np.float32(0.8391)] 
+2026-10-16 04:50:45.196860: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:50:45.516232: lr: 0.000702 
+2026-10-16 04:50:45.516476: This epoch took 278.806244 s
+ 
+2026-10-16 04:50:45.516561: 
+epoch:  325 
+2026-10-16 04:55:21.501158: train loss : -0.7894 
+2026-10-16 04:55:26.872673: validation loss: -0.5859 
+2026-10-16 04:55:26.873724: Average global foreground Dice: [np.float32(0.8961), np.float32(0.6014), np.float32(0.7301), np.float32(0.7142), np.float32(0.5609), np.float32(0.7619), np.float32(0.6378), np.float32(0.4083), np.float32(0.0), np.float32(0.4555), np.float32(0.5241), np.float32(0.617), np.float32(0.5989), np.float32(0.535), np.float32(0.0), np.float32(0.0), np.float32(0.5946), np.float32(0.4919), np.float32(0.6074), np.float32(0.6037), np.float32(0.5037), np.float32(0.6098), np.float32(0.8815), np.float32(0.8518), np.float32(0.441), np.float32(0.3972), np.float32(0.1751), np.float32(0.0), np.float32(0.4865), np.float32(0.5115), np.float32(0.0), np.float32(0.0), np.float32(0.5401), np.float32(0.5314), np.float32(0.7615), np.float32(0.7919)] 
+2026-10-16 04:55:26.873919: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:55:27.256123: lr: 0.000701 
+2026-10-16 04:55:27.256378: This epoch took 281.739759 s
+ 
+2026-10-16 04:55:27.256462: 
+epoch:  326 
+2026-10-16 04:59:37.579672: train loss : -0.7996 
+2026-10-16 04:59:42.913755: validation loss: -0.5559 
+2026-10-16 04:59:42.914736: Average global foreground Dice: [np.float32(0.9001), np.float32(0.576), np.float32(0.6301), np.float32(0.6485), np.float32(0.5257), np.float32(0.6724), np.float32(0.5429), np.float32(0.6832), np.float32(0.0), np.float32(0.4166), np.float32(0.4717), np.float32(0.5386), np.float32(0.5895), np.float32(0.5495), np.float32(0.0), np.float32(0.0), np.float32(0.474), np.float32(0.3726), np.float32(0.4508), np.float32(0.4319), np.float32(0.4583), np.float32(0.409), np.float32(0.8575), np.float32(0.8224), np.float32(0.3389), np.float32(0.3463), np.float32(0.0152), np.float32(0.0), np.float32(0.2847), np.float32(0.5355), np.float32(0.0), np.float32(0.0), np.float32(0.3805), np.float32(0.3914), np.float32(0.7153), np.float32(0.7252)] 
+2026-10-16 04:59:42.915071: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 04:59:43.240886: lr: 0.0007 
+2026-10-16 04:59:43.241397: This epoch took 255.984878 s
+ 
+2026-10-16 04:59:43.241724: 
+epoch:  327 
+2026-10-16 05:03:44.240600: train loss : -0.7818 
+2026-10-16 05:03:49.575191: validation loss: -0.5296 
+2026-10-16 05:03:49.576168: Average global foreground Dice: [np.float32(0.8997), np.float32(0.5946), np.float32(0.6882), np.float32(0.6495), np.float32(0.5517), np.float32(0.665), np.float32(0.5616), np.float32(0.5268), np.float32(0.0), np.float32(0.1233), np.float32(0.4535), np.float32(0.5556), np.float32(0.6242), np.float32(0.6113), np.float32(0.0), np.float32(0.0), np.float32(0.5038), np.float32(0.3887), np.float32(0.4734), np.float32(0.3865), np.float32(0.2041), np.float32(0.4261), np.float32(0.757), np.float32(0.7314), np.float32(0.3436), np.float32(0.3702), np.float32(0.0), np.float32(0.0), np.float32(0.2323), np.float32(0.441), np.float32(0.0), np.float32(0.0), np.float32(0.5308), np.float32(0.3625), np.float32(0.7032), np.float32(0.7588)] 
+2026-10-16 05:03:49.576496: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:03:49.900390: lr: 0.000699 
+2026-10-16 05:03:49.900651: This epoch took 246.658764 s
+ 
+2026-10-16 05:03:49.900735: 
+epoch:  328 
+2026-10-16 05:08:10.008145: train loss : -0.7873 
+2026-10-16 05:08:15.351234: validation loss: -0.5585 
+2026-10-16 05:08:15.352099: Average global foreground Dice: [np.float32(0.9086), np.float32(0.6166), np.float32(0.7131), np.float32(0.7091), np.float32(0.5623), np.float32(0.7298), np.float32(0.5895), np.float32(0.6581), np.float32(0.0), np.float32(0.472), np.float32(0.5197), np.float32(0.5931), np.float32(0.612), np.float32(0.5417), np.float32(0.0), np.float32(0.0), np.float32(0.5586), np.float32(0.4055), np.float32(0.5037), np.float32(0.464), np.float32(0.3581), np.float32(0.5079), np.float32(0.7946), np.float32(0.7847), np.float32(0.4479), np.float32(0.3533), np.float32(0.0822), np.float32(0.0), np.float32(0.3222), np.float32(0.5554), np.float32(0.0), np.float32(0.0), np.float32(0.5131), np.float32(0.3522), np.float32(0.7545), np.float32(0.777)] 
+2026-10-16 05:08:15.352263: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:08:15.718058: lr: 0.000698 
+2026-10-16 05:08:15.718541: This epoch took 265.817750 s
+ 
+2026-10-16 05:08:15.718844: 
+epoch:  329 
+2026-10-16 05:12:10.516409: train loss : -0.7979 
+2026-10-16 05:12:15.853191: validation loss: -0.5667 
+2026-10-16 05:12:15.854413: Average global foreground Dice: [np.float32(0.905), np.float32(0.6271), np.float32(0.698), np.float32(0.7162), np.float32(0.6123), np.float32(0.7284), np.float32(0.6259), np.float32(0.7128), np.float32(0.0272), np.float32(0.4717), np.float32(0.5474), np.float32(0.6007), np.float32(0.5796), np.float32(0.5127), np.float32(0.0), np.float32(0.0), np.float32(0.5849), np.float32(0.4209), np.float32(0.5512), np.float32(0.5061), np.float32(0.4386), np.float32(0.4754), np.float32(0.8144), np.float32(0.79), np.float32(0.4068), np.float32(0.4083), np.float32(0.0546), np.float32(0.0), np.float32(0.3374), np.float32(0.5945), np.float32(0.0), np.float32(0.0), np.float32(0.2968), np.float32(0.3604), np.float32(0.7533), np.float32(0.7759)] 
+2026-10-16 05:12:15.854817: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:12:16.173118: lr: 0.000697 
+2026-10-16 05:12:16.173540: This epoch took 240.454499 s
+ 
+2026-10-16 05:12:16.173896: 
+epoch:  330 
+2026-10-16 05:15:58.889704: train loss : -0.8082 
+2026-10-16 05:16:04.229412: validation loss: -0.5291 
+2026-10-16 05:16:04.230440: Average global foreground Dice: [np.float32(0.9024), np.float32(0.5826), np.float32(0.6768), np.float32(0.6871), np.float32(0.6038), np.float32(0.6812), np.float32(0.6106), np.float32(0.7231), np.float32(0.0231), np.float32(0.4407), np.float32(0.4809), np.float32(0.5374), np.float32(0.5722), np.float32(0.4365), np.float32(0.0), np.float32(0.0), np.float32(0.5832), np.float32(0.4079), np.float32(0.52), np.float32(0.503), np.float32(0.3842), np.float32(0.4965), np.float32(0.8385), np.float32(0.7951), np.float32(0.4468), np.float32(0.3288), np.float32(0.2054), np.float32(0.0), np.float32(0.3525), np.float32(0.571), np.float32(0.0), np.float32(0.0), np.float32(0.4295), np.float32(0.2697), np.float32(0.7478), np.float32(0.7583)] 
+2026-10-16 05:16:04.230832: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:16:04.548349: lr: 0.000696 
+2026-10-16 05:16:04.549087: This epoch took 228.374909 s
+ 
+2026-10-16 05:16:04.549388: 
+epoch:  331 
+2026-10-16 05:19:50.801321: train loss : -0.8035 
+2026-10-16 05:19:56.143192: validation loss: -0.4882 
+2026-10-16 05:19:56.144202: Average global foreground Dice: [np.float32(0.8852), np.float32(0.527), np.float32(0.6228), np.float32(0.5854), np.float32(0.4984), np.float32(0.5948), np.float32(0.6178), np.float32(0.7008), np.float32(0.0427), np.float32(0.3782), np.float32(0.4457), np.float32(0.557), np.float32(0.5203), np.float32(0.4322), np.float32(0.0), np.float32(0.0059), np.float32(0.621), np.float32(0.4632), np.float32(0.6002), np.float32(0.5912), np.float32(0.3597), np.float32(0.5004), np.float32(0.7329), np.float32(0.6848), np.float32(0.397), np.float32(0.3275), np.float32(0.0104), np.float32(0.0), np.float32(0.4152), np.float32(0.6395), np.float32(0.0), np.float32(0.0), np.float32(0.4074), np.float32(0.1062), np.float32(0.5984), np.float32(0.5365)] 
+2026-10-16 05:19:56.144442: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:19:56.495333: lr: 0.000696 
+2026-10-16 05:19:56.495605: This epoch took 231.946116 s
+ 
+2026-10-16 05:19:56.495831: 
+epoch:  332 
+2026-10-16 05:24:01.478861: train loss : -0.7958 
+2026-10-16 05:24:06.828854: validation loss: -0.5898 
+2026-10-16 05:24:06.830015: Average global foreground Dice: [np.float32(0.9078), np.float32(0.6486), np.float32(0.7565), np.float32(0.7709), np.float32(0.6816), np.float32(0.8084), np.float32(0.705), np.float32(0.5684), np.float32(0.0), np.float32(0.5659), np.float32(0.5571), np.float32(0.6322), np.float32(0.5911), np.float32(0.4902), np.float32(0.0), np.float32(0.0), np.float32(0.7392), np.float32(0.5859), np.float32(0.7256), np.float32(0.6695), np.float32(0.521), np.float32(0.5999), np.float32(0.8211), np.float32(0.7847), np.float32(0.4583), np.float32(0.4393), np.float32(0.0), np.float32(0.0), np.float32(0.3185), np.float32(0.5684), np.float32(0.0), np.float32(0.0), np.float32(0.5883), np.float32(0.5924), np.float32(0.7945), np.float32(0.8099)] 
+2026-10-16 05:24:06.830236: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:24:07.151515: lr: 0.000695 
+2026-10-16 05:24:07.151874: This epoch took 250.655807 s
+ 
+2026-10-16 05:24:07.152008: 
+epoch:  333 
+2026-10-16 05:28:18.084704: train loss : -0.8018 
+2026-10-16 05:28:23.448802: validation loss: -0.5678 
+2026-10-16 05:28:23.449802: Average global foreground Dice: [np.float32(0.9006), np.float32(0.6273), np.float32(0.7086), np.float32(0.7192), np.float32(0.6225), np.float32(0.7637), np.float32(0.6648), np.float32(0.7109), np.float32(0.0), np.float32(0.5991), np.float32(0.5124), np.float32(0.5843), np.float32(0.5187), np.float32(0.5183), np.float32(0.0), np.float32(0.0), np.float32(0.712), np.float32(0.5827), np.float32(0.6944), np.float32(0.6511), np.float32(0.4899), np.float32(0.6169), np.float32(0.829), np.float32(0.7874), np.float32(0.4761), np.float32(0.4759), np.float32(0.2387), np.float32(0.0), np.float32(0.2297), np.float32(0.6354), np.float32(0.0), np.float32(0.0), np.float32(0.5807), np.float32(0.6095), np.float32(0.7594), np.float32(0.7802)] 
+2026-10-16 05:28:23.449997: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:28:23.778500: lr: 0.000694 
+2026-10-16 05:28:23.778912: This epoch took 256.626833 s
+ 
+2026-10-16 05:28:23.779225: 
+epoch:  334 
+2026-10-16 05:32:12.792969: train loss : -0.7983 
+2026-10-16 05:32:18.140179: validation loss: -0.3663 
+2026-10-16 05:32:18.141037: Average global foreground Dice: [np.float32(0.9138), np.float32(0.4382), np.float32(0.3971), np.float32(0.394), np.float32(0.3411), np.float32(0.3806), np.float32(0.3364), np.float32(0.4536), np.float32(0.0), np.float32(0.373), np.float32(0.341), np.float32(0.3047), np.float32(0.5057), np.float32(0.3794), np.float32(0.0), np.float32(0.0), np.float32(0.36), np.float32(0.2835), np.float32(0.2908), np.float32(0.3104), np.float32(0.2386), np.float32(0.3066), np.float32(0.4898), np.float32(0.4778), np.float32(0.1996), np.float32(0.2409), np.float32(0.0261), np.float32(0.0), np.float32(0.0076), np.float32(0.3423), np.float32(0.0), np.float32(0.0), np.float32(0.3073), np.float32(0.2568), np.float32(0.4033), np.float32(0.3975)] 
+2026-10-16 05:32:18.141193: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:32:18.458708: lr: 0.000693 
+2026-10-16 05:32:18.459047: This epoch took 234.679566 s
+ 
+2026-10-16 05:32:18.459254: 
+epoch:  335 
+2026-10-16 05:36:52.828307: train loss : -0.7929 
+2026-10-16 05:36:58.174076: validation loss: -0.5847 
+2026-10-16 05:36:58.175203: Average global foreground Dice: [np.float32(0.9032), np.float32(0.6285), np.float32(0.7794), np.float32(0.8009), np.float32(0.6715), np.float32(0.8197), np.float32(0.6966), np.float32(0.6968), np.float32(0.0), np.float32(0.4558), np.float32(0.5456), np.float32(0.5951), np.float32(0.5557), np.float32(0.482), np.float32(0.0), np.float32(0.0), np.float32(0.7163), np.float32(0.5573), np.float32(0.6756), np.float32(0.6271), np.float32(0.5083), np.float32(0.5965), np.float32(0.8333), np.float32(0.8044), np.float32(0.5009), np.float32(0.3789), np.float32(0.2975), np.float32(0.0), np.float32(0.2137), np.float32(0.5989), np.float32(0.0), np.float32(0.0), np.float32(0.5895), np.float32(0.5862), np.float32(0.8093), np.float32(0.821)] 
+2026-10-16 05:36:58.175430: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:36:58.503354: lr: 0.000692 
+2026-10-16 05:36:58.503783: This epoch took 280.044433 s
+ 
+2026-10-16 05:36:58.504070: 
+epoch:  336 
+2026-10-16 05:41:15.899167: train loss : -0.7954 
+2026-10-16 05:41:21.265471: validation loss: -0.5713 
+2026-10-16 05:41:21.266425: Average global foreground Dice: [np.float32(0.9056), np.float32(0.6308), np.float32(0.7134), np.float32(0.7468), np.float32(0.6575), np.float32(0.7749), np.float32(0.6573), np.float32(0.8249), np.float32(0.0), np.float32(0.4019), np.float32(0.5548), np.float32(0.6029), np.float32(0.5822), np.float32(0.5036), np.float32(0.0), np.float32(0.0), np.float32(0.7121), np.float32(0.5592), np.float32(0.6867), np.float32(0.635), np.float32(0.3867), np.float32(0.5108), np.float32(0.7576), np.float32(0.7353), np.float32(0.4644), np.float32(0.4227), np.float32(0.0017), np.float32(0.0), np.float32(0.3368), np.float32(0.5879), np.float32(0.0), np.float32(0.0), np.float32(0.6005), np.float32(0.6917), np.float32(0.7552), np.float32(0.7784)] 
+2026-10-16 05:41:21.266682: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:41:21.593523: lr: 0.000691 
+2026-10-16 05:41:21.593783: This epoch took 263.089613 s
+ 
+2026-10-16 05:41:21.593874: 
+epoch:  337 
+2026-10-16 05:45:31.648269: train loss : -0.7948 
+2026-10-16 05:45:36.982970: validation loss: -0.5888 
+2026-10-16 05:45:36.984006: Average global foreground Dice: [np.float32(0.9139), np.float32(0.6579), np.float32(0.7491), np.float32(0.7763), np.float32(0.6687), np.float32(0.7982), np.float32(0.6851), np.float32(0.6969), np.float32(0.0122), np.float32(0.4903), np.float32(0.6016), np.float32(0.6326), np.float32(0.591), np.float32(0.5401), np.float32(0.0), np.float32(0.0), np.float32(0.7231), np.float32(0.5974), np.float32(0.6855), np.float32(0.6644), np.float32(0.4261), np.float32(0.5348), np.float32(0.8252), np.float32(0.8067), np.float32(0.4731), np.float32(0.4213), np.float32(0.245), np.float32(0.0), np.float32(0.4493), np.float32(0.6409), np.float32(0.0), np.float32(0.0), np.float32(0.5641), np.float32(0.603), np.float32(0.7946), np.float32(0.8104)] 
+2026-10-16 05:45:36.984318: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:45:37.308228: lr: 0.00069 
+2026-10-16 05:45:37.308716: This epoch took 255.714784 s
+ 
+2026-10-16 05:45:37.309002: 
+epoch:  338 
+2026-10-16 05:49:42.461017: train loss : -0.7965 
+2026-10-16 05:49:47.801999: validation loss: -0.5707 
+2026-10-16 05:49:47.803121: Average global foreground Dice: [np.float32(0.9005), np.float32(0.6038), np.float32(0.7581), np.float32(0.7675), np.float32(0.6785), np.float32(0.7842), np.float32(0.7067), np.float32(0.8028), np.float32(0.0137), np.float32(0.2763), np.float32(0.5606), np.float32(0.6407), np.float32(0.4975), np.float32(0.4269), np.float32(0.0), np.float32(0.0), np.float32(0.7329), np.float32(0.5879), np.float32(0.7211), np.float32(0.6673), np.float32(0.4582), np.float32(0.5757), np.float32(0.83), np.float32(0.8052), np.float32(0.4933), np.float32(0.4259), np.float32(0.2965), np.float32(0.0), np.float32(0.4897), np.float32(0.5046), np.float32(0.0), np.float32(0.0), np.float32(0.6253), np.float32(0.6413), np.float32(0.7983), np.float32(0.8175)] 
+2026-10-16 05:49:47.803530: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:49:48.131541: lr: 0.000689 
+2026-10-16 05:49:48.131777: This epoch took 250.822537 s
+ 
+2026-10-16 05:49:48.131852: 
+epoch:  339 
+2026-10-16 05:53:50.359061: train loss : -0.7976 
+2026-10-16 05:53:55.707513: validation loss: -0.5895 
+2026-10-16 05:53:55.708712: Average global foreground Dice: [np.float32(0.9088), np.float32(0.6227), np.float32(0.7657), np.float32(0.7683), np.float32(0.6918), np.float32(0.7863), np.float32(0.7133), np.float32(0.4103), np.float32(0.0), np.float32(0.5335), np.float32(0.602), np.float32(0.6218), np.float32(0.5649), np.float32(0.5131), np.float32(0.0), np.float32(0.0), np.float32(0.7385), np.float32(0.5981), np.float32(0.7047), np.float32(0.644), np.float32(0.5653), np.float32(0.6615), np.float32(0.8223), np.float32(0.7851), np.float32(0.4816), np.float32(0.4463), np.float32(0.2504), np.float32(0.0), np.float32(0.2986), np.float32(0.6192), np.float32(0.0), np.float32(0.0), np.float32(0.5407), np.float32(0.621), np.float32(0.803), np.float32(0.8191)] 
+2026-10-16 05:53:55.709010: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:53:56.026514: lr: 0.000688 
+2026-10-16 05:53:56.026838: This epoch took 247.894931 s
+ 
+2026-10-16 05:53:56.026988: 
+epoch:  340 
+2026-10-16 05:58:18.895760: train loss : -0.7920 
+2026-10-16 05:58:24.244141: validation loss: -0.5665 
+2026-10-16 05:58:24.245119: Average global foreground Dice: [np.float32(0.9094), np.float32(0.608), np.float32(0.7427), np.float32(0.7214), np.float32(0.6425), np.float32(0.7385), np.float32(0.6819), np.float32(0.4134), np.float32(0.2002), np.float32(0.342), np.float32(0.5798), np.float32(0.6007), np.float32(0.5978), np.float32(0.4798), np.float32(0.0), np.float32(0.0), np.float32(0.7341), np.float32(0.5398), np.float32(0.686), np.float32(0.6467), np.float32(0.5283), np.float32(0.6261), np.float32(0.7977), np.float32(0.7715), np.float32(0.4881), np.float32(0.4263), np.float32(0.1907), np.float32(0.0), np.float32(0.3184), np.float32(0.5942), np.float32(0.0), np.float32(0.0), np.float32(0.5172), np.float32(0.4778), np.float32(0.7767), np.float32(0.7947)] 
+2026-10-16 05:58:24.245280: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 05:58:24.567935: lr: 0.000687 
+2026-10-16 05:58:24.568166: This epoch took 268.541096 s
+ 
+2026-10-16 05:58:24.568251: 
+epoch:  341 
+2026-10-16 06:02:26.545129: train loss : -0.8011 
+2026-10-16 06:02:31.879355: validation loss: -0.5859 
+2026-10-16 06:02:31.880295: Average global foreground Dice: [np.float32(0.9136), np.float32(0.6222), np.float32(0.7471), np.float32(0.7557), np.float32(0.6506), np.float32(0.7823), np.float32(0.674), np.float32(0.4908), np.float32(0.0109), np.float32(0.5206), np.float32(0.5979), np.float32(0.6362), np.float32(0.6104), np.float32(0.5516), np.float32(0.0), np.float32(0.0), np.float32(0.7184), np.float32(0.5741), np.float32(0.6851), np.float32(0.6482), np.float32(0.4957), np.float32(0.5956), np.float32(0.7708), np.float32(0.7362), np.float32(0.4518), np.float32(0.4595), np.float32(0.2771), np.float32(0.0), np.float32(0.3854), np.float32(0.6307), np.float32(0.0), np.float32(0.0), np.float32(0.5549), np.float32(0.5593), np.float32(0.7719), np.float32(0.7974)] 
+2026-10-16 06:02:31.880449: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:02:32.222591: lr: 0.000686 
+2026-10-16 06:02:32.222942: This epoch took 247.654637 s
+ 
+2026-10-16 06:02:32.223072: 
+epoch:  342 
+2026-10-16 06:06:55.827373: train loss : -0.8029 
+2026-10-16 06:07:01.164764: validation loss: -0.5154 
+2026-10-16 06:07:01.165869: Average global foreground Dice: [np.float32(0.9158), np.float32(0.5696), np.float32(0.6607), np.float32(0.6775), np.float32(0.6302), np.float32(0.7031), np.float32(0.6281), np.float32(0.5544), np.float32(0.2452), np.float32(0.4023), np.float32(0.4463), np.float32(0.4946), np.float32(0.5135), np.float32(0.4644), np.float32(0.0), np.float32(0.0), np.float32(0.6914), np.float32(0.5872), np.float32(0.6683), np.float32(0.6328), np.float32(0.438), np.float32(0.5729), np.float32(0.7273), np.float32(0.6751), np.float32(0.4497), np.float32(0.3775), np.float32(0.1713), np.float32(0.0), np.float32(0.2818), np.float32(0.5907), np.float32(0.0), np.float32(0.0), np.float32(0.5549), np.float32(0.5529), np.float32(0.6905), np.float32(0.7305)] 
+2026-10-16 06:07:01.166349: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:07:01.488833: lr: 0.000685 
+2026-10-16 06:07:01.489071: This epoch took 269.265908 s
+ 
+2026-10-16 06:07:01.489151: 
+epoch:  343 
+2026-10-16 06:11:35.080194: train loss : -0.7950 
+2026-10-16 06:11:40.416376: validation loss: -0.6228 
+2026-10-16 06:11:40.417283: Average global foreground Dice: [np.float32(0.9187), np.float32(0.6688), np.float32(0.7964), np.float32(0.8074), np.float32(0.7577), np.float32(0.8387), np.float32(0.7499), np.float32(0.4482), np.float32(0.191), np.float32(0.5481), np.float32(0.5371), np.float32(0.6249), np.float32(0.5698), np.float32(0.536), np.float32(0.0), np.float32(0.0), np.float32(0.7692), np.float32(0.6101), np.float32(0.752), np.float32(0.6955), np.float32(0.5373), np.float32(0.6666), np.float32(0.8655), np.float32(0.881), np.float32(0.5333), np.float32(0.5243), np.float32(0.2388), np.float32(0.0), np.float32(0.2774), np.float32(0.708), np.float32(0.0), np.float32(0.0), np.float32(0.5866), np.float32(0.6173), np.float32(0.8457), np.float32(0.8583)] 
+2026-10-16 06:11:40.417641: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:11:40.749942: lr: 0.000684 
+2026-10-16 06:11:40.750436: This epoch took 279.261110 s
+ 
+2026-10-16 06:11:40.750858: 
+epoch:  344 
+2026-10-16 06:15:50.151077: train loss : -0.7892 
+2026-10-16 06:15:55.489383: validation loss: -0.5799 
+2026-10-16 06:15:55.490482: Average global foreground Dice: [np.float32(0.9042), np.float32(0.6176), np.float32(0.7584), np.float32(0.7756), np.float32(0.6803), np.float32(0.8078), np.float32(0.7228), np.float32(0.4833), np.float32(0.1219), np.float32(0.3792), np.float32(0.5735), np.float32(0.6202), np.float32(0.583), np.float32(0.4554), np.float32(0.0), np.float32(0.0), np.float32(0.7452), np.float32(0.5841), np.float32(0.7189), np.float32(0.6727), np.float32(0.5813), np.float32(0.6575), np.float32(0.7829), np.float32(0.7516), np.float32(0.5382), np.float32(0.4432), np.float32(0.1212), np.float32(0.0), np.float32(0.4769), np.float32(0.6718), np.float32(0.0), np.float32(0.0), np.float32(0.5804), np.float32(0.5958), np.float32(0.784), np.float32(0.7931)] 
+2026-10-16 06:15:55.490855: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:15:55.808298: lr: 0.000683 
+2026-10-16 06:15:55.808530: This epoch took 255.057399 s
+ 
+2026-10-16 06:15:55.808617: 
+epoch:  345 
+2026-10-16 06:20:26.990203: train loss : -0.7967 
+2026-10-16 06:20:32.344815: validation loss: -0.5847 
+2026-10-16 06:20:32.346189: Average global foreground Dice: [np.float32(0.9087), np.float32(0.6117), np.float32(0.7427), np.float32(0.7703), np.float32(0.6639), np.float32(0.8062), np.float32(0.7022), np.float32(0.7173), np.float32(0.1435), np.float32(0.6113), np.float32(0.5515), np.float32(0.5992), np.float32(0.5613), np.float32(0.4767), np.float32(0.0), np.float32(0.0), np.float32(0.7364), np.float32(0.5734), np.float32(0.7063), np.float32(0.6649), np.float32(0.5052), np.float32(0.5774), np.float32(0.7971), np.float32(0.7583), np.float32(0.5089), np.float32(0.3891), np.float32(0.0131), np.float32(0.0), np.float32(0.4988), np.float32(0.5891), np.float32(0.0), np.float32(0.0), np.float32(0.5656), np.float32(0.5193), np.float32(0.7636), np.float32(0.7785)] 
+2026-10-16 06:20:32.346585: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:20:32.671729: lr: 0.000682 
+2026-10-16 06:20:32.671968: This epoch took 276.863287 s
+ 
+2026-10-16 06:20:32.672042: 
+epoch:  346 
+2026-10-16 06:24:48.648148: train loss : -0.7946 
+2026-10-16 06:24:53.996374: validation loss: -0.6137 
+2026-10-16 06:24:53.997399: Average global foreground Dice: [np.float32(0.9147), np.float32(0.6954), np.float32(0.7598), np.float32(0.7861), np.float32(0.7163), np.float32(0.8244), np.float32(0.7346), np.float32(0.6678), np.float32(0.0734), np.float32(0.5578), np.float32(0.5986), np.float32(0.629), np.float32(0.6548), np.float32(0.4969), np.float32(0.0), np.float32(0.0013), np.float32(0.7463), np.float32(0.6191), np.float32(0.7137), np.float32(0.6836), np.float32(0.6109), np.float32(0.6518), np.float32(0.8686), np.float32(0.8649), np.float32(0.5012), np.float32(0.4552), np.float32(0.2464), np.float32(0.0), np.float32(0.3657), np.float32(0.5478), np.float32(0.0), np.float32(0.0), np.float32(0.5573), np.float32(0.5584), np.float32(0.8419), np.float32(0.854)] 
+2026-10-16 06:24:53.997857: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:24:54.318163: lr: 0.000681 
+2026-10-16 06:24:54.371073: saving checkpoint... 
+2026-10-16 06:24:54.508115: done, saving took 0.19 seconds 
+2026-10-16 06:24:54.509978: This epoch took 261.837881 s
+ 
+2026-10-16 06:24:54.510211: 
+epoch:  347 
+2026-10-16 06:29:15.418796: train loss : -0.7931 
+2026-10-16 06:29:20.767533: validation loss: -0.5132 
+2026-10-16 06:29:20.768713: Average global foreground Dice: [np.float32(0.881), np.float32(0.536), np.float32(0.6421), np.float32(0.6043), np.float32(0.4791), np.float32(0.5705), np.float32(0.4949), np.float32(0.4614), np.float32(0.1132), np.float32(0.4186), np.float32(0.4689), np.float32(0.5257), np.float32(0.6223), np.float32(0.5475), np.float32(0.0), np.float32(0.0), np.float32(0.5092), np.float32(0.369), np.float32(0.4963), np.float32(0.4913), np.float32(0.2985), np.float32(0.3941), np.float32(0.6461), np.float32(0.6251), np.float32(0.3399), np.float32(0.3058), np.float32(0.0066), np.float32(0.0), np.float32(0.3047), np.float32(0.5034), np.float32(0.0), np.float32(0.0), np.float32(0.4474), np.float32(0.3302), np.float32(0.6226), np.float32(0.6349)] 
+2026-10-16 06:29:20.769148: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:29:21.104495: lr: 0.00068 
+2026-10-16 06:29:21.104757: This epoch took 266.594379 s
+ 
+2026-10-16 06:29:21.104861: 
+epoch:  348 
+2026-10-16 06:33:42.977015: train loss : -0.7910 
+2026-10-16 06:33:48.318278: validation loss: -0.4811 
+2026-10-16 06:33:48.319194: Average global foreground Dice: [np.float32(0.8939), np.float32(0.5291), np.float32(0.6362), np.float32(0.6042), np.float32(0.407), np.float32(0.5484), np.float32(0.4094), np.float32(0.663), np.float32(0.0175), np.float32(0.4985), np.float32(0.4488), np.float32(0.4895), np.float32(0.5183), np.float32(0.3683), np.float32(0.0), np.float32(0.0), np.float32(0.4725), np.float32(0.3412), np.float32(0.4306), np.float32(0.482), np.float32(0.3648), np.float32(0.4974), np.float32(0.7449), np.float32(0.7107), np.float32(0.3583), np.float32(0.3254), np.float32(0.0505), np.float32(0.0), np.float32(0.3737), np.float32(0.5585), np.float32(0.0), np.float32(0.0), np.float32(0.3757), np.float32(0.3169), np.float32(0.635), np.float32(0.5791)] 
+2026-10-16 06:33:48.319462: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:33:48.640292: lr: 0.00068 
+2026-10-16 06:33:48.640934: This epoch took 267.536009 s
+ 
+2026-10-16 06:33:48.641239: 
+epoch:  349 
+2026-10-16 06:38:11.346350: train loss : -0.8025 
+2026-10-16 06:38:16.687968: validation loss: -0.4982 
+2026-10-16 06:38:16.689078: Average global foreground Dice: [np.float32(0.898), np.float32(0.5513), np.float32(0.6648), np.float32(0.6022), np.float32(0.4453), np.float32(0.6774), np.float32(0.5818), np.float32(0.6218), np.float32(0.0017), np.float32(0.4675), np.float32(0.4416), np.float32(0.5477), np.float32(0.4974), np.float32(0.3788), np.float32(0.0), np.float32(0.0), np.float32(0.5249), np.float32(0.4682), np.float32(0.5473), np.float32(0.5473), np.float32(0.4178), np.float32(0.417), np.float32(0.6266), np.float32(0.5694), np.float32(0.3549), np.float32(0.3161), np.float32(0.1296), np.float32(0.0), np.float32(0.427), np.float32(0.6152), np.float32(0.0), np.float32(0.0), np.float32(0.3688), np.float32(0.3761), np.float32(0.575), np.float32(0.5852)] 
+2026-10-16 06:38:16.689490: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:38:17.019928: lr: 0.000679 
+2026-10-16 06:38:17.020312: saving scheduled checkpoint file... 
+2026-10-16 06:38:17.029087: saving checkpoint... 
+2026-10-16 06:38:17.154396: done, saving took 0.13 seconds 
+2026-10-16 06:38:17.156678: done 
+2026-10-16 06:38:17.156846: This epoch took 268.515416 s
+ 
+2026-10-16 06:38:17.156919: 
+epoch:  350 
+2026-10-16 06:42:03.793469: train loss : -0.8052 
+2026-10-16 06:42:09.124900: validation loss: -0.5856 
+2026-10-16 06:42:09.125962: Average global foreground Dice: [np.float32(0.8905), np.float32(0.643), np.float32(0.7686), np.float32(0.7879), np.float32(0.679), np.float32(0.8221), np.float32(0.7394), np.float32(0.6609), np.float32(0.0011), np.float32(0.5488), np.float32(0.5378), np.float32(0.6294), np.float32(0.6058), np.float32(0.4998), np.float32(0.0), np.float32(0.0), np.float32(0.7272), np.float32(0.6062), np.float32(0.7009), np.float32(0.6589), np.float32(0.4918), np.float32(0.6216), np.float32(0.7826), np.float32(0.7351), np.float32(0.4977), np.float32(0.4137), np.float32(0.1589), np.float32(0.0), np.float32(0.2939), np.float32(0.4502), np.float32(0.0), np.float32(0.0), np.float32(0.5462), np.float32(0.5285), np.float32(0.7917), np.float32(0.8105)] 
+2026-10-16 06:42:09.126373: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:42:09.449006: lr: 0.000678 
+2026-10-16 06:42:09.449261: This epoch took 232.292276 s
+ 
+2026-10-16 06:42:09.449395: 
+epoch:  351 
+2026-10-16 06:46:18.278730: train loss : -0.8041 
+2026-10-16 06:46:23.620690: validation loss: -0.5745 
+2026-10-16 06:46:23.622019: Average global foreground Dice: [np.float32(0.9114), np.float32(0.6446), np.float32(0.7255), np.float32(0.7651), np.float32(0.6487), np.float32(0.8097), np.float32(0.6956), np.float32(0.5733), np.float32(0.0152), np.float32(0.5526), np.float32(0.5658), np.float32(0.6519), np.float32(0.605), np.float32(0.5336), np.float32(0.0), np.float32(0.0), np.float32(0.6999), np.float32(0.578), np.float32(0.6618), np.float32(0.6181), np.float32(0.5254), np.float32(0.5288), np.float32(0.7585), np.float32(0.6927), np.float32(0.4384), np.float32(0.3918), np.float32(0.1692), np.float32(0.0), np.float32(0.4402), np.float32(0.4693), np.float32(0.0), np.float32(0.0), np.float32(0.5255), np.float32(0.4949), np.float32(0.7307), np.float32(0.75)] 
+2026-10-16 06:46:23.622526: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:46:23.952054: lr: 0.000677 
+2026-10-16 06:46:23.952296: This epoch took 254.502709 s
+ 
+2026-10-16 06:46:23.952374: 
+epoch:  352 
+2026-10-16 06:50:36.767215: train loss : -0.8033 
+2026-10-16 06:50:42.110013: validation loss: -0.5647 
+2026-10-16 06:50:42.111064: Average global foreground Dice: [np.float32(0.9142), np.float32(0.6451), np.float32(0.7096), np.float32(0.7142), np.float32(0.6068), np.float32(0.743), np.float32(0.6401), np.float32(0.733), np.float32(0.0025), np.float32(0.5691), np.float32(0.5054), np.float32(0.5764), np.float32(0.5737), np.float32(0.5018), np.float32(0.0), np.float32(0.0), np.float32(0.6459), np.float32(0.5527), np.float32(0.6112), np.float32(0.5956), np.float32(0.478), np.float32(0.5204), np.float32(0.7584), np.float32(0.6814), np.float32(0.4431), np.float32(0.4198), np.float32(0.1641), np.float32(0.0), np.float32(0.4455), np.float32(0.534), np.float32(0.0), np.float32(0.0), np.float32(0.5061), np.float32(0.4879), np.float32(0.728), np.float32(0.741)] 
+2026-10-16 06:50:42.111423: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:50:42.461583: lr: 0.000676 
+2026-10-16 06:50:42.461809: This epoch took 258.509379 s
+ 
+2026-10-16 06:50:42.461890: 
+epoch:  353 
+2026-10-16 06:54:33.296060: train loss : -0.8020 
+2026-10-16 06:54:38.642395: validation loss: -0.5936 
+2026-10-16 06:54:38.643455: Average global foreground Dice: [np.float32(0.9106), np.float32(0.6682), np.float32(0.7527), np.float32(0.7291), np.float32(0.5809), np.float32(0.7654), np.float32(0.647), np.float32(0.5876), np.float32(0.2473), np.float32(0.5482), np.float32(0.5362), np.float32(0.6225), np.float32(0.6481), np.float32(0.5731), np.float32(0.0), np.float32(0.0), np.float32(0.6138), np.float32(0.523), np.float32(0.591), np.float32(0.5619), np.float32(0.5326), np.float32(0.5441), np.float32(0.8209), np.float32(0.792), np.float32(0.4258), np.float32(0.4409), np.float32(0.1819), np.float32(0.0), np.float32(0.3152), np.float32(0.5371), np.float32(0.0), np.float32(0.0), np.float32(0.531), np.float32(0.5312), np.float32(0.7743), np.float32(0.8002)] 
+2026-10-16 06:54:38.643914: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:54:38.968236: lr: 0.000675 
+2026-10-16 06:54:38.968456: This epoch took 236.506510 s
+ 
+2026-10-16 06:54:38.968537: 
+epoch:  354 
+2026-10-16 06:58:51.536720: train loss : -0.8067 
+2026-10-16 06:58:56.896052: validation loss: -0.5627 
+2026-10-16 06:58:56.897315: Average global foreground Dice: [np.float32(0.899), np.float32(0.6018), np.float32(0.7155), np.float32(0.7064), np.float32(0.5728), np.float32(0.7384), np.float32(0.6368), np.float32(0.521), np.float32(0.0), np.float32(0.457), np.float32(0.4993), np.float32(0.5814), np.float32(0.586), np.float32(0.5067), np.float32(0.0), np.float32(0.0), np.float32(0.6011), np.float32(0.4792), np.float32(0.5788), np.float32(0.5452), np.float32(0.4986), np.float32(0.5744), np.float32(0.8146), np.float32(0.7839), np.float32(0.4543), np.float32(0.4188), np.float32(0.2295), np.float32(0.0), np.float32(0.3616), np.float32(0.6257), np.float32(0.0), np.float32(0.0), np.float32(0.5329), np.float32(0.5099), np.float32(0.7355), np.float32(0.7598)] 
+2026-10-16 06:58:56.897768: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 06:58:57.229455: lr: 0.000674 
+2026-10-16 06:58:57.229778: This epoch took 258.261132 s
+ 
+2026-10-16 06:58:57.229970: 
+epoch:  355 
+2026-10-16 07:03:10.366683: train loss : -0.8015 
+2026-10-16 07:03:15.711187: validation loss: -0.6111 
+2026-10-16 07:03:15.712364: Average global foreground Dice: [np.float32(0.9054), np.float32(0.6691), np.float32(0.8212), np.float32(0.7802), np.float32(0.5799), np.float32(0.8346), np.float32(0.7162), np.float32(0.5944), np.float32(0.0232), np.float32(0.5061), np.float32(0.5767), np.float32(0.6688), np.float32(0.608), np.float32(0.506), np.float32(0.0), np.float32(0.0), np.float32(0.6135), np.float32(0.4576), np.float32(0.6227), np.float32(0.5653), np.float32(0.5625), np.float32(0.6057), np.float32(0.8618), np.float32(0.8369), np.float32(0.5196), np.float32(0.3881), np.float32(0.2535), np.float32(0.0), np.float32(0.4508), np.float32(0.4953), np.float32(0.0), np.float32(0.0), np.float32(0.4478), np.float32(0.5006), np.float32(0.8203), np.float32(0.8367)] 
+2026-10-16 07:03:15.712759: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:03:16.100061: lr: 0.000673 
+2026-10-16 07:03:16.100300: This epoch took 258.870244 s
+ 
+2026-10-16 07:03:16.100373: 
+epoch:  356 
+2026-10-16 07:07:43.641040: train loss : -0.8000 
+2026-10-16 07:07:48.976664: validation loss: -0.6274 
+2026-10-16 07:07:48.977591: Average global foreground Dice: [np.float32(0.891), np.float32(0.6844), np.float32(0.8175), np.float32(0.7963), np.float32(0.7763), np.float32(0.8424), np.float32(0.8073), np.float32(0.5703), np.float32(0.0423), np.float32(0.4833), np.float32(0.5003), np.float32(0.6274), np.float32(0.6634), np.float32(0.5722), np.float32(0.0), np.float32(0.0), np.float32(0.7644), np.float32(0.5841), np.float32(0.746), np.float32(0.6243), np.float32(0.5752), np.float32(0.669), np.float32(0.7865), np.float32(0.7589), np.float32(0.5004), np.float32(0.451), np.float32(0.1545), np.float32(0.0), np.float32(0.3493), np.float32(0.653), np.float32(0.0), np.float32(0.0), np.float32(0.586), np.float32(0.5902), np.float32(0.8437), np.float32(0.8553)] 
+2026-10-16 07:07:48.977938: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:07:49.321052: lr: 0.000672 
+2026-10-16 07:07:49.321450: This epoch took 273.221018 s
+ 
+2026-10-16 07:07:49.321796: 
+epoch:  357 
+2026-10-16 07:11:56.008466: train loss : -0.8026 
+2026-10-16 07:12:01.368750: validation loss: -0.5293 
+2026-10-16 07:12:01.369627: Average global foreground Dice: [np.float32(0.908), np.float32(0.6006), np.float32(0.6322), np.float32(0.6048), np.float32(0.4659), np.float32(0.6374), np.float32(0.518), np.float32(0.6747), np.float32(0.0), np.float32(0.5323), np.float32(0.4405), np.float32(0.4654), np.float32(0.588), np.float32(0.485), np.float32(0.0), np.float32(0.0), np.float32(0.4945), np.float32(0.3637), np.float32(0.4346), np.float32(0.421), np.float32(0.4586), np.float32(0.4505), np.float32(0.8137), np.float32(0.776), np.float32(0.2581), np.float32(0.3121), np.float32(0.0018), np.float32(0.0), np.float32(0.3885), np.float32(0.6245), np.float32(0.0), np.float32(0.0), np.float32(0.3747), np.float32(0.2305), np.float32(0.6537), np.float32(0.6698)] 
+2026-10-16 07:12:01.369957: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:12:01.689019: lr: 0.000671 
+2026-10-16 07:12:01.689230: This epoch took 252.367176 s
+ 
+2026-10-16 07:12:01.689311: 
+epoch:  358 
+2026-10-16 07:16:02.427334: train loss : -0.8042 
+2026-10-16 07:16:07.778226: validation loss: -0.5553 
+2026-10-16 07:16:07.779481: Average global foreground Dice: [np.float32(0.8977), np.float32(0.6529), np.float32(0.7138), np.float32(0.6983), np.float32(0.6047), np.float32(0.7194), np.float32(0.638), np.float32(0.673), np.float32(0.169), np.float32(0.5635), np.float32(0.4878), np.float32(0.5509), np.float32(0.5891), np.float32(0.4983), np.float32(0.0), np.float32(0.0), np.float32(0.6498), np.float32(0.508), np.float32(0.6145), np.float32(0.5684), np.float32(0.5045), np.float32(0.6409), np.float32(0.8467), np.float32(0.7994), np.float32(0.4369), np.float32(0.3826), np.float32(0.1204), np.float32(0.0), np.float32(0.3212), np.float32(0.6427), np.float32(0.0), np.float32(0.0), np.float32(0.4901), np.float32(0.3916), np.float32(0.7307), np.float32(0.7362)] 
+2026-10-16 07:16:07.779832: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:16:08.199171: lr: 0.00067 
+2026-10-16 07:16:08.199630: This epoch took 246.510262 s
+ 
+2026-10-16 07:16:08.199962: 
+epoch:  359 
+2026-10-16 07:20:18.759571: train loss : -0.8051 
+2026-10-16 07:20:24.119034: validation loss: -0.6100 
+2026-10-16 07:20:24.120170: Average global foreground Dice: [np.float32(0.8967), np.float32(0.6733), np.float32(0.7704), np.float32(0.7768), np.float32(0.7444), np.float32(0.8187), np.float32(0.7774), np.float32(0.6366), np.float32(0.0), np.float32(0.5716), np.float32(0.5462), np.float32(0.6102), np.float32(0.6145), np.float32(0.4683), np.float32(0.0), np.float32(0.0), np.float32(0.7354), np.float32(0.5655), np.float32(0.7171), np.float32(0.6519), np.float32(0.5362), np.float32(0.6611), np.float32(0.8637), np.float32(0.8274), np.float32(0.4855), np.float32(0.3734), np.float32(0.0), np.float32(0.0), np.float32(0.2603), np.float32(0.5379), np.float32(0.0), np.float32(0.0), np.float32(0.5236), np.float32(0.4645), np.float32(0.8529), np.float32(0.8575)] 
+2026-10-16 07:20:24.120697: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:20:24.451072: lr: 0.000669 
+2026-10-16 07:20:24.451409: This epoch took 256.251361 s
+ 
+2026-10-16 07:20:24.451665: 
+epoch:  360 
+2026-10-16 07:24:43.793457: train loss : -0.7984 
+2026-10-16 07:24:49.139301: validation loss: -0.5955 
+2026-10-16 07:24:49.140225: Average global foreground Dice: [np.float32(0.9054), np.float32(0.6609), np.float32(0.758), np.float32(0.7938), np.float32(0.705), np.float32(0.8257), np.float32(0.7338), np.float32(0.5125), np.float32(0.0017), np.float32(0.4682), np.float32(0.6222), np.float32(0.6582), np.float32(0.6417), np.float32(0.5525), np.float32(0.0), np.float32(0.0), np.float32(0.7291), np.float32(0.5836), np.float32(0.7057), np.float32(0.6346), np.float32(0.562), np.float32(0.6856), np.float32(0.7774), np.float32(0.7621), np.float32(0.4787), np.float32(0.416), np.float32(0.1097), np.float32(0.0), np.float32(0.2311), np.float32(0.6111), np.float32(0.0), np.float32(0.0), np.float32(0.5328), np.float32(0.4509), np.float32(0.7955), np.float32(0.8263)] 
+2026-10-16 07:24:49.140457: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:24:49.468030: lr: 0.000668 
+2026-10-16 07:24:49.468366: This epoch took 265.016603 s
+ 
+2026-10-16 07:24:49.468574: 
+epoch:  361 
+2026-10-16 07:29:08.558493: train loss : -0.7911 
+2026-10-16 07:29:13.909299: validation loss: -0.5962 
+2026-10-16 07:29:13.910243: Average global foreground Dice: [np.float32(0.9086), np.float32(0.6539), np.float32(0.7779), np.float32(0.8146), np.float32(0.7154), np.float32(0.8214), np.float32(0.7609), np.float32(0.636), np.float32(0.129), np.float32(0.6059), np.float32(0.6045), np.float32(0.6809), np.float32(0.5732), np.float32(0.4915), np.float32(0.0), np.float32(0.0351), np.float32(0.7522), np.float32(0.562), np.float32(0.7357), np.float32(0.6353), np.float32(0.5669), np.float32(0.691), np.float32(0.8611), np.float32(0.8451), np.float32(0.4891), np.float32(0.384), np.float32(0.0488), np.float32(0.0), np.float32(0.2223), np.float32(0.6466), np.float32(0.0), np.float32(0.0), np.float32(0.5402), np.float32(0.5348), np.float32(0.8398), np.float32(0.8468)] 
+2026-10-16 07:29:13.910552: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:29:14.300355: lr: 0.000667 
+2026-10-16 07:29:14.300611: This epoch took 264.831892 s
+ 
+2026-10-16 07:29:14.300701: 
+epoch:  362 
+2026-10-16 07:33:35.908614: train loss : -0.8017 
+2026-10-16 07:33:41.246051: validation loss: -0.5924 
+2026-10-16 07:33:41.247396: Average global foreground Dice: [np.float32(0.9105), np.float32(0.6614), np.float32(0.7346), np.float32(0.7624), np.float32(0.6541), np.float32(0.7812), np.float32(0.6944), np.float32(0.5902), np.float32(0.004), np.float32(0.601), np.float32(0.61), np.float32(0.646), np.float32(0.5592), np.float32(0.4713), np.float32(0.0), np.float32(0.0), np.float32(0.7129), np.float32(0.558), np.float32(0.6921), np.float32(0.6615), np.float32(0.526), np.float32(0.6306), np.float32(0.8747), np.float32(0.8433), np.float32(0.4799), np.float32(0.4238), np.float32(0.1393), np.float32(0.0), np.float32(0.333), np.float32(0.6439), np.float32(0.0), np.float32(0.0), np.float32(0.5614), np.float32(0.585), np.float32(0.8034), np.float32(0.8101)] 
+2026-10-16 07:33:41.247835: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:33:41.584571: lr: 0.000666 
+2026-10-16 07:33:41.584916: This epoch took 267.284158 s
+ 
+2026-10-16 07:33:41.585368: 
+epoch:  363 
+2026-10-16 07:37:42.889289: train loss : -0.7999 
+2026-10-16 07:37:48.226955: validation loss: -0.6400 
+2026-10-16 07:37:48.227976: Average global foreground Dice: [np.float32(0.9095), np.float32(0.7245), np.float32(0.795), np.float32(0.8058), np.float32(0.7136), np.float32(0.8496), np.float32(0.742), np.float32(0.6949), np.float32(0.0181), np.float32(0.4568), np.float32(0.6229), np.float32(0.6709), np.float32(0.6124), np.float32(0.5655), np.float32(0.0), np.float32(0.0), np.float32(0.7249), np.float32(0.6005), np.float32(0.6964), np.float32(0.6572), np.float32(0.6585), np.float32(0.6881), np.float32(0.8572), np.float32(0.8291), np.float32(0.4783), np.float32(0.4706), np.float32(0.0), np.float32(0.0), np.float32(0.5077), np.float32(0.6112), np.float32(0.0), np.float32(0.0), np.float32(0.5454), np.float32(0.588), np.float32(0.8548), np.float32(0.8596)] 
+2026-10-16 07:37:48.228412: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:37:48.589586: lr: 0.000665 
+2026-10-16 07:37:48.622824: saving checkpoint... 
+2026-10-16 07:37:48.764001: done, saving took 0.17 seconds 
+2026-10-16 07:37:48.765877: This epoch took 247.180280 s
+ 
+2026-10-16 07:37:48.766245: 
+epoch:  364 
+2026-10-16 07:42:08.166194: train loss : -0.7966 
+2026-10-16 07:42:13.522718: validation loss: -0.6167 
+2026-10-16 07:42:13.523972: Average global foreground Dice: [np.float32(0.9066), np.float32(0.6846), np.float32(0.759), np.float32(0.7933), np.float32(0.7161), np.float32(0.82), np.float32(0.7282), np.float32(0.6986), np.float32(0.0888), np.float32(0.4575), np.float32(0.5848), np.float32(0.596), np.float32(0.6119), np.float32(0.5827), np.float32(0.0), np.float32(0.0), np.float32(0.7234), np.float32(0.5967), np.float32(0.7103), np.float32(0.6515), np.float32(0.5308), np.float32(0.6349), np.float32(0.8716), np.float32(0.8478), np.float32(0.4329), np.float32(0.392), np.float32(0.0789), np.float32(0.0), np.float32(0.2876), np.float32(0.6345), np.float32(0.0), np.float32(0.0), np.float32(0.5426), np.float32(0.5388), np.float32(0.831), np.float32(0.8402)] 
+2026-10-16 07:42:13.524448: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:42:13.859282: lr: 0.000665 
+2026-10-16 07:42:13.866871: saving checkpoint... 
+2026-10-16 07:42:14.004503: done, saving took 0.14 seconds 
+2026-10-16 07:42:14.006255: This epoch took 265.239736 s
+ 
+2026-10-16 07:42:14.006398: 
+epoch:  365 
+2026-10-16 07:46:40.980345: train loss : -0.8059 
+2026-10-16 07:46:46.321457: validation loss: -0.5989 
+2026-10-16 07:46:46.322732: Average global foreground Dice: [np.float32(0.9032), np.float32(0.6412), np.float32(0.7725), np.float32(0.7789), np.float32(0.6988), np.float32(0.8118), np.float32(0.739), np.float32(0.6025), np.float32(0.0535), np.float32(0.579), np.float32(0.5497), np.float32(0.6285), np.float32(0.5979), np.float32(0.5602), np.float32(0.0), np.float32(0.0), np.float32(0.7458), np.float32(0.5821), np.float32(0.7291), np.float32(0.644), np.float32(0.5222), np.float32(0.6742), np.float32(0.8651), np.float32(0.8426), np.float32(0.4649), np.float32(0.5132), np.float32(0.1489), np.float32(0.0), np.float32(0.2158), np.float32(0.6253), np.float32(0.0), np.float32(0.0), np.float32(0.6154), np.float32(0.6082), np.float32(0.8268), np.float32(0.8434)] 
+2026-10-16 07:46:46.322938: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:46:46.653496: lr: 0.000664 
+2026-10-16 07:46:46.661628: saving checkpoint... 
+2026-10-16 07:46:46.796652: done, saving took 0.14 seconds 
+2026-10-16 07:46:46.798585: This epoch took 272.792128 s
+ 
+2026-10-16 07:46:46.798770: 
+epoch:  366 
+2026-10-16 07:51:12.267903: train loss : -0.8074 
+2026-10-16 07:51:17.613288: validation loss: -0.5790 
+2026-10-16 07:51:17.614269: Average global foreground Dice: [np.float32(0.9092), np.float32(0.6143), np.float32(0.7516), np.float32(0.7551), np.float32(0.6688), np.float32(0.7869), np.float32(0.6969), np.float32(0.5547), np.float32(0.0973), np.float32(0.5925), np.float32(0.5365), np.float32(0.6175), np.float32(0.5579), np.float32(0.4856), np.float32(0.0), np.float32(0.0), np.float32(0.725), np.float32(0.5342), np.float32(0.6936), np.float32(0.6389), np.float32(0.5632), np.float32(0.6702), np.float32(0.857), np.float32(0.8331), np.float32(0.4945), np.float32(0.4663), np.float32(0.1724), np.float32(0.0), np.float32(0.2883), np.float32(0.6978), np.float32(0.0), np.float32(0.0), np.float32(0.5692), np.float32(0.573), np.float32(0.797), np.float32(0.8145)] 
+2026-10-16 07:51:17.614624: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:51:17.952458: lr: 0.000663 
+2026-10-16 07:51:17.991900: saving checkpoint... 
+2026-10-16 07:51:18.132371: done, saving took 0.18 seconds 
+2026-10-16 07:51:18.134424: This epoch took 271.335559 s
+ 
+2026-10-16 07:51:18.134647: 
+epoch:  367 
+2026-10-16 07:55:19.705195: train loss : -0.8093 
+2026-10-16 07:55:25.069356: validation loss: -0.6009 
+2026-10-16 07:55:25.070482: Average global foreground Dice: [np.float32(0.9106), np.float32(0.6581), np.float32(0.7967), np.float32(0.8032), np.float32(0.7317), np.float32(0.8252), np.float32(0.7595), np.float32(0.6124), np.float32(0.0061), np.float32(0.3502), np.float32(0.6249), np.float32(0.6489), np.float32(0.6424), np.float32(0.521), np.float32(0.0), np.float32(0.0), np.float32(0.7645), np.float32(0.5984), np.float32(0.75), np.float32(0.6769), np.float32(0.5642), np.float32(0.6889), np.float32(0.8395), np.float32(0.8172), np.float32(0.4627), np.float32(0.5216), np.float32(0.2252), np.float32(0.0), np.float32(0.3543), np.float32(0.6321), np.float32(0.0), np.float32(0.0), np.float32(0.6), np.float32(0.6351), np.float32(0.8416), np.float32(0.8523)] 
+2026-10-16 07:55:25.070947: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 07:55:25.399097: lr: 0.000662 
+2026-10-16 07:55:25.430303: saving checkpoint... 
+2026-10-16 07:55:25.570774: done, saving took 0.17 seconds 
+2026-10-16 07:55:25.572772: This epoch took 247.438045 s
+ 
+2026-10-16 07:55:25.572890: 
+epoch:  368 
+2026-10-16 08:00:23.327052: train loss : -0.8043 
+2026-10-16 08:00:28.685457: validation loss: -0.5909 
+2026-10-16 08:00:28.686368: Average global foreground Dice: [np.float32(0.9123), np.float32(0.6143), np.float32(0.7649), np.float32(0.7827), np.float32(0.6886), np.float32(0.8038), np.float32(0.7268), np.float32(0.6246), np.float32(0.0665), np.float32(0.577), np.float32(0.6133), np.float32(0.6332), np.float32(0.5814), np.float32(0.5113), np.float32(0.0007), np.float32(0.0), np.float32(0.7388), np.float32(0.5299), np.float32(0.7235), np.float32(0.6129), np.float32(0.3572), np.float32(0.6105), np.float32(0.8662), np.float32(0.8546), np.float32(0.5208), np.float32(0.4219), np.float32(0.2288), np.float32(0.0), np.float32(0.3647), np.float32(0.6246), np.float32(0.0), np.float32(0.0), np.float32(0.5864), np.float32(0.6075), np.float32(0.8178), np.float32(0.8311)] 
+2026-10-16 08:00:28.686833: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:00:29.031838: lr: 0.000661 
+2026-10-16 08:00:29.080193: saving checkpoint... 
+2026-10-16 08:00:29.223155: done, saving took 0.19 seconds 
+2026-10-16 08:00:29.225445: This epoch took 303.652500 s
+ 
+2026-10-16 08:00:29.225671: 
+epoch:  369 
+2026-10-16 08:04:45.664480: train loss : -0.8072 
+2026-10-16 08:04:51.004991: validation loss: -0.6140 
+2026-10-16 08:04:51.006012: Average global foreground Dice: [np.float32(0.9032), np.float32(0.6824), np.float32(0.7847), np.float32(0.8012), np.float32(0.7189), np.float32(0.8325), np.float32(0.7424), np.float32(0.6952), np.float32(0.0118), np.float32(0.58), np.float32(0.6097), np.float32(0.6695), np.float32(0.5944), np.float32(0.5528), np.float32(0.0), np.float32(0.0), np.float32(0.7548), np.float32(0.611), np.float32(0.7317), np.float32(0.6672), np.float32(0.5328), np.float32(0.5959), np.float32(0.8963), np.float32(0.8614), np.float32(0.471), np.float32(0.4691), np.float32(0.1012), np.float32(0.0), np.float32(0.3981), np.float32(0.5844), np.float32(0.0), np.float32(0.0), np.float32(0.5843), np.float32(0.6076), np.float32(0.8423), np.float32(0.8558)] 
+2026-10-16 08:04:51.006211: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:04:51.341395: lr: 0.00066 
+2026-10-16 08:04:51.373437: saving checkpoint... 
+2026-10-16 08:04:51.501242: done, saving took 0.16 seconds 
+2026-10-16 08:04:51.503410: This epoch took 262.277598 s
+ 
+2026-10-16 08:04:51.503555: 
+epoch:  370 
+2026-10-16 08:08:58.114952: train loss : -0.8107 
+2026-10-16 08:09:03.462536: validation loss: -0.6037 
+2026-10-16 08:09:03.463374: Average global foreground Dice: [np.float32(0.9083), np.float32(0.6446), np.float32(0.7748), np.float32(0.7905), np.float32(0.7029), np.float32(0.8179), np.float32(0.7325), np.float32(0.6101), np.float32(0.0079), np.float32(0.6196), np.float32(0.5919), np.float32(0.6729), np.float32(0.6022), np.float32(0.4721), np.float32(0.0031), np.float32(0.0), np.float32(0.7489), np.float32(0.5829), np.float32(0.7316), np.float32(0.6476), np.float32(0.5898), np.float32(0.6695), np.float32(0.8099), np.float32(0.7885), np.float32(0.5048), np.float32(0.3777), np.float32(0.1714), np.float32(0.0), np.float32(0.5751), np.float32(0.7109), np.float32(0.0), np.float32(0.0), np.float32(0.5826), np.float32(0.6186), np.float32(0.8138), np.float32(0.8329)] 
+2026-10-16 08:09:03.463518: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:09:03.786614: lr: 0.000659 
+2026-10-16 08:09:03.811334: saving checkpoint... 
+2026-10-16 08:09:03.947027: done, saving took 0.16 seconds 
+2026-10-16 08:09:03.948714: This epoch took 252.445023 s
+ 
+2026-10-16 08:09:03.948905: 
+epoch:  371 
+2026-10-16 08:13:01.734104: train loss : -0.8091 
+2026-10-16 08:13:07.076964: validation loss: -0.5797 
+2026-10-16 08:13:07.078076: Average global foreground Dice: [np.float32(0.91), np.float32(0.6331), np.float32(0.7375), np.float32(0.754), np.float32(0.6751), np.float32(0.7776), np.float32(0.6846), np.float32(0.683), np.float32(0.0019), np.float32(0.3404), np.float32(0.5012), np.float32(0.6048), np.float32(0.5673), np.float32(0.5268), np.float32(0.007), np.float32(0.0702), np.float32(0.7173), np.float32(0.5369), np.float32(0.6886), np.float32(0.6386), np.float32(0.4916), np.float32(0.5619), np.float32(0.8341), np.float32(0.7776), np.float32(0.4387), np.float32(0.4549), np.float32(0.1322), np.float32(0.0), np.float32(0.4607), np.float32(0.6748), np.float32(0.0), np.float32(0.0), np.float32(0.5835), np.float32(0.61), np.float32(0.7812), np.float32(0.7937)] 
+2026-10-16 08:13:07.078300: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:13:07.410478: lr: 0.000658 
+2026-10-16 08:13:07.410923: This epoch took 243.461944 s
+ 
+2026-10-16 08:13:07.411208: 
+epoch:  372 
+2026-10-16 08:17:33.200084: train loss : -0.8117 
+2026-10-16 08:17:38.537341: validation loss: -0.4085 
+2026-10-16 08:17:38.538384: Average global foreground Dice: [np.float32(0.9159), np.float32(0.4366), np.float32(0.4718), np.float32(0.4456), np.float32(0.2984), np.float32(0.4815), np.float32(0.3675), np.float32(0.3923), np.float32(0.0404), np.float32(0.4897), np.float32(0.3624), np.float32(0.4087), np.float32(0.5672), np.float32(0.5316), np.float32(0.0135), np.float32(0.0021), np.float32(0.3214), np.float32(0.2514), np.float32(0.2647), np.float32(0.2681), np.float32(0.384), np.float32(0.3795), np.float32(0.6302), np.float32(0.5876), np.float32(0.1776), np.float32(0.2303), np.float32(0.0273), np.float32(0.0), np.float32(0.1911), np.float32(0.578), np.float32(0.0), np.float32(0.0), np.float32(0.3057), np.float32(0.2755), np.float32(0.4863), np.float32(0.5474)] 
+2026-10-16 08:17:38.538541: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:17:38.889681: lr: 0.000657 
+2026-10-16 08:17:38.890130: This epoch took 271.478732 s
+ 
+2026-10-16 08:17:38.890455: 
+epoch:  373 
+2026-10-16 08:22:06.028929: train loss : -0.8071 
+2026-10-16 08:22:11.389676: validation loss: -0.5094 
+2026-10-16 08:22:11.390603: Average global foreground Dice: [np.float32(0.9097), np.float32(0.554), np.float32(0.6035), np.float32(0.6023), np.float32(0.4205), np.float32(0.6489), np.float32(0.5013), np.float32(0.6627), np.float32(0.2694), np.float32(0.5199), np.float32(0.4637), np.float32(0.5391), np.float32(0.5807), np.float32(0.5425), np.float32(0.0), np.float32(0.0), np.float32(0.398), np.float32(0.2957), np.float32(0.3777), np.float32(0.2968), np.float32(0.4448), np.float32(0.3366), np.float32(0.7178), np.float32(0.6945), np.float32(0.2478), np.float32(0.3355), np.float32(0.0402), np.float32(0.0), np.float32(0.329), np.float32(0.6533), np.float32(0.0), np.float32(0.0), np.float32(0.3783), np.float32(0.383), np.float32(0.639), np.float32(0.6915)] 
+2026-10-16 08:22:11.390877: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:22:11.758223: lr: 0.000656 
+2026-10-16 08:22:11.758611: This epoch took 272.867944 s
+ 
+2026-10-16 08:22:11.758925: 
+epoch:  374 
+2026-10-16 08:26:13.366359: train loss : -0.8096 
+2026-10-16 08:26:18.729045: validation loss: -0.5387 
+2026-10-16 08:26:18.729928: Average global foreground Dice: [np.float32(0.9147), np.float32(0.6038), np.float32(0.733), np.float32(0.7072), np.float32(0.5762), np.float32(0.7344), np.float32(0.6132), np.float32(0.6295), np.float32(0.4558), np.float32(0.4997), np.float32(0.4798), np.float32(0.5623), np.float32(0.5357), np.float32(0.5604), np.float32(0.0), np.float32(0.0), np.float32(0.5579), np.float32(0.3846), np.float32(0.4963), np.float32(0.3714), np.float32(0.5278), np.float32(0.5823), np.float32(0.7487), np.float32(0.7378), np.float32(0.4516), np.float32(0.4077), np.float32(0.1306), np.float32(0.0), np.float32(0.3774), np.float32(0.6962), np.float32(0.0), np.float32(0.0), np.float32(0.4936), np.float32(0.4376), np.float32(0.7218), np.float32(0.7649)] 
+2026-10-16 08:26:18.730526: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:26:19.122016: lr: 0.000655 
+2026-10-16 08:26:19.122345: This epoch took 247.363159 s
+ 
+2026-10-16 08:26:19.122470: 
+epoch:  375 
+2026-10-16 08:30:21.383276: train loss : -0.8097 
+2026-10-16 08:30:26.735854: validation loss: -0.5332 
+2026-10-16 08:30:26.736882: Average global foreground Dice: [np.float32(0.9115), np.float32(0.5622), np.float32(0.6948), np.float32(0.695), np.float32(0.6013), np.float32(0.7103), np.float32(0.635), np.float32(0.4033), np.float32(0.0023), np.float32(0.4658), np.float32(0.5038), np.float32(0.5477), np.float32(0.5274), np.float32(0.5126), np.float32(0.0363), np.float32(0.0), np.float32(0.6266), np.float32(0.3981), np.float32(0.5397), np.float32(0.5042), np.float32(0.4511), np.float32(0.5982), np.float32(0.7659), np.float32(0.7446), np.float32(0.4994), np.float32(0.3331), np.float32(0.1589), np.float32(0.0), np.float32(0.3688), np.float32(0.6596), np.float32(0.0), np.float32(0.0), np.float32(0.485), np.float32(0.4334), np.float32(0.7359), np.float32(0.7534)] 
+2026-10-16 08:30:26.737242: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:30:27.073812: lr: 0.000654 
+2026-10-16 08:30:27.074090: This epoch took 247.951556 s
+ 
+2026-10-16 08:30:27.074179: 
+epoch:  376 
+2026-10-16 08:34:31.940869: train loss : -0.8100 
+2026-10-16 08:34:37.288432: validation loss: -0.5876 
+2026-10-16 08:34:37.289408: Average global foreground Dice: [np.float32(0.9163), np.float32(0.6475), np.float32(0.7031), np.float32(0.7293), np.float32(0.6474), np.float32(0.7451), np.float32(0.6542), np.float32(0.4392), np.float32(0.1984), np.float32(0.4213), np.float32(0.539), np.float32(0.6134), np.float32(0.5557), np.float32(0.5503), np.float32(0.0), np.float32(0.0), np.float32(0.6646), np.float32(0.5065), np.float32(0.6116), np.float32(0.5112), np.float32(0.5231), np.float32(0.6498), np.float32(0.8512), np.float32(0.8286), np.float32(0.4318), np.float32(0.4343), np.float32(0.0928), np.float32(0.0), np.float32(0.3782), np.float32(0.721), np.float32(0.0), np.float32(0.0), np.float32(0.4837), np.float32(0.5822), np.float32(0.768), np.float32(0.7892)] 
+2026-10-16 08:34:37.289810: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:34:37.636142: lr: 0.000653 
+2026-10-16 08:34:37.636390: This epoch took 250.562156 s
+ 
+2026-10-16 08:34:37.636466: 
+epoch:  377 
+2026-10-16 08:38:40.403726: train loss : -0.8106 
+2026-10-16 08:38:45.736264: validation loss: -0.5813 
+2026-10-16 08:38:45.737255: Average global foreground Dice: [np.float32(0.9054), np.float32(0.6471), np.float32(0.7268), np.float32(0.7695), np.float32(0.6637), np.float32(0.7881), np.float32(0.6922), np.float32(0.4717), np.float32(0.0491), np.float32(0.4898), np.float32(0.5507), np.float32(0.6187), np.float32(0.5829), np.float32(0.5173), np.float32(0.0), np.float32(0.0), np.float32(0.7022), np.float32(0.5524), np.float32(0.6641), np.float32(0.6375), np.float32(0.5825), np.float32(0.6536), np.float32(0.8348), np.float32(0.8053), np.float32(0.4287), np.float32(0.4004), np.float32(0.0), np.float32(0.0), np.float32(0.3548), np.float32(0.6574), np.float32(0.0), np.float32(0.0), np.float32(0.5102), np.float32(0.3999), np.float32(0.7903), np.float32(0.8057)] 
+2026-10-16 08:38:45.737454: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:38:46.097931: lr: 0.000652 
+2026-10-16 08:38:46.098167: This epoch took 248.461649 s
+ 
+2026-10-16 08:38:46.098261: 
+epoch:  378 
+2026-10-16 08:43:25.293800: train loss : -0.8046 
+2026-10-16 08:43:30.670650: validation loss: -0.5747 
+2026-10-16 08:43:30.671769: Average global foreground Dice: [np.float32(0.9097), np.float32(0.6703), np.float32(0.75), np.float32(0.7861), np.float32(0.6745), np.float32(0.8036), np.float32(0.7101), np.float32(0.6606), np.float32(0.2515), np.float32(0.5249), np.float32(0.5633), np.float32(0.6356), np.float32(0.5419), np.float32(0.5235), np.float32(0.0), np.float32(0.0), np.float32(0.7197), np.float32(0.5072), np.float32(0.6735), np.float32(0.6143), np.float32(0.5516), np.float32(0.6197), np.float32(0.8237), np.float32(0.8035), np.float32(0.5041), np.float32(0.4323), np.float32(0.1687), np.float32(0.0), np.float32(0.4292), np.float32(0.595), np.float32(0.0), np.float32(0.0), np.float32(0.5173), np.float32(0.5297), np.float32(0.8091), np.float32(0.8139)] 
+2026-10-16 08:43:30.672231: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:43:31.073107: lr: 0.000651 
+2026-10-16 08:43:31.073337: This epoch took 284.975015 s
+ 
+2026-10-16 08:43:31.073421: 
+epoch:  379 
+2026-10-16 08:47:54.803075: train loss : -0.8104 
+2026-10-16 08:48:00.166639: validation loss: -0.5626 
+2026-10-16 08:48:00.167547: Average global foreground Dice: [np.float32(0.918), np.float32(0.6587), np.float32(0.7655), np.float32(0.7565), np.float32(0.6623), np.float32(0.7869), np.float32(0.7018), np.float32(0.5219), np.float32(0.0888), np.float32(0.4768), np.float32(0.482), np.float32(0.5886), np.float32(0.5802), np.float32(0.5743), np.float32(0.0), np.float32(0.0), np.float32(0.6469), np.float32(0.4813), np.float32(0.6185), np.float32(0.5415), np.float32(0.5465), np.float32(0.6568), np.float32(0.7596), np.float32(0.7545), np.float32(0.4918), np.float32(0.4315), np.float32(0.0779), np.float32(0.0), np.float32(0.3394), np.float32(0.6137), np.float32(0.0), np.float32(0.0), np.float32(0.4392), np.float32(0.5161), np.float32(0.7824), np.float32(0.8147)] 
+2026-10-16 08:48:00.167702: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:48:00.505436: lr: 0.00065 
+2026-10-16 08:48:00.505704: This epoch took 269.432222 s
+ 
+2026-10-16 08:48:00.505790: 
+epoch:  380 
+2026-10-16 08:52:33.417907: train loss : -0.8150 
+2026-10-16 08:52:38.750416: validation loss: -0.6117 
+2026-10-16 08:52:38.751553: Average global foreground Dice: [np.float32(0.9188), np.float32(0.6797), np.float32(0.7651), np.float32(0.7918), np.float32(0.6996), np.float32(0.8231), np.float32(0.7203), np.float32(0.6458), np.float32(0.2183), np.float32(0.5482), np.float32(0.5941), np.float32(0.6602), np.float32(0.6263), np.float32(0.6073), np.float32(0.0), np.float32(0.0013), np.float32(0.7187), np.float32(0.517), np.float32(0.6952), np.float32(0.5968), np.float32(0.5964), np.float32(0.6352), np.float32(0.8018), np.float32(0.7713), np.float32(0.4594), np.float32(0.4696), np.float32(0.0429), np.float32(0.0), np.float32(0.3433), np.float32(0.6313), np.float32(0.0), np.float32(0.0), np.float32(0.5323), np.float32(0.5602), np.float32(0.8125), np.float32(0.8297)] 
+2026-10-16 08:52:38.751925: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:52:39.087974: lr: 0.000649 
+2026-10-16 08:52:39.088515: This epoch took 278.582664 s
+ 
+2026-10-16 08:52:39.088922: 
+epoch:  381 
+2026-10-16 08:56:59.258601: train loss : -0.8067 
+2026-10-16 08:57:04.618088: validation loss: -0.6145 
+2026-10-16 08:57:04.619217: Average global foreground Dice: [np.float32(0.9069), np.float32(0.683), np.float32(0.7983), np.float32(0.8111), np.float32(0.7024), np.float32(0.8406), np.float32(0.7193), np.float32(0.4939), np.float32(0.0), np.float32(0.3364), np.float32(0.5631), np.float32(0.6318), np.float32(0.6475), np.float32(0.6091), np.float32(0.0), np.float32(0.0), np.float32(0.7496), np.float32(0.5747), np.float32(0.7373), np.float32(0.6643), np.float32(0.6286), np.float32(0.6112), np.float32(0.837), np.float32(0.8102), np.float32(0.4409), np.float32(0.4518), np.float32(0.2488), np.float32(0.0), np.float32(0.3887), np.float32(0.5361), np.float32(0.0), np.float32(0.0), np.float32(0.5993), np.float32(0.6126), np.float32(0.8415), np.float32(0.8551)] 
+2026-10-16 08:57:04.619636: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 08:57:05.139428: lr: 0.000648 
+2026-10-16 08:57:05.139772: This epoch took 266.050595 s
+ 
+2026-10-16 08:57:05.139919: 
+epoch:  382 
+2026-10-16 09:01:36.189980: train loss : -0.8051 
+2026-10-16 09:01:41.548877: validation loss: -0.4748 
+2026-10-16 09:01:41.549990: Average global foreground Dice: [np.float32(0.9161), np.float32(0.5251), np.float32(0.5061), np.float32(0.5182), np.float32(0.3876), np.float32(0.5716), np.float32(0.4438), np.float32(0.484), np.float32(0.1514), np.float32(0.4148), np.float32(0.4208), np.float32(0.4885), np.float32(0.5511), np.float32(0.5332), np.float32(0.0), np.float32(0.0), np.float32(0.3712), np.float32(0.3026), np.float32(0.389), np.float32(0.3859), np.float32(0.486), np.float32(0.3751), np.float32(0.659), np.float32(0.6263), np.float32(0.1714), np.float32(0.2491), np.float32(0.0), np.float32(0.0), np.float32(0.2753), np.float32(0.4937), np.float32(0.0), np.float32(0.0), np.float32(0.3521), np.float32(0.3722), np.float32(0.5614), np.float32(0.6065)] 
+2026-10-16 09:01:41.550180: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:01:41.897395: lr: 0.000648 
+2026-10-16 09:01:41.897826: This epoch took 276.757835 s
+ 
+2026-10-16 09:01:41.898036: 
+epoch:  383 
+2026-10-16 09:06:13.137758: train loss : -0.8123 
+2026-10-16 09:06:18.473107: validation loss: -0.4486 
+2026-10-16 09:06:18.473971: Average global foreground Dice: [np.float32(0.9152), np.float32(0.5114), np.float32(0.4772), np.float32(0.5107), np.float32(0.3974), np.float32(0.5313), np.float32(0.4013), np.float32(0.5031), np.float32(0.0996), np.float32(0.5777), np.float32(0.3746), np.float32(0.4685), np.float32(0.5232), np.float32(0.4638), np.float32(0.0), np.float32(0.0), np.float32(0.3949), np.float32(0.3075), np.float32(0.369), np.float32(0.3622), np.float32(0.4468), np.float32(0.2794), np.float32(0.6777), np.float32(0.62), np.float32(0.2239), np.float32(0.2515), np.float32(0.0), np.float32(0.0), np.float32(0.2909), np.float32(0.4519), np.float32(0.0), np.float32(0.0), np.float32(0.4237), np.float32(0.4143), np.float32(0.5504), np.float32(0.5798)] 
+2026-10-16 09:06:18.474130: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:06:18.816039: lr: 0.000647 
+2026-10-16 09:06:18.816334: This epoch took 276.918117 s
+ 
+2026-10-16 09:06:18.816695: 
+epoch:  384 
+2026-10-16 09:10:38.452579: train loss : -0.8023 
+2026-10-16 09:10:43.796539: validation loss: -0.5035 
+2026-10-16 09:10:43.797533: Average global foreground Dice: [np.float32(0.8999), np.float32(0.5562), np.float32(0.5759), np.float32(0.6125), np.float32(0.4977), np.float32(0.6579), np.float32(0.5343), np.float32(0.4801), np.float32(0.1129), np.float32(0.4866), np.float32(0.4151), np.float32(0.4919), np.float32(0.5753), np.float32(0.5404), np.float32(0.0), np.float32(0.0), np.float32(0.4919), np.float32(0.3874), np.float32(0.4798), np.float32(0.4511), np.float32(0.4473), np.float32(0.4054), np.float32(0.6726), np.float32(0.6411), np.float32(0.2854), np.float32(0.3407), np.float32(0.0713), np.float32(0.0), np.float32(0.2337), np.float32(0.5652), np.float32(0.0), np.float32(0.0), np.float32(0.4661), np.float32(0.4401), np.float32(0.596), np.float32(0.6438)] 
+2026-10-16 09:10:43.797773: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:10:44.126431: lr: 0.000646 
+2026-10-16 09:10:44.126712: This epoch took 265.309788 s
+ 
+2026-10-16 09:10:44.126806: 
+epoch:  385 
+2026-10-16 09:14:54.752317: train loss : -0.8114 
+2026-10-16 09:15:00.091528: validation loss: -0.5511 
+2026-10-16 09:15:00.092684: Average global foreground Dice: [np.float32(0.9141), np.float32(0.6092), np.float32(0.7047), np.float32(0.7179), np.float32(0.5971), np.float32(0.7632), np.float32(0.6525), np.float32(0.653), np.float32(0.1125), np.float32(0.3796), np.float32(0.4819), np.float32(0.5908), np.float32(0.6097), np.float32(0.5448), np.float32(0.0955), np.float32(0.0), np.float32(0.6295), np.float32(0.4626), np.float32(0.623), np.float32(0.5612), np.float32(0.4395), np.float32(0.5153), np.float32(0.6971), np.float32(0.699), np.float32(0.3992), np.float32(0.3797), np.float32(0.1144), np.float32(0.0), np.float32(0.3327), np.float32(0.6052), np.float32(0.0), np.float32(0.0), np.float32(0.5416), np.float32(0.5819), np.float32(0.7071), np.float32(0.7642)] 
+2026-10-16 09:15:00.093119: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:15:00.424605: lr: 0.000645 
+2026-10-16 09:15:00.425017: This epoch took 256.298135 s
+ 
+2026-10-16 09:15:00.425277: 
+epoch:  386 
+2026-10-16 09:18:53.338260: train loss : -0.8111 
+2026-10-16 09:18:58.686876: validation loss: -0.5309 
+2026-10-16 09:18:58.687792: Average global foreground Dice: [np.float32(0.9139), np.float32(0.6063), np.float32(0.686), np.float32(0.7132), np.float32(0.5891), np.float32(0.7339), np.float32(0.619), np.float32(0.6224), np.float32(0.1332), np.float32(0.4498), np.float32(0.4624), np.float32(0.554), np.float32(0.5638), np.float32(0.538), np.float32(0.0171), np.float32(0.0), np.float32(0.617), np.float32(0.4868), np.float32(0.5646), np.float32(0.532), np.float32(0.5044), np.float32(0.5038), np.float32(0.7247), np.float32(0.6818), np.float32(0.4105), np.float32(0.4151), np.float32(0.0747), np.float32(0.0), np.float32(0.2297), np.float32(0.5783), np.float32(0.0), np.float32(0.0), np.float32(0.503), np.float32(0.5206), np.float32(0.7034), np.float32(0.7262)] 
+2026-10-16 09:18:58.687976: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:18:59.033823: lr: 0.000644 
+2026-10-16 09:18:59.034063: This epoch took 238.608610 s
+ 
+2026-10-16 09:18:59.034144: 
+epoch:  387 
+2026-10-16 09:23:02.322247: train loss : -0.8086 
+2026-10-16 09:23:07.669643: validation loss: -0.5911 
+2026-10-16 09:23:07.670717: Average global foreground Dice: [np.float32(0.9197), np.float32(0.636), np.float32(0.7748), np.float32(0.7962), np.float32(0.7011), np.float32(0.824), np.float32(0.74), np.float32(0.7919), np.float32(0.0492), np.float32(0.3991), np.float32(0.5171), np.float32(0.6054), np.float32(0.6278), np.float32(0.5214), np.float32(0.0), np.float32(0.0), np.float32(0.7558), np.float32(0.6076), np.float32(0.734), np.float32(0.6724), np.float32(0.4762), np.float32(0.613), np.float32(0.841), np.float32(0.8224), np.float32(0.4261), np.float32(0.4782), np.float32(0.2328), np.float32(0.0), np.float32(0.2291), np.float32(0.6639), np.float32(0.0), np.float32(0.0), np.float32(0.651), np.float32(0.6719), np.float32(0.8164), np.float32(0.8367)] 
+2026-10-16 09:23:07.671205: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:23:08.004973: lr: 0.000643 
+2026-10-16 09:23:08.005223: This epoch took 248.971027 s
+ 
+2026-10-16 09:23:08.005307: 
+epoch:  388 
+2026-10-16 09:27:22.425397: train loss : -0.8110 
+2026-10-16 09:27:27.777666: validation loss: -0.5907 
+2026-10-16 09:27:27.778619: Average global foreground Dice: [np.float32(0.9189), np.float32(0.626), np.float32(0.7373), np.float32(0.769), np.float32(0.6698), np.float32(0.7903), np.float32(0.7004), np.float32(0.7636), np.float32(0.0), np.float32(0.518), np.float32(0.56), np.float32(0.6252), np.float32(0.5892), np.float32(0.4928), np.float32(0.0742), np.float32(0.0), np.float32(0.7315), np.float32(0.5697), np.float32(0.6771), np.float32(0.6615), np.float32(0.5415), np.float32(0.6067), np.float32(0.8245), np.float32(0.8055), np.float32(0.4932), np.float32(0.4168), np.float32(0.2457), np.float32(0.0), np.float32(0.5068), np.float32(0.6712), np.float32(0.0), np.float32(0.0), np.float32(0.5826), np.float32(0.6019), np.float32(0.7954), np.float32(0.8135)] 
+2026-10-16 09:27:27.778815: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:27:28.134371: lr: 0.000642 
+2026-10-16 09:27:28.134794: This epoch took 260.129431 s
+ 
+2026-10-16 09:27:28.135042: 
+epoch:  389 
+2026-10-16 09:31:25.967158: train loss : -0.8134 
+2026-10-16 09:31:31.319219: validation loss: -0.5990 
+2026-10-16 09:31:31.320446: Average global foreground Dice: [np.float32(0.9188), np.float32(0.6527), np.float32(0.7641), np.float32(0.8043), np.float32(0.705), np.float32(0.8214), np.float32(0.7336), np.float32(0.7857), np.float32(0.0146), np.float32(0.3912), np.float32(0.5659), np.float32(0.6448), np.float32(0.5581), np.float32(0.528), np.float32(0.0), np.float32(0.0), np.float32(0.7276), np.float32(0.5628), np.float32(0.6819), np.float32(0.5977), np.float32(0.5665), np.float32(0.6862), np.float32(0.854), np.float32(0.8418), np.float32(0.4923), np.float32(0.4103), np.float32(0.1713), np.float32(0.0), np.float32(0.4152), np.float32(0.6815), np.float32(0.0), np.float32(0.0), np.float32(0.552), np.float32(0.6368), np.float32(0.8358), np.float32(0.8474)] 
+2026-10-16 09:31:31.321124: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:31:31.648873: lr: 0.000641 
+2026-10-16 09:31:31.649153: This epoch took 243.513905 s
+ 
+2026-10-16 09:31:31.649264: 
+epoch:  390 
+2026-10-16 09:35:39.276196: train loss : -0.8073 
+2026-10-16 09:35:44.627916: validation loss: -0.5630 
+2026-10-16 09:35:44.628963: Average global foreground Dice: [np.float32(0.9115), np.float32(0.597), np.float32(0.7448), np.float32(0.756), np.float32(0.6711), np.float32(0.7793), np.float32(0.6956), np.float32(0.7151), np.float32(0.135), np.float32(0.4344), np.float32(0.4814), np.float32(0.5616), np.float32(0.5817), np.float32(0.5856), np.float32(0.0122), np.float32(0.0), np.float32(0.6803), np.float32(0.5274), np.float32(0.6722), np.float32(0.6095), np.float32(0.5015), np.float32(0.6102), np.float32(0.8395), np.float32(0.8203), np.float32(0.4622), np.float32(0.4702), np.float32(0.2142), np.float32(0.0), np.float32(0.2326), np.float32(0.6167), np.float32(0.0), np.float32(0.0), np.float32(0.5771), np.float32(0.5943), np.float32(0.7869), np.float32(0.8136)] 
+2026-10-16 09:35:44.629362: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:35:44.990019: lr: 0.00064 
+2026-10-16 09:35:44.990315: This epoch took 253.340972 s
+ 
+2026-10-16 09:35:44.990431: 
+epoch:  391 
+2026-10-16 09:39:51.258312: train loss : -0.8116 
+2026-10-16 09:39:56.622456: validation loss: -0.5496 
+2026-10-16 09:39:56.623533: Average global foreground Dice: [np.float32(0.9061), np.float32(0.6083), np.float32(0.7192), np.float32(0.7578), np.float32(0.6543), np.float32(0.7795), np.float32(0.6885), np.float32(0.7377), np.float32(0.2161), np.float32(0.4899), np.float32(0.5095), np.float32(0.578), np.float32(0.5706), np.float32(0.4756), np.float32(0.0398), np.float32(0.0), np.float32(0.7209), np.float32(0.5919), np.float32(0.7024), np.float32(0.6583), np.float32(0.5131), np.float32(0.5999), np.float32(0.8371), np.float32(0.813), np.float32(0.5021), np.float32(0.4417), np.float32(0.2218), np.float32(0.0), np.float32(0.2539), np.float32(0.5319), np.float32(0.0), np.float32(0.0), np.float32(0.5446), np.float32(0.6266), np.float32(0.7874), np.float32(0.805)] 
+2026-10-16 09:39:56.623908: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:39:56.953164: lr: 0.000639 
+2026-10-16 09:39:56.953423: This epoch took 251.962935 s
+ 
+2026-10-16 09:39:56.953507: 
+epoch:  392 
+2026-10-16 09:44:01.920102: train loss : -0.8178 
+2026-10-16 09:44:07.267814: validation loss: -0.5416 
+2026-10-16 09:44:07.268970: Average global foreground Dice: [np.float32(0.9059), np.float32(0.5731), np.float32(0.6878), np.float32(0.6934), np.float32(0.5676), np.float32(0.7188), np.float32(0.6293), np.float32(0.66), np.float32(0.1309), np.float32(0.4112), np.float32(0.4489), np.float32(0.5324), np.float32(0.5472), np.float32(0.4503), np.float32(0.0), np.float32(0.0), np.float32(0.6472), np.float32(0.5139), np.float32(0.6516), np.float32(0.6154), np.float32(0.5164), np.float32(0.5537), np.float32(0.8196), np.float32(0.792), np.float32(0.4823), np.float32(0.4306), np.float32(0.2129), np.float32(0.0), np.float32(0.4313), np.float32(0.6271), np.float32(0.0), np.float32(0.0), np.float32(0.4761), np.float32(0.5105), np.float32(0.7257), np.float32(0.7604)] 
+2026-10-16 09:44:07.269183: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:44:07.613260: lr: 0.000638 
+2026-10-16 09:44:07.613684: This epoch took 250.660120 s
+ 
+2026-10-16 09:44:07.613838: 
+epoch:  393 
+2026-10-16 09:48:33.506259: train loss : -0.8120 
+2026-10-16 09:48:38.840372: validation loss: -0.5916 
+2026-10-16 09:48:38.841686: Average global foreground Dice: [np.float32(0.923), np.float32(0.6838), np.float32(0.7499), np.float32(0.7724), np.float32(0.6862), np.float32(0.7973), np.float32(0.7047), np.float32(0.7286), np.float32(0.0249), np.float32(0.4332), np.float32(0.5179), np.float32(0.6109), np.float32(0.623), np.float32(0.5844), np.float32(0.0), np.float32(0.0), np.float32(0.7257), np.float32(0.5813), np.float32(0.705), np.float32(0.6365), np.float32(0.5513), np.float32(0.5496), np.float32(0.8314), np.float32(0.805), np.float32(0.4494), np.float32(0.4881), np.float32(0.1681), np.float32(0.0), np.float32(0.3105), np.float32(0.6527), np.float32(0.0), np.float32(0.0), np.float32(0.5725), np.float32(0.6828), np.float32(0.802), np.float32(0.8175)] 
+2026-10-16 09:48:38.841923: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:48:39.185755: lr: 0.000637 
+2026-10-16 09:48:39.186046: This epoch took 271.572133 s
+ 
+2026-10-16 09:48:39.186141: 
+epoch:  394 
+2026-10-16 09:52:40.104738: train loss : -0.8181 
+2026-10-16 09:52:45.490465: validation loss: -0.5707 
+2026-10-16 09:52:45.491466: Average global foreground Dice: [np.float32(0.9247), np.float32(0.6352), np.float32(0.7685), np.float32(0.7712), np.float32(0.6682), np.float32(0.7825), np.float32(0.701), np.float32(0.6966), np.float32(0.0), np.float32(0.453), np.float32(0.4996), np.float32(0.5629), np.float32(0.605), np.float32(0.6086), np.float32(0.0), np.float32(0.0), np.float32(0.6157), np.float32(0.4012), np.float32(0.5597), np.float32(0.4674), np.float32(0.5409), np.float32(0.5866), np.float32(0.8204), np.float32(0.8066), np.float32(0.4729), np.float32(0.4795), np.float32(0.1803), np.float32(0.0), np.float32(0.3814), np.float32(0.6694), np.float32(0.0), np.float32(0.0), np.float32(0.5369), np.float32(0.5428), np.float32(0.8006), np.float32(0.8143)] 
+2026-10-16 09:52:45.491762: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:52:45.823272: lr: 0.000636 
+2026-10-16 09:52:45.823733: This epoch took 246.637233 s
+ 
+2026-10-16 09:52:45.824037: 
+epoch:  395 
+2026-10-16 09:57:11.830317: train loss : -0.8148 
+2026-10-16 09:57:17.164778: validation loss: -0.5930 
+2026-10-16 09:57:17.165677: Average global foreground Dice: [np.float32(0.911), np.float32(0.6241), np.float32(0.7459), np.float32(0.7474), np.float32(0.6472), np.float32(0.7734), np.float32(0.6889), np.float32(0.8031), np.float32(0.3283), np.float32(0.4893), np.float32(0.5414), np.float32(0.6008), np.float32(0.5718), np.float32(0.6009), np.float32(0.0), np.float32(0.0), np.float32(0.6436), np.float32(0.4599), np.float32(0.6437), np.float32(0.561), np.float32(0.5654), np.float32(0.5654), np.float32(0.817), np.float32(0.796), np.float32(0.4311), np.float32(0.4801), np.float32(0.1287), np.float32(0.0), np.float32(0.4181), np.float32(0.6385), np.float32(0.0), np.float32(0.0), np.float32(0.5393), np.float32(0.6153), np.float32(0.7788), np.float32(0.8036)] 
+2026-10-16 09:57:17.166064: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 09:57:17.501066: lr: 0.000635 
+2026-10-16 09:57:17.501331: This epoch took 271.677073 s
+ 
+2026-10-16 09:57:17.501437: 
+epoch:  396 
+2026-10-16 10:01:42.655534: train loss : -0.8133 
+2026-10-16 10:01:48.000651: validation loss: -0.5689 
+2026-10-16 10:01:48.001942: Average global foreground Dice: [np.float32(0.9096), np.float32(0.601), np.float32(0.73), np.float32(0.7384), np.float32(0.6176), np.float32(0.7639), np.float32(0.695), np.float32(0.8199), np.float32(0.0419), np.float32(0.5646), np.float32(0.5429), np.float32(0.5917), np.float32(0.5572), np.float32(0.4863), np.float32(0.0), np.float32(0.0), np.float32(0.6849), np.float32(0.5095), np.float32(0.6808), np.float32(0.6349), np.float32(0.5598), np.float32(0.6085), np.float32(0.7591), np.float32(0.716), np.float32(0.5244), np.float32(0.4473), np.float32(0.1701), np.float32(0.0), np.float32(0.356), np.float32(0.6246), np.float32(0.0), np.float32(0.0), np.float32(0.5369), np.float32(0.6029), np.float32(0.7541), np.float32(0.7728)] 
+2026-10-16 10:01:48.002354: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:01:48.333459: lr: 0.000634 
+2026-10-16 10:01:48.333820: This epoch took 270.832313 s
+ 
+2026-10-16 10:01:48.334042: 
+epoch:  397 
+2026-10-16 10:05:51.192159: train loss : -0.8117 
+2026-10-16 10:05:56.517305: validation loss: -0.5539 
+2026-10-16 10:05:56.518341: Average global foreground Dice: [np.float32(0.9099), np.float32(0.5949), np.float32(0.724), np.float32(0.7517), np.float32(0.6259), np.float32(0.77), np.float32(0.6817), np.float32(0.579), np.float32(0.0197), np.float32(0.4931), np.float32(0.483), np.float32(0.5898), np.float32(0.5544), np.float32(0.4784), np.float32(0.0), np.float32(0.0), np.float32(0.6784), np.float32(0.5315), np.float32(0.6644), np.float32(0.618), np.float32(0.5088), np.float32(0.523), np.float32(0.6864), np.float32(0.6311), np.float32(0.5), np.float32(0.4451), np.float32(0.2789), np.float32(0.0), np.float32(0.3072), np.float32(0.5966), np.float32(0.0), np.float32(0.0), np.float32(0.5778), np.float32(0.6409), np.float32(0.7321), np.float32(0.7502)] 
+2026-10-16 10:05:56.518543: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:05:56.856979: lr: 0.000633 
+2026-10-16 10:05:56.857234: This epoch took 248.523109 s
+ 
+2026-10-16 10:05:56.857341: 
+epoch:  398 
+2026-10-16 10:10:11.784120: train loss : -0.8173 
+2026-10-16 10:10:17.125152: validation loss: -0.5508 
+2026-10-16 10:10:17.126112: Average global foreground Dice: [np.float32(0.9096), np.float32(0.6257), np.float32(0.7323), np.float32(0.7362), np.float32(0.6049), np.float32(0.7517), np.float32(0.6576), np.float32(0.7149), np.float32(0.0305), np.float32(0.462), np.float32(0.5054), np.float32(0.5669), np.float32(0.595), np.float32(0.5256), np.float32(0.0007), np.float32(0.0), np.float32(0.588), np.float32(0.4107), np.float32(0.5563), np.float32(0.4875), np.float32(0.5739), np.float32(0.5523), np.float32(0.7901), np.float32(0.7702), np.float32(0.4786), np.float32(0.4133), np.float32(0.2319), np.float32(0.0), np.float32(0.291), np.float32(0.5665), np.float32(0.0), np.float32(0.0), np.float32(0.4753), np.float32(0.3709), np.float32(0.7475), np.float32(0.7626)] 
+2026-10-16 10:10:17.126298: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:10:17.463050: lr: 0.000632 
+2026-10-16 10:10:17.463514: This epoch took 260.606105 s
+ 
+2026-10-16 10:10:17.464041: 
+epoch:  399 
+2026-10-16 10:14:37.701534: train loss : -0.8174 
+2026-10-16 10:14:43.052391: validation loss: -0.5564 
+2026-10-16 10:14:43.053450: Average global foreground Dice: [np.float32(0.9045), np.float32(0.6149), np.float32(0.7341), np.float32(0.7407), np.float32(0.6201), np.float32(0.7684), np.float32(0.6614), np.float32(0.6959), np.float32(0.0461), np.float32(0.5463), np.float32(0.5153), np.float32(0.6041), np.float32(0.5498), np.float32(0.5071), np.float32(0.0085), np.float32(0.0), np.float32(0.6467), np.float32(0.4662), np.float32(0.6257), np.float32(0.555), np.float32(0.5426), np.float32(0.5617), np.float32(0.7979), np.float32(0.7753), np.float32(0.4996), np.float32(0.4225), np.float32(0.1841), np.float32(0.0), np.float32(0.3081), np.float32(0.6679), np.float32(0.0), np.float32(0.0), np.float32(0.5349), np.float32(0.4445), np.float32(0.7557), np.float32(0.7723)] 
+2026-10-16 10:14:43.054085: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:14:43.383297: lr: 0.000631 
+2026-10-16 10:14:43.383653: saving scheduled checkpoint file... 
+2026-10-16 10:14:43.391310: saving checkpoint... 
+2026-10-16 10:14:43.578628: done, saving took 0.19 seconds 
+2026-10-16 10:14:43.580571: done 
+2026-10-16 10:14:43.580787: This epoch took 266.116450 s
+ 
+2026-10-16 10:14:43.580875: 
+epoch:  400 
+2026-10-16 10:19:01.747030: train loss : -0.8218 
+2026-10-16 10:19:07.089377: validation loss: -0.6053 
+2026-10-16 10:19:07.090425: Average global foreground Dice: [np.float32(0.9018), np.float32(0.6514), np.float32(0.7705), np.float32(0.7952), np.float32(0.7018), np.float32(0.8234), np.float32(0.7339), np.float32(0.8811), np.float32(0.0252), np.float32(0.5598), np.float32(0.5698), np.float32(0.6337), np.float32(0.5791), np.float32(0.5653), np.float32(0.0155), np.float32(0.0), np.float32(0.7579), np.float32(0.6181), np.float32(0.7354), np.float32(0.6881), np.float32(0.5934), np.float32(0.6052), np.float32(0.7878), np.float32(0.7407), np.float32(0.5364), np.float32(0.4666), np.float32(0.1709), np.float32(0.0), np.float32(0.4615), np.float32(0.7109), np.float32(0.0), np.float32(0.0), np.float32(0.5899), np.float32(0.6593), np.float32(0.7959), np.float32(0.8028)] 
+2026-10-16 10:19:07.090823: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:19:07.439809: lr: 0.00063 
+2026-10-16 10:19:07.440248: This epoch took 263.859294 s
+ 
+2026-10-16 10:19:07.440573: 
+epoch:  401 
+2026-10-16 10:23:20.525426: train loss : -0.8149 
+2026-10-16 10:23:25.882708: validation loss: -0.3946 
+2026-10-16 10:23:25.883948: Average global foreground Dice: [np.float32(0.921), np.float32(0.4242), np.float32(0.4045), np.float32(0.4379), np.float32(0.3049), np.float32(0.4598), np.float32(0.3713), np.float32(0.6285), np.float32(0.0), np.float32(0.5537), np.float32(0.3277), np.float32(0.3807), np.float32(0.4913), np.float32(0.3898), np.float32(0.0042), np.float32(0.0), np.float32(0.2934), np.float32(0.2168), np.float32(0.2885), np.float32(0.2856), np.float32(0.3629), np.float32(0.2054), np.float32(0.5655), np.float32(0.4497), np.float32(0.1202), np.float32(0.1597), np.float32(0.0056), np.float32(0.0), np.float32(0.2717), np.float32(0.5313), np.float32(0.0), np.float32(0.0), np.float32(0.2927), np.float32(0.2909), np.float32(0.4607), np.float32(0.4494)] 
+2026-10-16 10:23:25.884300: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:23:26.224298: lr: 0.00063 
+2026-10-16 10:23:26.224538: This epoch took 258.783753 s
+ 
+2026-10-16 10:23:26.224795: 
+epoch:  402 
+2026-10-16 10:27:33.477775: train loss : -0.8203 
+2026-10-16 10:27:38.826019: validation loss: -0.4443 
+2026-10-16 10:27:38.826912: Average global foreground Dice: [np.float32(0.9104), np.float32(0.4948), np.float32(0.509), np.float32(0.4936), np.float32(0.3764), np.float32(0.5404), np.float32(0.4647), np.float32(0.4657), np.float32(0.0194), np.float32(0.5159), np.float32(0.3566), np.float32(0.4174), np.float32(0.5304), np.float32(0.4801), np.float32(0.0), np.float32(0.0094), np.float32(0.3542), np.float32(0.2823), np.float32(0.356), np.float32(0.3628), np.float32(0.4101), np.float32(0.277), np.float32(0.628), np.float32(0.5812), np.float32(0.176), np.float32(0.2385), np.float32(0.0), np.float32(0.0), np.float32(0.2879), np.float32(0.5912), np.float32(0.0), np.float32(0.0), np.float32(0.3588), np.float32(0.428), np.float32(0.5475), np.float32(0.5754)] 
+2026-10-16 10:27:38.827068: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:27:39.152794: lr: 0.000629 
+2026-10-16 10:27:39.153188: This epoch took 252.928167 s
+ 
+2026-10-16 10:27:39.153411: 
+epoch:  403 
+2026-10-16 10:32:08.024808: train loss : -0.8144 
+2026-10-16 10:32:13.363265: validation loss: -0.4973 
+2026-10-16 10:32:13.364449: Average global foreground Dice: [np.float32(0.9125), np.float32(0.5669), np.float32(0.5762), np.float32(0.6029), np.float32(0.4773), np.float32(0.6525), np.float32(0.5438), np.float32(0.5876), np.float32(0.0), np.float32(0.471), np.float32(0.4493), np.float32(0.5181), np.float32(0.5569), np.float32(0.4811), np.float32(0.002), np.float32(0.0286), np.float32(0.4614), np.float32(0.3286), np.float32(0.4503), np.float32(0.4567), np.float32(0.3628), np.float32(0.3832), np.float32(0.6684), np.float32(0.62), np.float32(0.3043), np.float32(0.3347), np.float32(0.0789), np.float32(0.0), np.float32(0.3697), np.float32(0.6368), np.float32(0.0), np.float32(0.0), np.float32(0.4329), np.float32(0.4974), np.float32(0.6308), np.float32(0.6574)] 
+2026-10-16 10:32:13.364712: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:32:13.716355: lr: 0.000628 
+2026-10-16 10:32:13.716697: This epoch took 274.563193 s
+ 
+2026-10-16 10:32:13.716907: 
+epoch:  404 
+2026-10-16 10:36:24.619163: train loss : -0.8097 
+2026-10-16 10:36:29.957757: validation loss: -0.5565 
+2026-10-16 10:36:29.958987: Average global foreground Dice: [np.float32(0.9156), np.float32(0.6199), np.float32(0.7057), np.float32(0.7247), np.float32(0.6121), np.float32(0.7468), np.float32(0.668), np.float32(0.6162), np.float32(0.0023), np.float32(0.4525), np.float32(0.5353), np.float32(0.5778), np.float32(0.5699), np.float32(0.519), np.float32(0.0), np.float32(0.0134), np.float32(0.6472), np.float32(0.413), np.float32(0.6249), np.float32(0.5269), np.float32(0.5439), np.float32(0.5327), np.float32(0.7002), np.float32(0.7127), np.float32(0.4527), np.float32(0.4322), np.float32(0.1683), np.float32(0.0), np.float32(0.4113), np.float32(0.6977), np.float32(0.0), np.float32(0.0), np.float32(0.4668), np.float32(0.5007), np.float32(0.7231), np.float32(0.7612)] 
+2026-10-16 10:36:29.959159: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:36:30.296263: lr: 0.000627 
+2026-10-16 10:36:30.296525: This epoch took 256.579513 s
+ 
+2026-10-16 10:36:30.296632: 
+epoch:  405 
+2026-10-16 10:40:49.444651: train loss : -0.8163 
+2026-10-16 10:40:54.802930: validation loss: -0.5332 
+2026-10-16 10:40:54.804109: Average global foreground Dice: [np.float32(0.9187), np.float32(0.6054), np.float32(0.6426), np.float32(0.6784), np.float32(0.5839), np.float32(0.7192), np.float32(0.6032), np.float32(0.5933), np.float32(0.0042), np.float32(0.3732), np.float32(0.4777), np.float32(0.5417), np.float32(0.5775), np.float32(0.5998), np.float32(0.0), np.float32(0.0), np.float32(0.5437), np.float32(0.397), np.float32(0.5179), np.float32(0.4741), np.float32(0.4335), np.float32(0.4727), np.float32(0.788), np.float32(0.7644), np.float32(0.3278), np.float32(0.3715), np.float32(0.0331), np.float32(0.0), np.float32(0.3365), np.float32(0.6427), np.float32(0.0), np.float32(0.0), np.float32(0.5148), np.float32(0.5561), np.float32(0.7038), np.float32(0.7356)] 
+2026-10-16 10:40:54.804419: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:40:55.137874: lr: 0.000626 
+2026-10-16 10:40:55.138218: This epoch took 264.841505 s
+ 
+2026-10-16 10:40:55.138319: 
+epoch:  406 
+2026-10-16 10:44:40.605464: train loss : -0.8224 
+2026-10-16 10:44:45.949949: validation loss: -0.5144 
+2026-10-16 10:44:45.950963: Average global foreground Dice: [np.float32(0.9183), np.float32(0.5866), np.float32(0.6179), np.float32(0.6196), np.float32(0.4031), np.float32(0.6869), np.float32(0.5425), np.float32(0.4967), np.float32(0.0), np.float32(0.5347), np.float32(0.4498), np.float32(0.5078), np.float32(0.5684), np.float32(0.5272), np.float32(0.0), np.float32(0.0), np.float32(0.3533), np.float32(0.2631), np.float32(0.4456), np.float32(0.4324), np.float32(0.4897), np.float32(0.4575), np.float32(0.5929), np.float32(0.6041), np.float32(0.2935), np.float32(0.3088), np.float32(0.051), np.float32(0.0), np.float32(0.3944), np.float32(0.6515), np.float32(0.0), np.float32(0.0), np.float32(0.4052), np.float32(0.4672), np.float32(0.5834), np.float32(0.6759)] 
+2026-10-16 10:44:45.951119: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:44:46.295440: lr: 0.000625 
+2026-10-16 10:44:46.295754: This epoch took 231.157374 s
+ 
+2026-10-16 10:44:46.295864: 
+epoch:  407 
+2026-10-16 10:48:42.882544: train loss : -0.8202 
+2026-10-16 10:48:48.223311: validation loss: -0.5521 
+2026-10-16 10:48:48.224267: Average global foreground Dice: [np.float32(0.9216), np.float32(0.602), np.float32(0.7065), np.float32(0.745), np.float32(0.5889), np.float32(0.7677), np.float32(0.6452), np.float32(0.7298), np.float32(0.0), np.float32(0.5561), np.float32(0.5336), np.float32(0.566), np.float32(0.5979), np.float32(0.4839), np.float32(0.2791), np.float32(0.0), np.float32(0.6247), np.float32(0.4533), np.float32(0.5733), np.float32(0.4738), np.float32(0.4692), np.float32(0.5306), np.float32(0.7559), np.float32(0.7545), np.float32(0.4969), np.float32(0.4518), np.float32(0.206), np.float32(0.0), np.float32(0.435), np.float32(0.6406), np.float32(0.0), np.float32(0.0), np.float32(0.5048), np.float32(0.5331), np.float32(0.7668), np.float32(0.7797)] 
+2026-10-16 10:48:48.224512: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:48:48.577499: lr: 0.000624 
+2026-10-16 10:48:48.577954: This epoch took 242.282031 s
+ 
+2026-10-16 10:48:48.578247: 
+epoch:  408 
+2026-10-16 10:52:55.161877: train loss : -0.8171 
+2026-10-16 10:53:00.510494: validation loss: -0.5990 
+2026-10-16 10:53:00.511469: Average global foreground Dice: [np.float32(0.9189), np.float32(0.6519), np.float32(0.7399), np.float32(0.7864), np.float32(0.6576), np.float32(0.8141), np.float32(0.6847), np.float32(0.704), np.float32(0.0), np.float32(0.5398), np.float32(0.5624), np.float32(0.6257), np.float32(0.6027), np.float32(0.5622), np.float32(0.0028), np.float32(0.0), np.float32(0.6774), np.float32(0.5138), np.float32(0.6329), np.float32(0.5792), np.float32(0.5391), np.float32(0.5728), np.float32(0.8455), np.float32(0.8316), np.float32(0.4923), np.float32(0.4599), np.float32(0.2468), np.float32(0.0), np.float32(0.4224), np.float32(0.6945), np.float32(0.0), np.float32(0.0), np.float32(0.5474), np.float32(0.5946), np.float32(0.8032), np.float32(0.8179)] 
+2026-10-16 10:53:00.511789: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:53:00.851673: lr: 0.000623 
+2026-10-16 10:53:00.852039: This epoch took 252.273556 s
+ 
+2026-10-16 10:53:00.852354: 
+epoch:  409 
+2026-10-16 10:56:56.478136: train loss : -0.8121 
+2026-10-16 10:57:01.814354: validation loss: -0.4747 
+2026-10-16 10:57:01.815544: Average global foreground Dice: [np.float32(0.9144), np.float32(0.5339), np.float32(0.5374), np.float32(0.5747), np.float32(0.4892), np.float32(0.6353), np.float32(0.5166), np.float32(0.7268), np.float32(0.0747), np.float32(0.4942), np.float32(0.4424), np.float32(0.4533), np.float32(0.612), np.float32(0.5628), np.float32(0.0), np.float32(0.0), np.float32(0.4424), np.float32(0.3491), np.float32(0.4314), np.float32(0.3956), np.float32(0.3989), np.float32(0.2858), np.float32(0.6586), np.float32(0.5966), np.float32(0.242), np.float32(0.301), np.float32(0.033), np.float32(0.0), np.float32(0.2132), np.float32(0.5562), np.float32(0.0), np.float32(0.0), np.float32(0.4134), np.float32(0.4407), np.float32(0.5625), np.float32(0.6115)] 
+2026-10-16 10:57:01.815742: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 10:57:02.168599: lr: 0.000622 
+2026-10-16 10:57:02.168962: This epoch took 241.316499 s
+ 
+2026-10-16 10:57:02.169176: 
+epoch:  410 
+2026-10-16 11:01:05.692011: train loss : -0.8114 
+2026-10-16 11:01:11.032867: validation loss: -0.3825 
+2026-10-16 11:01:11.033771: Average global foreground Dice: [np.float32(0.9052), np.float32(0.374), np.float32(0.4088), np.float32(0.4043), np.float32(0.2247), np.float32(0.4442), np.float32(0.3896), np.float32(0.4279), np.float32(0.0633), np.float32(0.5442), np.float32(0.2837), np.float32(0.3223), np.float32(0.4312), np.float32(0.2503), np.float32(0.0), np.float32(0.0), np.float32(0.2495), np.float32(0.177), np.float32(0.2856), np.float32(0.2699), np.float32(0.3627), np.float32(0.307), np.float32(0.5324), np.float32(0.5504), np.float32(0.1975), np.float32(0.1802), np.float32(0.0405), np.float32(0.0), np.float32(0.3126), np.float32(0.5985), np.float32(0.0), np.float32(0.0), np.float32(0.2435), np.float32(0.2647), np.float32(0.4435), np.float32(0.4426)] 
+2026-10-16 11:01:11.034132: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:01:11.373482: lr: 0.000621 
+2026-10-16 11:01:11.373980: This epoch took 249.204720 s
+ 
+2026-10-16 11:01:11.374310: 
+epoch:  411 
+2026-10-16 11:05:10.385263: train loss : -0.8124 
+2026-10-16 11:05:15.726310: validation loss: -0.5657 
+2026-10-16 11:05:15.727530: Average global foreground Dice: [np.float32(0.9067), np.float32(0.6186), np.float32(0.7404), np.float32(0.7456), np.float32(0.6061), np.float32(0.7699), np.float32(0.6766), np.float32(0.5031), np.float32(0.0552), np.float32(0.562), np.float32(0.565), np.float32(0.5993), np.float32(0.6033), np.float32(0.4752), np.float32(0.0047), np.float32(0.0), np.float32(0.6267), np.float32(0.4353), np.float32(0.5441), np.float32(0.5119), np.float32(0.5521), np.float32(0.6179), np.float32(0.7757), np.float32(0.7666), np.float32(0.5392), np.float32(0.4393), np.float32(0.2822), np.float32(0.0), np.float32(0.3398), np.float32(0.631), np.float32(0.0), np.float32(0.0), np.float32(0.5261), np.float32(0.5251), np.float32(0.7642), np.float32(0.7686)] 
+2026-10-16 11:05:15.727927: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:05:16.086414: lr: 0.00062 
+2026-10-16 11:05:16.086896: This epoch took 244.712358 s
+ 
+2026-10-16 11:05:16.087093: 
+epoch:  412 
+2026-10-16 11:09:46.676522: train loss : -0.8160 
+2026-10-16 11:09:52.026414: validation loss: -0.5752 
+2026-10-16 11:09:52.027478: Average global foreground Dice: [np.float32(0.9205), np.float32(0.6022), np.float32(0.7421), np.float32(0.7745), np.float32(0.67), np.float32(0.7995), np.float32(0.6988), np.float32(0.5399), np.float32(0.0), np.float32(0.5826), np.float32(0.5469), np.float32(0.6113), np.float32(0.5227), np.float32(0.505), np.float32(0.0141), np.float32(0.0), np.float32(0.7193), np.float32(0.52), np.float32(0.7022), np.float32(0.6519), np.float32(0.5224), np.float32(0.5821), np.float32(0.7985), np.float32(0.793), np.float32(0.5181), np.float32(0.4598), np.float32(0.1918), np.float32(0.0), np.float32(0.3821), np.float32(0.6383), np.float32(0.0), np.float32(0.0), np.float32(0.5641), np.float32(0.588), np.float32(0.7817), np.float32(0.8032)] 
+2026-10-16 11:09:52.027873: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:09:52.362025: lr: 0.000619 
+2026-10-16 11:09:52.362315: This epoch took 276.275127 s
+ 
+2026-10-16 11:09:52.362417: 
+epoch:  413 
+2026-10-16 11:13:55.703725: train loss : -0.8172 
+2026-10-16 11:14:01.055377: validation loss: -0.6019 
+2026-10-16 11:14:01.056553: Average global foreground Dice: [np.float32(0.9111), np.float32(0.6325), np.float32(0.7576), np.float32(0.7916), np.float32(0.687), np.float32(0.8175), np.float32(0.7194), np.float32(0.609), np.float32(0.0335), np.float32(0.4765), np.float32(0.5782), np.float32(0.6324), np.float32(0.5433), np.float32(0.493), np.float32(0.1886), np.float32(0.0), np.float32(0.7444), np.float32(0.5929), np.float32(0.7205), np.float32(0.6686), np.float32(0.5491), np.float32(0.6275), np.float32(0.8509), np.float32(0.8349), np.float32(0.5068), np.float32(0.4653), np.float32(0.07), np.float32(0.0), np.float32(0.3968), np.float32(0.646), np.float32(0.0), np.float32(0.0), np.float32(0.5975), np.float32(0.6176), np.float32(0.8134), np.float32(0.8252)] 
+2026-10-16 11:14:01.057010: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:14:01.395769: lr: 0.000618 
+2026-10-16 11:14:01.396184: This epoch took 249.033633 s
+ 
+2026-10-16 11:14:01.396469: 
+epoch:  414 
+2026-10-16 11:18:05.656899: train loss : -0.8206 
+2026-10-16 11:18:10.994810: validation loss: -0.5653 
+2026-10-16 11:18:10.995758: Average global foreground Dice: [np.float32(0.8875), np.float32(0.5746), np.float32(0.7522), np.float32(0.7847), np.float32(0.7033), np.float32(0.8084), np.float32(0.7258), np.float32(0.6875), np.float32(0.0033), np.float32(0.5263), np.float32(0.4997), np.float32(0.5942), np.float32(0.5177), np.float32(0.4567), np.float32(0.0233), np.float32(0.0), np.float32(0.7531), np.float32(0.5596), np.float32(0.7032), np.float32(0.6258), np.float32(0.5043), np.float32(0.6191), np.float32(0.8129), np.float32(0.7858), np.float32(0.504), np.float32(0.4047), np.float32(0.2009), np.float32(0.0), np.float32(0.2632), np.float32(0.6435), np.float32(0.0), np.float32(0.0), np.float32(0.6225), np.float32(0.4371), np.float32(0.8024), np.float32(0.8176)] 
+2026-10-16 11:18:10.995938: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:18:11.320159: lr: 0.000617 
+2026-10-16 11:18:11.320388: This epoch took 249.923688 s
+ 
+2026-10-16 11:18:11.320692: 
+epoch:  415 
+2026-10-16 11:22:43.052914: train loss : -0.8170 
+2026-10-16 11:22:48.404828: validation loss: -0.5545 
+2026-10-16 11:22:48.405813: Average global foreground Dice: [np.float32(0.9178), np.float32(0.5458), np.float32(0.7319), np.float32(0.7479), np.float32(0.6595), np.float32(0.7892), np.float32(0.7014), np.float32(0.6482), np.float32(0.0173), np.float32(0.6037), np.float32(0.5327), np.float32(0.5877), np.float32(0.4379), np.float32(0.4745), np.float32(0.168), np.float32(0.0), np.float32(0.731), np.float32(0.5484), np.float32(0.6939), np.float32(0.6523), np.float32(0.5112), np.float32(0.6081), np.float32(0.7777), np.float32(0.7855), np.float32(0.4806), np.float32(0.3385), np.float32(0.0175), np.float32(0.0), np.float32(0.3412), np.float32(0.6727), np.float32(0.0), np.float32(0.0), np.float32(0.5489), np.float32(0.5956), np.float32(0.7781), np.float32(0.7997)] 
+2026-10-16 11:22:48.406160: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:22:48.732800: lr: 0.000616 
+2026-10-16 11:22:48.733129: This epoch took 277.412270 s
+ 
+2026-10-16 11:22:48.733259: 
+epoch:  416 
+2026-10-16 11:27:03.387810: train loss : -0.8155 
+2026-10-16 11:27:08.729824: validation loss: -0.5669 
+2026-10-16 11:27:08.730824: Average global foreground Dice: [np.float32(0.9178), np.float32(0.6563), np.float32(0.7366), np.float32(0.7747), np.float32(0.7044), np.float32(0.8018), np.float32(0.7044), np.float32(0.676), np.float32(0.0435), np.float32(0.5471), np.float32(0.5456), np.float32(0.6093), np.float32(0.6131), np.float32(0.5896), np.float32(0.1771), np.float32(0.0115), np.float32(0.7514), np.float32(0.5946), np.float32(0.7051), np.float32(0.662), np.float32(0.5335), np.float32(0.5765), np.float32(0.6889), np.float32(0.6504), np.float32(0.472), np.float32(0.326), np.float32(0.093), np.float32(0.0), np.float32(0.2286), np.float32(0.6094), np.float32(0.0), np.float32(0.0), np.float32(0.5707), np.float32(0.6348), np.float32(0.7583), np.float32(0.775)] 
+2026-10-16 11:27:08.731256: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:27:09.061608: lr: 0.000615 
+2026-10-16 11:27:09.061855: This epoch took 260.328528 s
+ 
+2026-10-16 11:27:09.062094: 
+epoch:  417 
+2026-10-16 11:31:17.931479: train loss : -0.8190 
+2026-10-16 11:31:23.274127: validation loss: -0.5738 
+2026-10-16 11:31:23.275176: Average global foreground Dice: [np.float32(0.9031), np.float32(0.6101), np.float32(0.7358), np.float32(0.7554), np.float32(0.6729), np.float32(0.7922), np.float32(0.7084), np.float32(0.6349), np.float32(0.0017), np.float32(0.559), np.float32(0.5009), np.float32(0.5801), np.float32(0.5667), np.float32(0.5264), np.float32(0.0945), np.float32(0.0), np.float32(0.7309), np.float32(0.5745), np.float32(0.7153), np.float32(0.6582), np.float32(0.5418), np.float32(0.5862), np.float32(0.7767), np.float32(0.7579), np.float32(0.4698), np.float32(0.4434), np.float32(0.0699), np.float32(0.0), np.float32(0.1791), np.float32(0.6003), np.float32(0.0), np.float32(0.0), np.float32(0.6093), np.float32(0.639), np.float32(0.7804), np.float32(0.8021)] 
+2026-10-16 11:31:23.275667: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:31:23.603994: lr: 0.000614 
+2026-10-16 11:31:23.604256: This epoch took 254.542074 s
+ 
+2026-10-16 11:31:23.604438: 
+epoch:  418 
+2026-10-16 11:35:18.016599: train loss : -0.8165 
+2026-10-16 11:35:23.351239: validation loss: -0.5774 
+2026-10-16 11:35:23.352277: Average global foreground Dice: [np.float32(0.9097), np.float32(0.6027), np.float32(0.7339), np.float32(0.7639), np.float32(0.676), np.float32(0.7857), np.float32(0.695), np.float32(0.6009), np.float32(0.014), np.float32(0.6188), np.float32(0.5088), np.float32(0.5801), np.float32(0.5089), np.float32(0.4182), np.float32(0.0), np.float32(0.0), np.float32(0.7353), np.float32(0.5886), np.float32(0.7031), np.float32(0.6735), np.float32(0.5348), np.float32(0.6068), np.float32(0.7625), np.float32(0.7678), np.float32(0.5281), np.float32(0.44), np.float32(0.241), np.float32(0.0), np.float32(0.3559), np.float32(0.6387), np.float32(0.0), np.float32(0.0), np.float32(0.6135), np.float32(0.6779), np.float32(0.7871), np.float32(0.7989)] 
+2026-10-16 11:35:23.352862: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:35:23.681186: lr: 0.000613 
+2026-10-16 11:35:23.681405: This epoch took 240.076802 s
+ 
+2026-10-16 11:35:23.681487: 
+epoch:  419 
+2026-10-16 11:39:35.308934: train loss : -0.8192 
+2026-10-16 11:39:40.673038: validation loss: -0.5835 
+2026-10-16 11:39:40.674334: Average global foreground Dice: [np.float32(0.92), np.float32(0.625), np.float32(0.7172), np.float32(0.7361), np.float32(0.6335), np.float32(0.7574), np.float32(0.6627), np.float32(0.6197), np.float32(0.0), np.float32(0.6343), np.float32(0.5718), np.float32(0.6013), np.float32(0.5513), np.float32(0.4624), np.float32(0.0011), np.float32(0.0), np.float32(0.7146), np.float32(0.5774), np.float32(0.6807), np.float32(0.6628), np.float32(0.5697), np.float32(0.5959), np.float32(0.8189), np.float32(0.797), np.float32(0.5072), np.float32(0.4856), np.float32(0.1024), np.float32(0.0), np.float32(0.2906), np.float32(0.6459), np.float32(0.0), np.float32(0.0), np.float32(0.5987), np.float32(0.6582), np.float32(0.7624), np.float32(0.7822)] 
+2026-10-16 11:39:40.674519: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:39:41.007835: lr: 0.000612 
+2026-10-16 11:39:41.008349: This epoch took 257.326800 s
+ 
+2026-10-16 11:39:41.008706: 
+epoch:  420 
+2026-10-16 11:43:34.248931: train loss : -0.8249 
+2026-10-16 11:43:39.620865: validation loss: -0.3026 
+2026-10-16 11:43:39.622179: Average global foreground Dice: [np.float32(0.9126), np.float32(0.2866), np.float32(0.3146), np.float32(0.21), np.float32(0.0471), np.float32(0.378), np.float32(0.3015), np.float32(0.2333), np.float32(0.0121), np.float32(0.5567), np.float32(0.2144), np.float32(0.321), np.float32(0.3441), np.float32(0.2317), np.float32(0.0), np.float32(0.0), np.float32(0.0952), np.float32(0.1612), np.float32(0.2242), np.float32(0.1586), np.float32(0.2885), np.float32(0.2095), np.float32(0.2484), np.float32(0.3968), np.float32(0.0769), np.float32(0.1574), np.float32(0.0033), np.float32(0.0), np.float32(0.1394), np.float32(0.4263), np.float32(0.0), np.float32(0.0), np.float32(0.0721), np.float32(0.2022), np.float32(0.1939), np.float32(0.4333)] 
+2026-10-16 11:43:39.622381: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:43:39.955745: lr: 0.000612 
+2026-10-16 11:43:39.956261: This epoch took 238.947328 s
+ 
+2026-10-16 11:43:39.956595: 
+epoch:  421 
+2026-10-16 11:47:57.070552: train loss : -0.8135 
+2026-10-16 11:48:02.414101: validation loss: -0.5206 
+2026-10-16 11:48:02.415135: Average global foreground Dice: [np.float32(0.9121), np.float32(0.5503), np.float32(0.6415), np.float32(0.6256), np.float32(0.5263), np.float32(0.6683), np.float32(0.5736), np.float32(0.5292), np.float32(0.0), np.float32(0.4711), np.float32(0.4589), np.float32(0.5507), np.float32(0.5204), np.float32(0.4481), np.float32(0.4154), np.float32(0.0), np.float32(0.6037), np.float32(0.47), np.float32(0.5849), np.float32(0.5448), np.float32(0.4727), np.float32(0.4592), np.float32(0.6777), np.float32(0.6523), np.float32(0.4116), np.float32(0.3681), np.float32(0.0064), np.float32(0.0), np.float32(0.2949), np.float32(0.5817), np.float32(0.0), np.float32(0.0), np.float32(0.4521), np.float32(0.5385), np.float32(0.6465), np.float32(0.7007)] 
+2026-10-16 11:48:02.415451: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:48:02.739857: lr: 0.000611 
+2026-10-16 11:48:02.740282: This epoch took 262.783434 s
+ 
+2026-10-16 11:48:02.740452: 
+epoch:  422 
+2026-10-16 11:51:54.746544: train loss : -0.8159 
+2026-10-16 11:52:00.085602: validation loss: -0.5161 
+2026-10-16 11:52:00.086866: Average global foreground Dice: [np.float32(0.9136), np.float32(0.5255), np.float32(0.5715), np.float32(0.5881), np.float32(0.4583), np.float32(0.6331), np.float32(0.5395), np.float32(0.5159), np.float32(0.0928), np.float32(0.4983), np.float32(0.4686), np.float32(0.5402), np.float32(0.5871), np.float32(0.5337), np.float32(0.0), np.float32(0.0), np.float32(0.418), np.float32(0.3714), np.float32(0.4354), np.float32(0.4193), np.float32(0.4378), np.float32(0.4028), np.float32(0.6854), np.float32(0.6829), np.float32(0.218), np.float32(0.265), np.float32(0.0), np.float32(0.0), np.float32(0.2272), np.float32(0.6568), np.float32(0.0), np.float32(0.0), np.float32(0.4215), np.float32(0.4628), np.float32(0.6217), np.float32(0.6677)] 
+2026-10-16 11:52:00.087099: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:52:00.487764: lr: 0.00061 
+2026-10-16 11:52:00.488075: This epoch took 237.747426 s
+ 
+2026-10-16 11:52:00.488206: 
+epoch:  423 
+2026-10-16 11:56:34.193204: train loss : -0.8138 
+2026-10-16 11:56:39.537478: validation loss: -0.5524 
+2026-10-16 11:56:39.538495: Average global foreground Dice: [np.float32(0.9105), np.float32(0.5443), np.float32(0.6583), np.float32(0.6692), np.float32(0.5241), np.float32(0.6993), np.float32(0.6274), np.float32(0.5133), np.float32(0.0815), np.float32(0.4898), np.float32(0.4664), np.float32(0.5733), np.float32(0.5516), np.float32(0.5146), np.float32(0.0052), np.float32(0.0), np.float32(0.5725), np.float32(0.4402), np.float32(0.5432), np.float32(0.4573), np.float32(0.4699), np.float32(0.4053), np.float32(0.7686), np.float32(0.7642), np.float32(0.3876), np.float32(0.3892), np.float32(0.1003), np.float32(0.0), np.float32(0.2414), np.float32(0.6545), np.float32(0.0), np.float32(0.0), np.float32(0.4045), np.float32(0.4948), np.float32(0.7153), np.float32(0.7531)] 
+2026-10-16 11:56:39.538784: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 11:56:39.884112: lr: 0.000609 
+2026-10-16 11:56:39.884526: This epoch took 279.396013 s
+ 
+2026-10-16 11:56:39.884758: 
+epoch:  424 
+2026-10-16 12:00:45.533014: train loss : -0.8226 
+2026-10-16 12:00:50.890213: validation loss: -0.5755 
+2026-10-16 12:00:50.891418: Average global foreground Dice: [np.float32(0.9072), np.float32(0.6163), np.float32(0.7435), np.float32(0.7439), np.float32(0.6639), np.float32(0.7901), np.float32(0.6983), np.float32(0.6871), np.float32(0.1025), np.float32(0.5069), np.float32(0.5022), np.float32(0.583), np.float32(0.5559), np.float32(0.4894), np.float32(0.0), np.float32(0.0), np.float32(0.7387), np.float32(0.6037), np.float32(0.7165), np.float32(0.6756), np.float32(0.4915), np.float32(0.5737), np.float32(0.7916), np.float32(0.7652), np.float32(0.4916), np.float32(0.4679), np.float32(0.1955), np.float32(0.0), np.float32(0.3016), np.float32(0.6739), np.float32(0.0), np.float32(0.0), np.float32(0.5962), np.float32(0.6545), np.float32(0.7816), np.float32(0.8047)] 
+2026-10-16 12:00:50.891797: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:00:51.228238: lr: 0.000608 
+2026-10-16 12:00:51.228596: This epoch took 251.343757 s
+ 
+2026-10-16 12:00:51.228812: 
+epoch:  425 
+2026-10-16 12:04:53.141825: train loss : -0.8108 
+2026-10-16 12:04:58.476643: validation loss: -0.5765 
+2026-10-16 12:04:58.477573: Average global foreground Dice: [np.float32(0.9028), np.float32(0.5819), np.float32(0.7185), np.float32(0.727), np.float32(0.6368), np.float32(0.7449), np.float32(0.6717), np.float32(0.4852), np.float32(0.0062), np.float32(0.4337), np.float32(0.5257), np.float32(0.6181), np.float32(0.5692), np.float32(0.5325), np.float32(0.0), np.float32(0.0), np.float32(0.7), np.float32(0.5582), np.float32(0.6729), np.float32(0.628), np.float32(0.5485), np.float32(0.5673), np.float32(0.7789), np.float32(0.7637), np.float32(0.4733), np.float32(0.3991), np.float32(0.0685), np.float32(0.0), np.float32(0.3281), np.float32(0.6699), np.float32(0.0), np.float32(0.0), np.float32(0.5422), np.float32(0.511), np.float32(0.7696), np.float32(0.7923)] 
+2026-10-16 12:04:58.477735: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:04:58.807132: lr: 0.000607 
+2026-10-16 12:04:58.807471: This epoch took 247.578560 s
+ 
+2026-10-16 12:04:58.807848: 
+epoch:  426 
+2026-10-16 12:09:19.096321: train loss : -0.8119 
+2026-10-16 12:09:24.460570: validation loss: -0.5964 
+2026-10-16 12:09:24.461684: Average global foreground Dice: [np.float32(0.9059), np.float32(0.6139), np.float32(0.7679), np.float32(0.7872), np.float32(0.7109), np.float32(0.8231), np.float32(0.7355), np.float32(0.4619), np.float32(0.0), np.float32(0.3517), np.float32(0.5048), np.float32(0.6099), np.float32(0.5791), np.float32(0.5464), np.float32(0.0), np.float32(0.0), np.float32(0.7463), np.float32(0.5842), np.float32(0.7214), np.float32(0.6177), np.float32(0.5043), np.float32(0.6238), np.float32(0.8584), np.float32(0.8385), np.float32(0.4923), np.float32(0.4127), np.float32(0.0034), np.float32(0.0), np.float32(0.3374), np.float32(0.6848), np.float32(0.0), np.float32(0.0), np.float32(0.5938), np.float32(0.5337), np.float32(0.8277), np.float32(0.8421)] 
+2026-10-16 12:09:24.462048: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:09:24.796160: lr: 0.000606 
+2026-10-16 12:09:24.796422: This epoch took 265.988427 s
+ 
+2026-10-16 12:09:24.796607: 
+epoch:  427 
+2026-10-16 12:13:40.688013: train loss : -0.8181 
+2026-10-16 12:13:46.039957: validation loss: -0.6356 
+2026-10-16 12:13:46.041089: Average global foreground Dice: [np.float32(0.9127), np.float32(0.6625), np.float32(0.8086), np.float32(0.8079), np.float32(0.7576), np.float32(0.8417), np.float32(0.7685), np.float32(0.5036), np.float32(0.0), np.float32(0.4444), np.float32(0.6005), np.float32(0.653), np.float32(0.655), np.float32(0.6354), np.float32(0.0906), np.float32(0.0), np.float32(0.7696), np.float32(0.6119), np.float32(0.7342), np.float32(0.6533), np.float32(0.5494), np.float32(0.5896), np.float32(0.8939), np.float32(0.8809), np.float32(0.4699), np.float32(0.5086), np.float32(0.241), np.float32(0.0), np.float32(0.4021), np.float32(0.7007), np.float32(0.0), np.float32(0.0), np.float32(0.5647), np.float32(0.6464), np.float32(0.8596), np.float32(0.871)] 
+2026-10-16 12:13:46.041577: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:13:46.374286: lr: 0.000605 
+2026-10-16 12:13:46.374613: This epoch took 261.577933 s
+ 
+2026-10-16 12:13:46.374912: 
+epoch:  428 
+2026-10-16 12:17:58.084484: train loss : -0.8187 
+2026-10-16 12:18:03.422438: validation loss: -0.3757 
+2026-10-16 12:18:03.423552: Average global foreground Dice: [np.float32(0.9156), np.float32(0.3625), np.float32(0.3437), np.float32(0.3272), np.float32(0.274), np.float32(0.3974), np.float32(0.3194), np.float32(0.4643), np.float32(0.0), np.float32(0.3975), np.float32(0.2944), np.float32(0.3874), np.float32(0.5603), np.float32(0.5544), np.float32(0.0), np.float32(0.0), np.float32(0.2193), np.float32(0.2199), np.float32(0.2586), np.float32(0.2441), np.float32(0.2665), np.float32(0.1192), np.float32(0.6497), np.float32(0.6165), np.float32(0.1332), np.float32(0.2016), np.float32(0.0203), np.float32(0.0), np.float32(0.0972), np.float32(0.4798), np.float32(0.0), np.float32(0.0), np.float32(0.3208), np.float32(0.3594), np.float32(0.3763), np.float32(0.4777)] 
+2026-10-16 12:18:03.423750: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:18:03.751243: lr: 0.000604 
+2026-10-16 12:18:03.751476: This epoch took 257.376336 s
+ 
+2026-10-16 12:18:03.751558: 
+epoch:  429 
+2026-10-16 12:22:01.342982: train loss : -0.8104 
+2026-10-16 12:22:06.689499: validation loss: -0.5613 
+2026-10-16 12:22:06.690500: Average global foreground Dice: [np.float32(0.9083), np.float32(0.5922), np.float32(0.7133), np.float32(0.7448), np.float32(0.6749), np.float32(0.751), np.float32(0.6916), np.float32(0.4494), np.float32(0.0897), np.float32(0.3475), np.float32(0.4938), np.float32(0.5733), np.float32(0.5558), np.float32(0.5107), np.float32(0.0241), np.float32(0.0), np.float32(0.7304), np.float32(0.5748), np.float32(0.698), np.float32(0.6611), np.float32(0.5422), np.float32(0.6233), np.float32(0.8416), np.float32(0.8271), np.float32(0.4727), np.float32(0.4027), np.float32(0.1697), np.float32(0.0), np.float32(0.2479), np.float32(0.5497), np.float32(0.0), np.float32(0.0), np.float32(0.622), np.float32(0.5709), np.float32(0.7763), np.float32(0.7971)] 
+2026-10-16 12:22:06.690790: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:22:07.039812: lr: 0.000603 
+2026-10-16 12:22:07.040265: This epoch took 243.288640 s
+ 
+2026-10-16 12:22:07.040875: 
+epoch:  430 
+2026-10-16 12:26:23.815804: train loss : -0.8092 
+2026-10-16 12:26:29.170385: validation loss: -0.5809 
+2026-10-16 12:26:29.171410: Average global foreground Dice: [np.float32(0.9037), np.float32(0.6186), np.float32(0.7352), np.float32(0.7584), np.float32(0.6789), np.float32(0.7877), np.float32(0.7027), np.float32(0.5167), np.float32(0.0086), np.float32(0.4465), np.float32(0.5398), np.float32(0.5959), np.float32(0.5675), np.float32(0.5241), np.float32(0.0), np.float32(0.0), np.float32(0.734), np.float32(0.6085), np.float32(0.6791), np.float32(0.6439), np.float32(0.5379), np.float32(0.5836), np.float32(0.8459), np.float32(0.8118), np.float32(0.5128), np.float32(0.4682), np.float32(0.1607), np.float32(0.0), np.float32(0.3614), np.float32(0.6034), np.float32(0.0), np.float32(0.0), np.float32(0.6274), np.float32(0.5829), np.float32(0.7892), np.float32(0.8089)] 
+2026-10-16 12:26:29.171710: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:26:29.512004: lr: 0.000602 
+2026-10-16 12:26:29.512962: This epoch took 262.471874 s
+ 
+2026-10-16 12:26:29.513179: 
+epoch:  431 
+2026-10-16 12:30:38.502626: train loss : -0.8206 
+2026-10-16 12:30:43.855801: validation loss: -0.5674 
+2026-10-16 12:30:43.856859: Average global foreground Dice: [np.float32(0.9056), np.float32(0.599), np.float32(0.7178), np.float32(0.7604), np.float32(0.6566), np.float32(0.768), np.float32(0.6945), np.float32(0.5632), np.float32(0.0026), np.float32(0.4296), np.float32(0.5323), np.float32(0.6001), np.float32(0.5568), np.float32(0.4741), np.float32(0.0), np.float32(0.0), np.float32(0.7148), np.float32(0.6009), np.float32(0.6626), np.float32(0.64), np.float32(0.5139), np.float32(0.6398), np.float32(0.7383), np.float32(0.6936), np.float32(0.5188), np.float32(0.4358), np.float32(0.1074), np.float32(0.0), np.float32(0.3566), np.float32(0.612), np.float32(0.0), np.float32(0.0), np.float32(0.5935), np.float32(0.6195), np.float32(0.7492), np.float32(0.7671)] 
+2026-10-16 12:30:43.857335: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:30:44.224581: lr: 0.000601 
+2026-10-16 12:30:44.224970: This epoch took 254.711699 s
+ 
+2026-10-16 12:30:44.225084: 
+epoch:  432 
+2026-10-16 12:35:08.955612: train loss : -0.8197 
+2026-10-16 12:35:14.294979: validation loss: -0.6181 
+2026-10-16 12:35:14.295853: Average global foreground Dice: [np.float32(0.907), np.float32(0.6441), np.float32(0.7795), np.float32(0.8184), np.float32(0.7404), np.float32(0.8052), np.float32(0.7533), np.float32(0.5373), np.float32(0.002), np.float32(0.3336), np.float32(0.5939), np.float32(0.625), np.float32(0.6118), np.float32(0.5423), np.float32(0.0015), np.float32(0.0), np.float32(0.7621), np.float32(0.6394), np.float32(0.7246), np.float32(0.6834), np.float32(0.5598), np.float32(0.6355), np.float32(0.8752), np.float32(0.8503), np.float32(0.5267), np.float32(0.5024), np.float32(0.1789), np.float32(0.0), np.float32(0.3368), np.float32(0.6746), np.float32(0.0), np.float32(0.0), np.float32(0.593), np.float32(0.6236), np.float32(0.8412), np.float32(0.8526)] 
+2026-10-16 12:35:14.296019: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:35:14.666816: lr: 0.0006 
+2026-10-16 12:35:14.667241: This epoch took 270.441902 s
+ 
+2026-10-16 12:35:14.667530: 
+epoch:  433 
+2026-10-16 12:39:22.575861: train loss : -0.8190 
+2026-10-16 12:39:27.914018: validation loss: -0.5754 
+2026-10-16 12:39:27.915175: Average global foreground Dice: [np.float32(0.9072), np.float32(0.6023), np.float32(0.7198), np.float32(0.7491), np.float32(0.6567), np.float32(0.7773), np.float32(0.6751), np.float32(0.5354), np.float32(0.0), np.float32(0.3258), np.float32(0.4901), np.float32(0.5522), np.float32(0.5862), np.float32(0.5297), np.float32(0.0857), np.float32(0.0), np.float32(0.6833), np.float32(0.5769), np.float32(0.6504), np.float32(0.6203), np.float32(0.5212), np.float32(0.5716), np.float32(0.8008), np.float32(0.7752), np.float32(0.4734), np.float32(0.4727), np.float32(0.0955), np.float32(0.0), np.float32(0.3222), np.float32(0.6833), np.float32(0.0), np.float32(0.0), np.float32(0.6003), np.float32(0.6182), np.float32(0.7648), np.float32(0.7918)] 
+2026-10-16 12:39:27.915642: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:39:28.250747: lr: 0.000599 
+2026-10-16 12:39:28.251125: This epoch took 253.583327 s
+ 
+2026-10-16 12:39:28.251265: 
+epoch:  434 
+2026-10-16 12:43:57.475247: train loss : -0.8164 
+2026-10-16 12:44:02.831377: validation loss: -0.6281 
+2026-10-16 12:44:02.832419: Average global foreground Dice: [np.float32(0.9046), np.float32(0.6774), np.float32(0.7646), np.float32(0.7993), np.float32(0.681), np.float32(0.8149), np.float32(0.7143), np.float32(0.4836), np.float32(0.0), np.float32(0.4316), np.float32(0.612), np.float32(0.6562), np.float32(0.6172), np.float32(0.5647), np.float32(0.0), np.float32(0.0), np.float32(0.7375), np.float32(0.5675), np.float32(0.6975), np.float32(0.6439), np.float32(0.5868), np.float32(0.6516), np.float32(0.8397), np.float32(0.8312), np.float32(0.4751), np.float32(0.4593), np.float32(0.1675), np.float32(0.0), np.float32(0.4408), np.float32(0.6883), np.float32(0.0), np.float32(0.0), np.float32(0.5841), np.float32(0.6735), np.float32(0.8211), np.float32(0.8295)] 
+2026-10-16 12:44:02.832676: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:44:03.169747: lr: 0.000598 
+2026-10-16 12:44:03.170152: This epoch took 274.918815 s
+ 
+2026-10-16 12:44:03.170390: 
+epoch:  435 
+2026-10-16 12:48:05.687466: train loss : -0.8229 
+2026-10-16 12:48:11.074479: validation loss: -0.5760 
+2026-10-16 12:48:11.075388: Average global foreground Dice: [np.float32(0.9153), np.float32(0.6506), np.float32(0.7203), np.float32(0.7664), np.float32(0.6829), np.float32(0.7774), np.float32(0.7069), np.float32(0.5464), np.float32(0.0136), np.float32(0.4354), np.float32(0.5708), np.float32(0.6183), np.float32(0.6009), np.float32(0.543), np.float32(0.0), np.float32(0.0), np.float32(0.7049), np.float32(0.5854), np.float32(0.6926), np.float32(0.6421), np.float32(0.5147), np.float32(0.5759), np.float32(0.8042), np.float32(0.7785), np.float32(0.493), np.float32(0.4637), np.float32(0.0424), np.float32(0.0), np.float32(0.167), np.float32(0.5243), np.float32(0.0), np.float32(0.0), np.float32(0.5545), np.float32(0.6419), np.float32(0.7929), np.float32(0.8038)] 
+2026-10-16 12:48:11.075522: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:48:11.408175: lr: 0.000597 
+2026-10-16 12:48:11.408436: This epoch took 248.237962 s
+ 
+2026-10-16 12:48:11.408514: 
+epoch:  436 
+2026-10-16 12:52:42.705114: train loss : -0.8237 
+2026-10-16 12:52:48.046303: validation loss: -0.5684 
+2026-10-16 12:52:48.047305: Average global foreground Dice: [np.float32(0.9101), np.float32(0.589), np.float32(0.6948), np.float32(0.7349), np.float32(0.6114), np.float32(0.7525), np.float32(0.6667), np.float32(0.4254), np.float32(0.0204), np.float32(0.4611), np.float32(0.5242), np.float32(0.5799), np.float32(0.5469), np.float32(0.4849), np.float32(0.0), np.float32(0.0028), np.float32(0.6682), np.float32(0.5313), np.float32(0.6423), np.float32(0.6022), np.float32(0.5164), np.float32(0.551), np.float32(0.7438), np.float32(0.7192), np.float32(0.4595), np.float32(0.4178), np.float32(0.0224), np.float32(0.0), np.float32(0.4491), np.float32(0.6465), np.float32(0.0), np.float32(0.0), np.float32(0.5496), np.float32(0.5653), np.float32(0.7429), np.float32(0.7622)] 
+2026-10-16 12:52:48.047573: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:52:48.381592: lr: 0.000596 
+2026-10-16 12:52:48.381819: This epoch took 276.973250 s
+ 
+2026-10-16 12:52:48.381897: 
+epoch:  437 
+2026-10-16 12:56:54.116542: train loss : -0.8102 
+2026-10-16 12:56:59.467994: validation loss: -0.1753 
+2026-10-16 12:56:59.469136: Average global foreground Dice: [np.float32(0.9128), np.float32(0.0545), np.float32(0.1577), np.float32(0.0019), np.float32(0.0107), np.float32(0.1598), np.float32(0.1523), np.float32(0.0017), np.float32(0.0308), np.float32(0.449), np.float32(0.0482), np.float32(0.1926), np.float32(0.3696), np.float32(0.1546), np.float32(0.0), np.float32(0.0), np.float32(0.0431), np.float32(0.0496), np.float32(0.1277), np.float32(0.1885), np.float32(0.0386), np.float32(0.195), np.float32(0.1664), np.float32(0.1735), np.float32(0.0123), np.float32(0.097), np.float32(0.0), np.float32(0.0), np.float32(0.013), np.float32(0.2741), np.float32(0.0), np.float32(0.0), np.float32(0.0878), np.float32(0.0538), np.float32(0.0422), np.float32(0.1341)] 
+2026-10-16 12:56:59.469512: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 12:56:59.804273: lr: 0.000595 
+2026-10-16 12:56:59.804731: This epoch took 251.422780 s
+ 
+2026-10-16 12:56:59.805058: 
+epoch:  438 
+2026-10-16 13:00:51.033487: train loss : -0.8171 
+2026-10-16 13:00:56.371659: validation loss: -0.5390 
+2026-10-16 13:00:56.372900: Average global foreground Dice: [np.float32(0.9072), np.float32(0.5754), np.float32(0.671), np.float32(0.6873), np.float32(0.5925), np.float32(0.7272), np.float32(0.6355), np.float32(0.5288), np.float32(0.0024), np.float32(0.3894), np.float32(0.476), np.float32(0.5746), np.float32(0.5612), np.float32(0.474), np.float32(0.0), np.float32(0.0), np.float32(0.6162), np.float32(0.4435), np.float32(0.5855), np.float32(0.5373), np.float32(0.5128), np.float32(0.528), np.float32(0.7492), np.float32(0.6711), np.float32(0.4391), np.float32(0.3698), np.float32(0.0226), np.float32(0.0), np.float32(0.3939), np.float32(0.7107), np.float32(0.0), np.float32(0.0), np.float32(0.5464), np.float32(0.4818), np.float32(0.6855), np.float32(0.7065)] 
+2026-10-16 13:00:56.373122: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:00:56.724955: lr: 0.000594 
+2026-10-16 13:00:56.725195: This epoch took 236.919893 s
+ 
+2026-10-16 13:00:56.725291: 
+epoch:  439 
+2026-10-16 13:04:53.054163: train loss : -0.8147 
+2026-10-16 13:04:58.411407: validation loss: -0.5586 
+2026-10-16 13:04:58.412381: Average global foreground Dice: [np.float32(0.9162), np.float32(0.6283), np.float32(0.7221), np.float32(0.7477), np.float32(0.6582), np.float32(0.7562), np.float32(0.6878), np.float32(0.4716), np.float32(0.1008), np.float32(0.3629), np.float32(0.543), np.float32(0.5849), np.float32(0.5444), np.float32(0.5235), np.float32(0.0606), np.float32(0.0), np.float32(0.6926), np.float32(0.5223), np.float32(0.6407), np.float32(0.5963), np.float32(0.4934), np.float32(0.5204), np.float32(0.8097), np.float32(0.7874), np.float32(0.4515), np.float32(0.4204), np.float32(0.1942), np.float32(0.0), np.float32(0.2911), np.float32(0.5415), np.float32(0.0), np.float32(0.0), np.float32(0.5357), np.float32(0.5731), np.float32(0.767), np.float32(0.7728)] 
+2026-10-16 13:04:58.412712: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:04:58.758122: lr: 0.000593 
+2026-10-16 13:04:58.758450: This epoch took 242.033089 s
+ 
+2026-10-16 13:04:58.758791: 
+epoch:  440 
+2026-10-16 13:08:43.744165: train loss : -0.8157 
+2026-10-16 13:08:49.093098: validation loss: -0.5576 
+2026-10-16 13:08:49.094045: Average global foreground Dice: [np.float32(0.9014), np.float32(0.6063), np.float32(0.7098), np.float32(0.7601), np.float32(0.6491), np.float32(0.7738), np.float32(0.6835), np.float32(0.6219), np.float32(0.0699), np.float32(0.4119), np.float32(0.5441), np.float32(0.5855), np.float32(0.5745), np.float32(0.4698), np.float32(0.0), np.float32(0.0), np.float32(0.6886), np.float32(0.5373), np.float32(0.6526), np.float32(0.6383), np.float32(0.5315), np.float32(0.6009), np.float32(0.7516), np.float32(0.7107), np.float32(0.491), np.float32(0.3431), np.float32(0.1234), np.float32(0.0), np.float32(0.3469), np.float32(0.6461), np.float32(0.0), np.float32(0.0), np.float32(0.5365), np.float32(0.6252), np.float32(0.7644), np.float32(0.7722)] 
+2026-10-16 13:08:49.094202: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:08:49.422933: lr: 0.000592 
+2026-10-16 13:08:49.423444: This epoch took 230.664490 s
+ 
+2026-10-16 13:08:49.423854: 
+epoch:  441 
+2026-10-16 13:12:47.738662: train loss : -0.8248 
+2026-10-16 13:12:53.085073: validation loss: -0.5854 
+2026-10-16 13:12:53.086078: Average global foreground Dice: [np.float32(0.9125), np.float32(0.6277), np.float32(0.7216), np.float32(0.7754), np.float32(0.6668), np.float32(0.7846), np.float32(0.6919), np.float32(0.4699), np.float32(0.0056), np.float32(0.4721), np.float32(0.5554), np.float32(0.6023), np.float32(0.5512), np.float32(0.4917), np.float32(0.0), np.float32(0.0), np.float32(0.7196), np.float32(0.591), np.float32(0.6827), np.float32(0.634), np.float32(0.5659), np.float32(0.5584), np.float32(0.817), np.float32(0.7831), np.float32(0.4599), np.float32(0.3788), np.float32(0.0649), np.float32(0.0), np.float32(0.2867), np.float32(0.7141), np.float32(0.0), np.float32(0.0), np.float32(0.5193), np.float32(0.568), np.float32(0.7819), np.float32(0.8019)] 
+2026-10-16 13:12:53.086525: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:12:53.427228: lr: 0.000592 
+2026-10-16 13:12:53.427463: This epoch took 244.003380 s
+ 
+2026-10-16 13:12:53.427539: 
+epoch:  442 
+2026-10-16 13:17:23.391720: train loss : -0.8199 
+2026-10-16 13:17:28.752865: validation loss: -0.5278 
+2026-10-16 13:17:28.753839: Average global foreground Dice: [np.float32(0.9179), np.float32(0.5707), np.float32(0.5766), np.float32(0.6373), np.float32(0.529), np.float32(0.6572), np.float32(0.5521), np.float32(0.5332), np.float32(0.0473), np.float32(0.392), np.float32(0.4806), np.float32(0.512), np.float32(0.5824), np.float32(0.5368), np.float32(0.0), np.float32(0.0), np.float32(0.5074), np.float32(0.3999), np.float32(0.5051), np.float32(0.5359), np.float32(0.4849), np.float32(0.4183), np.float32(0.7983), np.float32(0.7506), np.float32(0.2715), np.float32(0.3045), np.float32(0.0437), np.float32(0.0), np.float32(0.3327), np.float32(0.6629), np.float32(0.0), np.float32(0.0), np.float32(0.4445), np.float32(0.497), np.float32(0.6675), np.float32(0.6834)] 
+2026-10-16 13:17:28.754009: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:17:29.088199: lr: 0.000591 
+2026-10-16 13:17:29.088694: This epoch took 275.661092 s
+ 
+2026-10-16 13:17:29.089020: 
+epoch:  443 
+2026-10-16 13:21:34.927158: train loss : -0.8223 
+2026-10-16 13:21:40.283775: validation loss: -0.5258 
+2026-10-16 13:21:40.285156: Average global foreground Dice: [np.float32(0.9097), np.float32(0.5351), np.float32(0.5894), np.float32(0.6045), np.float32(0.5246), np.float32(0.642), np.float32(0.562), np.float32(0.4224), np.float32(0.0495), np.float32(0.4915), np.float32(0.4496), np.float32(0.5166), np.float32(0.5692), np.float32(0.4797), np.float32(0.0007), np.float32(0.0), np.float32(0.5708), np.float32(0.4153), np.float32(0.5461), np.float32(0.558), np.float32(0.4668), np.float32(0.4398), np.float32(0.7975), np.float32(0.7712), np.float32(0.3329), np.float32(0.2762), np.float32(0.0), np.float32(0.0), np.float32(0.2095), np.float32(0.564), np.float32(0.0), np.float32(0.0), np.float32(0.4651), np.float32(0.4873), np.float32(0.672), np.float32(0.6896)] 
+2026-10-16 13:21:40.285431: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:21:40.623282: lr: 0.00059 
+2026-10-16 13:21:40.623553: This epoch took 251.534300 s
+ 
+2026-10-16 13:21:40.623654: 
+epoch:  444 
+2026-10-16 13:25:28.686327: train loss : -0.8189 
+2026-10-16 13:25:34.029374: validation loss: -0.2942 
+2026-10-16 13:25:34.030270: Average global foreground Dice: [np.float32(0.9262), np.float32(0.3418), np.float32(0.3197), np.float32(0.2398), np.float32(0.1883), np.float32(0.3015), np.float32(0.1717), np.float32(0.3294), np.float32(0.0094), np.float32(0.3485), np.float32(0.2027), np.float32(0.2269), np.float32(0.3962), np.float32(0.2681), np.float32(0.0), np.float32(0.0), np.float32(0.1397), np.float32(0.1142), np.float32(0.1857), np.float32(0.235), np.float32(0.2566), np.float32(0.213), np.float32(0.494), np.float32(0.4362), np.float32(0.0983), np.float32(0.1375), np.float32(0.0), np.float32(0.0), np.float32(0.1131), np.float32(0.549), np.float32(0.0), np.float32(0.0), np.float32(0.2068), np.float32(0.1219), np.float32(0.3116), np.float32(0.3634)] 
+2026-10-16 13:25:34.030510: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:25:34.370912: lr: 0.000589 
+2026-10-16 13:25:34.371124: This epoch took 233.747401 s
+ 
+2026-10-16 13:25:34.371202: 
+epoch:  445 
+2026-10-16 13:29:44.230501: train loss : -0.8232 
+2026-10-16 13:29:49.574957: validation loss: -0.3971 
+2026-10-16 13:29:49.575937: Average global foreground Dice: [np.float32(0.9303), np.float32(0.4507), np.float32(0.4477), np.float32(0.411), np.float32(0.2633), np.float32(0.4492), np.float32(0.2988), np.float32(0.4302), np.float32(0.1733), np.float32(0.4592), np.float32(0.3637), np.float32(0.3757), np.float32(0.4722), np.float32(0.2947), np.float32(0.0), np.float32(0.0), np.float32(0.2306), np.float32(0.1926), np.float32(0.2694), np.float32(0.3012), np.float32(0.412), np.float32(0.2876), np.float32(0.5822), np.float32(0.5824), np.float32(0.1636), np.float32(0.2126), np.float32(0.0087), np.float32(0.0), np.float32(0.2664), np.float32(0.5819), np.float32(0.0), np.float32(0.0), np.float32(0.2831), np.float32(0.259), np.float32(0.4678), np.float32(0.5028)] 
+2026-10-16 13:29:49.576264: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:29:49.914307: lr: 0.000588 
+2026-10-16 13:29:49.914555: This epoch took 255.543299 s
+ 
+2026-10-16 13:29:49.914634: 
+epoch:  446 
+2026-10-16 13:34:06.260004: train loss : -0.8176 
+2026-10-16 13:34:11.600005: validation loss: -0.4855 
+2026-10-16 13:34:11.600999: Average global foreground Dice: [np.float32(0.9138), np.float32(0.5267), np.float32(0.5233), np.float32(0.5199), np.float32(0.4404), np.float32(0.5855), np.float32(0.4816), np.float32(0.512), np.float32(0.0), np.float32(0.4484), np.float32(0.4218), np.float32(0.5169), np.float32(0.5828), np.float32(0.5447), np.float32(0.0), np.float32(0.0), np.float32(0.4066), np.float32(0.3677), np.float32(0.4188), np.float32(0.4219), np.float32(0.4289), np.float32(0.3387), np.float32(0.6779), np.float32(0.6187), np.float32(0.1922), np.float32(0.2845), np.float32(0.0186), np.float32(0.0), np.float32(0.1838), np.float32(0.5531), np.float32(0.0), np.float32(0.0), np.float32(0.4395), np.float32(0.3552), np.float32(0.5609), np.float32(0.6097)] 
+2026-10-16 13:34:11.601316: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:34:11.932868: lr: 0.000587 
+2026-10-16 13:34:11.933352: This epoch took 262.018666 s
+ 
+2026-10-16 13:34:11.933667: 
+epoch:  447 
+2026-10-16 13:38:39.216547: train loss : -0.8215 
+2026-10-16 13:38:44.577474: validation loss: -0.4748 
+2026-10-16 13:38:44.578625: Average global foreground Dice: [np.float32(0.9225), np.float32(0.5045), np.float32(0.5432), np.float32(0.5444), np.float32(0.3968), np.float32(0.5807), np.float32(0.4683), np.float32(0.4674), np.float32(0.1906), np.float32(0.4626), np.float32(0.4627), np.float32(0.51), np.float32(0.532), np.float32(0.4466), np.float32(0.0), np.float32(0.0), np.float32(0.4013), np.float32(0.3582), np.float32(0.4128), np.float32(0.4474), np.float32(0.3942), np.float32(0.3735), np.float32(0.6662), np.float32(0.6539), np.float32(0.2179), np.float32(0.293), np.float32(0.0099), np.float32(0.0), np.float32(0.3524), np.float32(0.5844), np.float32(0.0), np.float32(0.0), np.float32(0.3346), np.float32(0.203), np.float32(0.5862), np.float32(0.6243)] 
+2026-10-16 13:38:44.578910: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:38:44.923955: lr: 0.000586 
+2026-10-16 13:38:44.924164: This epoch took 272.990232 s
+ 
+2026-10-16 13:38:44.924241: 
+epoch:  448 
+2026-10-16 13:42:39.267660: train loss : -0.8227 
+2026-10-16 13:42:44.605494: validation loss: -0.5137 
+2026-10-16 13:42:44.606578: Average global foreground Dice: [np.float32(0.9291), np.float32(0.6034), np.float32(0.5713), np.float32(0.5993), np.float32(0.493), np.float32(0.6469), np.float32(0.5246), np.float32(0.568), np.float32(0.0402), np.float32(0.4871), np.float32(0.5037), np.float32(0.546), np.float32(0.6013), np.float32(0.5447), np.float32(0.0), np.float32(0.0), np.float32(0.459), np.float32(0.4109), np.float32(0.4569), np.float32(0.4671), np.float32(0.4542), np.float32(0.3622), np.float32(0.7329), np.float32(0.6956), np.float32(0.2421), np.float32(0.3379), np.float32(0.0029), np.float32(0.0), np.float32(0.2281), np.float32(0.6198), np.float32(0.0), np.float32(0.0), np.float32(0.4064), np.float32(0.3596), np.float32(0.6431), np.float32(0.6708)] 
+2026-10-16 13:42:44.606827: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:42:44.936524: lr: 0.000585 
+2026-10-16 13:42:44.936903: This epoch took 240.012606 s
+ 
+2026-10-16 13:42:44.937201: 
+epoch:  449 
+2026-10-16 13:47:03.931026: train loss : -0.8216 
+2026-10-16 13:47:09.288328: validation loss: -0.5191 
+2026-10-16 13:47:09.289454: Average global foreground Dice: [np.float32(0.9159), np.float32(0.5652), np.float32(0.5851), np.float32(0.5952), np.float32(0.4747), np.float32(0.6427), np.float32(0.5298), np.float32(0.3876), np.float32(0.0235), np.float32(0.4065), np.float32(0.4795), np.float32(0.531), np.float32(0.5401), np.float32(0.4745), np.float32(0.0), np.float32(0.0), np.float32(0.4688), np.float32(0.3961), np.float32(0.4663), np.float32(0.4507), np.float32(0.495), np.float32(0.4039), np.float32(0.7523), np.float32(0.7017), np.float32(0.2156), np.float32(0.3399), np.float32(0.0033), np.float32(0.0), np.float32(0.2938), np.float32(0.6445), np.float32(0.0), np.float32(0.0), np.float32(0.4283), np.float32(0.4627), np.float32(0.6405), np.float32(0.6831)] 
+2026-10-16 13:47:09.289705: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:47:09.629492: lr: 0.000584 
+2026-10-16 13:47:09.629828: saving scheduled checkpoint file... 
+2026-10-16 13:47:09.637961: saving checkpoint... 
+2026-10-16 13:47:09.764677: done, saving took 0.13 seconds 
+2026-10-16 13:47:09.766569: done 
+2026-10-16 13:47:09.766823: This epoch took 264.829389 s
+ 
+2026-10-16 13:47:09.766901: 
+epoch:  450 
+2026-10-16 13:51:31.161168: train loss : -0.8176 
+2026-10-16 13:51:36.491917: validation loss: -0.4458 
+2026-10-16 13:51:36.492917: Average global foreground Dice: [np.float32(0.9098), np.float32(0.4402), np.float32(0.4592), np.float32(0.4557), np.float32(0.3567), np.float32(0.5087), np.float32(0.4074), np.float32(0.6708), np.float32(0.1584), np.float32(0.4448), np.float32(0.3456), np.float32(0.4525), np.float32(0.5289), np.float32(0.4472), np.float32(0.0), np.float32(0.0), np.float32(0.3157), np.float32(0.2689), np.float32(0.3234), np.float32(0.322), np.float32(0.353), np.float32(0.2921), np.float32(0.6759), np.float32(0.6426), np.float32(0.1474), np.float32(0.2378), np.float32(0.0058), np.float32(0.0), np.float32(0.2319), np.float32(0.6569), np.float32(0.0), np.float32(0.0), np.float32(0.3871), np.float32(0.3619), np.float32(0.501), np.float32(0.5656)] 
+2026-10-16 13:51:36.493165: (interpret this as an estimate for the Dice of the different classes. This is not exact.) 
+2026-10-16 13:51:36.834778: lr: 0.000583 
+2026-10-16 13:51:36.835020: This epoch took 267.068063 s
+ 
+2026-10-16 13:51:36.835094: 
+epoch:  451