Upload 149 files

Added the nnunet dataset containing the trained model for use in the Zebrafish OCT measurer

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+Dataset002_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_0/progress.png filter=lfs diff=lfs merge=lfs -text
+Dataset002_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_1/progress.png filter=lfs diff=lfs merge=lfs -text
+Dataset002_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_2/progress.png filter=lfs diff=lfs merge=lfs -text
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+Dataset002_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_4/progress.png filter=lfs diff=lfs merge=lfs -text

Dataset002_zebrafish/nnUNetTrainer__nnUNetPlans__2d/dataset.json

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+{
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+        "0": "\u00b5OCT"
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+        "Retina": 1,
+        "VCD": 2,
+        "lens": 3,
+        "cornea": 4
+    },
+    "numTraining": 108,
+    "file_ending": ".png"
+}

Dataset002_zebrafish/nnUNetTrainer__nnUNetPlans__2d/dataset_fingerprint.json

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Dataset002_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_0/checkpoint_best.pth

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Dataset002_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_0/debug.json

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+    "configuration_name": "2d",
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+    "dataloader_val.num_processes": "6",
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+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7f92fd346f00>",
+    "local_rank": "0",
+    "log_file": "/hpc/rlav440/NNUNET_DATA/results/Dataset001_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_0/training_log_2025_10_30_08_33_14.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f92fcd0a420>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): OptimizedModule(\n      (_orig_mod): MemoryEfficientSoftDiceLoss()\n    )\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f92fd257530>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset001_zebrafish', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [999.0, 1.0, 1.0], 'original_median_shape_after_transp': [1, 1024, 102], 'image_reader_writer': 'NaturalImage2DIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 5, 'patch_size': [1024, 112], 'median_image_size_in_voxels': [1024.0, 102.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 1], [2, 1], [2, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 255.0, 'mean': 49.659568786621094, 'median': 45.0, 'min': 0.0, 'percentile_00_5': 10.0, 'percentile_99_5': 163.0, 'std': 24.689043045043945}}}, 'configuration': '2d', 'fold': 0, 'dataset_json': {'channel_names': {'0': '\u00b5OCT'}, 'labels': {'background': 0, 'Retina': 1, 'VCD': 2, 'lens': 3, 'cornea': 4}, 'numTraining': 108, 'file_ending': '.png'}, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "num_epochs": "1000",
+    "num_input_channels": "1",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 0.01\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/hpc/rlav440/NNUNET_DATA/results/Dataset001_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_0",
+    "output_folder_base": "/hpc/rlav440/NNUNET_DATA/results/Dataset001_zebrafish/nnUNetTrainer__nnUNetPlans__2d",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset001_zebrafish', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [999.0, 1.0, 1.0], 'original_median_shape_after_transp': [1, 1024, 102], 'image_reader_writer': 'NaturalImage2DIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 5, 'patch_size': [1024, 112], 'median_image_size_in_voxels': [1024.0, 102.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 1], [2, 1], [2, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 255.0, 'mean': 49.659568786621094, 'median': 45.0, 'min': 0.0, 'percentile_00_5': 10.0, 'percentile_99_5': 163.0, 'std': 24.689043045043945}}}",
+    "preprocessed_dataset_folder": "/hpc/rlav440/NNUNET_DATA/processed/Dataset001_zebrafish/nnUNetPlans_2d",
+    "preprocessed_dataset_folder_base": "/hpc/rlav440/NNUNET_DATA/processed/Dataset001_zebrafish",
+    "probabilistic_oversampling": "False",
+    "save_every": "50",
+    "torch_version": "2.5.1+cu121",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset002_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_0/training_log_2025_10_30_08_30_37.txt

@@ -0,0 +1,23 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2025-10-30 08:30:39.758686: Using torch.compile... 
+2025-10-30 08:30:40.806764: do_dummy_2d_data_aug: False 
+2025-10-30 08:30:40.812904: Creating new 5-fold cross-validation split... 
+2025-10-30 08:30:40.818653: Desired fold for training: 0 
+2025-10-30 08:30:40.820158: This split has 86 training and 22 validation cases. 
+
+This is the configuration used by this training:
+Configuration name: 2d
+ {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 5, 'patch_size': [1024, 112], 'median_image_size_in_voxels': [1024.0, 102.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 1], [2, 1], [2, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset001_zebrafish', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [999.0, 1.0, 1.0], 'original_median_shape_after_transp': [1, 1024, 102], 'image_reader_writer': 'NaturalImage2DIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 255.0, 'mean': 49.659568786621094, 'median': 45.0, 'min': 0.0, 'percentile_00_5': 10.0, 'percentile_99_5': 163.0, 'std': 24.689043045043945}}} 
+ 
+2025-10-30 08:30:42.544917: Unable to plot network architecture: nnUNet_compile is enabled! 
+2025-10-30 08:30:42.566393:  
+2025-10-30 08:30:42.568064: Epoch 0 
+2025-10-30 08:30:42.569908: Current learning rate: 0.01 

Dataset002_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_0/training_log_2025_10_30_08_31_30.txt

@@ -0,0 +1,24 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2025-10-30 08:31:32.228938: Using torch.compile... 
+2025-10-30 08:31:33.240147: do_dummy_2d_data_aug: False 
+2025-10-30 08:31:33.242795: Using splits from existing split file: /hpc/rlav440/NNUNET_DATA/processed/Dataset001_zebrafish/splits_final.json 
+2025-10-30 08:31:33.244770: The split file contains 5 splits. 
+2025-10-30 08:31:33.246291: Desired fold for training: 0 
+2025-10-30 08:31:33.247709: This split has 86 training and 22 validation cases. 
+
+This is the configuration used by this training:
+Configuration name: 2d
+ {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 5, 'patch_size': [1024, 112], 'median_image_size_in_voxels': [1024.0, 102.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 1], [2, 1], [2, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True} 
+ 
+These are the global plan.json settings:
+ {'dataset_name': 'Dataset001_zebrafish', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [999.0, 1.0, 1.0], 'original_median_shape_after_transp': [1, 1024, 102], 'image_reader_writer': 'NaturalImage2DIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 255.0, 'mean': 49.659568786621094, 'median': 45.0, 'min': 0.0, 'percentile_00_5': 10.0, 'percentile_99_5': 163.0, 'std': 24.689043045043945}}} 
+ 
+2025-10-30 08:31:35.049255: Unable to plot network architecture: nnUNet_compile is enabled! 
+2025-10-30 08:31:35.064852:  
+2025-10-30 08:31:35.066397: Epoch 0 
+2025-10-30 08:31:35.067992: Current learning rate: 0.01 

Dataset002_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_0/validation/summary.json

@@ -0,0 +1,1068 @@
+{
+    "foreground_mean": {
+        "Dice": 0.9381609999853798,
+        "FN": 141.70454545454544,
+        "FP": 128.14772727272728,
+        "IoU": 0.8973557637396948,
+        "TN": 92626.94318181818,
+        "TP": 11551.204545454544,
+        "n_pred": 11679.352272727274,
+        "n_ref": 11692.909090909092
+    },
+    "mean": {
+        "1": {
+            "Dice": 0.9820573832820152,
+            "FN": 87.22727272727273,
+            "FP": 130.6818181818182,
+            "IoU": 0.9650193424393703,
+            "TN": 98239.04545454546,
+            "TP": 5991.045454545455,
+            "n_pred": 6121.727272727273,
+            "n_ref": 6078.272727272727
+        },
+        "2": {
+            "Dice": 0.9883763726706863,
+            "FN": 153.77272727272728,
+            "FP": 88.54545454545455,
+            "IoU": 0.9771657747700953,
+            "TN": 93366.63636363637,
+            "TP": 10839.045454545454,
+            "n_pred": 10927.59090909091,
+            "n_ref": 10992.818181818182
+        },
+        "3": {
+            "Dice": 0.9945642882669948,
+            "FN": 178.9090909090909,
+            "FP": 129.3181818181818,
+            "IoU": 0.9892015915985823,
+            "TN": 75350.68181818182,
+            "TP": 28789.090909090908,
+            "n_pred": 28918.409090909092,
+            "n_ref": 28968.0
+        },
+        "4": {
+            "Dice": 0.7876459557218229,
+            "FN": 146.9090909090909,
+            "FP": 164.04545454545453,
+            "IoU": 0.6580363461507317,
+            "TN": 103551.40909090909,
+            "TP": 585.6363636363636,
+            "n_pred": 749.6818181818181,
+            "n_ref": 732.5454545454545
+        }
+    },
+    "metric_per_case": [
+        {
+            "metrics": {
+                "1": {
+                    "Dice": 0.9967478385024193,
+                    "FN": 41,
+                    "FP": 0,
+                    "IoU": 0.993516761543327,
+                    "TN": 98124,
+                    "TP": 6283,
+                    "n_pred": 6283,
+                    "n_ref": 6324
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+                    "n_ref": 15402
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+                    "TP": 37026,
+                    "n_pred": 37091,
+                    "n_ref": 37026
+                },
+                "4": {
+                    "Dice": 0.8942465753424658,
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+                    "FP": 193,
+                    "IoU": 0.8087215064420218,
+                    "TN": 103439,
+                    "TP": 816,
+                    "n_pred": 1009,
+                    "n_ref": 816
+                }
+            },
+            "prediction_file": "/hpc/rlav440/NNUNET_DATA/results/Dataset001_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_0/validation/fish0004.png",
+            "reference_file": "/hpc/rlav440/NNUNET_DATA/processed/Dataset001_zebrafish/gt_segmentations/fish0004.png"
+        },
+        {
+            "metrics": {
+                "1": {
+                    "Dice": 0.9855023600809171,
+                    "FN": 70,
+                    "FP": 102,
+                    "IoU": 0.9714190761050183,
+                    "TN": 98430,
+                    "TP": 5846,
+                    "n_pred": 5948,
+                    "n_ref": 5916
+                },
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+                    "Dice": 0.9951391957578436,
+                    "FN": 144,
+                    "FP": 21,
+                    "IoU": 0.990325417766051,
+                    "TN": 87393,
+                    "TP": 16890,
+                    "n_pred": 16911,
+                    "n_ref": 17034
+                },
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+                    "Dice": 0.9931855290323451,
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+                    "FP": 484,
+                    "IoU": 0.9864633034900552,
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+                    "TP": 36801,
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+                    "n_ref": 36822
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+                    "Dice": 0.7848678213309025,
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+                    "TN": 103115,
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+                    "n_ref": 1326
+                }
+            },
+            "prediction_file": "/hpc/rlav440/NNUNET_DATA/results/Dataset001_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_0/validation/fish0009.png",
+            "reference_file": "/hpc/rlav440/NNUNET_DATA/processed/Dataset001_zebrafish/gt_segmentations/fish0009.png"
+        },
+        {
+            "metrics": {
+                "1": {
+                    "Dice": 0.991869918699187,
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+                    "IoU": 0.9838709677419355,
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+                    "n_ref": 9996
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+                    "TP": 26790,
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+                    "n_ref": 26826
+                },
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+                    "IoU": 0.5217864923747276,
+                    "TN": 103530,
+                    "TP": 479,
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+                    "n_ref": 918
+                }
+            },
+            "prediction_file": "/hpc/rlav440/NNUNET_DATA/results/Dataset001_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_0/validation/fish0013.png",
+            "reference_file": "/hpc/rlav440/NNUNET_DATA/processed/Dataset001_zebrafish/gt_segmentations/fish0013.png"
+        },
+        {
+            "metrics": {
+                "1": {
+                    "Dice": 0.9885645741703318,
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+                    "FP": 0,
+                    "IoU": 0.9773877292852625,
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+                    "n_pred": 6181,
+                    "n_ref": 6324
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+                    "n_ref": 9180
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+                "3": {
+                    "Dice": 0.9960629921259843,
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+            "reference_file": "/hpc/rlav440/NNUNET_DATA/processed/Dataset001_zebrafish/gt_segmentations/fish0094.png"
+        }
+    ]
+}

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+    "_best_ema": "None",
+    "batch_size": "5",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 5, 'patch_size': [1024, 112], 'median_image_size_in_voxels': [1024.0, 102.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], 'use_mask_for_norm': [False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.PlainConvUNet', 'arch_kwargs': {'n_stages': 8, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 1], [2, 1], [2, 1]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2, 2], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}",
+    "configuration_name": "2d",
+    "cudnn_version": 90100,
+    "current_epoch": "0",
+    "dataloader_train": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7f72e55ce570>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader.nnUNetDataLoader object at 0x7f72dc3d96d0>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "None",
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+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader.nnUNetDataLoader object at 0x7f72dc4e9280>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "None",
+    "dataset_json": "{'channel_names': {'0': '\u00b5OCT'}, 'labels': {'background': 0, 'Retina': 1, 'VCD': 2, 'lens': 3, 'cornea': 4}, 'numTraining': 108, 'file_ending': '.png'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
+    "fold": "1",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA A100 80GB PCIe",
+    "grad_scaler": "<torch.amp.grad_scaler.GradScaler object at 0x7f72e55a6300>",
+    "hostname": "hpc5",
+    "inference_allowed_mirroring_axes": "(0, 1)",
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+    "local_rank": "0",
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+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f72e5241430>",
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+    "output_folder_base": "/hpc/rlav440/NNUNET_DATA/results/Dataset001_zebrafish/nnUNetTrainer__nnUNetPlans__2d",
+    "oversample_foreground_percent": "0.33",
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+    "preprocessed_dataset_folder": "/hpc/rlav440/NNUNET_DATA/processed/Dataset001_zebrafish/nnUNetPlans_2d",
+    "preprocessed_dataset_folder_base": "/hpc/rlav440/NNUNET_DATA/processed/Dataset001_zebrafish",
+    "probabilistic_oversampling": "False",
+    "save_every": "50",
+    "torch_version": "2.5.1+cu121",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

Dataset002_zebrafish/nnUNetTrainer__nnUNetPlans__2d/fold_1/training_log_2025_10_30_08_32_08.txt

@@ -0,0 +1,12 @@
+
+#######################################################################
+Please cite the following paper when using nnU-Net:
+Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
+#######################################################################
+ 
+2025-10-30 08:32:10.865681: Using torch.compile... 
+2025-10-30 08:32:11.762300: do_dummy_2d_data_aug: False 
+2025-10-30 08:32:11.764857: Using splits from existing split file: /hpc/rlav440/NNUNET_DATA/processed/Dataset001_zebrafish/splits_final.json 
+2025-10-30 08:32:11.766589: The split file contains 5 splits. 
+2025-10-30 08:32:11.768494: Desired fold for training: 1 
+2025-10-30 08:32:11.769945: This split has 86 training and 22 validation cases.