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+    "_best_ema": "None",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [227.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8700,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7fd24452c340>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fd24452c1c0>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "Compose ( [SpatialTransform( independent_scale_for_each_axis = False, p_rot_per_sample = 0.2, p_scale_per_sample = 0.2, p_el_per_sample = 0, data_key = 'data', label_key = 'seg', patch_size = [128, 128, 128], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7fd24452ce50>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7fd24452ce20>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "Compose ( [RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'name': 'Segmentation of X', 'description': 'Segmentation', 'reference': 'Jakob', 'licence': '-', 'release': '0.0', 'labels': {'background': 0, 'spleen': 1, 'kidney_right': 2, 'kidney_left': 3, 'gallbladder': 4, 'liver': 5, 'stomach': 6, 'pancreas': 7, 'adrenal_gland_right': 8, 'adrenal_gland_left': 9, 'lung_upper_lobe_left': 10, 'lung_lower_lobe_left': 11, 'lung_upper_lobe_right': 12, 'lung_middle_lobe_right': 13, 'lung_lower_lobe_right': 14, 'esophagus': 15, 'trachea': 16, 'thyroid_gland': 17, 'small_bowel': 18, 'duodenum': 19, 'colon': 20, 'urinary_bladder': 21, 'prostate': 22, 'kidney_cyst_left': 23, 'kidney_cyst_right': 24}, 'numTraining': 1559, 'channel_names': {'0': 'CT'}, 'file_ending': '.nii.gz', 'overwrite_image_reader_writer': 'NibabelIOWithReorient'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA A100-SXM4-80GB MIG 7g.80gb",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7fd255327e20>",
+    "hostname": "rndapollolp01.uhbs.ch",
+    "inference_allowed_mirroring_axes": "None",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7fd255327ee0>",
+    "local_rank": "0",
+    "log_file": "/mnt/nor/nnunet/results_v2/Dataset291_TotalSegmentator_part1_organs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/training_log_2023_5_13_11_18_49.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7fd255327d90>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7fd341069310>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset291_TotalSegmentator_part1_organs_1559subj', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [227, 227, 240], 'image_reader_writer': 'NibabelIOWithReorient', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 46, 'patch_size': [256, 256], 'median_image_size_in_voxels': [227.0, 239.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, '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}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [196, 196, 206], 'spacing': [1.7389111114500002, 1.7389111114500002, 1.7389111114500002], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [227.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3606.0, 'mean': -370.00039267657144, 'median': -249.0, 'min': -3139.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 276.0, 'std': 436.5998675471528}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'name': 'Segmentation of X', 'description': 'Segmentation', 'reference': 'Jakob', 'licence': '-', 'release': '0.0', 'labels': {'background': 0, 'spleen': 1, 'kidney_right': 2, 'kidney_left': 3, 'gallbladder': 4, 'liver': 5, 'stomach': 6, 'pancreas': 7, 'adrenal_gland_right': 8, 'adrenal_gland_left': 9, 'lung_upper_lobe_left': 10, 'lung_lower_lobe_left': 11, 'lung_upper_lobe_right': 12, 'lung_middle_lobe_right': 13, 'lung_lower_lobe_right': 14, 'esophagus': 15, 'trachea': 16, 'thyroid_gland': 17, 'small_bowel': 18, 'duodenum': 19, 'colon': 20, 'urinary_bladder': 21, 'prostate': 22, 'kidney_cyst_left': 23, 'kidney_cyst_right': 24}, 'numTraining': 1559, 'channel_names': {'0': 'CT'}, 'file_ending': '.nii.gz', 'overwrite_image_reader_writer': 'NibabelIOWithReorient'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "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    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": "/mnt/nor/nnunet/results_v2/Dataset291_TotalSegmentator_part1_organs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0",
+    "output_folder_base": "/mnt/nor/nnunet/results_v2/Dataset291_TotalSegmentator_part1_organs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset291_TotalSegmentator_part1_organs_1559subj', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [227, 227, 240], 'image_reader_writer': 'NibabelIOWithReorient', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 46, 'patch_size': [256, 256], 'median_image_size_in_voxels': [227.0, 239.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, '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}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [196, 196, 206], 'spacing': [1.7389111114500002, 1.7389111114500002, 1.7389111114500002], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [227.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3606.0, 'mean': -370.00039267657144, 'median': -249.0, 'min': -3139.0, 'percentile_00_5': -1024.0, 'percentile_99_5': 276.0, 'std': 436.5998675471528}}}",
+    "preprocessed_dataset_folder": "/dojo/Dataset291_TotalSegmentator_part1_organs_1559subj/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/dojo/Dataset291_TotalSegmentator_part1_organs_1559subj",
+    "save_every": "50",
+    "torch_version": "2.0.0",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}
\ No newline at end of file
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new file mode 100644
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Binary files /dev/null and b/Dataset291_TotalSegmentator_part1_organs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/progress.png differ
diff --git a/Dataset291_TotalSegmentator_part1_organs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json b/Dataset291_TotalSegmentator_part1_organs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json
new file mode 100644
index 0000000000000000000000000000000000000000..619f628b55ec93329a401f9a9cf191963a6340d2
--- /dev/null
+++ b/Dataset291_TotalSegmentator_part1_organs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json
@@ -0,0 +1,444 @@
+{
+    "dataset_name": "Dataset291_TotalSegmentator_part1_organs_1559subj",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        1.5,
+        1.5,
+        1.5
+    ],
+    "original_median_shape_after_transp": [
+        227,
+        227,
+        240
+    ],
+    "image_reader_writer": "NibabelIOWithReorient",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 46,
+            "patch_size": [
+                256,
+                256
+            ],
+            "median_image_size_in_voxels": [
+                227.0,
+                239.0
+            ],
+            "spacing": [
+                1.5,
+                1.5
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                6,
+                6
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 512,
+            "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": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": true
+        },
+        "3d_lowres": {
+            "data_identifier": "nnUNetPlans_3d_lowres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                128,
+                128,
+                128
+            ],
+            "median_image_size_in_voxels": [
+                196,
+                196,
+                206
+            ],
+            "spacing": [
+                1.7389111114500002,
+                1.7389111114500002,
+                1.7389111114500002
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                5,
+                5,
+                5
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 320,
+            "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": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": false,
+            "next_stage": "3d_cascade_fullres"
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                128,
+                128,
+                128
+            ],
+            "median_image_size_in_voxels": [
+                227.0,
+                227.0,
+                239.0
+            ],
+            "spacing": [
+                1.5,
+                1.5,
+                1.5
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                5,
+                5,
+                5
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 320,
+            "resampling_fn_data": "resample_data_or_seg_to_shape",
+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
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diff --git a/Dataset292_TotalSegmentator_part2_vertebrae_1532subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/debug.json b/Dataset292_TotalSegmentator_part2_vertebrae_1532subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/debug.json
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+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [225.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8700,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f0fa01ff190>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f0fa01ff0d0>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "Compose ( [SpatialTransform( independent_scale_for_each_axis = False, p_rot_per_sample = 0.2, p_scale_per_sample = 0.2, p_el_per_sample = 0, data_key = 'data', label_key = 'seg', patch_size = [128, 128, 128], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f0fa01ffd60>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f0fa01ffcd0>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "Compose ( [RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'name': 'Segmentation of X', 'description': 'Segmentation', 'reference': 'Jakob', 'licence': '-', 'release': '0.0', 'labels': {'background': 0, 'sacrum': 1, 'vertebrae_S1': 2, 'vertebrae_L5': 3, 'vertebrae_L4': 4, 'vertebrae_L3': 5, 'vertebrae_L2': 6, 'vertebrae_L1': 7, 'vertebrae_T12': 8, 'vertebrae_T11': 9, 'vertebrae_T10': 10, 'vertebrae_T9': 11, 'vertebrae_T8': 12, 'vertebrae_T7': 13, 'vertebrae_T6': 14, 'vertebrae_T5': 15, 'vertebrae_T4': 16, 'vertebrae_T3': 17, 'vertebrae_T2': 18, 'vertebrae_T1': 19, 'vertebrae_C7': 20, 'vertebrae_C6': 21, 'vertebrae_C5': 22, 'vertebrae_C4': 23, 'vertebrae_C3': 24, 'vertebrae_C2': 25, 'vertebrae_C1': 26}, 'numTraining': 1532, 'channel_names': {'0': 'CT'}, 'file_ending': '.nii.gz', 'overwrite_image_reader_writer': 'NibabelIOWithReorient'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA A100-SXM4-80GB MIG 7g.80gb",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f0fa98b76a0>",
+    "hostname": "rndapollolp01.uhbs.ch",
+    "inference_allowed_mirroring_axes": "None",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7f0fa98b7640>",
+    "local_rank": "0",
+    "log_file": "/mnt/nor/nnunet/results_v2/Dataset292_TotalSegmentator_part2_vertebrae_1532subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/training_log_2023_6_28_11_19_44.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f0fa98b78b0>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f0fa9a65f40>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset292_TotalSegmentator_part2_vertebrae_1532subj', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [225, 227, 239], 'image_reader_writer': 'NibabelIOWithReorient', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 46, 'patch_size': [256, 256], 'median_image_size_in_voxels': [227.0, 239.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, '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}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [194, 196, 206], 'spacing': [1.7389111114500002, 1.7389111114500002, 1.7389111114500002], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [225.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 36911.0, 'mean': 367.32929817099546, 'median': 283.0, 'min': -34461.0, 'percentile_00_5': -96.0, 'percentile_99_5': 1514.0, 'std': 320.8587389393513}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'name': 'Segmentation of X', 'description': 'Segmentation', 'reference': 'Jakob', 'licence': '-', 'release': '0.0', 'labels': {'background': 0, 'sacrum': 1, 'vertebrae_S1': 2, 'vertebrae_L5': 3, 'vertebrae_L4': 4, 'vertebrae_L3': 5, 'vertebrae_L2': 6, 'vertebrae_L1': 7, 'vertebrae_T12': 8, 'vertebrae_T11': 9, 'vertebrae_T10': 10, 'vertebrae_T9': 11, 'vertebrae_T8': 12, 'vertebrae_T7': 13, 'vertebrae_T6': 14, 'vertebrae_T5': 15, 'vertebrae_T4': 16, 'vertebrae_T3': 17, 'vertebrae_T2': 18, 'vertebrae_T1': 19, 'vertebrae_C7': 20, 'vertebrae_C6': 21, 'vertebrae_C5': 22, 'vertebrae_C4': 23, 'vertebrae_C3': 24, 'vertebrae_C2': 25, 'vertebrae_C1': 26}, 'numTraining': 1532, 'channel_names': {'0': 'CT'}, 'file_ending': '.nii.gz', 'overwrite_image_reader_writer': 'NibabelIOWithReorient'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "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    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": "/mnt/nor/nnunet/results_v2/Dataset292_TotalSegmentator_part2_vertebrae_1532subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0",
+    "output_folder_base": "/mnt/nor/nnunet/results_v2/Dataset292_TotalSegmentator_part2_vertebrae_1532subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
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+    "preprocessed_dataset_folder": "/dojo/Dataset292_TotalSegmentator_part2_vertebrae_1532subj/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/dojo/Dataset292_TotalSegmentator_part2_vertebrae_1532subj",
+    "save_every": "50",
+    "torch_version": "2.0.0",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}
\ No newline at end of file
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diff --git a/Dataset292_TotalSegmentator_part2_vertebrae_1532subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json b/Dataset292_TotalSegmentator_part2_vertebrae_1532subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json
new file mode 100644
index 0000000000000000000000000000000000000000..337b724f61849f8a12bb8ec1fcf40b390bd45c16
--- /dev/null
+++ b/Dataset292_TotalSegmentator_part2_vertebrae_1532subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json
@@ -0,0 +1,444 @@
+{
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+}
\ No newline at end of file
diff --git a/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset.json b/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset.json
new file mode 100644
index 0000000000000000000000000000000000000000..faeaff55627b2c066c99966709de2cd0b609ad26
--- /dev/null
+++ b/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset.json
@@ -0,0 +1,34 @@
+{
+    "name": "Segmentation of X",
+    "description": "Segmentation",
+    "reference": "Jakob",
+    "licence": "-",
+    "release": "0.0",
+    "channel_names": {
+        "0": "CT"
+    },
+    "labels": {
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+        "heart": 1,
+        "aorta": 2,
+        "pulmonary_vein": 3,
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+        "subclavian_artery_left": 6,
+        "common_carotid_artery_right": 7,
+        "common_carotid_artery_left": 8,
+        "brachiocephalic_vein_left": 9,
+        "brachiocephalic_vein_right": 10,
+        "atrial_appendage_left": 11,
+        "superior_vena_cava": 12,
+        "inferior_vena_cava": 13,
+        "portal_vein_and_splenic_vein": 14,
+        "iliac_artery_left": 15,
+        "iliac_artery_right": 16,
+        "iliac_vena_left": 17,
+        "iliac_vena_right": 18
+    },
+    "numTraining": 1559,
+    "file_ending": ".nii.gz",
+    "overwrite_image_reader_writer": "NibabelIOWithReorient"
+}
\ No newline at end of file
diff --git a/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset_fingerprint.json b/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset_fingerprint.json
new file mode 100644
index 0000000000000000000000000000000000000000..7583b9bc9c86934aecb37d217cbdcf3deebdcd64
--- /dev/null
+++ b/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset_fingerprint.json
@@ -0,0 +1,15608 @@
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index 0000000000000000000000000000000000000000..dd06eb220215c56fa73f1aeffbffbf092eb9cded
--- /dev/null
+++ b/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/debug.json
@@ -0,0 +1,52 @@
+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [227.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8700,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f6f27071eb0>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f6f270710a0>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "Compose ( [SpatialTransform( independent_scale_for_each_axis = False, p_rot_per_sample = 0.2, p_scale_per_sample = 0.2, p_el_per_sample = 0, data_key = 'data', label_key = 'seg', patch_size = [128, 128, 128], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f6f27071760>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f6f270716d0>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "Compose ( [RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'name': 'Segmentation of X', 'description': 'Segmentation', 'reference': 'Jakob', 'licence': '-', 'release': '0.0', 'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'heart': 1, 'aorta': 2, 'pulmonary_vein': 3, 'brachiocephalic_trunk': 4, 'subclavian_artery_right': 5, 'subclavian_artery_left': 6, 'common_carotid_artery_right': 7, 'common_carotid_artery_left': 8, 'brachiocephalic_vein_left': 9, 'brachiocephalic_vein_right': 10, 'atrial_appendage_left': 11, 'superior_vena_cava': 12, 'inferior_vena_cava': 13, 'portal_vein_and_splenic_vein': 14, 'iliac_artery_left': 15, 'iliac_artery_right': 16, 'iliac_vena_left': 17, 'iliac_vena_right': 18}, 'numTraining': 1559, 'file_ending': '.nii.gz', 'overwrite_image_reader_writer': 'NibabelIOWithReorient'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA A100-SXM4-80GB MIG 7g.80gb",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f6f37e7eca0>",
+    "hostname": "rndapollolp01.uhbs.ch",
+    "inference_allowed_mirroring_axes": "None",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7f6f37e7ed90>",
+    "local_rank": "0",
+    "log_file": "/mnt/nor/nnunet/results_v2/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/training_log_2023_8_8_11_15_45.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f6f37e7ef40>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f6f37e7edf0>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset293_TotalSegmentator_part3_cardiac_1559subj', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [227, 227, 240], 'image_reader_writer': 'NibabelIOWithReorient', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 47, 'patch_size': [256, 256], 'median_image_size_in_voxels': [227.0, 239.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, '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}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [196, 196, 206], 'spacing': [1.7389111114500002, 1.7389111114500002, 1.7389111114500002], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [227.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 6666.0, 'mean': 142.1977119916391, 'median': 108.0, 'min': -1551.0, 'percentile_00_5': -101.0, 'percentile_99_5': 813.0, 'std': 154.74032651622053}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'name': 'Segmentation of X', 'description': 'Segmentation', 'reference': 'Jakob', 'licence': '-', 'release': '0.0', 'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'heart': 1, 'aorta': 2, 'pulmonary_vein': 3, 'brachiocephalic_trunk': 4, 'subclavian_artery_right': 5, 'subclavian_artery_left': 6, 'common_carotid_artery_right': 7, 'common_carotid_artery_left': 8, 'brachiocephalic_vein_left': 9, 'brachiocephalic_vein_right': 10, 'atrial_appendage_left': 11, 'superior_vena_cava': 12, 'inferior_vena_cava': 13, 'portal_vein_and_splenic_vein': 14, 'iliac_artery_left': 15, 'iliac_artery_right': 16, 'iliac_vena_left': 17, 'iliac_vena_right': 18}, 'numTraining': 1559, 'file_ending': '.nii.gz', 'overwrite_image_reader_writer': 'NibabelIOWithReorient'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "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    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": "/mnt/nor/nnunet/results_v2/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0",
+    "output_folder_base": "/mnt/nor/nnunet/results_v2/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset293_TotalSegmentator_part3_cardiac_1559subj', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [227, 227, 240], 'image_reader_writer': 'NibabelIOWithReorient', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 47, 'patch_size': [256, 256], 'median_image_size_in_voxels': [227.0, 239.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, '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}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [196, 196, 206], 'spacing': [1.7389111114500002, 1.7389111114500002, 1.7389111114500002], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [227.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 6666.0, 'mean': 142.1977119916391, 'median': 108.0, 'min': -1551.0, 'percentile_00_5': -101.0, 'percentile_99_5': 813.0, 'std': 154.74032651622053}}}",
+    "preprocessed_dataset_folder": "/dojo/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/dojo/Dataset293_TotalSegmentator_part3_cardiac_1559subj",
+    "save_every": "50",
+    "torch_version": "2.0.0",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}
\ No newline at end of file
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new file mode 100644
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diff --git a/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json b/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json
new file mode 100644
index 0000000000000000000000000000000000000000..cd81f5c1a9bd3949ed65fdf98d5d8774819d6711
--- /dev/null
+++ b/Dataset293_TotalSegmentator_part3_cardiac_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json
@@ -0,0 +1,444 @@
+{
+    "dataset_name": "Dataset293_TotalSegmentator_part3_cardiac_1559subj",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        1.5,
+        1.5,
+        1.5
+    ],
+    "original_median_shape_after_transp": [
+        227,
+        227,
+        240
+    ],
+    "image_reader_writer": "NibabelIOWithReorient",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 47,
+            "patch_size": [
+                256,
+                256
+            ],
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diff --git a/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset.json b/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset.json
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index 0000000000000000000000000000000000000000..32a401f91233ecb1216cad3353c8626a5968f7f3
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diff --git a/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset_fingerprint.json b/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset_fingerprint.json
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diff --git a/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/debug.json b/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/debug.json
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+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [227.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8700,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f2f6c3131c0>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f2f6c313d60>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "Compose ( [SpatialTransform( independent_scale_for_each_axis = False, p_rot_per_sample = 0.2, p_scale_per_sample = 0.2, p_el_per_sample = 0, data_key = 'data', label_key = 'seg', patch_size = [128, 128, 128], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7f2f6c313100>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f2f6c313370>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "Compose ( [RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'name': 'Segmentation of X', 'description': 'Segmentation', 'reference': 'Jakob', 'licence': '-', 'release': '0.0', 'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'humerus_left': 1, 'humerus_right': 2, 'scapula_left': 3, 'scapula_right': 4, 'clavicula_left': 5, 'clavicula_right': 6, 'femur_left': 7, 'femur_right': 8, 'hip_left': 9, 'hip_right': 10, 'spinal_cord': 11, 'gluteus_maximus_left': 12, 'gluteus_maximus_right': 13, 'gluteus_medius_left': 14, 'gluteus_medius_right': 15, 'gluteus_minimus_left': 16, 'gluteus_minimus_right': 17, 'autochthon_left': 18, 'autochthon_right': 19, 'iliopsoas_left': 20, 'iliopsoas_right': 21, 'brain': 22, 'skull': 23}, 'numTraining': 1559, 'file_ending': '.nii.gz', 'overwrite_image_reader_writer': 'NibabelIOWithReorient'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA A100-SXM4-80GB MIG 7g.80gb",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f2f727c0dc0>",
+    "hostname": "rndapollolp01.uhbs.ch",
+    "inference_allowed_mirroring_axes": "None",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7f2f727c0e50>",
+    "local_rank": "0",
+    "log_file": "/mnt/nor/nnunet/results_v2/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/training_log_2023_8_8_12_49_07.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f2f727c06a0>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f2f7270c2b0>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset294_TotalSegmentator_part4_muscles_1559subj', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [227, 227, 240], 'image_reader_writer': 'NibabelIOWithReorient', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 46, 'patch_size': [256, 256], 'median_image_size_in_voxels': [227.0, 239.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, '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}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [196, 196, 206], 'spacing': [1.7389111114500002, 1.7389111114500002, 1.7389111114500002], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [227.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 44594.0, 'mean': 174.82106003593222, 'median': 52.0, 'min': -49831.0, 'percentile_00_5': -197.0, 'percentile_99_5': 1729.0, 'std': 362.496044938386}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'name': 'Segmentation of X', 'description': 'Segmentation', 'reference': 'Jakob', 'licence': '-', 'release': '0.0', 'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'humerus_left': 1, 'humerus_right': 2, 'scapula_left': 3, 'scapula_right': 4, 'clavicula_left': 5, 'clavicula_right': 6, 'femur_left': 7, 'femur_right': 8, 'hip_left': 9, 'hip_right': 10, 'spinal_cord': 11, 'gluteus_maximus_left': 12, 'gluteus_maximus_right': 13, 'gluteus_medius_left': 14, 'gluteus_medius_right': 15, 'gluteus_minimus_left': 16, 'gluteus_minimus_right': 17, 'autochthon_left': 18, 'autochthon_right': 19, 'iliopsoas_left': 20, 'iliopsoas_right': 21, 'brain': 22, 'skull': 23}, 'numTraining': 1559, 'file_ending': '.nii.gz', 'overwrite_image_reader_writer': 'NibabelIOWithReorient'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "PlainConvUNet",
+    "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    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": "/mnt/nor/nnunet/results_v2/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0",
+    "output_folder_base": "/mnt/nor/nnunet/results_v2/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset294_TotalSegmentator_part4_muscles_1559subj', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [227, 227, 240], 'image_reader_writer': 'NibabelIOWithReorient', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 46, 'patch_size': [256, 256], 'median_image_size_in_voxels': [227.0, 239.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, '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}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [196, 196, 206], 'spacing': [1.7389111114500002, 1.7389111114500002, 1.7389111114500002], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [227.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 44594.0, 'mean': 174.82106003593222, 'median': 52.0, 'min': -49831.0, 'percentile_00_5': -197.0, 'percentile_99_5': 1729.0, 'std': 362.496044938386}}}",
+    "preprocessed_dataset_folder": "/dojo/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/dojo/Dataset294_TotalSegmentator_part4_muscles_1559subj",
+    "save_every": "50",
+    "torch_version": "2.0.0",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}
\ No newline at end of file
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Binary files /dev/null and b/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/progress.png differ
diff --git a/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json b/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json
new file mode 100644
index 0000000000000000000000000000000000000000..770b7435cad71007f16efcee33d0fc4be12ec22a
--- /dev/null
+++ b/Dataset294_TotalSegmentator_part4_muscles_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/plans.json
@@ -0,0 +1,444 @@
+{
+    "dataset_name": "Dataset294_TotalSegmentator_part4_muscles_1559subj",
+    "plans_name": "nnUNetPlans",
+    "original_median_spacing_after_transp": [
+        1.5,
+        1.5,
+        1.5
+    ],
+    "original_median_shape_after_transp": [
+        227,
+        227,
+        240
+    ],
+    "image_reader_writer": "NibabelIOWithReorient",
+    "transpose_forward": [
+        0,
+        1,
+        2
+    ],
+    "transpose_backward": [
+        0,
+        1,
+        2
+    ],
+    "configurations": {
+        "2d": {
+            "data_identifier": "nnUNetPlans_2d",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 46,
+            "patch_size": [
+                256,
+                256
+            ],
+            "median_image_size_in_voxels": [
+                227.0,
+                239.0
+            ],
+            "spacing": [
+                1.5,
+                1.5
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                6,
+                6
+            ],
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+                [
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2
+                ],
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+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
+                [
+                    3,
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+                ],
+                [
+                    3,
+                    3
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+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3
+                ],
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+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 512,
+            "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": null
+            },
+            "resampling_fn_seg_kwargs": {
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+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
+                "is_seg": false,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": true
+        },
+        "3d_lowres": {
+            "data_identifier": "nnUNetPlans_3d_lowres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                128,
+                128,
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+                196,
+                196,
+                206
+            ],
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+                1.7389111114500002,
+                1.7389111114500002,
+                1.7389111114500002
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                5,
+                5,
+                5
+            ],
+            "pool_op_kernel_sizes": [
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+                    1,
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+                    1
+                ],
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+                ],
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+                    2,
+                    2,
+                    2
+                ],
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+                    2,
+                    2,
+                    2
+                ],
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+                    2,
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
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+                    3
+                ],
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+                    3,
+                    3
+                ],
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+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ]
+            ],
+            "unet_max_num_features": 320,
+            "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": null
+            },
+            "resampling_fn_seg_kwargs": {
+                "is_seg": true,
+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+            "resampling_fn_probabilities_kwargs": {
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+                "order": 1,
+                "order_z": 0,
+                "force_separate_z": null
+            },
+            "batch_dice": false,
+            "next_stage": "3d_cascade_fullres"
+        },
+        "3d_fullres": {
+            "data_identifier": "nnUNetPlans_3d_fullres",
+            "preprocessor_name": "DefaultPreprocessor",
+            "batch_size": 2,
+            "patch_size": [
+                128,
+                128,
+                128
+            ],
+            "median_image_size_in_voxels": [
+                227.0,
+                227.0,
+                239.0
+            ],
+            "spacing": [
+                1.5,
+                1.5,
+                1.5
+            ],
+            "normalization_schemes": [
+                "CTNormalization"
+            ],
+            "use_mask_for_norm": [
+                false
+            ],
+            "UNet_class_name": "PlainConvUNet",
+            "UNet_base_num_features": 32,
+            "n_conv_per_stage_encoder": [
+                2,
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "n_conv_per_stage_decoder": [
+                2,
+                2,
+                2,
+                2,
+                2
+            ],
+            "num_pool_per_axis": [
+                5,
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+                5
+            ],
+            "pool_op_kernel_sizes": [
+                [
+                    1,
+                    1,
+                    1
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ],
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+                    2,
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+                    2
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+                [
+                    2,
+                    2,
+                    2
+                ],
+                [
+                    2,
+                    2,
+                    2
+                ]
+            ],
+            "conv_kernel_sizes": [
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
+                ],
+                [
+                    3,
+                    3,
+                    3
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+}
\ No newline at end of file
diff --git a/Dataset295_TotalSegmentator_part5_ribs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset.json b/Dataset295_TotalSegmentator_part5_ribs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset.json
new file mode 100644
index 0000000000000000000000000000000000000000..5d3bf56067876ec3d88bec88fc098be5a6c39e91
--- /dev/null
+++ b/Dataset295_TotalSegmentator_part5_ribs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset.json
@@ -0,0 +1,42 @@
+{
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+    "description": "Segmentation",
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+    "licence": "-",
+    "release": "0.0",
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+    "file_ending": ".nii.gz",
+    "overwrite_image_reader_writer": "NibabelIOWithReorient"
+}
\ No newline at end of file
diff --git a/Dataset295_TotalSegmentator_part5_ribs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset_fingerprint.json b/Dataset295_TotalSegmentator_part5_ribs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset_fingerprint.json
new file mode 100644
index 0000000000000000000000000000000000000000..47d5030a9b06a0992e6789021ce0f5888718261f
--- /dev/null
+++ b/Dataset295_TotalSegmentator_part5_ribs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/dataset_fingerprint.json
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+{
+    "_best_ema": "None",
+    "batch_size": "2",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [227.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}",
+    "configuration_name": "3d_fullres",
+    "cudnn_version": 8700,
+    "current_epoch": "0",
+    "dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7efb2425f4f0>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7efb2425f400>",
+    "dataloader_train.num_processes": "12",
+    "dataloader_train.transform": "Compose ( [SpatialTransform( independent_scale_for_each_axis = False, p_rot_per_sample = 0.2, p_scale_per_sample = 0.2, p_el_per_sample = 0, data_key = 'data', label_key = 'seg', patch_size = [128, 128, 128], patch_center_dist_from_border = None, do_elastic_deform = False, alpha = (0, 0), sigma = (0, 0), do_rotation = True, angle_x = (-0.5235987755982988, 0.5235987755982988), angle_y = (-0.5235987755982988, 0.5235987755982988), angle_z = (-0.5235987755982988, 0.5235987755982988), do_scale = True, scale = (0.7, 1.4), border_mode_data = 'constant', border_cval_data = 0, order_data = 3, border_mode_seg = 'constant', border_cval_seg = -1, order_seg = 1, random_crop = False, p_rot_per_axis = 1, p_independent_scale_per_axis = 1 ), GaussianNoiseTransform( p_per_sample = 0.1, data_key = 'data', noise_variance = (0, 0.1), p_per_channel = 1, per_channel = False ), GaussianBlurTransform( p_per_sample = 0.2, different_sigma_per_channel = True, p_per_channel = 0.5, data_key = 'data', blur_sigma = (0.5, 1.0), different_sigma_per_axis = False, p_isotropic = 0 ), BrightnessMultiplicativeTransform( p_per_sample = 0.15, data_key = 'data', multiplier_range = (0.75, 1.25), per_channel = True ), ContrastAugmentationTransform( p_per_sample = 0.15, data_key = 'data', contrast_range = (0.75, 1.25), preserve_range = True, per_channel = True, p_per_channel = 1 ), SimulateLowResolutionTransform( order_upsample = 3, order_downsample = 0, channels = None, per_channel = True, p_per_channel = 0.5, p_per_sample = 0.25, data_key = 'data', zoom_range = (0.5, 1), ignore_axes = None ), GammaTransform( p_per_sample = 0.1, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = True ), GammaTransform( p_per_sample = 0.3, retain_stats = True, per_channel = True, data_key = 'data', gamma_range = (0.7, 1.5), invert_image = False ), RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x7efb2425f790>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7efb2425f490>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "Compose ( [RemoveLabelTransform( output_key = 'seg', input_key = 'seg', replace_with = 0, remove_label = -1 ), RenameTransform( delete_old = True, out_key = 'target', in_key = 'seg' ), DownsampleSegForDSTransform2( axes = None, output_key = 'target', input_key = 'target', order = 0, ds_scales = [[1.0, 1.0, 1.0], [0.5, 0.5, 0.5], [0.25, 0.25, 0.25], [0.125, 0.125, 0.125], [0.0625, 0.0625, 0.0625]] ), NumpyToTensor( keys = ['data', 'target'], cast_to = 'float' )] )",
+    "dataset_json": "{'name': 'Segmentation of X', 'description': 'Segmentation', 'reference': 'Jakob', 'licence': '-', 'release': '0.0', 'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'rib_left_1': 1, 'rib_left_2': 2, 'rib_left_3': 3, 'rib_left_4': 4, 'rib_left_5': 5, 'rib_left_6': 6, 'rib_left_7': 7, 'rib_left_8': 8, 'rib_left_9': 9, 'rib_left_10': 10, 'rib_left_11': 11, 'rib_left_12': 12, 'rib_right_1': 13, 'rib_right_2': 14, 'rib_right_3': 15, 'rib_right_4': 16, 'rib_right_5': 17, 'rib_right_6': 18, 'rib_right_7': 19, 'rib_right_8': 20, 'rib_right_9': 21, 'rib_right_10': 22, 'rib_right_11': 23, 'rib_right_12': 24, 'sternum': 25, 'costal_cartilages': 26}, 'numTraining': 1559, 'file_ending': '.nii.gz', 'overwrite_image_reader_writer': 'NibabelIOWithReorient'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "fold": "0",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA A100-SXM4-80GB MIG 7g.80gb",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7efb2b960dc0>",
+    "hostname": "rndapollolp01.uhbs.ch",
+    "inference_allowed_mirroring_axes": "None",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7efb2b960d60>",
+    "local_rank": "0",
+    "log_file": "/mnt/nor/nnunet/results_v2/Dataset295_TotalSegmentator_part5_ribs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0/training_log_2023_8_14_05_58_11.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7efb2b9606a0>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): MemoryEfficientSoftDiceLoss()\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7efb2b8ac2b0>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset295_TotalSegmentator_part5_ribs_1559subj', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.5, 1.5, 1.5], 'original_median_shape_after_transp': [227, 227, 240], 'image_reader_writer': 'NibabelIOWithReorient', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 46, 'patch_size': [256, 256], 'median_image_size_in_voxels': [227.0, 239.0], 'spacing': [1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2, 2], 'num_pool_per_axis': [6, 6], 'pool_op_kernel_sizes': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'conv_kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'unet_max_num_features': 512, '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}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [196, 196, 206], 'spacing': [1.7389111114500002, 1.7389111114500002, 1.7389111114500002], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [227.0, 227.0, 239.0], 'spacing': [1.5, 1.5, 1.5], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [False], 'UNet_class_name': 'PlainConvUNet', 'UNet_base_num_features': 32, 'n_conv_per_stage_encoder': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], 'num_pool_per_axis': [5, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'unet_max_num_features': 320, '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}, 'batch_dice': True}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 21501.0, 'mean': 291.98966240726804, 'median': 220.0, 'min': -2437.0, 'percentile_00_5': -110.0, 'percentile_99_5': 1302.0, 'std': 261.53174290110513}}}, 'configuration': '3d_fullres', 'fold': 0, 'dataset_json': {'name': 'Segmentation of X', 'description': 'Segmentation', 'reference': 'Jakob', 'licence': '-', 'release': '0.0', 'channel_names': {'0': 'CT'}, 'labels': {'background': 0, 'rib_left_1': 1, 'rib_left_2': 2, 'rib_left_3': 3, 'rib_left_4': 4, 'rib_left_5': 5, 'rib_left_6': 6, 'rib_left_7': 7, 'rib_left_8': 8, 'rib_left_9': 9, 'rib_left_10': 10, 'rib_left_11': 11, 'rib_left_12': 12, 'rib_right_1': 13, 'rib_right_2': 14, 'rib_right_3': 15, 'rib_right_4': 16, 'rib_right_5': 17, 'rib_right_6': 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+    "network": "PlainConvUNet",
+    "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    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": "/mnt/nor/nnunet/results_v2/Dataset295_TotalSegmentator_part5_ribs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres/fold_0",
+    "output_folder_base": "/mnt/nor/nnunet/results_v2/Dataset295_TotalSegmentator_part5_ribs_1559subj/nnUNetTrainerNoMirroring__nnUNetPlans__3d_fullres",
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+    "preprocessed_dataset_folder": "/dojo/Dataset295_TotalSegmentator_part5_ribs_1559subj/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/dojo/Dataset295_TotalSegmentator_part5_ribs_1559subj",
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+    "torch_version": "2.0.0",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}
\ No newline at end of file
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+{
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+            "resampling_fn_seg": "resample_data_or_seg_to_shape",
+            "resampling_fn_data_kwargs": {
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+        }
+    },
+    "experiment_planner_used": "ExperimentPlanner",
+    "label_manager": "LabelManager",
+    "foreground_intensity_properties_per_channel": {
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+            "std": 261.53174290110513
+        }
+    }
+}
\ No newline at end of file
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