Upload 7 files
Browse files- nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset.json +162 -0
- nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset_fingerprint.json +0 -0
- nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/checkpoint_best.pth +3 -0
- nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/checkpoint_final.pth +3 -0
- nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/debug.json +52 -0
- nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/progress.png +0 -0
- nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/plans.json +434 -0
nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset.json
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}
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nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/dataset_fingerprint.json
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nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/checkpoint_best.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:d9dc75c78d69fa6812d755c8b7431d5f9e0ee0f42995a61c6981caa57039268b
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size 257684770
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nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/checkpoint_final.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:c6cfc7ff22eb9eca2983663f7898c6c8284119fb9612fc750fa8ef016bf4f2dd
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size 257684990
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nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/debug.json
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{
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"_best_ema": "None",
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"batch_size": "2",
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"configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 80], 'median_image_size_in_voxels': [249.0, 240.0, 179.0], 'spacing': [1.0, 1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], '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, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 1]], '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}",
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"configuration_name": "3d_fullres",
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"cudnn_version": 8902,
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"current_epoch": "0",
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"dataloader_train": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x2b54f0f5c6d0>",
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"dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x2b54f1712710>",
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"dataloader_train.num_processes": "12",
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"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, 80], 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 ), MirrorTransform( p_per_sample = 1, data_key = 'data', label_key = 'seg', axes = (0, 1, 2) ), 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' )] )",
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"dataloader_val": "<nnunetv2.training.data_augmentation.custom_transforms.limited_length_multithreaded_augmenter.LimitedLenWrapper object at 0x2b54f0f5cdd0>",
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"dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x2b54f1712b50>",
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"dataloader_val.num_processes": "6",
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"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' )] )",
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| 16 |
+
"dataset_json": "{'channel_names': {'0': 'MRI'}, 'file_ending': '.nii.gz', 'labels': {'background': 0, 'class1': 1, 'class10': 10, 'class100': 100, 'class101': 101, 'class102': 102, 'class103': 103, 'class104': 104, 'class105': 105, 'class106': 106, 'class107': 107, 'class108': 108, 'class109': 109, 'class11': 11, 'class110': 110, 'class111': 111, 'class112': 112, 'class113': 113, 'class114': 114, 'class115': 115, 'class116': 116, 'class117': 117, 'class118': 118, 'class119': 119, 'class12': 12, 'class120': 120, 'class121': 121, 'class122': 122, 'class123': 123, 'class124': 124, 'class125': 125, 'class126': 126, 'class127': 127, 'class128': 128, 'class129': 129, 'class13': 13, 'class130': 130, 'class131': 131, 'class132': 132, 'class133': 133, 'class134': 134, 'class135': 135, 'class136': 136, 'class137': 137, 'class138': 138, 'class139': 139, 'class14': 14, 'class140': 140, 'class141': 141, 'class142': 142, 'class143': 143, 'class144': 144, 'class145': 145, 'class146': 146, 'class147': 147, 'class148': 148, 'class149': 149, 'class15': 15, 'class150': 150, 'class151': 151, 'class152': 152, 'class16': 16, 'class17': 17, 'class18': 18, 'class19': 19, 'class2': 2, 'class20': 20, 'class21': 21, 'class22': 22, 'class23': 23, 'class24': 24, 'class25': 25, 'class26': 26, 'class27': 27, 'class28': 28, 'class29': 29, 'class3': 3, 'class30': 30, 'class31': 31, 'class32': 32, 'class33': 33, 'class34': 34, 'class35': 35, 'class36': 36, 'class37': 37, 'class38': 38, 'class39': 39, 'class4': 4, 'class40': 40, 'class41': 41, 'class42': 42, 'class43': 43, 'class44': 44, 'class45': 45, 'class46': 46, 'class47': 47, 'class48': 48, 'class49': 49, 'class5': 5, 'class50': 50, 'class51': 51, 'class52': 52, 'class53': 53, 'class54': 54, 'class55': 55, 'class56': 56, 'class57': 57, 'class58': 58, 'class59': 59, 'class6': 6, 'class60': 60, 'class61': 61, 'class62': 62, 'class63': 63, 'class64': 64, 'class65': 65, 'class66': 66, 'class67': 67, 'class68': 68, 'class69': 69, 'class7': 7, 'class70': 70, 'class71': 71, 'class72': 72, 'class73': 73, 'class74': 74, 'class75': 75, 'class76': 76, 'class77': 77, 'class78': 78, 'class79': 79, 'class8': 8, 'class80': 80, 'class81': 81, 'class82': 82, 'class83': 83, 'class84': 84, 'class85': 85, 'class86': 86, 'class87': 87, 'class88': 88, 'class89': 89, 'class9': 9, 'class90': 90, 'class91': 91, 'class92': 92, 'class93': 93, 'class94': 94, 'class95': 95, 'class96': 96, 'class97': 97, 'class98': 98, 'class99': 99}, 'numTraining': 1886}",
|
| 17 |
+
"device": "cuda:0",
|
| 18 |
+
"disable_checkpointing": "False",
|
| 19 |
+
"fold": "0",
|
| 20 |
+
"folder_with_segs_from_previous_stage": "None",
|
| 21 |
+
"gpu_name": "NVIDIA A100 80GB PCIe",
|
| 22 |
+
"grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x2b54f0f78a50>",
|
| 23 |
+
"hostname": "2117ga002",
|
| 24 |
+
"inference_allowed_mirroring_axes": "(0, 1, 2)",
|
| 25 |
+
"initial_lr": "0.01",
|
| 26 |
+
"is_cascaded": "False",
|
| 27 |
+
"is_ddp": "False",
|
| 28 |
+
"label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x2b54f081b7d0>",
|
| 29 |
+
"local_rank": "0",
|
| 30 |
+
"log_file": "/cbica/home/baikk/Projects/DLICVMUSE/nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/training_log_2024_11_7_09_37_20.txt",
|
| 31 |
+
"logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x2b54f00b33d0>",
|
| 32 |
+
"loss": "DeepSupervisionWrapper(\n (loss): DC_and_CE_loss(\n (ce): RobustCrossEntropyLoss()\n (dc): MemoryEfficientSoftDiceLoss()\n )\n)",
|
| 33 |
+
"lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x2b54f08f6ed0>",
|
| 34 |
+
"my_init_kwargs": "{'plans': {'dataset_name': 'Dataset904_Dataset005_DLICVMUSE', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [255, 255, 176], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 45, 'patch_size': [256, 192], 'median_image_size_in_voxels': [240.0, 179.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], '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], 'pool_op_kernel_sizes': [[1, 1], [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]], 'unet_max_num_features': 512, 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device(type='cuda')}",
|
| 35 |
+
"network": "PlainConvUNet",
|
| 36 |
+
"num_epochs": "1000",
|
| 37 |
+
"num_input_channels": "1",
|
| 38 |
+
"num_iterations_per_epoch": "250",
|
| 39 |
+
"num_val_iterations_per_epoch": "50",
|
| 40 |
+
"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)",
|
| 41 |
+
"output_folder": "/cbica/home/baikk/Projects/DLICVMUSE/nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0",
|
| 42 |
+
"output_folder_base": "/cbica/home/baikk/Projects/DLICVMUSE/nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres",
|
| 43 |
+
"oversample_foreground_percent": "0.33",
|
| 44 |
+
"plans_manager": "{'dataset_name': 'Dataset904_Dataset005_DLICVMUSE', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [1.0, 1.0, 1.0], 'original_median_shape_after_transp': [255, 255, 176], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'configurations': {'2d': {'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 45, 'patch_size': [256, 192], 'median_image_size_in_voxels': [240.0, 179.0], 'spacing': [1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], '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], 'pool_op_kernel_sizes': [[1, 1], [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]], '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, 80], 'median_image_size_in_voxels': [191, 184, 137], 'spacing': [1.304773183829245, 1.304773183829245, 1.304773183829245], 'normalization_schemes': ['ZScoreNormalization'], '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, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 1]], '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, 80], 'median_image_size_in_voxels': [249.0, 240.0, 179.0], 'spacing': [1.0, 1.0, 1.0], 'normalization_schemes': ['ZScoreNormalization'], '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, 4], 'pool_op_kernel_sizes': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 1]], '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': 9650.3291015625, 'mean': 522.7075805664062, 'median': 313.0, 'min': -24.0, 'percentile_00_5': 20.0, 'percentile_99_5': 4022.3749035644832, 'std': 644.3214721679688}}}",
|
| 45 |
+
"preprocessed_dataset_folder": "/cbica/home/baikk/Projects/DLICVMUSE/nnunet_preprocessed/Dataset904_Dataset005_DLICVMUSE/nnUNetPlans_3d_fullres",
|
| 46 |
+
"preprocessed_dataset_folder_base": "/cbica/home/baikk/Projects/DLICVMUSE/nnunet_preprocessed/Dataset904_Dataset005_DLICVMUSE",
|
| 47 |
+
"save_every": "50",
|
| 48 |
+
"torch_version": "2.1.0+cu121",
|
| 49 |
+
"unpack_dataset": "True",
|
| 50 |
+
"was_initialized": "True",
|
| 51 |
+
"weight_decay": "3e-05"
|
| 52 |
+
}
|
nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/fold_0/progress.png
ADDED
|
nnunet_results/Dataset904_Dataset005_DLICVMUSE/nnUNetTrainer__nnUNetPlans__3d_fullres/plans.json
ADDED
|
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|
| 1 |
+
{
|
| 2 |
+
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|
| 3 |
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|
| 4 |
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| 5 |
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1.0,
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| 8 |
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| 9 |
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| 10 |
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| 11 |
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255,
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| 12 |
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| 13 |
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],
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| 14 |
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|
| 15 |
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|
| 16 |
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0,
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1,
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| 18 |
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| 19 |
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],
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| 20 |
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| 21 |
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0,
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| 22 |
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| 23 |
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| 24 |
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],
|
| 25 |
+
"configurations": {
|
| 26 |
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|
| 27 |
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|
| 28 |
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"preprocessor_name": "DefaultPreprocessor",
|
| 29 |
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| 30 |
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|
| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 38 |
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| 39 |
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| 41 |
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| 42 |
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| 44 |
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| 45 |
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| 46 |
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 54 |
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| 59 |
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| 65 |
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| 143 |
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| 144 |
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| 145 |
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| 146 |
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| 147 |
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80
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| 153 |
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| 154 |
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| 155 |
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| 156 |
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| 157 |
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137
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| 160 |
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| 163 |
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| 167 |
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| 168 |
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false
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| 169 |
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],
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| 170 |
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| 171 |
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| 172 |
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