add trained model weights

README.md

@@ -2,4 +2,4 @@
 license: apache-2.0
 ---
 
-##Sine-Wave-Transformations-for-Deep-Learning-Based-Tumor-Segmentation-in-CT-PET-Imaging
+## Sine-Wave-Transformations-for-Deep-Learning-Based-Tumor-Segmentation-in-CT-PET-Imaging

nnUNet_results/Dataset101_autopet/nnUNetTrainerUmambaSinNorm__nnUNetResEncUNetMPlans__3d_fullres_bs8/dataset.json

@@ -0,0 +1,16 @@
+{
+    "channel_names": {
+        "0": "CT",
+        "1": "rescale_to_0_1"
+    },
+    "labels": {
+        "background": 0,
+        "tumor": 1
+    },
+    "numTraining": 1611,
+    "file_ending": ".nii.gz",
+    "name": "Combined",
+    "reference": "https://autopet-iii.grand-challenge.org/",
+    "release": "release",
+    "description": "AutoPET3 Multicenter Multitracer Generalization"
+}

nnUNet_results/Dataset101_autopet/nnUNetTrainerUmambaSinNorm__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_all/checkpoint_best.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3396e040442729231e3c62aae79d391b804d374f3690932f787a7e984d696612
+size 831791110

nnUNet_results/Dataset101_autopet/nnUNetTrainerUmambaSinNorm__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_all/debug.json

@@ -0,0 +1,56 @@
+{
+    "_best_ema": "0.8076631",
+    "accumulation_steps": "16",
+    "batch_size": "8",
+    "configuration_manager": "{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 8, 'patch_size': [112, 160, 128], 'median_image_size_in_voxels': [300.0, 400.0, 400.0], 'spacing': [3.0, 2.0364201068878174, 2.0364201068878174], 'normalization_schemes': ['CTNormalization', 'RescaleTo01Normalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}, 'deep_supervision': True}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True, 'inherits_from': '3d_fullres'}",
+    "configuration_name": "3d_fullres_bs8",
+    "cudnn_version": 90100,
+    "current_epoch": "1000",
+    "current_step": "0",
+    "dataloader_train": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7f30abd159f0>",
+    "dataloader_train.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f30abd16cb0>",
+    "dataloader_train.num_processes": "16",
+    "dataloader_train.transform": "None",
+    "dataloader_val": "<batchgenerators.dataloading.nondet_multi_threaded_augmenter.NonDetMultiThreadedAugmenter object at 0x7f30abd16b90>",
+    "dataloader_val.generator": "<nnunetv2.training.dataloading.data_loader_3d.nnUNetDataLoader3D object at 0x7f30abd16bf0>",
+    "dataloader_val.num_processes": "6",
+    "dataloader_val.transform": "None",
+    "dataset_json": "{'channel_names': {'0': 'CT', '1': 'rescale_to_0_1'}, 'labels': {'background': 0, 'tumor': 1}, 'numTraining': 1611, 'file_ending': '.nii.gz', 'name': 'Combined', 'reference': 'https://autopet-iii.grand-challenge.org/', 'release': 'release', 'description': 'AutoPET3 Multicenter Multitracer Generalization'}",
+    "device": "cuda:0",
+    "disable_checkpointing": "False",
+    "enable_deep_supervision": "True",
+    "fold": "all",
+    "folder_with_segs_from_previous_stage": "None",
+    "gpu_name": "NVIDIA A40",
+    "grad_scaler": "<torch.cuda.amp.grad_scaler.GradScaler object at 0x7f3091140d00>",
+    "hostname": "athena",
+    "inference_allowed_mirroring_axes": "(0, 1, 2)",
+    "initial_lr": "0.01",
+    "is_cascaded": "False",
+    "is_ddp": "False",
+    "label_manager": "<nnunetv2.utilities.label_handling.label_handling.LabelManager object at 0x7f3091140e20>",
+    "local_rank": "0",
+    "log_file": "/processing/jintao/nnUNet_results/Dataset101_autopet/nnUNetTrainerUmambaSinNorm__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_all/training_log_2024_9_15_22_29_31.txt",
+    "logger": "<nnunetv2.training.logging.nnunet_logger.nnUNetLogger object at 0x7f30763ec820>",
+    "loss": "DeepSupervisionWrapper(\n  (loss): DC_and_CE_loss(\n    (ce): RobustCrossEntropyLoss()\n    (dc): OptimizedModule(\n      (_orig_mod): MemoryEfficientSoftDiceLoss()\n    )\n  )\n)",
+    "lr_scheduler": "<nnunetv2.training.lr_scheduler.polylr.PolyLRScheduler object at 0x7f30763d6bc0>",
+    "my_init_kwargs": "{'plans': {'dataset_name': 'Dataset101_autopet', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [3.0, 2.0364201068878174, 2.0364201068878174], 'original_median_shape_after_transp': [326, 400, 400], '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': 16, 'patch_size': [448, 448], 'median_image_size_in_voxels': [400.0, 400.0], 'spacing': [2.0364201068878174, 2.0364201068878174], 'normalization_schemes': ['CTNormalization', 'RescaleTo01Normalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetMPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 160, 128], 'median_image_size_in_voxels': [171, 228, 228], 'spacing': [5.260518159231303, 3.5708749840357066, 3.5708749840357066], 'normalization_schemes': ['CTNormalization', 'RescaleTo01Normalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 160, 128], 'median_image_size_in_voxels': [300.0, 400.0, 400.0], 'spacing': [3.0, 2.0364201068878174, 2.0364201068878174], 'normalization_schemes': ['CTNormalization', 'RescaleTo01Normalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres_bs4': {'inherits_from': '3d_fullres', 'batch_size': 4}, '3d_fullres_bs8': {'inherits_from': '3d_fullres', 'batch_size': 8}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3624.52685546875, 'mean': 107.73438968591431, 'median': 71.05622100830078, 'min': -1357.7672119140625, 'percentile_00_5': -832.062744140625, 'percentile_99_5': 1127.758544921875, 'std': 286.34403119451997}, '1': {'max': 392.79351806640625, 'mean': 7.063827929027176, 'median': 4.681584358215332, 'min': 0.09483639150857925, 'percentile_00_5': 1.0433332920074463, 'percentile_99_5': 51.211158752441406, 'std': 7.960414805306728}}}, 'configuration': '3d_fullres_bs8', 'fold': 'all', 'dataset_json': {'channel_names': {'0': 'CT', '1': 'rescale_to_0_1'}, 'labels': {'background': 0, 'tumor': 1}, 'numTraining': 1611, 'file_ending': '.nii.gz', 'name': 'Combined', 'reference': 'https://autopet-iii.grand-challenge.org/', 'release': 'release', 'description': 'AutoPET3 Multicenter Multitracer Generalization'}, 'unpack_dataset': True, 'device': device(type='cuda')}",
+    "network": "OptimizedModule",
+    "new_num_epochs": "1200",
+    "num_epochs": "1000",
+    "num_input_channels": "2",
+    "num_iterations_per_epoch": "250",
+    "num_val_iterations_per_epoch": "50",
+    "optimizer": "SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    initial_lr: 0.01\n    lr: 1.995262314968881e-05\n    maximize: False\n    momentum: 0.99\n    nesterov: True\n    weight_decay: 3e-05\n)",
+    "output_folder": "/processing/jintao/nnUNet_results/Dataset101_autopet/nnUNetTrainerUmambaSinNorm__nnUNetResEncUNetMPlans__3d_fullres_bs8/fold_all",
+    "output_folder_base": "/processing/jintao/nnUNet_results/Dataset101_autopet/nnUNetTrainerUmambaSinNorm__nnUNetResEncUNetMPlans__3d_fullres_bs8",
+    "oversample_foreground_percent": "0.33",
+    "plans_manager": "{'dataset_name': 'Dataset101_autopet', 'plans_name': 'nnUNetResEncUNetMPlans', 'original_median_spacing_after_transp': [3.0, 2.0364201068878174, 2.0364201068878174], 'original_median_shape_after_transp': [326, 400, 400], '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': 16, 'patch_size': [448, 448], 'median_image_size_in_voxels': [400.0, 400.0], 'spacing': [2.0364201068878174, 2.0364201068878174], 'normalization_schemes': ['CTNormalization', 'RescaleTo01Normalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 7, 'features_per_stage': [32, 64, 128, 256, 512, 512, 512], 'conv_op': 'torch.nn.modules.conv.Conv2d', 'kernel_sizes': [[3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3], [3, 3]], 'strides': [[1, 1], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2], [2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm2d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_lowres': {'data_identifier': 'nnUNetResEncUNetMPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 160, 128], 'median_image_size_in_voxels': [171, 228, 228], 'spacing': [5.260518159231303, 3.5708749840357066, 3.5708749840357066], 'normalization_schemes': ['CTNormalization', 'RescaleTo01Normalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': False, 'next_stage': '3d_cascade_fullres'}, '3d_fullres': {'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [112, 160, 128], 'median_image_size_in_voxels': [300.0, 400.0, 400.0], 'spacing': [3.0, 2.0364201068878174, 2.0364201068878174], 'normalization_schemes': ['CTNormalization', 'RescaleTo01Normalization'], 'use_mask_for_norm': [False, False], 'resampling_fn_data': 'resample_data_or_seg_to_shape', 'resampling_fn_seg': 'resample_data_or_seg_to_shape', 'resampling_fn_data_kwargs': {'is_seg': False, 'order': 3, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_seg_kwargs': {'is_seg': True, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'resampling_fn_probabilities': 'resample_data_or_seg_to_shape', 'resampling_fn_probabilities_kwargs': {'is_seg': False, 'order': 1, 'order_z': 0, 'force_separate_z': None}, 'architecture': {'network_class_name': 'dynamic_network_architectures.architectures.unet.ResidualEncoderUNet', 'arch_kwargs': {'n_stages': 6, 'features_per_stage': [32, 64, 128, 256, 320, 320], 'conv_op': 'torch.nn.modules.conv.Conv3d', 'kernel_sizes': [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]], 'strides': [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'n_blocks_per_stage': [1, 3, 4, 6, 6, 6], 'n_conv_per_stage_decoder': [1, 1, 1, 1, 1], 'conv_bias': True, 'norm_op': 'torch.nn.modules.instancenorm.InstanceNorm3d', 'norm_op_kwargs': {'eps': 1e-05, 'affine': True}, 'dropout_op': None, 'dropout_op_kwargs': None, 'nonlin': 'torch.nn.LeakyReLU', 'nonlin_kwargs': {'inplace': True}}, '_kw_requires_import': ['conv_op', 'norm_op', 'dropout_op', 'nonlin']}, 'batch_dice': True}, '3d_fullres_bs4': {'inherits_from': '3d_fullres', 'batch_size': 4}, '3d_fullres_bs8': {'inherits_from': '3d_fullres', 'batch_size': 8}, '3d_fullres_bs16': {'inherits_from': '3d_fullres', 'batch_size': 16}, '3d_cascade_fullres': {'inherits_from': '3d_fullres', 'previous_stage': '3d_lowres'}}, 'experiment_planner_used': 'nnUNetPlannerResEncM', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3624.52685546875, 'mean': 107.73438968591431, 'median': 71.05622100830078, 'min': -1357.7672119140625, 'percentile_00_5': -832.062744140625, 'percentile_99_5': 1127.758544921875, 'std': 286.34403119451997}, '1': {'max': 392.79351806640625, 'mean': 7.063827929027176, 'median': 4.681584358215332, 'min': 0.09483639150857925, 'percentile_00_5': 1.0433332920074463, 'percentile_99_5': 51.211158752441406, 'std': 7.960414805306728}}}",
+    "preprocessed_dataset_folder": "/processing/jintao/nnUNet_preprocessed/Dataset101_autopet/nnUNetPlans_3d_fullres",
+    "preprocessed_dataset_folder_base": "/processing/jintao/nnUNet_preprocessed/Dataset101_autopet",
+    "save_every": "50",
+    "torch_version": "2.4.1",
+    "unpack_dataset": "True",
+    "was_initialized": "True",
+    "weight_decay": "3e-05"
+}

nnUNet_results/Dataset101_autopet/nnUNetTrainerUmambaSinNorm__nnUNetResEncUNetMPlans__3d_fullres_bs8/plans.json

@@ -0,0 +1,548 @@
+{
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