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2025-01-21 05:06:36.981287: predicting image_791 |
2025-01-21 05:06:39.631922: predicting image_797 |
2025-01-21 05:06:42.262951: predicting image_800 |
2025-01-21 05:06:44.902789: predicting image_801 |
2025-01-21 05:06:47.554479: predicting image_811 |
2025-01-21 05:06:50.202246: predicting image_822 |
2025-01-21 05:06:52.899313: predicting image_823 |
2025-01-21 05:06:55.551948: predicting image_834 |
2025-01-21 05:06:58.194482: predicting image_835 |
2025-01-21 05:07:00.835891: predicting image_841 |
2025-01-21 05:07:03.514181: predicting image_847 |
2025-01-21 05:07:06.172353: predicting image_85 |
2025-01-21 05:07:08.859811: predicting image_850 |
2025-01-21 05:07:11.494952: predicting image_854 |
2025-01-21 05:07:14.152591: predicting image_857 |
2025-01-21 05:07:16.799411: predicting image_868 |
2025-01-21 05:07:19.481480: predicting image_869 |
2025-01-21 05:07:22.118010: predicting image_874 |
2025-01-21 05:07:24.774720: predicting image_875 |
2025-01-21 05:07:27.422157: predicting image_884 |
2025-01-21 05:07:30.077412: predicting image_885 |
2025-01-21 05:07:32.717672: predicting image_888 |
2025-01-21 05:07:35.363754: predicting image_896 |
2025-01-21 05:07:38.051916: predicting image_899 |
2025-01-21 05:07:40.688547: predicting image_90 |
2025-01-21 05:07:43.383483: predicting image_900 |
2025-01-21 05:07:46.025166: predicting image_905 |
2025-01-21 05:07:48.725361: predicting image_91 |
2025-01-21 05:07:51.369204: predicting image_910 |
2025-01-21 05:07:54.015398: predicting image_915 |
2025-01-21 05:07:56.651405: predicting image_918 |
2025-01-21 05:07:59.296809: predicting image_927 |
2025-01-21 05:08:01.957754: predicting image_928 |
2025-01-21 05:08:04.617650: predicting image_933 |
2025-01-21 05:08:07.251575: predicting image_934 |
2025-01-21 05:08:09.888778: predicting image_939 |
2025-01-21 05:08:12.541586: predicting image_97 |
2025-01-21 05:08:35.964244: Validation complete |
2025-01-21 05:08:35.967590: Mean Validation Dice: 0.752500536524901 |
####################################################################### |
Please cite the following paper when using nnU-Net: |
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. |
####################################################################### |
This is the configuration used by this training: |
Configuration name: 2d |
{'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 13, 'patch_size': [448, 576], 'median_image_size_in_voxels': [2464.0, 3280.0], '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, 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} |
These are the global plan.json settings: |
{'dataset_name': 'Dataset999_ChronoRootTest', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [999.0, 1.0, 1.0], 'original_median_shape_after_transp': [1, 2464, 3280], 'image_reader_writer': 'NaturalImage2DIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 227.0, 'mean': 144.27783203125, 'median': 147.0, 'min': 0.0, 'percentile_00_5': 44.0, 'percentile_99_5': 201.0, 'std': 27.187984466552734}}} |
2023-11-09 09:28:06.041052: unpacking dataset... |
####################################################################### |
Please cite the following paper when using nnU-Net: |
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. |
####################################################################### |
This is the configuration used by this training: |
Configuration name: 2d |
{'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 13, 'patch_size': [448, 576], 'median_image_size_in_voxels': [2464.0, 3280.0], '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, 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} |
These are the global plan.json settings: |
{'dataset_name': 'Dataset999_ChronoRootTest', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [999.0, 1.0, 1.0], 'original_median_shape_after_transp': [1, 2464, 3280], 'image_reader_writer': 'NaturalImage2DIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 227.0, 'mean': 144.27783203125, 'median': 147.0, 'min': 0.0, 'percentile_00_5': 44.0, 'percentile_99_5': 201.0, 'std': 27.187984466552734}}} |
2023-05-31 23:39:01.165428: unpacking dataset... |
####################################################################### |
Please cite the following paper when using nnU-Net: |
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. |
####################################################################### |
This is the configuration used by this training: |
Configuration name: 2d |
{'data_identifier': 'nnUNetPlans_2d', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 13, 'patch_size': [448, 576], 'median_image_size_in_voxels': [2464.0, 3280.0], '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, 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} |
These are the global plan.json settings: |
{'dataset_name': 'Dataset999_ChronoRootTest', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [999.0, 1.0, 1.0], 'original_median_shape_after_transp': [1, 2464, 3280], 'image_reader_writer': 'NaturalImage2DIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 227.0, 'mean': 144.27783203125, 'median': 147.0, 'min': 0.0, 'percentile_00_5': 44.0, 'percentile_99_5': 201.0, 'std': 27.187984466552734}}} |
2023-05-31 23:45:35.153800: unpacking dataset... |
2023-05-31 23:45:48.310938: unpacking done... |
2023-05-31 23:45:48.314584: do_dummy_2d_data_aug: False |
2023-05-31 23:45:48.316683: Creating new 5-fold cross-validation split... |
2023-05-31 23:45:48.318486: Desired fold for training: 0 |
2023-05-31 23:45:48.318544: This split has 420 training and 106 validation cases. |
2023-05-31 23:45:57.222927: Unable to plot network architecture: |
2023-05-31 23:45:57.223130: module 'torch.onnx' has no attribute '_optimize_trace' |
2023-05-31 23:45:57.271339: |
2023-05-31 23:45:57.271410: Epoch 0 |
2023-05-31 23:45:57.271488: Current learning rate: 0.01 |
2023-05-31 23:58:55.741701: train_loss 0.0007 |
2023-05-31 23:58:55.741876: val_loss -0.3272 |
2023-05-31 23:58:55.742007: Pseudo dice [0.3262] |
2023-05-31 23:58:55.742095: Epoch time: 778.47 s |
2023-05-31 23:58:55.742163: Yayy! New best EMA pseudo Dice: 0.3262 |
2023-05-31 23:58:57.036986: |
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