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####################################################################### |
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: 3d_fullres |
{'data_identifier': 'nnUNetPlans_3d_fullres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [64, 128, 128], 'median_image_size_in_voxels': [64.0, 113.0, 113.0], 'spacing': [0.9765625, 0.5, 0.48828125], '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': [4, 5, 5], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 'conv_kernel_sizes': [[1, 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} |
These are the global plan.json settings: |
{'dataset_name': 'Dataset303_3TT1WMSegASHSGT', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [0.9765625, 0.5, 0.48828125], 'original_median_shape_after_transp': [64, 113, 113], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [1, 0, 2], 'transpose_backward': [1, 0, 2], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 685.0, 'mean': 356.69915771484375, 'median': 396.0, 'min': 117.0, 'percentile_00_5': 149.0, 'percentile_99_5': 570.0, 'std': 120.57451629638672}}} |
2023-08-02 21:58:34.149126: unpacking dataset... |
2023-08-02 21:58:34.473478: unpacking done... |
2023-08-02 21:58:34.474119: do_dummy_2d_data_aug: False |
2023-08-02 21:58:34.474694: Using splits from existing split file: /data/liyue7/Data/M1_nnUNet/nnUNet_preprocessed/Dataset303_3TT1WMSegASHSGT/splits_final.json |
2023-08-02 21:58:34.474854: The split file contains 5 splits. |
2023-08-02 21:58:34.474900: Desired fold for training: 0 |
2023-08-02 21:58:34.474939: This split has 46 training and 12 validation cases. |
2023-08-02 21:58:34.480075: Unable to plot network architecture: |
2023-08-02 21:58:34.480146: No module named 'hiddenlayer' |
2023-08-02 21:58:34.519761: |
2023-08-02 21:58:34.519815: Epoch 0 |
2023-08-02 21:58:34.519891: Current learning rate: 0.01 |
2023-08-02 22:00:03.004448: train_loss -0.1602 |
2023-08-02 22:00:03.004621: val_loss -0.6431 |
2023-08-02 22:00:03.004667: Pseudo dice [0.7123] |
2023-08-02 22:00:03.004718: Epoch time: 88.49 s |
2023-08-02 22:00:03.004757: Yayy! New best EMA pseudo Dice: 0.7123 |
2023-08-02 22:00:04.079823: |
2023-08-02 22:00:04.079919: Epoch 1 |
2023-08-02 22:00:04.080002: Current learning rate: 0.00998 |
2023-08-02 22:01:06.331137: train_loss -0.6851 |
2023-08-02 22:01:06.331275: val_loss -0.7601 |
2023-08-02 22:01:06.331314: Pseudo dice [0.8097] |
2023-08-02 22:01:06.331359: Epoch time: 62.25 s |
2023-08-02 22:01:06.331396: Yayy! New best EMA pseudo Dice: 0.7221 |
2023-08-02 22:01:08.636403: |
2023-08-02 22:01:08.636506: Epoch 2 |
2023-08-02 22:01:08.636586: Current learning rate: 0.00995 |
2023-08-02 22:02:11.011815: train_loss -0.7477 |
2023-08-02 22:02:11.011961: val_loss -0.8005 |
2023-08-02 22:02:11.012002: Pseudo dice [0.8433] |
2023-08-02 22:02:11.012047: Epoch time: 62.38 s |
2023-08-02 22:02:11.012084: Yayy! New best EMA pseudo Dice: 0.7342 |
2023-08-02 22:02:13.056767: |
2023-08-02 22:02:13.056870: Epoch 3 |
2023-08-02 22:02:13.056954: Current learning rate: 0.00993 |
2023-08-02 22:03:15.491260: train_loss -0.7857 |
2023-08-02 22:03:15.491742: val_loss -0.8215 |
2023-08-02 22:03:15.491786: Pseudo dice [0.8605] |
2023-08-02 22:03:15.491831: Epoch time: 62.44 s |
2023-08-02 22:03:15.491870: Yayy! New best EMA pseudo Dice: 0.7468 |
2023-08-02 22:03:17.505135: |
2023-08-02 22:03:17.505237: Epoch 4 |
2023-08-02 22:03:17.505322: Current learning rate: 0.00991 |
2023-08-02 22:04:19.921209: train_loss -0.8016 |
2023-08-02 22:04:19.921346: val_loss -0.8273 |
2023-08-02 22:04:19.921388: Pseudo dice [0.8615] |
2023-08-02 22:04:19.921433: Epoch time: 62.42 s |
2023-08-02 22:04:19.921470: Yayy! New best EMA pseudo Dice: 0.7583 |
2023-08-02 22:04:21.908744: |
2023-08-02 22:04:21.908846: Epoch 5 |
2023-08-02 22:04:21.908923: Current learning rate: 0.00989 |
2023-08-02 22:05:24.340911: train_loss -0.8115 |
2023-08-02 22:05:24.341050: val_loss -0.8428 |
2023-08-02 22:05:24.341090: Pseudo dice [0.8746] |
2023-08-02 22:05:24.341135: Epoch time: 62.43 s |
2023-08-02 22:05:24.341172: Yayy! New best EMA pseudo Dice: 0.7699 |
2023-08-02 22:05:26.416294: |
2023-08-02 22:05:26.416399: Epoch 6 |
2023-08-02 22:05:26.416478: Current learning rate: 0.00986 |
2023-08-02 22:06:28.831770: train_loss -0.8274 |
2023-08-02 22:06:28.831909: val_loss -0.8475 |
2023-08-02 22:06:28.831949: Pseudo dice [0.8801] |
2023-08-02 22:06:28.831995: Epoch time: 62.42 s |
2023-08-02 22:06:28.832032: Yayy! New best EMA pseudo Dice: 0.7809 |
2023-08-02 22:06:30.999928: |
2023-08-02 22:06:31.000031: Epoch 7 |
2023-08-02 22:06:31.000112: Current learning rate: 0.00984 |
2023-08-02 22:07:33.432782: train_loss -0.8394 |
2023-08-02 22:07:33.432915: val_loss -0.8555 |
2023-08-02 22:07:33.432956: Pseudo dice [0.8845] |
2023-08-02 22:07:33.433001: Epoch time: 62.43 s |
2023-08-02 22:07:33.433038: Yayy! New best EMA pseudo Dice: 0.7913 |
2023-08-02 22:07:35.622762: |
2023-08-02 22:07:35.622863: Epoch 8 |
2023-08-02 22:07:35.622944: Current learning rate: 0.00982 |
2023-08-02 22:08:38.060140: train_loss -0.838 |
2023-08-02 22:08:38.060285: val_loss -0.8542 |
2023-08-02 22:08:38.060333: Pseudo dice [0.883] |
2023-08-02 22:08:38.060412: Epoch time: 62.44 s |
2023-08-02 22:08:38.060493: Yayy! New best EMA pseudo Dice: 0.8005 |
2023-08-02 22:08:40.178575: |
2023-08-02 22:08:40.178674: Epoch 9 |
2023-08-02 22:08:40.178754: Current learning rate: 0.0098 |
2023-08-02 22:09:42.615384: train_loss -0.8516 |
2023-08-02 22:09:42.615526: val_loss -0.8548 |
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