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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. |
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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': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreN... |
These are the global plan.json settings: |
{'dataset_name': 'Dataset001_Prostate158', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.4017857015132904, 0.4017857015132904], 'original_median_shape_after_transp': [25, 270, 270], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [0, 1, 2], 'transpose_backward': [0, 1, 2], 'ex... |
2023-07-24 11:07:14.227682: unpacking dataset... |
2023-07-24 11:07:45.176286: unpacking done... |
2023-07-24 11:07:45.179194: do_dummy_2d_data_aug: True |
2023-07-24 11:07:45.180955: Creating new 5-fold cross-validation split... |
2023-07-24 11:07:45.184430: Desired fold for training: 2 |
2023-07-24 11:07:45.184525: This split has 111 training and 28 validation cases. |
2023-07-24 11:08:14.775316: Unable to plot network architecture: |
2023-07-24 11:08:14.776303: module 'torch.onnx' has no attribute '_optimize_trace' |
2023-07-24 11:08:14.931230: |
2023-07-24 11:08:14.931455: Epoch 0 |
2023-07-24 11:08:14.931655: Current learning rate: 0.01 |
2023-07-24 11:21:17.329870: train_loss 0.0116 |
2023-07-24 11:21:17.331226: val_loss -0.1306 |
2023-07-24 11:21:17.331535: Pseudo dice [0.6784, 0.1535, 0.0] |
2023-07-24 11:21:17.331795: Epoch time: 782.4 s |
2023-07-24 11:21:17.331986: Yayy! New best EMA pseudo Dice: 0.2773 |
2023-07-24 11:21:21.456689: |
2023-07-24 11:21:21.456983: Epoch 1 |
2023-07-24 11:21:21.457272: Current learning rate: 0.00999 |
2023-07-24 11:31:34.266789: train_loss -0.2092 |
2023-07-24 11:31:34.267082: val_loss -0.2198 |
2023-07-24 11:31:34.267474: Pseudo dice [0.676, 0.4748, 0.0] |
2023-07-24 11:31:34.267631: Epoch time: 612.81 s |
2023-07-24 11:31:34.267788: Yayy! New best EMA pseudo Dice: 0.2879 |
2023-07-24 11:31:42.957169: |
2023-07-24 11:31:42.957382: Epoch 2 |
2023-07-24 11:31:42.957557: Current learning rate: 0.00998 |
2023-07-24 11:42:47.672608: train_loss -0.2888 |
2023-07-24 11:42:47.676551: val_loss -0.3046 |
2023-07-24 11:42:47.676843: Pseudo dice [0.7847, 0.5907, 0.0] |
2023-07-24 11:42:47.677034: Epoch time: 664.73 s |
2023-07-24 11:42:47.677184: Yayy! New best EMA pseudo Dice: 0.305 |
2023-07-24 11:42:52.473037: |
2023-07-24 11:42:52.473370: Epoch 3 |
2023-07-24 11:42:52.473569: Current learning rate: 0.00997 |
2023-07-24 11:53:03.992183: train_loss -0.3445 |
2023-07-24 11:53:03.992498: val_loss -0.3752 |
2023-07-24 11:53:03.992702: Pseudo dice [0.7928, 0.6501, 0.1828] |
2023-07-24 11:53:03.992877: Epoch time: 611.52 s |
2023-07-24 11:53:03.993030: Yayy! New best EMA pseudo Dice: 0.3287 |
2023-07-24 11:53:11.643694: |
2023-07-24 11:53:11.644740: Epoch 4 |
2023-07-24 11:53:11.644959: Current learning rate: 0.00996 |
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