Dataset Viewer
text
stringlengths 0
1.33k
|
|---|
#######################################################################
|
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': [28, 256, 256], 'median_image_size_in_voxels': [25.0, 270.0, 270.0], 'spacing': [2.999998998641968, 0.4017857015132904, 0.4017857015132904], 'normalization_schemes': ['ZScoreNormalization', 'ZScoreNormalization', 'ZScoreNormalization'], 'use_mask_for_norm': [False, False, 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': [2, 6, 6], 'pool_op_kernel_sizes': [[1, 1, 1], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [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': '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], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 1694.0, 'mean': 267.35308837890625, 'median': 242.0, 'min': 0.0, 'percentile_00_5': 36.0, 'percentile_99_5': 768.0, 'std': 136.11251831054688}, '1': {'max': 3557.286865234375, 'mean': 1215.81591796875, 'median': 1203.8331298828125, 'min': 0.0, 'percentile_00_5': 0.0, 'percentile_99_5': 2259.82861328125, 'std': 338.6748352050781}, '2': {'max': 198.95455932617188, 'mean': 72.26309204101562, 'median': 70.3214340209961, 'min': 0.0, 'percentile_00_5': 34.534385681152344, 'percentile_99_5': 132.71939086914062, 'std': 18.909290313720703}}}
|
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
|
No dataset card yet
- Downloads last month
- 10