text
stringlengths 0
1.69k
|
|---|
####################################################################### |
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_lowres |
{'data_identifier': 'nnUNetPlans_3d_lowres', 'preprocessor_name': 'DefaultPreprocessor', 'batch_size': 2, 'patch_size': [128, 128, 128], 'median_image_size_in_voxels': [204, 199, 199], 'spacing': [2.0118091537065514, 2.0117834028789936, 2.0117834028789936], 'normalization_schemes': ['CTNormalization'], 'use_mask_for_norm': [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.PlainConvUNet', '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], [2, 2, 2]], 'n_conv_per_stage': [2, 2, 2, 2, 2, 2], 'n_conv_per_stage_decoder': [2, 2, 2, 2, 2], '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': False, 'next_stage': '3d_cascade_fullres'} |
These are the global plan.json settings: |
{'dataset_name': 'Dataset040_KiTS', 'plans_name': 'nnUNetPlans', 'original_median_spacing_after_transp': [3.0, 0.78125, 0.78125], 'original_median_shape_after_transp': [108, 512, 512], 'image_reader_writer': 'SimpleITKIO', 'transpose_forward': [2, 0, 1], 'transpose_backward': [1, 2, 0], 'experiment_planner_used': 'ExperimentPlanner', 'label_manager': 'LabelManager', 'foreground_intensity_properties_per_channel': {'0': {'max': 3071.0, 'mean': 102.5714111328125, 'median': 103.0, 'min': -1015.0, 'percentile_00_5': -75.0, 'percentile_99_5': 295.0, 'std': 73.64986419677734}}} |
2024-03-24 16:23:33.907423: unpacking dataset... |
2024-03-24 16:24:09.368879: unpacking done... |
2024-03-24 16:24:09.391333: do_dummy_2d_data_aug: False |
2024-03-24 16:24:09.393250: Creating new 5-fold cross-validation split... |
2024-03-24 16:24:09.395936: Desired fold for training: 1 |
2024-03-24 16:24:09.396037: This split has 168 training and 42 validation cases. |
2024-03-24 16:24:09.404678: Unable to plot network architecture: |
2024-03-24 16:24:09.404788: No module named 'hiddenlayer' |
2024-03-24 16:24:09.412364: |
2024-03-24 16:24:09.412531: Epoch 0 |
2024-03-24 16:24:09.412707: Current learning rate: 0.01 |
2024-03-24 16:27:07.256240: train_loss 0.1091 |
2024-03-24 16:27:07.256982: val_loss -0.0539 |
2024-03-24 16:27:07.257176: Pseudo dice [0.0, 0.0] |
2024-03-24 16:27:07.257360: Epoch time: 177.85 s |
2024-03-24 16:27:07.257494: Yayy! New best EMA pseudo Dice: 0.0 |
2024-03-24 16:27:10.317428: |
2024-03-24 16:27:10.317623: Epoch 1 |
2024-03-24 16:27:10.317797: Current learning rate: 0.00999 |
2024-03-24 16:29:56.174564: train_loss -0.1914 |
2024-03-24 16:29:56.174967: val_loss -0.3155 |
2024-03-24 16:29:56.175123: Pseudo dice [0.7804, 0.0] |
2024-03-24 16:29:56.175256: Epoch time: 165.86 s |
2024-03-24 16:29:56.175370: Yayy! New best EMA pseudo Dice: 0.039 |
2024-03-24 16:30:00.346499: |
2024-03-24 16:30:00.346745: Epoch 2 |
2024-03-24 16:30:00.346931: Current learning rate: 0.00998 |
2024-03-24 16:32:44.736431: train_loss -0.3149 |
2024-03-24 16:32:44.745651: val_loss -0.3736 |
2024-03-24 16:32:44.745988: Pseudo dice [0.8401, 0.0] |
2024-03-24 16:32:44.746165: Epoch time: 164.39 s |
2024-03-24 16:32:44.746289: Yayy! New best EMA pseudo Dice: 0.0771 |
2024-03-24 16:32:48.818360: |
2024-03-24 16:32:48.818691: Epoch 3 |
2024-03-24 16:32:48.818992: Current learning rate: 0.00997 |
2024-03-24 16:35:35.933227: train_loss -0.3816 |
2024-03-24 16:35:35.935286: val_loss -0.3661 |
2024-03-24 16:35:35.935525: Pseudo dice [0.8206, 0.3088] |
2024-03-24 16:35:35.935701: Epoch time: 167.12 s |
2024-03-24 16:35:35.935875: Yayy! New best EMA pseudo Dice: 0.1259 |
2024-03-24 16:35:39.705547: |
2024-03-24 16:35:39.705734: Epoch 4 |
2024-03-24 16:35:39.705885: Current learning rate: 0.00996 |
2024-03-24 16:38:19.357284: train_loss -0.4298 |
2024-03-24 16:38:19.357689: val_loss -0.4284 |
2024-03-24 16:38:19.357903: Pseudo dice [0.8375, 0.4081] |
2024-03-24 16:38:19.358058: Epoch time: 159.65 s |
2024-03-24 16:38:19.358193: Yayy! New best EMA pseudo Dice: 0.1756 |
2024-03-24 16:38:24.072187: |
2024-03-24 16:38:24.072365: Epoch 5 |
2024-03-24 16:38:24.072483: Current learning rate: 0.00995 |
2024-03-24 16:41:02.982842: train_loss -0.4475 |
2024-03-24 16:41:02.983249: val_loss -0.4362 |
2024-03-24 16:41:02.983436: Pseudo dice [0.8722, 0.405] |
2024-03-24 16:41:02.983598: Epoch time: 158.91 s |
2024-03-24 16:41:02.983744: Yayy! New best EMA pseudo Dice: 0.2219 |
2024-03-24 16:41:06.399036: |
2024-03-24 16:41:06.399239: Epoch 6 |
2024-03-24 16:41:06.399403: Current learning rate: 0.00995 |
2024-03-24 16:43:44.046497: train_loss -0.4701 |
2024-03-24 16:43:44.058377: val_loss -0.543 |
2024-03-24 16:43:44.058634: Pseudo dice [0.8961, 0.5832] |
2024-03-24 16:43:44.058777: Epoch time: 157.65 s |
2024-03-24 16:43:44.058884: Yayy! New best EMA pseudo Dice: 0.2737 |
2024-03-24 16:43:47.689247: |
2024-03-24 16:43:47.689440: Epoch 7 |
2024-03-24 16:43:47.689574: Current learning rate: 0.00994 |
2024-03-24 16:46:30.363983: train_loss -0.4922 |
2024-03-24 16:46:30.364378: val_loss -0.4981 |
2024-03-24 16:46:30.364542: Pseudo dice [0.8544, 0.5696] |
2024-03-24 16:46:30.364694: Epoch time: 162.68 s |
2024-03-24 16:46:30.364833: Yayy! New best EMA pseudo Dice: 0.3175 |
2024-03-24 16:46:33.709532: |
2024-03-24 16:46:33.709749: Epoch 8 |
2024-03-24 16:46:33.709939: Current learning rate: 0.00993 |
2024-03-24 16:49:24.680205: train_loss -0.5211 |
2024-03-24 16:49:24.680538: val_loss -0.5475 |
2024-03-24 16:49:24.680671: Pseudo dice [0.8922, 0.5681] |
2024-03-24 16:49:24.680870: Epoch time: 170.97 s |
2024-03-24 16:49:24.680990: Yayy! New best EMA pseudo Dice: 0.3588 |
2024-03-24 16:49:29.747880: |
2024-03-24 16:49:29.748293: Epoch 9 |
2024-03-24 16:49:29.748466: Current learning rate: 0.00992 |
2024-03-24 16:52:00.722276: train_loss -0.5371 |
2024-03-24 16:52:00.722630: val_loss -0.5438 |
2024-03-24 16:52:00.722799: Pseudo dice [0.9008, 0.5743] |
End of preview. Expand
in Data Studio
README.md exists but content is empty.
- Downloads last month
- 49