Image Segmentation
Transformers
PyTorch
repcontur
feature-extraction
medical
radiology
ct
3d
tumor-segmentation
referring-segmentation
report-generation
visual-question-answering
custom_code
Instructions to use Oncosoft-Research/RepConTUR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Oncosoft-Research/RepConTUR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Oncosoft-Research/RepConTUR", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Oncosoft-Research/RepConTUR", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download plans.json from Oncosoft-Research/RepConTUR: direct link, hf CLI and curl.
- Browser
- Download file 4.14 kB
-
https://huggingface.co/Oncosoft-Research/RepConTUR/resolve/refs%2Fpr%2F1/plans.json
- Command line
-
hf download hf://Oncosoft-Research/RepConTUR@refs/pr/1/plans.json
-
curl -L -o plans.json https://huggingface.co/Oncosoft-Research/RepConTUR/resolve/refs%2Fpr%2F1/plans.json
4.14 kB
| { | |
| "dataset_name": "VoxTellDataset", | |
| "image_reader_writer": "NibabelIOWithReorient", | |
| "configurations": { | |
| "3d_fullres": { | |
| "data_identifier": "nnUNetResEncUNetLPlans_noResampling_3d_fullres", | |
| "preprocessor_name": "DefaultPreprocessor", | |
| "batch_size": 2, | |
| "patch_size": [ | |
| 192, | |
| 192, | |
| 192 | |
| ], | |
| "normalization_schemes": [ | |
| "ZScoreNormalization" | |
| ], | |
| "architecture": { | |
| "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet", | |
| "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_blocks_per_stage": [ | |
| 1, | |
| 3, | |
| 4, | |
| 6, | |
| 6, | |
| 6 | |
| ], | |
| "n_conv_per_stage_decoder": [ | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| 1 | |
| ], | |
| "conv_bias": true, | |
| "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d", | |
| "norm_op_kwargs": { | |
| "eps": 1e-05, | |
| "affine": true | |
| }, | |
| "dropout_op": null, | |
| "dropout_op_kwargs": null, | |
| "nonlin": "torch.nn.LeakyReLU", | |
| "nonlin_kwargs": { | |
| "inplace": true | |
| } | |
| }, | |
| "_kw_requires_import": [ | |
| "conv_op", | |
| "norm_op", | |
| "dropout_op", | |
| "nonlin" | |
| ] | |
| }, | |
| "batch_dice": false | |
| } | |
| }, | |
| "experiment_planner_used": "nnUNetPlannerResEncL_noResampling" | |
| } |