Instructions to use darragh/swinunetr-btcv-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darragh/swinunetr-btcv-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("darragh/swinunetr-btcv-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from darragh/swinunetr-btcv-base: direct link, hf CLI and curl.
- Browser
- Download file 382 Bytes
-
https://huggingface.co/darragh/swinunetr-btcv-base/resolve/main/config.json
- Command line
-
hf download hf://darragh/swinunetr-btcv-base/config.json
-
curl -L -o config.json https://huggingface.co/darragh/swinunetr-btcv-base/resolve/main/config.json
382 Bytes
| { | |
| "architecture": "SwinUNETR", | |
| "img_size": 96, | |
| "in_channels": 1, | |
| "out_channels": 14, | |
| "depths": [ | |
| 2, | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| "num_heads": [ | |
| 3, | |
| 6, | |
| 12, | |
| 24 | |
| ], | |
| "feature_size": 48, | |
| "norm_name": "instance", | |
| "drop_rate": 0.0, | |
| "attn_drop_rate": 0.0, | |
| "dropout_path_rate": 0.0, | |
| "normalize": true, | |
| "use_checkpoint": false, | |
| "spatial_dims": 3 | |
| } | |