Instructions to use anhaltai/swinunetrv2_BraTS2021_mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anhaltai/swinunetrv2_BraTS2021_mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="anhaltai/swinunetrv2_BraTS2021_mini", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("anhaltai/swinunetrv2_BraTS2021_mini", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "SwinUNETRv2Model" | |
| ], | |
| "attn_drop_rate": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_swinvunetr2.SwinUNETRv2Config", | |
| "AutoModel": "modeling_swinunetrv2.SwinUNETRv2Model" | |
| }, | |
| "depths": [ | |
| 2, | |
| 2, | |
| 2, | |
| 2 | |
| ], | |
| "downsample": "merging", | |
| "drop_rate": 0.0, | |
| "dropout_path_rate": 0.0, | |
| "feature_size": 48, | |
| "in_channels": 1, | |
| "mlp_ratio": 4.0, | |
| "model_type": "swinunetrv2", | |
| "norm_name": "instance", | |
| "normalize": true, | |
| "num_heads": [ | |
| 3, | |
| 6, | |
| 12, | |
| 24 | |
| ], | |
| "num_layers": 4, | |
| "out_channels": 5, | |
| "out_features": [ | |
| "stage4" | |
| ], | |
| "out_indices": [ | |
| 4 | |
| ], | |
| "patch_norm": false, | |
| "patch_size": 2, | |
| "qkv_bias": true, | |
| "spatial_dims": 3, | |
| "stage_names": [ | |
| "stem", | |
| "stage1", | |
| "stage2", | |
| "stage3", | |
| "stage4" | |
| ], | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.55.2", | |
| "use_checkpoint": false, | |
| "window_size": 7 | |
| } | |