Feature Extraction
Transformers
Safetensors
PyTorch
brain-mri-siglip
medical-imaging
mri
brain-mri
siglip
vision-language
contrastive-learning
custom-code
custom_code
Instructions to use shenxiaochen/brain-mri-siglip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shenxiaochen/brain-mri-siglip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="shenxiaochen/brain-mri-siglip", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shenxiaochen/brain-mri-siglip", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "canonicalize_orientation": true, | |
| "clip_percentiles": [ | |
| 0.5, | |
| 99.5 | |
| ], | |
| "crop_margin": 4, | |
| "do_clip": true, | |
| "do_crop_foreground": true, | |
| "do_normalize": true, | |
| "effective_pad_value": -1.0, | |
| "foreground_threshold": 0.001, | |
| "image_processor_type": "BrainMRISiglipVolumeProcessor", | |
| "interpolation_mode": "trilinear", | |
| "max_channel_dim": 4, | |
| "output_range": [ | |
| -1.0, | |
| 1.0 | |
| ], | |
| "pad_value": null, | |
| "path_background_value": -1.0, | |
| "path_crop_margin_mm": 5.0, | |
| "path_foreground_strategy": "largest_component_nonzero", | |
| "path_foreground_threshold": 0.001, | |
| "path_generic_cache_version": 1, | |
| "path_generic_recipe_id": "generic_foreground_128x192x192_fp16_v1", | |
| "path_recipe_mode": "auto", | |
| "path_target_shape": [ | |
| 128, | |
| 192, | |
| 192 | |
| ], | |
| "path_target_spacing": [ | |
| 1.25, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "prefer_nibabel_resample": false, | |
| "resize_strategy": "pad_or_crop", | |
| "spacing": [ | |
| 1.25, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "spacing_tolerance": 0.001, | |
| "use_foreground_intensity_stats": true, | |
| "volume_size": [ | |
| 128, | |
| 192, | |
| 192 | |
| ] | |
| } | |