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
| { | |
| "processor_class": "BrainMRISiglipProcessor", | |
| "auto_map": { | |
| "AutoProcessor": "processing_brain_mri_siglip.BrainMRISiglipProcessor" | |
| }, | |
| "offline_aligned_preprocessing": { | |
| "path_recipe_mode": "auto", | |
| "path_target_shape": [ | |
| 128, | |
| 192, | |
| 192 | |
| ], | |
| "path_target_spacing": [ | |
| 1.25, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "path_crop_margin_mm": 5.0, | |
| "path_foreground_threshold": 0.001, | |
| "path_background_value": -1.0, | |
| "path_foreground_strategy": "largest_component_nonzero", | |
| "path_generic_recipe_id": "generic_foreground_128x192x192_fp16_v1", | |
| "path_generic_cache_version": 1 | |
| } | |
| } |