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
File size: 1,078 Bytes
8360541 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | {
"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
]
}
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