Zero-Shot Image Classification
Transformers
Safetensors
siglip
vision
medical
radiology
dermatology
pathology
ophthalmology
chest-x-ray
Instructions to use fokan/MedSigLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fokan/MedSigLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="fokan/MedSigLIP") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("fokan/MedSigLIP") model = AutoModelForZeroShotImageClassification.from_pretrained("fokan/MedSigLIP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 360 Bytes
4522ddc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [0.5, 0.5, 0.5],
"image_processor_type": "SiglipImageProcessor",
"image_std": [0.5, 0.5, 0.5],
"processor_class": "SiglipProcessor",
"resample": 3,
"rescale_factor": 0.00392156862,
"size": {
"height": 448,
"width": 448
}
}
|