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
| { | |
| "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 | |
| } | |
| } | |