Instructions to use alecocc/medsiglip-448-ft-carisbo-prova with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alecocc/medsiglip-448-ft-carisbo-prova with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="alecocc/medsiglip-448-ft-carisbo-prova") 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("alecocc/medsiglip-448-ft-carisbo-prova") model = AutoModelForZeroShotImageClassification.from_pretrained("alecocc/medsiglip-448-ft-carisbo-prova", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b22855c8e123837d24563d91f36fb4dfb97d0f09aae8c6ca01798a1a51213e81
- Size of remote file:
- 5.84 kB
- SHA256:
- 0cdd0d140d31dbc4d0ae21c4032907fdedb48fc8afbc1ec8edb5a8e36eae3f4e
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