Instructions to use mtilerisoyy/medsiglip-448-ft-crc100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtilerisoyy/medsiglip-448-ft-crc100k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="mtilerisoyy/medsiglip-448-ft-crc100k") 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("mtilerisoyy/medsiglip-448-ft-crc100k") model = AutoModelForZeroShotImageClassification.from_pretrained("mtilerisoyy/medsiglip-448-ft-crc100k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f2ce53165beb21d1331b2132b5012183250fa9e20403484d4d03d955468164c
- Size of remote file:
- 5.37 kB
- SHA256:
- ca4fd631a11a64611d27f65a8d8888480c1675c56260520f84a8401fd04cd20c
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