Instructions to use yshsdfv/siglip2-base-patch16-naflex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yshsdfv/siglip2-base-patch16-naflex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="yshsdfv/siglip2-base-patch16-naflex") 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("yshsdfv/siglip2-base-patch16-naflex") model = AutoModelForZeroShotImageClassification.from_pretrained("yshsdfv/siglip2-base-patch16-naflex") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07a0b303a10b5e19f2cc84a7e05c5c30092dceb2751e9f4299bbadf5dc4187e1
- Size of remote file:
- 34.4 MB
- SHA256:
- 58a1696e79c9d97937389ed116f552a15c84811d7b8023918b86f4bc5775b1b0
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