Instructions to use google/siglip-base-patch16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip-base-patch16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/siglip-base-patch16-256") 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("google/siglip-base-patch16-256") model = AutoModelForZeroShotImageClassification.from_pretrained("google/siglip-base-patch16-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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# SigLIP (base-sized model)
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license: apache-2.0
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tags:
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- vision
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widget:
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
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candidate_labels: playing music, playing sports
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example_title: Cat & Dog
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# SigLIP (base-sized model)
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