Instructions to use google/siglip-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/siglip-base-patch16-224") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") model = AutoModelForZeroShotImageClassification.from_pretrained("google/siglip-base-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from google/siglip-base-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 813 MB
-
https://huggingface.co/google/siglip-base-patch16-224/resolve/main/model.safetensors
- Command line
-
hf download hf://google/siglip-base-patch16-224/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/google/siglip-base-patch16-224/resolve/main/model.safetensors
813 MB
- Xet hash:
- 421489ed67220ff3cf58fefe883271f5dd6fd1bca1f2d24157453b5cc8f82f88
- Size of remote file:
- 813 MB
- SHA256:
- 2c63cb7d1f2e95ba501893cbb8faeb4ea9a3af295498d35097126228659c2af8
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