Instructions to use Veritone/siglip2-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Veritone/siglip2-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Veritone/siglip2-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("Veritone/siglip2-base-patch16-224") model = AutoModelForZeroShotImageClassification.from_pretrained("Veritone/siglip2-base-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Mirror google/siglip2-base-patch16-224
Verified signature00cc30c -
Remove stray text-only duplicate (see model.safetensors fix)
Verified signatured7397e4 -
Mirror google/siglip2-base-patch16-224
Verified signature1b0da8d
-
Mirror google/siglip2-base-patch16-224
Verified signature7b764f9 -
initial commit
Verified signaturea7e8508