Instructions to use dleemiller/siglip2-math-base-patch16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dleemiller/siglip2-math-base-patch16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="dleemiller/siglip2-math-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("dleemiller/siglip2-math-base-patch16-256") model = AutoModelForZeroShotImageClassification.from_pretrained("dleemiller/siglip2-math-base-patch16-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8f8f093af7261b739202889c052b88e7e9e25add2a1a85f9c1900cbd80f070a
- Size of remote file:
- 5.78 kB
- SHA256:
- f8a81294b2d7656fd78faa648731093f50738db7a2e554a56c7aff24cc2161bf
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