Instructions to use prithivMLmods/SigLIP2-ImageShield-2n-large-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/SigLIP2-ImageShield-2n-large-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/SigLIP2-ImageShield-2n-large-256") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/SigLIP2-ImageShield-2n-large-256") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/SigLIP2-ImageShield-2n-large-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/misalignment_distribution_pie.png from prithivMLmods/SigLIP2-ImageShield-2n-large-256: direct link, hf CLI and curl.
- Browser
- Download file 47.4 kB
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https://huggingface.co/prithivMLmods/SigLIP2-ImageShield-2n-large-256/resolve/main/assets/misalignment_distribution_pie.png
- Command line
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hf download hf://prithivMLmods/SigLIP2-ImageShield-2n-large-256/assets/misalignment_distribution_pie.png
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curl -L -o misalignment_distribution_pie.png https://huggingface.co/prithivMLmods/SigLIP2-ImageShield-2n-large-256/resolve/main/assets/misalignment_distribution_pie.png
47.4 kB
