Instructions to use prithivMLmods/SigLIP2-ImageShield-90M-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/SigLIP2-ImageShield-90M-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/SigLIP2-ImageShield-90M-224") 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-90M-224") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/SigLIP2-ImageShield-90M-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/training_eval_graph.png from prithivMLmods/SigLIP2-ImageShield-90M-224: direct link, hf CLI and curl.
- Browser
- Download file 104 kB
-
https://huggingface.co/prithivMLmods/SigLIP2-ImageShield-90M-224/resolve/main/assets/training_eval_graph.png
- Command line
-
hf download hf://prithivMLmods/SigLIP2-ImageShield-90M-224/assets/training_eval_graph.png
-
curl -L -o training_eval_graph.png https://huggingface.co/prithivMLmods/SigLIP2-ImageShield-90M-224/resolve/main/assets/training_eval_graph.png
104 kB

- Xet hash:
- 7c952b38b1a074481732d02e940b1c2d9f1083da2e5dc6c668c453ba622c7d39
- Size of remote file:
- 104 kB
- SHA256:
- c4db98a86027aaa4369d80ae592bc2615eb7ec60e9575dec9ecd1c5815667e9a
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