Instructions to use bn22/siglip2vision-small-patch16-naflex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bn22/siglip2vision-small-patch16-naflex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bn22/siglip2vision-small-patch16-naflex") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("bn22/siglip2vision-small-patch16-naflex") model = AutoModelForImageClassification.from_pretrained("bn22/siglip2vision-small-patch16-naflex", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8eb3ccebf8a58a81108c08fe8191f18834144e964628db1772749833ac78d43e
- Size of remote file:
- 111 MB
- SHA256:
- 4476f4083fcc4b9e3cfa5e59623eb9ece74505c1a9bef397c5c12fcaeaa85642
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