Instructions to use UCSC-VLAA/openvision-vit-small-patch16-160 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-VLAA/openvision-vit-small-patch16-160 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="UCSC-VLAA/openvision-vit-small-patch16-160")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/openvision-vit-small-patch16-160", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b053b0b685ad5a3e020e07db12d9805f37137310930e8a1a0da5ce6b97cbfb9e
- Size of remote file:
- 222 MB
- SHA256:
- 6d77e6cfe69312306cec8fcc3720dd9b873c153fd28b450d4118e0f5a22ec708
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