Instructions to use UCSC-VLAA/openvision-vit-tiny-patch8-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-VLAA/openvision-vit-tiny-patch8-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="UCSC-VLAA/openvision-vit-tiny-patch8-384")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/openvision-vit-tiny-patch8-384", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d500eadc5682a56f2d2063eeeaeba94fd805e70084363ab88a3f8e0560afa74b
- Size of remote file:
- 69.7 MB
- SHA256:
- c66334404cf2859d26c80fbc4e88500930c719e236a1e1c076b141217833a75c
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