Instructions to use UCSC-VLAA/openvision-vit-tiny-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-tiny-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-tiny-patch16-160")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/openvision-vit-tiny-patch16-160", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68b6e7a0d65ffdbea67d8e83f97bda1fe907d6a4d0b6518d74f18de481f670b9
- Size of remote file:
- 68.4 MB
- SHA256:
- 62d5759980fc87b411f308d9d7a8cd3bfcb186801e9a3984418359e7964eff8a
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