Instructions to use UCSC-VLAA/openvision-vit-base-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-base-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-base-patch8-384")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/openvision-vit-base-patch8-384", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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by nielsr HF Staff - opened
README.md
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---
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pipeline_tag: image-feature-extraction
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library_name: transformers
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license: apache-2.0
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---
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This repository contains the OpenVision model, a fully-open and cost-effective family of advanced vision encoders for multimodal learning, as described in the paper [OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision Encoders for Multimodal Learning](https://huggingface.co/papers/2505.04601).
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Project Page: https://ucsc-vlaa.github.io/OpenVision/
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Code: https://github.com/UCSC-VLAA/OpenVision
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