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
| license: apache-2.0 | |
| pipeline_tag: image-feature-extraction | |
| library_name: transformers | |
| # OpenVision | |
| This repository contains the OpenVision model described in the paper [OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision Encoders for Multimodal Learning](https://huggingface.co/papers/2505.04601). | |
| Project page: https://ucsc-vlaa.github.io/OpenVision | |
| Code: https://github.com/UCSC-VLAA/OpenVision |