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
Add model card
#1
by nielsr HF Staff - opened
This PR adds a model card for the paper OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision Encoders for Multimodal Learning.
It also adds a relevant pipeline tag and sets Apache 2.0 as the license.
Xianhang changed pull request status to merged