Oryx ViT

--- base_model: - google/siglip-so400m-patch14-384 language: - en - zh license: apache-2.0 pipeline_tag: image-feature-extraction --- # Oryx-ViT ## Model Summary The Oryx-ViT model is trained on 200M data and can seamlessly and efficiently process visual inputs with arbitrary spatial sizes and temporal lengths. It is described in the paper [Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution](https://arxiv.org/abs/2409.12961). - **Repository:** https://github.com/Oryx-mllm/Oryx - **Project Page:** https://oryx-mllm.github.io - **Languages:** English, Chinese ### Model Architecture - **Architecture:** SigLip - **Data:** a mixture of 200M data, 2 epoch - **Precision:** BFloat16 #### Hardware & Software - **Hardware:** 64 * NVIDIA Tesla A100 - **Orchestration:** HuggingFace Trainer - **Code:** Pytorch ## Citation ```bibtex @article{liu2024oryx, title={Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution}, author={Liu, Zuyan and Dong, Yuhao and Liu, Ziwei and Hu, Winston and Lu, Jiwen and Rao, Yongming}, journal={arXiv preprint arXiv:2409.12961}, year={2024} } ```

Source context: 0 downloads · 8 likes · Pipeline image-feature-extraction · Repo THUdyh/Oryx-ViT