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--- |
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base_model: |
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- google/siglip-so400m-patch14-384 |
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language: |
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- en |
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- zh |
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license: apache-2.0 |
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pipeline_tag: image-feature-extraction |
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--- |
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# Oryx-ViT |
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## Model Summary |
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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). |
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- **Repository:** https://github.com/Oryx-mllm/Oryx |
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- **Project Page:** https://oryx-mllm.github.io |
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- **Languages:** English, Chinese |
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### Model Architecture |
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- **Architecture:** SigLip |
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- **Data:** a mixture of 200M data, 2 epoch |
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- **Precision:** BFloat16 |
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#### Hardware & Software |
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- **Hardware:** 64 * NVIDIA Tesla A100 |
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- **Orchestration:** HuggingFace Trainer |
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- **Code:** Pytorch |
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## Citation |
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```bibtex |
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@article{liu2024oryx, |
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title={Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution}, |
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author={Liu, Zuyan and Dong, Yuhao and Liu, Ziwei and Hu, Winston and Lu, Jiwen and Rao, Yongming}, |
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journal={arXiv preprint arXiv:2409.12961}, |
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year={2024} |
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} |
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``` |
