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# ViP-LLaVA Model Card |
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## Model details |
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**Model type:** |
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ViP-LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on both image level instruction data and region-level instruction data annotated with visual prompts. |
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It is an auto-regressive language model, based on the transformer architecture. |
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**Model date:** |
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ViP-LLaVA-13B was trained in November 2023. |
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**Paper or resources for more information:** |
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https://vip-llava.github.io/ |
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## License |
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Llama 2 is licensed under the LLAMA 2 Community License, |
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Copyright (c) Meta Platforms, Inc. All Rights Reserved. |
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**Where to send questions or comments about the model:** |
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https://github.com/mu-cai/ViP-LLaVA/issues |
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## Intended use |
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**Primary intended uses:** |
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The primary use of ViP-LLaVA is research on large multimodal models and chatbots. |
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**Primary intended users:** |
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The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. |
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## Training dataset |
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- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. |
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- 665K image level instruction data from LLaVA-1.5. |
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- 520K image-text pairs marked with visual prompts. |
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- 13K region-level instruction data generated from GPT-4V. |
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## Evaluation dataset |
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ViP-LLaVA achieves state-of-the-art performance in 4 academic region-level benchmarks and our newly proposed RegionBench. |
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