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The Beijing Academy of Artificial Intelligence (hereinafter referred to as "we" or "BAAI") provides you with an open-source dataset (hereinafter referred to as "dataset") through the SVIT HuggingFace repository (https://huggingface.co/datasets/BAAI/SVIT). You can download the dataset you need and use it for purposes such as learning, research, and business, while abiding by the usage rules of each original dataset.
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Dataset Card for SVIT
Scale up visual instruction tuning to millions by GPT-4.
Introduction
We Scale up Visual Instruction Tuning (SVIT) and propose a large-scale dataset with 4.2 million informative instruction tuning data, including 1.6M conversation QA pairs, 1.6M complex reasoning QA pairs, 106K detailed descriptions and 1.0M referring QA pairs, by prompting GPT-4 with the abundant manual annotations of image.
The dataset is built based on Visual Genome and MS-COCO. The original images and the annotations from Visual Genome and MS-COCO are in "raw" folder. The instructions and responses generated by GPT-4 are in "data" folder. Details about the dataset can be found in GitHub or the paper.
- GitHub: https://github.com/BAAI-DCAI/Visual-Instruction-Tuning
- Paper: https://arxiv.org/pdf/2307.04087.pdf
License
The dataset is licensed under a Creative Commons Attribution 4.0 License. It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use. The use of original images and annotations from Visual Genome and MS-COCO should comply with the original licenses.
Contact us
If you have any comments or questions about the dataset, feel free to create an issue in GitHub: https://github.com/BAAI-DCAI/Visual-Instruction-Tuning/issues.
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