LLaVA Visual Instruct CC3M 595K Pretrain Dataset Card

Dataset details

Dataset type: LLaVA Visual Instruct CC3M Pretrain 595K is a subset of CC-3M dataset, filtered with a more balanced concept coverage distribution. Captions are also associated with BLIP synthetic caption for reference. It is constructed for the pretraining stage for feature alignment in visual instruction tuning. We aim to build large multimodal towards GPT-4 vision/language capability.

Dataset date: LLaVA Visual Instruct CC3M Pretrain 595K was created in April 2023.

Dataset structure:

• chat.json contains the multimodal synthesized conversation from the image-caption pairs, by adding randomly selected instructions like: "Describe this image". It is used for pretraining in LLaVA. We use the raw CC-3M caption as the default answer.
• metadata.json contains the meta data of the image index in CC-3M, image file name, image URL, original CC-3M caption, synthetic BLIP caption. Note that ~10% of the samples are not associated with BLIP caption yet in this release.
• images.zip contains all raw images of the filtered subset from CC-3M. Important notice: Upon the request from the community, as ~15% images of the original CC-3M dataset are no longer accessible, we upload images.zip for better reproducing our work in research community. It should not be used for any other purpose. The use of these images must comply with the CC-3M license. This may be taken down when requested by the original CC-3M dataset owner or owners of the referenced images.

Paper or resources for more information: https://llava-vl.github.io/

License: Must comply with license of CC-3M , BLIP (if you use their synthetic caption).

CC-3M The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Where to send questions or comments about the model: https://github.com/haotian-liu/LLaVA/issues

Intended use

Primary intended uses: The primary use of LLaVA is research on large multimodal models and chatbots.

Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.