VisualWebInstruct is a large-scale, diverse multimodal instruction dataset designed to enhance vision-language models' reasoning capabilities. The dataset contains approximately 900K question-answer (QA) pairs, with 40% consisting of visual QA pairs associated with 163,743 unique images, while the remaining 60% are text-only QA pairs.
Please also checkout our more recent verified version at Huggingface.… See the full description on the dataset page:
https://huggingface.co/datasets/TIGER-Lab/VisualWebInstruct.
