From Pixels to Prose: A Large Dataset of Dense Image Captions
Training large vision-language models requires extensive, high-quality
image-text pairs. Existing web-scraped datasets, however, are noisy and lack
detailed image descriptions. To bridge this gap, we introduce PixelProse, a
comprehensive dataset of over 16M (million) synthetically generated captions,
leveraging cutting-edge vision-language models for detailed and accurate
descriptions. To ensure data integrity, we rigorously analyze our dataset for
problematic content, including child sexual abuse material (CSAM), personally
identifiable information (PII), and toxicity. We also provide valuable metadata
such as watermark presence and aesthetic scores, aiding in further dataset
filtering. We hope PixelProse will be a valuable resource for future
vision-language research. PixelProse is available at
https://huggingface.co/datasets/tomg-group-umd/pixelprose
6670ff4b244fc188ec2ee635
Vasu Singla
claimed_verified
2024-07-11T14:19:03.578Z
6352f7f90422304c3e7bf098
/avatars/90bff66d85240415bb43d02c329ffbca.svg
Vasu Singla
user
vasusingla
6670ff4b244fc188ec2ee636
Kaiyu Yue
claimed_verified
2024-06-20T07:34:40.773Z
640d0dbc8036cc2142273a83
https://cdn-avatars.huggingface.co/v1/production/uploads/640d0dbc8036cc2142273a83/cicTWJVqqvQv_DgDucWgY.jpeg
Kaiyu Yue
user
kaiyuyue
6670ff4b244fc188ec2ee637
Sukriti Paul
6670ff4b244fc188ec2ee638
Reza Shirkavand
claimed_verified
2024-06-19T20:21:14.265Z
65466e8b4d1931dc93dbf9a6
/avatars/8626417bac65f431bc50345a024dd570.svg
Reza Shirkavand
user
rezashkv
6670ff4b244fc188ec2ee639
Mayuka Jayawardhana
6670ff4b244fc188ec2ee63a
Alireza Ganjdanesh
6670ff4b244fc188ec2ee63b
Heng Huang
6670ff4b244fc188ec2ee63c
Abhinav Bhatele
6670ff4b244fc188ec2ee63d
Gowthami Somepalli
6670ff4b244fc188ec2ee63e
Tom Goldstein
From Pixels to Prose: A Large Dataset of Dense Image Captions
Training large vision-language models requires extensive, high-quality
image-text pairs. Existing web-scraped datasets, however, are noisy and lack
detailed image descriptions. To bridge this gap, we introduce PixelProse, a
comprehensive dataset of over 16M (million) synthetically generated captions,
leveraging cutting-edge vision-language models for detailed and accurate
descriptions. To ensure data integrity, we rigorously analyze our dataset for
problematic content, including child sexual abuse material (CSAM), personally
identifiable information (PII), and toxicity. We also provide valuable metadata
such as watermark presence and aesthetic scores, aiding in further dataset
filtering. We hope PixelProse will be a valuable resource for future
vision-language research. PixelProse is available at
https://huggingface.co/datasets/tomg-group-umd/pixelprose
vision-language models image-text pairs web-scraped datasets synthetically generated captions data integrity child sexual abuse material personally identifiable information toxicity metadata watermark presence aesthetic scores dataset filtering