Dataset Card for Recap-DataComp-1B

Recap-DataComp-1B is a large-scale image-text dataset that has been recaptioned using an advanced LLaVA-1.5-LLaMA3-8B model to enhance the alignment and detail of textual descriptions.

Dataset Details

Dataset Description

Our paper aims to bridge this community effort, leveraging the powerful and open-sourced LLaMA-3, a GPT-4 level LLM. Our recaptioning pipeline is simple: first, we fine-tune a LLaMA-3-8B powered LLaVA-1.5 and then employ it to recaption 1.3 billion images from the DataComp-1B dataset. Our empirical results confirm that this enhanced dataset, Recap-DataComp-1B, offers substantial benefits in training advanced vision-language models. For discriminative models like CLIP, we observe enhanced zero-shot performance in cross-modal retrieval tasks. For generative models like text-to-image Diffusion Transformers, the generated images exhibit a significant improvement in alignment with users' text instructions, especially in following complex queries.

• Curated by: Xianhang Li, Haoqin Tu, Mude Hui, Zeyu Wang, Bingchen Zhao, Junfei Xiao, Sucheng Ren, Jieru Mei, Qing Liu, Huangjie Zheng, Yuyin Zhou, Cihang Xie
• License: cc-by-4.0

Dataset Sources

• Repository: https://github.com/UCSC-VLAA/Recap-DataComp-1B
• Paper: https://arxiv.org/abs/2406.08478

Uses

Direct Use

Recap-DataComp-1B is intended for training advanced vision-language models, including discriminative models like CLIP and generative models such as text-to-image Diffusion Transformers. It can be used for tasks such as zero-shot classification, cross-modal retrieval, and text-to-image generation.

Out-of-Scope Use

The dataset is not suitable for applications requiring highly accurate and sensitive personal data, as the recaptioned data may still contain noise and inaccuracies from the original web-crawled data.

Dataset Structure

The dataset contains fields for image URLs, original captions, recaptioned text, and other metadata such as sha256 hashes. It is structured to facilitate easy access and use for training vision-language models.

Dataset Creation

Curation Rationale

The dataset was created to address the noise and misalignment issues present in web-crawled image-text pairs, aiming to improve the performance of vision-language models by providing more semantically rich and well-aligned captions.

Source Data

The source data is web-crawled image-text pairs from the DataComp-1B dataset, which has been curated from a larger collection of 12.8 billion image-text pairs.

Data Collection and Processing

Data was collected through web crawling and subjected to rigorous preprocessing, including safety checks, deduplication, and filtering based on CLIP scores and image-based criteria. The recaptioning was done using a fine-tuned LLaMA-3-8B powered LLaVA-1.5 model.

Annotations

Annotation process

Annotations in the form of recaptioned text were generated using an advanced language model, LLaVA-1.5-LLaMA3-8B. The recaptioning process involved auto-regressive generation with greedy decoding, aimed at producing detailed and semantically rich captions.

Who are the annotators?

The annotations were generated by the LLaVA-1.5-LLaMA3-8B model.

Personal and Sensitive Information

The dataset has undergone safety checks to filter out harmful content, but users should still exercise caution as some personal or sensitive information may be present due to the nature of web-crawled data.

Bias, Risks, and Limitations

While the recaptioned dataset aims to improve data quality, it may still contain biases and inaccuracies inherent in the original web-crawled data. Users should be aware of these limitations and the potential for misalignment or noise in the captions.

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation

BibTeX:

@article{li2024recaption,
      title={What If We Recaption Billions of Web Images with LLaMA-3?}, 
      author={Xianhang Li and Haoqin Tu and Mude Hui and Zeyu Wang and Bingchen Zhao and Junfei Xiao and Sucheng Ren and Jieru Mei and Qing Liu and Huangjie Zheng and Yuyin Zhou and Cihang Xie},
      journal={arXiv preprint arXiv:2406.08478},
      year={2024}
}

Dataset Card Authors

Xianhang Li, Haoqin Tu, Mude Hui, Zeyu Wang, Bingchen Zhao, Junfei Xiao, Sucheng Ren, Jieru Mei, Qing Liu, Huangjie Zheng, Yuyin Zhou, Cihang Xie

Dataset Card Contact

xli421@ucsc.edu