WAON: Large-Scale and High-Quality Japanese Image-Text Pair Dataset for Vision-Language Models

| 🤗 HuggingFace  | 📄 Paper  | 🧑‍💻 Code  |

Introduction

WAON is a Japanese (image, text) pair dataset containing approximately 155M examples, crawled from Common Crawl. It is built from snapshots taken in 2025-18, 2025-08, 2024-51, 2024-42, 2024-33, and 2024-26. The dataset is high-quality and diverse, constructed through a sophisticated data processing pipeline. We apply filtering based on image size and SigLIP scores, and perform deduplication using URLs, captions, and perceptual hashes (pHash).

How to Use

from datasets import load_dataset

ds = load_dataset("speed/WAON")

Format

• url: URL of the image
• caption: Caption associated with the image
• page_title: Title of the page containing the image
• page_url: URL of the page
• punsafe: Probability that the image is unsafe
• quality: The quality of the text in the text column
• width: Width (in pixels) of the resized image used for computing pHash
• height: Height (in pixels) of the resized image used for computing pHash
• original_width: Original width of the image
• original_height: Original height of the image
• sha256: SHA-256 hash of the original image file
• phash: Perceptual hash (pHash) computed from the resized image

Dataset Construction Pipeline

We construct WAON dataset through the following steps (The numbers in parentheses indicate the remaining data count after each processing step (based on the 2025-18 snapshot):

LICENSE

This dataset is licensed under the Apache License 2.0 and governed by Japanese law. Its use is limited to “information analysis” as defined in Article 30-4 of the Japanese Copyright Act.

Citation

@misc{sugiura2025waonlargescalehighqualityjapanese,
      title={WAON: Large-Scale and High-Quality Japanese Image-Text Pair Dataset for Vision-Language Models}, 
      author={Issa Sugiura and Shuhei Kurita and Yusuke Oda and Daisuke Kawahara and Yasuo Okabe and Naoaki Okazaki},
      year={2025},
      eprint={2510.22276},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.22276}, 
}