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

Clone the repository:

git clone https://gitlab.llm-jp.nii.ac.jp/datasets/waon.git
cd waon

Load the dataset using the datasets library:

from datasets import load_dataset

ds = load_dataset("parquet", data_dir="data")

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:

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},
}