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---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: url
dtype: string
- name: caption
dtype: string
- name: similarity
dtype: float64
- name: page_title
dtype: string
- name: page_url
dtype: string
- name: punsafe
dtype: float64
- name: width
dtype: float64
- name: height
dtype: float64
- name: original_width
dtype: float64
- name: original_height
dtype: float64
- name: sha256
dtype: string
- name: phash
dtype: string
splits:
- name: train
num_bytes: 72405439283
num_examples: 153942892
download_size: 46743814850
dataset_size: 72405439283
license: apache-2.0
language:
- ja
size_categories:
- 100M<n<1B
---
<div align="center" style="line-height: 1;">
<h1>WAON: Large-Scale and High-Quality Japanese Image-Text Pair Dataset for Vision-Language Models </h1>
|
<a href="https://huggingface.co/collections/speed/waon" target="_blank">🤗 HuggingFace</a>
|
<a href="https://arxiv.org/abs/2510.22276" target="_blank">📄 Paper</a>
|
<a href="https://github.com/llm-jp/WAON" target="_blank">🧑💻 Code</a>
|
<br/>
<img src="validation_top1_accuracy.svg" width="50%"/>
</div>
## 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
```python
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):
<div align="center">
<img src="waon-pipeline.svg" width="50%"/>
</div>
## 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
```bibtex
@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},
}
``` |