README.md

---
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/llm-jp/waon" target="_blank">🤗 HuggingFace</a>
  &nbsp;|
  <a href="https://arxiv.org/abs/2510.22276" target="_blank">📄 Paper</a>
  &nbsp;|
  <a href="https://github.com/llm-jp/WAON" target="_blank">🧑‍💻 Code</a>
  &nbsp;|

  <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

Clone the repository:
```bash
git clone https://gitlab.llm-jp.nii.ac.jp/datasets/waon.git
cd waon
```

Load the dataset using the `datasets` library:
```python
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 (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 (not including images themselves) 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},
}
```