README.md

---
dataset_info:
  features:
  - name: class
    dtype: string
  - name: url
    dtype: string
  - name: category
    dtype: string
  splits:
  - name: train
    num_bytes: 218995
    num_examples: 1870
  download_size: 177502
  dataset_size: 218995
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# WAON-Bench: Japanese Cultural Image Classification Dataset


<div align="center" style="line-height: 1;">

  |
  <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/>

</div>

<img src="WAON-Bench.jpg" alt="Overview of WAON-Bench" width="100%"/>

WAON-Bench is a manually curated image classification dataset designed to benchmark Vision-Language models on Japanese culture.
The dataset contains 374 classes across 8 categories (animals, buildings, events, everyday life, food, nature, scenery, and traditions), with 5 images per class, totaling 1,870 examples.

## How to Use

```python
from datasets import load_dataset

ds = load_dataset("llm-jp/WAON-Bench")
```

## Data Collection Pipeline
We followed the pipeline below to construct the dataset:

1. **Class Definition**: A total of 374 class names were manually defined and grouped into eight top-level categories:
animal, building, event, everyday, food, nature, scenery, and tradition.
2. **Image Selection**: For each class, 5 images were manually retrieved using Google Image Search. \
Images were selected based on the following criteria:
    - The image should clearly represent the intended class.
    - It should not contain elements that could be easily confused with other classes.

## Dataset Format
Each sample includes:

- `class`: Class name
- `url`: Image URL
- `category`: Class category

Example:
```
{'class': '柴犬', 'url': 'https://img.wanqol.com/2020/11/6e489894-main.jpg?auto=format', 'category': 'animal'}
```

## Dataset Statistics

- **Total classes**: 374

- **Total images**: 1,870

- **Class num per category**
| **class** | animal | building | event | everyday | food | nature | scenery | tradition | total |
|----------:|-------:|---------:|------:|---------:|-----:|-------:|--------:|----------:|------:|
| **count** |     41 |       40 |    29 |       45 |   55 |     27 |      75 |        62 |   374 |



- **Example Class Names per Category**
|category | class names|   
|:-----------|--------:|
| animal   | '柴犬', 'エゾシカ', 'ニホンカモシカ', 'イノシシ', ...|
| building | '鳥居', '茶室', '合掌造り', '町家', '縁側', ...|
| event    | '花見', '花火大会', '盆踊り', '運動会', '卒業式', '成人式', ...|
| everyday | 'カラオケ', '温泉', '屋台', '洗濯物', '敷布団', ...|
| food     | '茄子', 'しらす', 'ラーメン', '焼き鳥', '焼肉', ...|
| nature   | '桜', '梅', '藤', '松, '噴火', ...|
| scenery  | '茶畑', '雪国の街並み', '漁港', '砂防ダム', '石垣', ...|
| tradition| '華道', 剣道', '柔道', '弓道', ...|

- **t-SNE Visualization of SigLIP2 Embeddings**


The figure below shows a 2D t-SNE projection of image embeddings generated using [google/siglip2-base-patch16-256](https://huggingface.co/google/siglip2-base-patch16-256). Each point represents one image in the dataset. 

<img src="siglip_tsne_visualization.png" alt="t-SNE Visualization" width="50%"/>


## LICENSE
This dataset is licensed under the Apache License 2.0.

## 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}, 
}
```