class
stringclasses 374
values | url
stringlengths 34
460
| category
stringclasses 8
values |
|---|---|---|
柴犬
|
animal
|
|
柴犬
|
animal
|
|
柴犬
|
animal
|
|
柴犬
|
animal
|
|
柴犬
|
animal
|
|
秋田犬
|
animal
|
|
秋田犬
|
animal
|
|
秋田犬
|
animal
|
|
秋田犬
|
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSylLh3VHdT2iYIIkbxVK_78s9vtSSu86Qj9A&s
|
animal
|
秋田犬
|
animal
|
|
日本スピッツ
|
animal
|
|
日本スピッツ
|
animal
|
|
日本スピッツ
|
animal
|
|
日本スピッツ
|
animal
|
|
日本スピッツ
|
animal
|
|
日本猫
|
animal
|
|
日本猫
|
animal
|
|
日本猫
|
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSPZV5nKK_3FtvnehC-xrTlyfon8O--tTRtMQ&s
|
animal
|
日本猫
|
animal
|
|
日本猫
|
animal
|
|
ニホンザル
|
animal
|
|
ニホンザル
|
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTBeWZwgEIEl4ArMVo0x_tyGqZBvuMFSu9xAQ&s
|
animal
|
ニホンザル
|
animal
|
|
ニホンザル
|
animal
|
|
ニホンザル
|
animal
|
|
エゾシカ
|
animal
|
|
エゾシカ
|
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQR13H5_v2wzrrQ07cH1s1Cer4qi42VBTM2rg&s
|
animal
|
エゾシカ
|
https://readyfor.jp/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBc245IiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--5b0946bda0cb0fbf5a7eaaf375cd7f0f2b781864/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdDRG9MWm05eWJXRjBPZ2wzWldKd09oTnlaWE5wZW1WZmRHOWZabWxzYkZzSGFRTGdBbWtDbmdFNkNuTmhkbVZ5ZXdZNkRIRjFZV3hwZEhscGFRPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--e45cd96fc7488bba669cee05438012af55164512/p4997-key-visual.png
|
animal
|
エゾシカ
|
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcT_3VV_n0JIADbxd6mwsjZlJg5LHV1Y-1KyLA&s
|
animal
|
エゾシカ
|
animal
|
|
ニホンカモシカ
|
animal
|
|
ニホンカモシカ
|
animal
|
|
ニホンカモシカ
|
animal
|
|
ニホンカモシカ
|
animal
|
|
ニホンカモシカ
|
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTP7NvOtVC5OAt7vorwY6O-Rpiy757yguHNow&s
|
animal
|
イノシシ
|
animal
|
|
イノシシ
|
animal
|
|
イノシシ
|
animal
|
|
イノシシ
|
animal
|
|
イノシシ
|
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ5EW6XqQA0FSWYDmzqTKY8Yp1y2PYBSac88A&s
|
animal
|
タヌキ
|
animal
|
|
タヌキ
|
animal
|
|
タヌキ
|
animal
|
|
タヌキ
|
animal
|
|
タヌキ
|
animal
|
|
キツネ
|
animal
|
|
キツネ
|
animal
|
|
キツネ
|
animal
|
|
キツネ
|
animal
|
|
キツネ
|
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcT7sXAJU1JEzQwzWUMnCGJ1YnMfudqQttNZnQ&s
|
animal
|
ツキノワグマ
|
animal
|
|
ツキノワグマ
|
animal
|
|
ツキノワグマ
|
animal
|
|
ツキノワグマ
|
animal
|
|
ツキノワグマ
|
animal
|
|
ニホンリス
|
animal
|
|
ニホンリス
|
animal
|
|
ニホンリス
|
animal
|
|
ニホンリス
|
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQVfIgYesFYhlFpH54H5zaj3ZIcAlSOoLDsFA&s
|
animal
|
ニホンリス
|
animal
|
|
モモンガ
|
animal
|
|
モモンガ
|
animal
|
|
モモンガ
|
animal
|
|
モモンガ
|
animal
|
|
モモンガ
|
animal
|
|
イタチ
|
animal
|
|
イタチ
|
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSk2dUBP2D2ELUq-j01HYDAUfU3pCEBM2Eyeg&s
|
animal
|
イタチ
|
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ_oKNOj-gv5e_YGureXgw5Tbg1JEYZ16uy2w&s
|
animal
|
イタチ
|
animal
|
|
イタチ
|
animal
|
|
ウサギ
|
animal
|
|
ウサギ
|
animal
|
|
ウサギ
|
animal
|
|
ウサギ
|
animal
|
|
ウサギ
|
animal
|
|
コウモリ
|
animal
|
|
コウモリ
|
animal
|
|
コウモリ
|
animal
|
|
コウモリ
|
animal
|
|
コウモリ
|
animal
|
|
トキ
|
https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSON3473x91aWVC16C0nVQ1FgPStkIqjCE5Ew&s
|
animal
|
トキ
|
animal
|
|
トキ
|
animal
|
|
トキ
|
animal
|
|
トキ
|
animal
|
|
ツル
|
animal
|
|
ツル
|
animal
|
|
ツル
|
animal
|
|
ツル
|
animal
|
|
ツル
|
animal
|
|
ウグイス
|
animal
|
|
ウグイス
|
animal
|
|
ウグイス
|
animal
|
|
ウグイス
|
animal
|
|
ウグイス
|
animal
|
|
ハト
|
animal
|
|
ハト
|
animal
|
|
ハト
|
animal
|
|
ハト
|
animal
|
|
ハト
|
animal
|
WAON-Bench: Japanese Cultural Image Classification Dataset
| 🤗 HuggingFace | 📄 Paper | 🧑💻 Code |
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. Each class includes 5 images.
How to Use
from datasets import load_dataset
ds = load_dataset("llm-jp/WAON-Bench")
Data Collection Pipeline
We followed the pipeline below to construct the dataset:
- 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.
- 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 nameurl: Image URLcategory: 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. Each point represents one image in the dataset.
LICENSE
This dataset is licensed under the Apache License 2.0.
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},
}
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