speed/WAON-Bench-with-images

WAON-Bench: Japanese Cultural Image Classification Dataset

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("speed/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
• jpg: PIL Image object

Example:

{'class': '柴犬', 'url': 'https://img.wanqol.com/2020/11/6e489894-main.jpg?auto=format', 'category': 'animal', 'jpg': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=1440x1000 at 0x7F8FA9E10170>}

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

The images are subject to the licenses specified by their respective rights holders. All other components are provided under the Apache License 2.0.