Dataset Card for KMNIST

Dataset Details

Dataset Description

This dataset contains two variants, Kuzushiji-MNIST and Kuzushiji-49.

Kuzushiji-MNIST is a drop-in replacement for the MNIST dataset.

Kuzushiji-49, as the name suggests, has 49 classes, is a much larger, but imbalanced dataset containing 48 Hiragana characters and one Hiragana iteration mark.

• License: CC BY-SA 4.0

Dataset Sources

• Homepage: https://github.com/rois-codh/kmnist
• Paper: Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., & Ha, D. (2018). Deep learning for classical japanese literature. arXiv preprint arXiv:1812.01718.

Dataset Structure

Kuzushiji-MNIST:

Total images: 70,000

Classes: 10 categories

Splits:

• Train: 60,000 images

• Test: 10,000 images

Image specs: 28×28 pixels, grayscale

Kuzushiji-49:

Total images: 270,912

Classes: 49 categories

Splits:

• Train: 232,365 images

• Test: 38,547 images

Image specs: 28×28 pixels, grayscale

Example Usage

Below is a quick example of how to load this dataset via the Hugging Face Datasets library.

from datasets import load_dataset  

# Load the dataset  
dataset = load_dataset("randall-lab/kmnist", name="kmnist", split="train", trust_remote_code=True)   
# dataset = load_dataset("randall-lab/kmnist", name="kmnist", split="test", trust_remote_code=True)
# dataset = load_dataset("randall-lab/kmnist", name="k49mnist", split="train", trust_remote_code=True)   
# dataset = load_dataset("randall-lab/kmnist", name="k49mnist", split="test", trust_remote_code=True)  

# Access a sample from the dataset  
example = dataset[0]  
image = example["image"]  
label = example["label"]  

image.show()  # Display the image  
print(f"Label: {label}")

Citation

BibTeX:

@article{clanuwat2018deep, title={Deep learning for classical japanese literature}, author={Clanuwat, Tarin and Bober-Irizar, Mikel and Kitamoto, Asanobu and Lamb, Alex and Yamamoto, Kazuaki and Ha, David}, journal={arXiv preprint arXiv:1812.01718}, year={2018} }