README.md

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# Dataset Card for KMNIST

<!-- Provide a quick summary of the dataset. -->

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
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

<!-- Provide the basic links for the dataset. -->

- **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

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

#### 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

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**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}
}