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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}
}
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