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# Dataset Card for KMNIST |
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<!-- Provide a quick summary of the dataset. --> |
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## Dataset Details |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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This dataset contains two variants, **Kuzushiji-MNIST** and **Kuzushiji-49**. |
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**Kuzushiji-MNIST** is a drop-in replacement for the MNIST dataset. |
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**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. |
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- **License:** CC BY-SA 4.0 |
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### Dataset Sources |
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<!-- Provide the basic links for the dataset. --> |
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- **Homepage:** https://github.com/rois-codh/kmnist |
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- **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. |
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## Dataset Structure |
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<!-- 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. --> |
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#### Kuzushiji-MNIST: |
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Total images: 70,000 |
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Classes: 10 categories |
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Splits: |
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- **Train:** 60,000 images |
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- **Test:** 10,000 images |
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Image specs: 28×28 pixels, grayscale |
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#### Kuzushiji-49: |
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Total images: 270,912 |
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Classes: 49 categories |
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Splits: |
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- **Train:** 232,365 images |
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- **Test:** 38,547 images |
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Image specs: 28×28 pixels, grayscale |
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## Example Usage |
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Below is a quick example of how to load this dataset via the Hugging Face Datasets library. |
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``` |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("randall-lab/kmnist", name="kmnist", split="train", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/kmnist", name="kmnist", split="test", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/kmnist", name="k49mnist", split="train", trust_remote_code=True) |
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# dataset = load_dataset("randall-lab/kmnist", name="k49mnist", split="test", trust_remote_code=True) |
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# Access a sample from the dataset |
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example = dataset[0] |
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image = example["image"] |
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label = example["label"] |
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image.show() # Display the image |
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print(f"Label: {label}") |
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``` |
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## Citation |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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@article{clanuwat2018deep, |
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title={Deep learning for classical japanese literature}, |
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author={Clanuwat, Tarin and Bober-Irizar, Mikel and Kitamoto, Asanobu and Lamb, Alex and Yamamoto, Kazuaki and Ha, David}, |
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journal={arXiv preprint arXiv:1812.01718}, |
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year={2018} |
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} |
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