kmnist
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Kuzushiji-MNIST is a drop-in replacement for the MNIST dataset (28x28 grayscale,
70,000 images), provided in the original MNIST format as well as a NumPy format.
Since MNIST restricts us to 10 classes, we chose one character to represent each
of the 10 rows of Hiragana when creating Kuzushiji-MNIST.
| Split |
Examples |
'test' |
10,000 |
'train' |
60,000 |
FeaturesDict({
'image': Image(shape=(28, 28, 1), dtype=uint8),
'label': ClassLabel(shape=(), dtype=int64, num_classes=10),
})
| Feature |
Class |
Shape |
Dtype |
Description |
|
FeaturesDict |
|
|
|
| image |
Image |
(28, 28, 1) |
uint8 |
|
| label |
ClassLabel |
|
int64 |
|
@online{clanuwat2018deep,
author = {Tarin Clanuwat and Mikel Bober-Irizar and Asanobu Kitamoto and Alex Lamb and Kazuaki Yamamoto and David Ha},
title = {Deep Learning for Classical Japanese Literature},
date = {2018-12-03},
year = {2018},
eprintclass = {cs.CV},
eprinttype = {arXiv},
eprint = {cs.CV/1812.01718},
}
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Last updated 2024-06-01 UTC.
[null,null,["Last updated 2024-06-01 UTC."],[],[],null,["# kmnist\n\n\u003cbr /\u003e\n\n- **Description**:\n\nKuzushiji-MNIST is a drop-in replacement for the MNIST dataset (28x28 grayscale,\n70,000 images), provided in the original MNIST format as well as a NumPy format.\nSince MNIST restricts us to 10 classes, we chose one character to represent each\nof the 10 rows of Hiragana when creating Kuzushiji-MNIST.\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/kuzushiji-mnist)\n\n- **Homepage** :\n \u003chttp://codh.rois.ac.jp/kmnist/index.html.en\u003e\n\n- **Source code** :\n [`tfds.image_classification.KMNIST`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/image_classification/mnist.py)\n\n- **Versions**:\n\n - **`3.0.1`** (default): No release notes.\n- **Download size** : `20.26 MiB`\n\n- **Dataset size** : `31.76 MiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n Yes\n\n- **Splits**:\n\n| Split | Examples |\n|-----------|----------|\n| `'test'` | 10,000 |\n| `'train'` | 60,000 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'image': Image(shape=(28, 28, 1), dtype=uint8),\n 'label': ClassLabel(shape=(), dtype=int64, num_classes=10),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|---------|--------------|-------------|-------|-------------|\n| | FeaturesDict | | | |\n| image | Image | (28, 28, 1) | uint8 | |\n| label | ClassLabel | | int64 | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `('image', 'label')`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n\n- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @online{clanuwat2018deep,\n author = {Tarin Clanuwat and Mikel Bober-Irizar and Asanobu Kitamoto and Alex Lamb and Kazuaki Yamamoto and David Ha},\n title = {Deep Learning for Classical Japanese Literature},\n date = {2018-12-03},\n year = {2018},\n eprintclass = {cs.CV},\n eprinttype = {arXiv},\n eprint = {cs.CV/1812.01718},\n }"]]
