fashion_mnist

Visualization: Explore in Know Your Data
Description:

Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.

Additional Documentation: Explore on Papers With Code
Homepage: https://github.com/zalandoresearch/fashion-mnist
Source code: tfds.image_classification.FashionMNIST
Versions:
- 3.0.1 (default): No release notes.
Download size: 29.45 MiB
Dataset size: 36.42 MiB
Auto-cached (documentation): Yes
Splits:

Split	Examples
`'test'`	10,000
`'train'`	60,000

Feature structure:

FeaturesDict({
    'image': Image(shape=(28, 28, 1), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=10),
})

Feature documentation:

Feature	Class	Shape	Dtype
	FeaturesDict
image	Image	(28, 28, 1)	uint8
label	ClassLabel		int64

Supervised keys (See as_supervised doc): ('image', 'label')
Figure (tfds.show_examples):

Visualization

Examples (tfds.as_dataframe):

Citation:

@article{DBLP:journals/corr/abs-1708-07747,
  author    = {Han Xiao and
               Kashif Rasul and
               Roland Vollgraf},
  title     = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
               Algorithms},
  journal   = {CoRR},
  volume    = {abs/1708.07747},
  year      = {2017},
  url       = {http://arxiv.org/abs/1708.07747},
  archivePrefix = {arXiv},
  eprint    = {1708.07747},
  timestamp = {Mon, 13 Aug 2018 16:47:27 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}