Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Han Xiao, Kashif Rasul, Roland Vollgraf
8/25/2017
cs.LGcs.CVstat.ML

Official code: zalandoresearch/fashion-mnist (12,856 stars, MIT, last commit 2022-06-13).

Abstract

We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits. The dataset is freely available at https://github.com/zalandoresearch/fashion-mnist

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Code Implementations(1)

zalandoresearch/fashion-mnistOfficial100%
MIT

A MNIST-like fashion product database. Benchmark :point_down:

12,8563,069CSS, HTMLAug 25, 20174 years agoMIT
benchmarkcomputer-visionconvolutional-neural-networksdatasetdeep-learning+6 more

Cite this paper

@article{xiao2017fashionmnist,
  title  = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms},
  author = {Han Xiao and Kashif Rasul and Roland Vollgraf},
  year   = {2017},
  eprint = {1708.07747},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1708.07747}
}

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