Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Machine Learning
2017-09-19 v2 Computer Vision and Pattern Recognition
Machine Learning
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
Cite
@article{arxiv.1708.07747,
title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms},
author = {Han Xiao and Kashif Rasul and Roland Vollgraf},
journal= {arXiv preprint arXiv:1708.07747},
year = {2017}
}
Comments
Dataset is freely available at https://github.com/zalandoresearch/fashion-mnist Benchmark is available at http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/