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arXiv:1708.07747cs.LG

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

Han Xiao, Kashif Rasul, Roland Vollgraf

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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

arXiv 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/
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.CVComputer Vision and Pattern Recognition stat.MLMachine Learning (Statistics)

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  1. An MNIST-like fashion product dataset 1 comment

    • This might help. Their academic paper is basically a description of how they made the dataset (and why) and includes a great example of the steps they went through to convert the images. https://arxiv.org/pdf/1708.07747.pdf

      evolve2kView on HNParent context

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