%0 Generic %1 xiao2017fashionmnist %A Xiao, Han %A Rasul, Kashif %A Vollgraf, Roland %D 2017 %K deep-learning %T Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms %U http://arxiv.org/abs/1708.07747 %X 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

@misc{xiao2017fashionmnist, 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}, added-at = {2020-04-17T20:29:38.000+0200}, author = {Xiao, Han and Rasul, Kashif and Vollgraf, Roland}, biburl = {https://www.bibsonomy.org/bibtex/2de51af2f6c7d8b0f4cd84a428bb17967/mo_xime}, description = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms}, interhash = {0c81f9a6170118f14703b6796101ce40}, intrahash = {de51af2f6c7d8b0f4cd84a428bb17967}, keywords = {deep-learning}, note = {cite arxiv:1708.07747Comment: 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/}, timestamp = {2020-04-17T20:29:38.000+0200}, title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms}, url = {http://arxiv.org/abs/1708.07747}, year = 2017 }