Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
This provides a new benchmark for machine learning researchers, though it is incremental as it builds on the existing MNIST framework.
They tackled the need for a more challenging benchmark dataset by creating Fashion-MNIST, a dataset of 70,000 fashion images in 10 categories, which serves as a drop-in replacement for MNIST.
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