This dataset contains grayscale images for clothing generated by Zalando (https://jobs.zalando.com/tech/). The dataset is created to be a substitute for the original MNIST dataset for machine learning algorithms. This substitution seems necessary because achieving very high classification accuracies is easy by classical machine learning algorithms. Also, MNIST might have been overused. As a result, Fashion MNIST shares the same image size, training and test sizes and number of classes with original MNIST.
Here is some information regarding the Fashion MNIST dataset:
Number of images in the dataset: 70,000 (60,000 images for the training set and 10,000 images for the test set)
Image size: 28×28
Number of classes: 10 (T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot)
4 data files are available for download from https://github.com/zalandoresearch/fashion-mnist which contain training set images, training set labels, test set images and test set labels. Instead of downloading the dataset, you might clone the GitHub repository in the same address provided above. More details and loading commands can be found in the same GitHub repository. You might also be interested to take a look at the Kaggle page https://www.kaggle.com/zalando-research/fashionmnist/home.
If you use this dataset, make sure to cite the paper:
Han Xiao, Kashif Rasul, Roland Vollgraf. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms, 2017.