The Fashion-MNIST dataset

Those who have experience with deep learning have most likely heard about the MNIST dataset. It is one of the most widely-used image datasets, serving as a benchmark for tasks such as image classification and image generation, and is used by many computer vision models:

There are several problems with MNIST, however. First of all, the dataset is too easy, since a simple convolutional neural network is able to achieve 99% test accuracy. In spite of this, the dataset is used far too often in research and benchmarks. The F-MNIST dataset, produced by the online fashion retailer Zalando, is a more complex, much-needed upgrade to MNIST:

Instead of digits, the F-MNIST dataset includes photos of ten different clothing types (ranging from t-shirts to shoes) compressed in to 28x28 monochrome thumbnails. Hence, F-MNIST serves as a convenient drop-in replacement to MNIST and is increasingly gaining popularity in the community. Hence we will train our CNN on F-MNIST as well. The preceding table maps each label index to its class

In the following subsections, we will design a convolutional neural network that will learn to classify data from this dataset.