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

Introduction

The MNIST dataset comprising of 10-class handwritten digits, was first introduced by LeCun et al. (1998) in 1998. At that time one could not have foreseen the stellar rise of deep learning techniques and their performance. Despite the fact that today deep learning can do so much the simple MNIST dataset has become the most widely used testbed in deep learning, surpassing CIFAR-10 (Krizhevsky and Hinton, 2009) and ImageNet (Deng et al., 2009) in its popularity via Google trendshttps://trends.google.com/trends/explore?date=all&q=mnist,CIFAR,ImageNet. Despite its simplicity its usage does not seem to be decreasing despite calls for it in the deep learning community.

The reason MNIST is so popular has to do with its size, allowing deep learning researchers to quickly check and prototype their algorithms. This is also complemented by the fact that all machine learning libraries (e.g. scikit-learn) and deep learning frameworks (e.g. Tensorflow, Pytorch) provide helper functions and convenient examples that use MNIST out of the box.

Our aim with this work is to create a good benchmark dataset which has all the accessibility of MNIST, namely its small size, straightforward encoding and permissive license. We took the approach of sticking to the $10$ classes $70,000$ grayscale images in the size of $28\times 28$ as in the original MNIST. In fact, the only change one needs to use this dataset is to change the URL from where the MNIST dataset is fetched. Moreover, Fashion-MNIST poses a more challenging classification task than the simple MNIST digits data, whereas the latter has been trained to accuracies above 99.7% as reported in Wan et al. (2013); Ciregan et al. (2012).

We also looked at the EMNIST dataset provided by Cohen et al. (2017), an extended version of MNIST that extends the number of classes by introducing uppercase and lowercase characters. However, to be able to use it seamlessly one needs to not only extend the deep learning framework’s MNIST helpers, but also change the underlying deep neural network to classify these extra classes.

Fashion-MNIST Dataset

Fashion-MNIST is based on the assortment on Zalando’s websiteZalando is the Europe’s largest online fashion platform. http://www.zalando.com. Every fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit. The original picture has a light-gray background (hexadecimal color: #fdfdfd) and stored in $762\times 1000$ JPEG format. For efficiently serving different frontend components, the original picture is resampled with multiple resolutions, e.g. large, medium, small, thumbnail and tiny.

We use the front look thumbnail images of $70,000$ unique products to build Fashion-MNIST. Those products come from different gender groups: men, women, kids and neutral. In particular, white-color products are not included in the dataset as they have low contrast to the background. The thumbnails ($51\times 73$) are then fed into the following conversion pipeline, which is visualized in Figure 1.

Trimming any edges that are close to the color of the corner pixels. The “closeness” is defined by the distance within $5\%$ of the maximum possible intensity in RGB space.

Resizing the longest edge of the image to $28$ by subsampling the pixels, i.e. some rows and columns are skipped over.

Sharpening pixels using a Gaussian operator of the radius and standard deviation of $1.0$, with increasing effect near outlines.

Extending the shortest edge to $28$ and put the image to the center of the canvas.

Converting the image to 8-bit grayscale pixels.

For the class labels, we use the silhouette code of the product. The silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando. Each product contains only one silhouette code. Table 2 gives a summary of all class labels in Fashion-MNIST with examples for each class.

Finally, the dataset is divided into a training and a test set. The training set receives a randomly-selected $6,000$ examples from each class. Images and labels are stored in the same file format as the MNIST data set, which is designed for storing vectors and multidimensional matrices. The result files are listed in Table 1. We sort examples by their labels while storing, resulting in smaller label files after compression comparing to the MNIST. It is also easier to retrieve examples with a certain class label. The data shuffling job is therefore left to the algorithm developer.

Experiments

We provide some classification results in LABEL:tbl:benchmark to form a benchmark on this data set. All algorithms are repeated $5$ times by shuffling the training data and the average accuracy on the test set is reported. The benchmark on the MNIST dataset is also included for a side-by-side comparison. A more comprehensive table with explanations on the algorithms can be found on https://github.com/zalandoresearch/fashion-mnist.

Conclusions

This paper introduced Fashion-MNIST, a fashion product images dataset intended to be a drop-in replacement of MNIST and whilst providing a more challenging alternative for benchmarking machine learning algorithm. The images in Fashion-MNIST are converted to a format that matches that of the MNIST dataset, making it immediately compatible with any machine learning package capable of working with the original MNIST dataset.

References