Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

Here's an example of how the data looks (each class takes three-rows):

The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. "If it doesn't work on MNIST, it won't work at all", they said. "Well, if it does work on MNIST, it may still fail on others."

Seriously, we are talking about replacing MNIST. Here are some good reasons:

• MNIST is too easy. Convolutional nets can achieve 99.7% on MNIST. Classic machine learning algorithms can also achieve 97% easily. Check out our side-by-side benchmark for Fashion-MNIST vs. MNIST, and read "Most pairs of MNIST digits can be distinguished pretty well by just one pixel."
• MNIST is overused. In this April 2017 Twitter thread, Google Brain research scientist and deep learning expert Ian Goodfellow calls for people to move away from MNIST.
• MNIST can not represent modern CV tasks, as noted in this April 2017 Twitter thread, deep learning expert/Keras author François Chollet.

Many ML libraries already include Fashion-MNIST data/API, give it a try!

You can use direct links to download the dataset. The data is stored in the same format as the original MNIST data.

Alternatively, you can clone this GitHub repository; the dataset appears under data/fashion. This repo also contains some scripts for benchmark and visualization.

git clone git@github.com:zalandoresearch/fashion-mnist.git

Each training and test example is assigned to one of the following labels:

Use utils/mnist_reader in this repo:

import mnist_reader
X_train, y_train = mnist_reader.load_mnist('data/fashion', kind='train')
X_test, y_test = mnist_reader.load_mnist('data/fashion', kind='t10k')

Make sure you have downloaded the data and placed it in data/fashion. Otherwise, Tensorflow will download and use the original MNIST.

from tensorflow.examples.tutorials.mnist import input_data
data = input_data.read_data_sets('data/fashion')

data.train.next_batch(BATCH_SIZE)

Note, Tensorflow supports passing in a source url to the read_data_sets. You may use:

data = input_data.read_data_sets('data/fashion', source_url='http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/')

Also, an official Tensorflow tutorial of using tf.keras, a high-level API to train Fashion-MNIST can be found here.

To date, the following libraries have included Fashion-MNIST as a built-in dataset. Therefore, you don't need to download Fashion-MNIST by yourself. Just follow their API and you are ready to go.

• Activeloop Hub
• Apache MXNet Gluon
• TensorFlow.js
• Kaggle
• Pytorch
• Keras
• Edward
• Tensorflow
• TensorFlow Datasets
• Torch
• JuliaML
• Chainer
• HuggingFace Datasets

You are welcome to make pull requests to other open-source machine learning packages, improving their support to Fashion-MNIST dataset.

As one of the Machine Learning community's most popular datasets, MNIST has inspired people to implement loaders in many different languages. You can use these loaders with the Fashion-MNIST dataset as well. (Note: may require decompressing first.) To date, we haven't yet tested all of these loaders with Fashion-MNIST.

• C
• C++
• Java
• Python and this
• Scala
• Go
• C#
• NodeJS and this
• Swift
• R and this
• Matlab
• Ruby
• Rust

We built an automatic benchmarking system based on scikit-learn that covers 129 classifiers (but no deep learning) with different parameters. Find the results here.

You can reproduce the results by running benchmark/runner.py. We recommend building and deploying this Dockerfile.

You are welcome to submit your benchmark; simply create a new issue and we'll list your results here. Before doing that, please make sure it does not already appear in this list. Visit our contributor guidelines for additional details.

The table below collects the submitted benchmarks. Note that we haven't yet tested these results. You are welcome to validate the results using the code provided by the submitter. Test accuracy may differ due to the number of epoch, batch size, etc. To correct this table, please create a new issue.

• Tensorflow implementation of various GANs and VAEs. (Recommend to read! Note how various GANs generate different results on Fashion-MNIST, which can not be easily observed on the original MNIST.)
• Make a ghost wardrobe using DCGAN
• fashion-mnist的gan玩具
• CGAN output after 5000 steps
• GAN Playground - Explore Generative Adversarial Nets in your Browser

• Xifeng Guo's implementation of Unsupervised Deep Embedding for Clustering Analysis (DEC)
• Leland McInnes's Uniform Manifold Approximation and Projection (UMAP)

Machine Learning Meets Fashion by Yufeng G @ Google Cloud

Introduction to Kaggle Kernels by Yufeng G @ Google Cloud

动手学深度学习 by Mu Li @ Amazon AI

Apache MXNet으로 배워보는 딥러닝(Deep Learning) - 김무현 (AWS 솔루션즈아키텍트)

Thanks for your interest in contributing! There are many ways to get involved; start with our contributor guidelines and then check these open issues for specific tasks.

To discuss the dataset, please use .

If you use Fashion-MNIST in a scientific publication, we would appreciate references to the following paper:

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. Han Xiao, Kashif Rasul, Roland Vollgraf. arXiv:1708.07747

Biblatex entry:

@online{xiao2017/online,
  author       = {Han Xiao and Kashif Rasul and Roland Vollgraf},
  title        = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms},
  date         = {2017-08-28},
  year         = {2017},
  eprintclass  = {cs.LG},
  eprinttype   = {arXiv},
  eprint       = {cs.LG/1708.07747},
}

Who is citing Fashion-MNIST?

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