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. Fashion-MNIST is intended to serve as a direct drop-in replacement of the original MNIST dataset for benchmarking machine learning algorithms.
Here is an example how the data looks like (each class takes three-rows):
The original MNIST dataset contains a lot of handwritten digits. People from 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 they would try on. "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."
Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset to benchmark machine learning algorithms, as it shares the same image size and the structure of training and testing splits.
Seriously, we are talking about replacing MNIST. Here are some good reasons:
- MNIST is too easy. Convolutional nets can get 99.7% on MNIST. Classic machine learning algorithms can also get 97% easily. Check out our side-by-side benchmark for Fashion-MNIST vs. MNIST, and read about "Most pairs of MNIST digits can be distinguished pretty well by just one pixel"
- MNIST is overused. Check out "Ian Goodfellow wants people to move away from mnist."
- MNIST can not represent modern CV tasks. Check out "François Cholle: Ideas on MNIST do not transfer to real CV."
You can use direct links to download the the dataset. The data is stored in the same format as the original MNIST data.
| Name | Content | Examples | Size | Link |
|---|---|---|---|---|
train-images-idx3-ubyte.gz |
training set images | 60,000 | 26 MBytes | Download |
train-labels-idx1-ubyte.gz |
training set labels | 60,000 | 29 KBytes | Download |
t10k-images-idx3-ubyte.gz |
test set images | 10,000 | 4.2 MBytes | Download |
t10k-labels-idx1-ubyte.gz |
test set labels | 10,000 | 5.0 KBytes | Download |
Or you can clone this repository, the dataset is under data/fashion. This repo contains some scripts for benchmark and visualization.
git clone git@github.com:zalandoresearch/fashion-mnist.gitEach training and test example is assigned to one of the following labels:
| Label | Description |
|---|---|
| 0 | T-shirt/top |
| 1 | Trouser |
| 2 | Pullover |
| 3 | Dress |
| 4 | Coat |
| 5 | Sandal |
| 6 | Shirt |
| 7 | Sneaker |
| 8 | Bag |
| 9 | Ankle boot |
- use
utils/mnist_readerin 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')from tensorflow.examples.tutorials.mnist import input_data
data = input_data.read_data_sets('data/fashion')
data.train.next_batch(100)As one of the most popular dataset in the Machine Learning community, people have implemented MNIST loader in many languages. They can be used to load Fashion-MNIST dataset as well (may require decompressing first). Note that they are not tested by us.
We build an automatic benchmarking system based on scikit-learn, covering 128 classifiers (but no deep learning) with different parameters. Results can be found here.
You can reproduce the results by running benchmark/runner.py. A recommend way is to build and deploy this docker container.
You are welcome to submit your benchmark. Please create a new issue, your results will be listed here. Check out the Contributing section for details. Before submitting a benchmark, please make sure it is not listed in this list.
| Classifier | Preprocessing | Test accuracy | Submitter | Code |
|---|---|---|---|---|
| 2 Conv Layers with max pooling (Keras) | None | 0.876 | Kashif Rasul | zalando_mnist_cnn |
| 2 Conv Layers with max pooling (Tensorflow) >300 epochs | None | 0.916 | Tensorflow's doc | convnet |
| Simple 2 layer convnet <100K parameter | None | 0.925 | @hardmaru | pytorch_tiny_custom_mnist_adam |
| GRU+SVM | None | 0.888 | @AFAgarap | gru_svm_zalando |
| GRU+SVM with dropout | None | 0.855 | @AFAgarap | gru_svm_zalando_dropout |
| WRN40-4 8.9M params | standard preprocessing (mean/std subtraction/division) and augmentation (random crops/horizontal flips) | 0.967 | @ajbrock | WideResNet & Freezout |
| DenseNet-BC 768K params | standard preprocessing (mean/std subtraction/division) and augmentation (random crops/horizontal flips) | 0.954 | @ajbrock | DenseNets & Freezout |
| MobileNet | augmentation (horizontal flips) | 0.950 | @苏剑林 | Chinese blog post |
- Make a ghost wardrobe using DCGAN
- fashion-mnist的gan玩具
- CGAN output after 5000 steps
- Tensorflow implementation of various GANs and VAEs.
Thanks for your interest in contributing! There are many ways to contribute to this project. Get started here! And please check these open issues for specific tasks.
For discussion on 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},
}The MIT License (MIT) Copyright © [2017] Zalando SE, https://tech.zalando.com
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