Fashion-MNIST: a Drop-In Replacement of MNIST for Benchmarking Machine Learning Algorithms
The dataset is here: https://github.com/zalandoresearch/fashion-mnist
We would appreciate references to the following paper if you use this dataset in publications:
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. Han Xiao, Kashif Rasul, Roland Vollgraf. arXiv: cs.LG/1708.07747
What is Fashion-MNIST?
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.
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.
To Serious Machine Learning Researchers
Seriously, we are talking about replacing MNIST. Here are some good reasons:
- MNIST is too easy. Check out our side-by-side benchmark and “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 “Ideas on MNIST do not transfer to real CV”
Get the Data
You can use direct links to download the the dataset. The data is stored in the same format as the original MNIST data.
|training set images||60,000||26 MBytes||Download|
|training set labels||60,000||29 KBytes||Download|
|test set images||10,000||4.2 MBytes||Download|
|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 [email protected]:zalandoresearch/fashion-mnist.git
Each training and test example is assigned to one of the following labels:
Loading data with Python (
numpy is required)
utils/mnist_readerin this repo:
X_train, y_train = mnist_reader.load_mnist('data/fashion', kind='train')
X_test, y_test = mnist_reader.load_mnist('data/fashion', kind='t10k')
Loading data with Tensorflow
from tensorflow.examples.tutorials.mnist import input_data
Loading data with other languages
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 125 classifiers 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.
t-SNE on Fashion-MNIST (left) and original MNIST (right)
PCA on Fashion-MNIST (left) and original MNIST (right)
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