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Fashion-MNIST
创建来自Data Decorators / AChenQ
概要
活动

Overview

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.

Instruction

Get the Data

  • Labels

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

LabelDescription
0T-shirt/top
1Trouser
2Pullover
3Dress
4Coat
5Sandal
6Shirt
7Sneaker
8Bag
9Ankle boot

Usage

  • Loading data with Python (requires NumPy)

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')
  • Loading data with Tensorflow

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.

  • Loading data with other machine learning libraries

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.

  1. Apache MXNet Gluon
  2. deeplearn.js
  3. Kaggle
  4. Pytorch
  5. Keras
  6. Edward
  7. Tensorflow
  8. Torch
  9. JuliaML
  10. Chainer

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

  • Loading data with other languages

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.

  1. C
  2. C++
  3. Java
  4. Python and this and this
  5. Scala
  6. Go
  7. C#
  8. NodeJS and this
  9. Swift
  10. R and this
  11. Matlab
  12. Ruby

Citation

Please use the following citation when referencing the dataset:

@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},
}

License

MIT

数据预览
查看数据
数据集信息
应用场景MNISTFashion
标注类型Classification
LicenseMIT
更新时间2020-12-31 17:25:40
数据概要
数据格式Image
数据数量70k
文件大小35MB
标注数量70000
版权归属方
Zalando
标注方
未知
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