Fashion-MNIST
Classification
MNIST
|Fashion
|...
许可协议: MIT

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:

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

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

数据概要
数据格式
Image,
数据量
70K
文件大小
29.45MB
发布方
Zalando
Founded 2008 in Berlin by David Schneider and Robert Gentz Zalando transformed from an e-commerce company into a multi-service platform for fashion. From logistics, to big brands, to fashion bloggers - we’re building the engine that connects all people and the fashion ecosystem.
数据集反馈
| 254 | 数据量 70K | 大小 29.45MB
Fashion-MNIST
Classification
MNIST | Fashion
许可协议: MIT

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:

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

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

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