Fashion MNIST Computer Vision Project
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Authors:
- Han Xiao, Kashif Rasul and Roland Vollgraf
- https://arxiv.org/abs/1708.07747
Dataset Obtained From: https://github.com/zalandoresearch/fashion-mnist
All images were sized 28x28 in the original dataset
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):
Version 1 (original-images_Original-FashionMNIST-Splits):
- Original images, with the original splits for MNIST:
train
(86% of images - 60,000 images) set andtest
(14% of images - 10,000 images) set only. - This version was not trained
Version 3 (original-images_trainSetSplitBy80_20):
- Original, raw images, with the
train
set split to provide 80% of its images to the training set and 20% of its images to the validation set - https://blog.roboflow.com/train-test-split/
Citation:
@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},
}
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