View source: R/fashion-mnist.R
download_fashion_mnist | R Documentation |
Download Fashion-MNIST database of images of fashion products.
download_fashion_mnist(base_url = fashion_mnist_url, verbose = FALSE)
base_url |
Base URL that the files are located at. |
verbose |
If |
A data frame with 786 variables:
px1
, px2
, px3
... px784
Integer pixel value, from 0 (white) to 255 (black).
Label
The fashion item represented by the image, in the range 0-9.
Description
The name of the fashion item associated with the
Label
Pixels are organized row-wise. The Label
variable is stored as a
factor. The labels correspond to:
0
T-shirt/top
1
Trouser
2
Pullover
3
Dress
4
Coat
5
Sandal
6
Shirt
7
Sneaker
8
Bag
9
Ankle boot
and are also present as the Description
factor.
There are 70,000 items in the data set. The first 60,000 are the training
set, as found in the train-images-idx3-ubyte.gz
file. The remaining
10,000 are the test set, from the t10k-images-idx3-ubyte.gz
file.
Items in the dataset can be visualized with the
show_mnist_digit
function.
For more information see https://github.com/zalandoresearch/fashion-mnist.
Downloads the image and label files for the training and test datasets and converts them to a data frame. The dataset is intended to be a drop-in replacement for the MNIST digits dataset but with more relevance for benchmarking machine learning algorithms (i.e. it's more difficult).
Data frame containing Fashion-MNIST.
Originally based on a function by Brendan O'Connor.
Xiao, H., Kashif, R., & Vollgraf, R. (2017). Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv preprint arXiv:1708.07747. https://github.com/zalandoresearch/fashion-mnist/
## Not run:
# download the data set
fashion <- download_fashion_mnist()
# first 60,000 instances are the training set
fashion_train <- head(fashion, 60000)
# the remaining 10,000 are the test set
fashion_test <- tail(fashion, 10000)
# PCA on 1000 examples
fashion_r1000 <- fashion[sample(nrow(fashion), 1000), ]
pca <- prcomp(fashion_r1000[, 1:784], retx = TRUE, rank. = 2)
# plot the scores of the first two components
plot(pca$x[, 1:2], type = "n")
text(pca$x[, 1:2],
labels = fashion_r1000$Label,
col = rainbow(length(levels(fashion$Label)))[fashion_r1000$Label]
)
## End(Not run)
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