download_cifar10: Download CIFAR-10

In jlmelville/snedata: SNE Simulation Dataset Functions

Download CIFAR-10

Description

Download CIFAR-10 database of images.

Usage

download_cifar10(
  url = "https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz",
  destfile = tempfile(),
  cleanup = TRUE,
  verbose = FALSE
)

Arguments

url	URL of the CIFAR-10 data.
destfile	Filename for where to download the CIFAR-10 tarfile. It will be untarred and processed in the same directory.
cleanup	If TRUE, then destfile and the untarred data will be deleted before the function returns. Only worth setting to FALSE to debug problems.
verbose	If TRUE, then download progress will be logged as a message.

Format

A data frame with 3074 variables:

r1, r2, r3 ... r1024

Integer pixel value of the red channel of the image, from 0 to 255.

g1, g2, g3 ... g1024

Integer pixel value of the green channel of the image, from 0 to 255.

b1, b2, b3 ... b1024

Integer pixel value of the blue channel of the image, from 0 to 255.

Label

The image category, represented by a factor in the range 0-9.

Description

The name of the image category associated with Label, represented by a factor.

The pixel features are organized row-wise from the top left of each image. The Label levels correspond to the following class names (stored in the Description column):

0

Airplane

1

Automobile

2

Bird

3

Cat

4

Deer

5

Dog

6

Frog

7

Horse

8

Ship

9

Truck

There are 60,000 items in the data set. The first 50,000 are the training set, and the remaining 10,000 are the testing set.

Items in the dataset can be visualized with the show_cifar function.

For more information see https://www.cs.toronto.edu/~kriz/cifar.html.

Details

Downloads the image and label files for the training and test datasets and converts them to a data frame.

The CIFAR-10 dataset contains 60000 32 x 32 color images, divided into ten different classes, with 6000 images per class.

Value

Data frame containing the CIFAR-10 dataset.

References

The CIFAR-10 dataset https://www.cs.toronto.edu/~kriz/cifar.html

Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images (Vol. 1, No. 4, p. 7). Technical report, University of Toronto.

Examples

## Not run: 
# download the data set
cifar10 <- download_cifar10(verbose = TRUE)

# first 50,000 instances are the training set
cifar10_train <- head(cifar10, 50000)
# the remaining 24,300 are the test set
cifar10_test <- tail(cifar10, 10000)

# PCA on 1000 examples
cifar10_r1000 <- cifar10[sample(nrow(cifar10), 1000), ]
pca <- prcomp(cifar10_r1000[, 1:(32 * 32)], 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 = cifar10_r1000$Label,
  col = rainbow(length(levels(cifar10$Label)))[cifar10_r1000$Label]
)

## End(Not run)

jlmelville/snedata documentation built on Jan. 13, 2024, 2:06 a.m.