Skip to contents

Download CIFAR-10

Source: R/cifar.R
download_cifar10.Rd

Download CIFAR-10 database of images.

Usage

download_cifar10(
  url = "https://cave.cs.toronto.edu/kriz/cifar-10-binary.tar.gz",
  destfile = NULL,
  cleanup = TRUE,
  verbose = FALSE,
  as = c("data.frame", "list"),
  timeout = 1800
)

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. Canonical list results carry this identity in meta$split.

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

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

Arguments

url

URL of the CIFAR-10 data.

destfile

Filename for where to download the CIFAR-10 tarfile. If NULL, a file in a temporary work directory is used. The archive is always extracted to a separate temporary work 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.

as

Return format. Use "data.frame" for the original data frame shape, or "list" for the canonical image result described in download_mnist(). The integer pixel matrix uses about 0.69 GiB; the wide data-frame result needs additional memory. Use "list" if that result is sufficient.

timeout

Minimum download timeout in seconds. The default accommodates the large archive on slower connections; a larger existing global R timeout is preserved.

Value

If as = "data.frame", a data frame containing the CIFAR-10 dataset. If as = "list", a canonical image result with factor class labels and descriptions in meta.

Details

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

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

References

The CIFAR-10 dataset https://cave.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

if (FALSE) { # \dontrun{
# download the data set
cifar10 <- download_cifar10(verbose = TRUE)

# first 50,000 instances are the training set
cifar10_train <- head(cifar10, 50000)
# the remaining 10,000 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 * 3)], 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]
)
} # }