mnist_dataset: MNIST and Derived Datasets

In torchvision: Models, Datasets and Transformations for Images

MNIST and Derived Datasets

Description

Prepares various MNIST-style image classification datasets and optionally downloads them. Images are thumbnails images of 28 x 28 pixels of grayscale values encoded as integer.

Usage

mnist_dataset(
  root = tempdir(),
  train = TRUE,
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)

kmnist_dataset(
  root = tempdir(),
  train = TRUE,
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)

qmnist_dataset(
  root = tempdir(),
  split = "train",
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)

fashion_mnist_dataset(
  root = tempdir(),
  train = TRUE,
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)

emnist_collection(
  root = tempdir(),
  split = "test",
  dataset = "balanced",
  transform = NULL,
  target_transform = NULL,
  download = FALSE
)

emnist_dataset(kind, ...)

Arguments

root	Root directory for dataset storage. The dataset will be stored under ⁠root/<dataset-name>⁠. Defaults to tempdir().
train	Logical. If TRUE, use the training set; otherwise, use the test set. Not applicable to all datasets.
transform	Optional. A function that takes an image and returns a transformed version (e.g., normalization, cropping).
target_transform	Optional. A function that transforms the label.
download	Logical. If TRUE, downloads the dataset to ⁠root/⁠. If the dataset is already present, download is skipped.
split	Character. Used in emnist_dataset() and qmnist_dataset() to specify the subset. See individual descriptions for valid values.
dataset	one of "byclass", "bymerge", "balanced" representing the subset of emnist collection made of a set of classes. You can look at dataset attribute ⁠$classes⁠ to see the actual classes.
kind	the dataset in emnist_collection.
...	the other emnist_collection parameters.

Details

• MNIST: Original handwritten digit dataset.

• Fashion-MNIST: Clothing item images for classification.

• Kuzushiji-MNIST: Japanese cursive character dataset.

• QMNIST: Extended MNIST with high-precision NIST data.

• EMNIST: A collection of letters and digits with multiple datasets and splits.

Value

A torch dataset object, where each items is a list of x (image) and y (label).

Functions

• kmnist_dataset(): Kuzushiji-MNIST cursive Japanese character dataset.

• qmnist_dataset(): Extended MNIST dataset with high-precision test data (QMNIST).

• fashion_mnist_dataset(): Fashion-MNIST clothing image dataset.

• emnist_collection(): EMNIST collection with digits and letters arranged in multiple datasets.

• emnist_dataset(): Deprecated. Please use emnist_collection.

Supported datasets for emnist_collection()

• "byclass": 62 classes (digits + uppercase + lowercase)

• "bymerge": 47 classes (merged uppercase and lowercase)

• "balanced": 47 classes, balanced digits and letters

• "letters": 26 uppercase letters

• "digits": 10 digit classes

• "mnist": Standard MNIST digit classes

Supported splits for qmnist_dataset()

• "train": 60,000 training samples (MNIST-compatible)

• "test": Extended test set

• "nist": Full NIST digit set

See Also

Other classification_dataset: caltech_dataset, cifar10_dataset(), eurosat_dataset(), fer_dataset(), fgvc_aircraft_dataset(), flowers102_dataset(), image_folder_dataset(), lfw_dataset, oxfordiiitpet_dataset(), places365_dataset(), tiny_imagenet_dataset(), whoi_plankton_dataset(), whoi_small_coralnet_dataset()

Examples

## Not run: 
ds <- mnist_dataset(download = TRUE)
item <- ds[1]
item$x  # image
item$y  # label

qmnist <- qmnist_dataset(split = "train", download = TRUE)
item <- qmnist[1]
item$x
item$y

emnist <- emnist_collection(dataset = "balanced", split = "test", download = TRUE)
item <- emnist[1]
item$x
item$y

kmnist <- kmnist_dataset(download = TRUE, train = FALSE)
fmnist <- fashion_mnist_dataset(download = TRUE, train = TRUE)

## End(Not run)

torchvision documentation built on Nov. 6, 2025, 9:07 a.m.