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.
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_dataset(
root = tempdir(),
split = "balanced",
transform = NULL,
target_transform = NULL,
download = FALSE
)Root directory for dataset storage. The dataset will be stored under root/<dataset-name>. Defaults to tempdir().
Logical. If TRUE, use the training set; otherwise, use the test set. Not applicable to all datasets.
Optional. A function that takes an image and returns a transformed version (e.g., normalization, cropping).
Optional. A function that transforms the label.
Logical. If TRUE, downloads the dataset to root/. If the dataset is already present, download is skipped.
Character. Used in emnist_dataset() and qmnist_dataset() to specify the subset. See individual descriptions for valid values.
A torch dataset object, where each items is a list of x (image) and y (label).
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: Letters and digits with multiple label splits.
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_dataset(): EMNIST dataset with digits and letters and multiple split modes.
emnist_dataset()"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
qmnist_dataset()"train": 60,000 training samples (MNIST-compatible)
"test": Extended test set
"nist": Full NIST digit set
Other classification_dataset:
caltech_dataset,
cifar10_dataset(),
eurosat_dataset(),
fer_dataset(),
fgvc_aircraft_dataset(),
flowers102_dataset(),
oxfordiiitpet_dataset(),
tiny_imagenet_dataset()
if (FALSE) { # \dontrun{
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_dataset(split = "balanced", download = TRUE)
item <- emnist[1]
item$x
item$y
kmnist <- kmnist_dataset(download = TRUE)
fmnist <- fashion_mnist_dataset(download = TRUE)
} # }