The CIFAR-10 and CIFAR-100 datasets. A subset of the 80 million tiny images dataset
with noisy labels was supplied to student labelers, who were asked to filter out
incorrectly labeled images.
The images are have datatype torch_long().
CIFAR-10 contains 10 classes. CIFAR-100 contains 100 classes, which may be partitioned into 20 superclasses of 5 classes each. The CIFAR-10 and CIFAR-100 classes are mutually exclusive. See Chapter 3.1 of the technical report for more details.
The data is obtained from torchvision::cifar10_dataset() (or torchvision::cifar100_dataset()).
Format
R6::R6Class inheriting from mlr3::TaskClassif.
Download
The task's backend is a DataBackendLazy which will download the data once it is requested.
Other meta-data is already available before that.
You can cache these datasets by setting the mlr3torch.cache option to TRUE or to a specific path to be used
as the cache directory.
Properties
Task type: “classif”
Properties: “multiclass”
Has Missings: no
Target: “class”
Features: “image”
Data Dimension: 60000x4
References
Krizhevsky, Alex (2009). “Learning Multiple Layers of Features from Tiny Images.” Master's thesis, Department of Computer Science, University of Toronto.
Examples
task_cifar10 = tsk("cifar10")
task_cifar100 = tsk("cifar100")
print(task_cifar10)
#> <TaskClassif:cifar10> (60000 x 2): CIFAR-10 Classification
#> * Target: class
#> * Properties: multiclass
#> * Features (1):
#> - lt (1): image
print(task_cifar100)
#> <TaskClassif:cifar100> (60000 x 2): CIFAR-100 Classification
#> * Target: class
#> * Properties: multiclass
#> * Features (1):
#> - lt (1): image