Classic MNIST image classification.
The underlying DataBackend contains columns "label", "image", "row_id", "split", where the last column
indicates whether the row belongs to the train or test set.
The first 60000 rows belong to the training set, the last 10000 rows to the test set.
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: “label”
Features: “image”
Data Dimension: 70000x4
References
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P. (1998). “Gradient-based learning applied to document recognition.” Proceedings of the IEEE, 86(11), 2278-2324. doi:10.1109/5.726791 .
Examples
task = tsk("mnist")
task
#> <TaskClassif:mnist> (70000 x 2): MNIST Digit Classification
#> * Target: label
#> * Properties: multiclass
#> * Features (1):
#> - lt (1): image