Datasets
DD-Ranking provides a set of commonly used datasets in existing dataset distillation methods. Users can flexibly use these datasets for evaluation. The interface to load datasets is as follows:
dd_ranking.utils.get_dataset(dataset: str, data_path: str, im_size: tuple, use_zca: bool, custom_val_trans: Optional[Callable], device: str) [SOURCE]
Parameters
- dataset(str): Name of the dataset.
- data_path(str): Path to the dataset.
- im_size(tuple): Image size.
- use_zca(bool): Whether to use ZCA whitening. When set to True, the dataset will not be normalized using the mean and standard deviation of the training set.
- custom_val_trans(Optional[Callable]): Custom transformation on the validation set.
- device(str): Device for performing ZCA whitening.
Currently, we support the following datasets with default settings. We will keep updating this section with more datasets.
- CIFAR10
- channels:
3 - im_size:
(32, 32) - num_classes:
10 - mean:
[0.4914, 0.4822, 0.4465] - std:
[0.2023, 0.1994, 0.2010]
- channels:
- CIFAR100
- channels:
3 - im_size:
(32, 32) - num_classes:
100 - mean:
[0.4914, 0.4822, 0.4465] - std:
[0.2023, 0.1994, 0.2010]
- channels:
- TinyImageNet
- channels:
3 - im_size:
(64, 64) - num_classes:
200 - mean:
[0.485, 0.456, 0.406] - std:
[0.229, 0.224, 0.225]
- channels: