You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.
Batch augmentation is the augmentation which involve multiple samples, such as Mixup and CutMix.
In MMClassification, these batch augmentation is used as a part of Classifier. A typical usage is as below:
model = dict( backbone = ..., neck = ..., head = ..., train_cfg=dict(augments=[ dict(type='BatchMixup', alpha=0.8, prob=0.5, num_classes=num_classes), dict(type='BatchCutMix', alpha=1.0, prob=0.5, num_classes=num_classes), ])) )