Abstract: Data augmentation has become a standard component of vision pre-trained models to capture the invariance between augmented views. In practice, augmentation techniques that mask regions of a sample with zero/mean values or patches from other samples are commonly employed in pre-trained models with self-/semi-/fully-supervised contrastive losses.However, the underlying mechanism behind the effectiveness of these augmentation techniques remains poorly explored.To investigate the problems, we conduct an empirical study to quantify how data augmentation affects performance. Concretely, we apply 4 types of data augmentations termed with $\texttt{Random Erasing}$, $\texttt{CutOut}$, $\texttt{CutMix}$ and $\texttt{MixUp}$ to a series of self-/semi-/fully- supervised pre-trained models. We report their performance on vision tasks such as image classification, object detection, instance segmentation, and semantic segmentation. We then explicitly evaluate the invariance and diversity of the feature embedding.We observe that: 1) Masking regions of the images decreases the invariance of the learned feature embedding while providing a more considerable diversity. 2) Manual annotations do not change the invariance or diversity of the learned feature embedding. 3) The $\texttt{MixUp}$ approach improves the diversity significantly, with only a marginal decrease in terms of the invariance.