Data augmentation is a technique used in machine learning and deep learning to improve the performance of models by increasing the size of the training dataset without collecting new data. It involves applying various transformations to existing images or videos to generate new, augmented examples that can help reduce overfitting and improve model generalization. The goal is to fill out the underlying distribution from which the images come from in the dataset, refining the model's decision boundaries. Data augmentation can be performed on image datasets, video datasets, and even text datasets. It can also be used to address class imbalance problems by augmenting the smaller classes more to make all classes the same size. The technique is widely used in computer vision tasks and has been shown to improve model performance and robustness. However, it's essential to use data augmentation carefully, as excessive transformations can result in unrealistic images that may not be useful for training models.