Data augmentation has emerged as a cornerstone of successful deep learning models in computer vision, significantly enhancing their robustness, generalization, and overall performance. While various augmentation techniques have proven effective, the key to unlocking their full potential lies in careful evaluation and optimization. This chapter will delve into crucial aspects of evaluating and optimizing deep learning image augmentation strategies, exploring key considerations, metrics, and best practices for selecting and combining augmentation methods to achieve optimal model performance and improve the overall robustness and generalizability of deep learning models.