Is it worth the energy? An in-depth study on the energy efficiency of data augmentation strategies for finetuning-based low/few-shot object detection

Current methods for low- and few-shot object detection have primarily focused on enhancing model performance for detecting objects. One common approach to achieve this is by combining model finetuning with data augmentation strategies. However, little attention has been given to the energy efficiency of these approaches in data-scarce regimes. This paper seeks to conduct a comprehensive empirical study that examines both model performance and energy efficiency of custom data augmentations and automated data augmentation selection strategies when combined with a lightweight object detector. The methods are evaluated in four different benchmark datasets in terms of their performance and energy consumption, providing valuable insights regarding reaching an optimal tradeoff between these two objectives. Additionally, to better quantify this tradeoff, we propose a novel metric named modified Efficiency Factor that combines both of these conflicting objectives in a single metric and thus enables gaining insights into the effectiveness of the examined models and data augmentation strategies when considering both performance and efficiency. Consequently, it is shown that while some broader guidelines regarding appropriate data augmentation selections can be provided based on the obtained performance and energy efficiency results, in many cases, the performance gains of data augmentation strategies are overshadowed by their increased energy usage, necessitating the development of more energy-efficient data augmentation strategies to address data scarcity.