A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection

TL;DR

This paper empirically evaluates the trade-offs between performance improvements and energy consumption of data augmentation strategies in low/few-shot object detection, highlighting the need for energy-efficient methods.

Contribution

It provides a comprehensive analysis of how different data augmentation strategies impact both accuracy and energy efficiency in low/few-shot object detection.

Findings

Data augmentation often increases energy consumption significantly.

Performance gains are sometimes overshadowed by energy costs.

Energy-efficient augmentation strategies are needed for data-scarce scenarios.

Abstract

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 three different benchmark datasets in terms of their performance and energy consumption, and the Efficiency Factor is employed to gain insights into their effectiveness considering both performance and efficiency. Consequently, it is shown that 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.