{"title":"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","authors":"Vladislav Li , Georgios Tsoumplekas , Ilias Siniosoglou , Panagiotis Sarigiannidis , Vasileios Argyriou","doi":"10.1016/j.sysarc.2025.103484","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50027,"journal":{"name":"Journal of Systems Architecture","volume":"167 ","pages":"Article 103484"},"PeriodicalIF":3.7000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Systems Architecture","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1383762125001560","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
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 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.
期刊介绍:
The Journal of Systems Architecture: Embedded Software Design (JSA) is a journal covering all design and architectural aspects related to embedded systems and software. It ranges from the microarchitecture level via the system software level up to the application-specific architecture level. Aspects such as real-time systems, operating systems, FPGA programming, programming languages, communications (limited to analysis and the software stack), mobile systems, parallel and distributed architectures as well as additional subjects in the computer and system architecture area will fall within the scope of this journal. Technology will not be a main focus, but its use and relevance to particular designs will be. Case studies are welcome but must contribute more than just a design for a particular piece of software.
Design automation of such systems including methodologies, techniques and tools for their design as well as novel designs of software components fall within the scope of this journal. Novel applications that use embedded systems are also central in this journal. While hardware is not a part of this journal hardware/software co-design methods that consider interplay between software and hardware components with and emphasis on software are also relevant here.