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

IF 3.7 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Vladislav Li , Georgios Tsoumplekas , Ilias Siniosoglou , Panagiotis Sarigiannidis , Vasileios Argyriou
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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.
值得花精力吗?基于微调的低/少镜头目标检测中数据增强策略能量效率的深入研究
目前的低镜头和少镜头目标检测方法主要集中在提高模型检测目标的性能上。实现这一目标的一种常用方法是将模型调优与数据增强策略相结合。然而,在数据匮乏的情况下,很少有人注意到这些方法的能源效率。本文试图进行一项全面的实证研究,在结合轻量级对象检测器的情况下,检查自定义数据增强和自动数据增强选择策略的模型性能和能源效率。在四个不同的基准数据集中对这些方法的性能和能耗进行了评估,为实现这两个目标之间的最佳权衡提供了有价值的见解。此外,为了更好地量化这种权衡,我们提出了一个名为modified Efficiency Factor的新指标,它将这两个相互冲突的目标结合在一个指标中,从而能够在考虑性能和效率时深入了解所检查的模型和数据增强策略的有效性。因此,研究表明,虽然可以根据获得的性能和能源效率结果提供有关适当数据增强选择的一些更广泛的指导方针,但在许多情况下,数据增强策略的性能收益被其增加的能源使用所掩盖,因此需要开发更节能的数据增强策略来解决数据稀缺问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Systems Architecture
Journal of Systems Architecture 工程技术-计算机:硬件
CiteScore
8.70
自引率
15.60%
发文量
226
审稿时长
46 days
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.
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