In the ever-evolving era of Artificial Intelligence (AI), model performance has constituted a key metric driving innovation, leading to an exponential growth in model size and complexity. However, sustainability and energy efficiency have been critical requirements during deployment in contemporary industrial settings, necessitating the use of data-efficient approaches such as few-shot learning. In this paper, to alleviate the burden of lengthy model training and minimize energy consumption, a finetuning approach to adapt standard object detection models to downstream tasks is examined. Subsequently, a thorough case study and evaluation of the energy demands of the developed models, applied in object detection benchmark datasets from volatile industrial environments is presented. Specifically, different finetuning strategies as well as utilization of ancillary evaluation data during training are examined, and the trade-off between performance and efficiency is highlighted in this low-data regime. Finally, this paper introduces a novel way to quantify this trade-off through a customized Efficiency Factor metric.

翻译：在人工智能（AI）不断发展的时代，模型性能已成为驱动创新的关键指标，导致模型规模和复杂性呈指数级增长。然而，在当代工业部署中，可持续性和能效已成为关键需求，必须采用数据高效的方法，例如小样本学习。为缓解冗长模型训练的负担并最小化能耗，本文研究了一种微调方法，用于使标准目标检测模型适应下游任务。随后，针对从动态工业环境中选取的目标检测基准数据集，对所开发模型的能耗进行了深入的案例研究和评估。具体而言，考察了不同的微调策略以及训练过程中辅助评估数据的利用情况，并在此低数据场景下突出了性能与效率之间的权衡。最后，本文提出了一种通过自定义效率因子指标来量化这种权衡的新方法。