评估工业环境中目标检测少样本学习的能源效率
Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings
AI总结：
本文研究工业目标检测中少样本微调的能耗问题，比较不同策略与辅助数据使用，并提出 Efficiency Factor 指标以量化性能与效率权衡。
摘要：
在人工智能（AI）不断发展的时代，模型性能已成为推动创新的关键指标，导致模型规模和复杂性呈指数级增长。然而，在当代工业环境中部署时，可持续性和能源效率一直是关键要求，因此有必要使用少样本学习等数据高效方法。本文为减轻冗长模型训练的负担并最大限度降低能耗，研究了一种用于使标准目标检测模型适应下游任务的微调方法。随后，针对所开发模型在来自多变工业环境的目标检测基准数据集上的应用，对其能源需求进行了全面案例研究和评估。具体而言，研究了不同微调策略以及训练期间辅助评估数据的使用，并强调了这种低数据场景下性能与效率之间的权衡。最后，本文提出了一种通过定制的 Efficiency Factor（效率因子）指标来量化该权衡的新方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
