Current research in the computer vision field mainly focuses on improving Deep Learning (DL) correctness and inference time performance. However, there is still little work on the huge carbon footprint that has training DL models. This study aims to analyze the impact of the model architecture and training environment when training greener computer vision models. We divide this goal into two research questions. First, we analyze the effects of model architecture on achieving greener models while keeping correctness at optimal levels. Second, we study the influence of the training environment on producing greener models. To investigate these relationships, we collect multiple metrics related to energy efficiency and model correctness during the models' training. Then, we outline the trade-offs between the measured energy efficiency and the models' correctness regarding model architecture, and their relationship with the training environment. We conduct this research in the context of a computer vision system for image classification. In conclusion, we show that selecting the proper model architecture and training environment can reduce energy consumption dramatically (up to 98.83%) at the cost of negligible decreases in correctness. Also, we find evidence that GPUs should scale with the models' computational complexity for better energy efficiency.

翻译：当前计算机视觉领域的研究主要聚焦于提升深度学习（DL）的正确性与推理时间性能。然而，针对训练深度学习模型所产生的巨大碳足迹，相关研究仍然较为匮乏。本研究旨在分析模型架构与训练环境对构建更绿色计算机视觉模型的影响。我们将此目标分解为两个研究问题：首先，分析模型架构在保持最优正确性的前提下对实现更绿色模型的影响；其次，研究训练环境对生成更绿色模型的作用。为探究这些关联，我们在模型训练过程中收集了与能效和模型正确性相关的多项指标。随后，我们概述了测量所得的能效与模型正确性在模型架构层面的权衡关系，以及它们与训练环境之间的关联。本研究以图像分类的计算机视觉系统为背景展开。结论表明，选择适当的模型架构与训练环境可在正确性几乎不下降的前提下大幅降低能耗（最高达98.83%）。此外，我们发现GPU的计算能力应与模型的计算复杂度保持匹配，以获得更优的能效表现。