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 81.38%) 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模型的正确性及推理时间性能。然而，关于训练深度学习模型产生的巨大碳足迹，相关研究仍十分匮乏。本研究旨在分析模型架构与训练环境对训练更环保的计算机视觉模型的影响。我们将目标分解为两个研究问题：首先，分析模型架构在保证最优正确性的前提下实现更环保模型的效果；其次，研究训练环境对产生更环保模型的影响。为探究这些关系，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类指标，随后概述了模型架构方面测量到的能效与模型正确性之间的权衡，以及它们与训练环境的关系。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，选择合适的模型架构与训练环境可大幅降低能耗（最高达81.38%），而正确性损失可忽略不计。此外，我们发现GPU的性能应与模型计算复杂度相匹配，以实现更优的能效。