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侧重于提升深度学习（DL）的准确性与推理时间性能，但针对训练深度学习模型所产生的巨大碳足迹仍鲜有探讨。本研究旨在分析模型架构与训练环境对训练更绿色计算机视觉模型的影响，并将其分解为两个研究问题：首先，分析在保持最优准确性的前提下，模型架构对实现更绿色模型的作用；其次，研究训练环境对生成更绿色模型的影响。为探究这些关系，我们在模型训练过程中收集了与能源效率和模型准确性相关的多项指标，随后在图像分类的计算机视觉系统背景下，概括了所测得的能源效率与模型准确性之间关于模型架构的权衡关系，及其与训练环境的关联。结论表明，选择合适的模型架构与训练环境可在仅牺牲极小准确性的情况下大幅降低能耗（最高达98.83%）。同时，我们发现GPU的计算能力应与模型的复杂度相匹配，以实现更优的能源效率。