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Current research in the computer vision field mainly focuses on improvingDeep Learning (DL) correctness and inference time performance. However, thereis 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 trainingenvironment when training greener computer vision models. We divide this goalinto two research questions. First, we analyze the effects of modelarchitecture on achieving greener models while keeping correctness at optimallevels. Second, we study the influence of the training environment on producinggreener models. To investigate these relationships, we collect multiple metricsrelated to energy efficiency and model correctness during the models' training.Then, we outline the trade-offs between the measured energy efficiency and themodels' correctness regarding model architecture, and their relationship withthe training environment. We conduct this research in the context of a computervision system for image classification. In conclusion, we show that selectingthe proper model architecture and training environment can reduce energyconsumption dramatically (up to 81.38%) at the cost of negligible decreases incorrectness. Also, we find evidence that GPUs should scale with the models'computational complexity for better energy efficiency.

Do DL models and training environments have an impact on energy consumption?