The energy requirements of neural network learning are growing at a rapid rate. Increased energy demands have caused a global need to seek ways to improve energy efficiency of neural network learning. This paper aims to establish a baseline on how adjusting basic parameters can affect energy consumption in neural network learning on Computer Vision tasks. In this article, we catalog the effects of various adjustments, from simple batch size adjustments to more complicated hardware settings (e.g., power capping). Based on our characterizations, we have found numerous avenues to adjust computer vision algorithm energy expenditure. For example, switching from a single precision model to mixed precision training can result in energy reductions of nearly 40 %. dditionally, power capping the Graphical Processing Unit (GPU) can reduce energy cost by an additional 10%.