  In the ever-evolving era of Artificial Intelligence (AI), model performance
has constituted a key metric driving innovation, leading to an exponential
growth in model size and complexity. However, sustainability and energy
efficiency have been critical requirements during deployment in contemporary
industrial settings, necessitating the use of data-efficient approaches such as
few-shot learning. In this paper, to alleviate the burden of lengthy model
training and minimize energy consumption, a finetuning approach to adapt
standard object detection models to downstream tasks is examined. Subsequently,
a thorough case study and evaluation of the energy demands of the developed
models, applied in object detection benchmark datasets from volatile industrial
environments is presented. Specifically, different finetuning strategies as
well as utilization of ancillary evaluation data during training are examined,
and the trade-off between performance and efficiency is highlighted in this
low-data regime. Finally, this paper introduces a novel way to quantify this
trade-off through a customized Efficiency Factor metric.
