  Efficient detectors for edge devices are often optimized for parameters or
speed count metrics, which remain in weak correlation with the energy of
detectors.
  However, some vision applications of convolutional neural networks, such as
always-on surveillance cameras, are critical for energy constraints.
  This paper aims to serve as a baseline by designing detectors to reach
tradeoffs between energy and performance from two perspectives:
  1) We extensively analyze various CNNs to identify low-energy architectures,
including selecting activation functions, convolutions operators, and feature
fusion structures on necks. These underappreciated details in past work
seriously affect the energy consumption of detectors;
  2) To break through the dilemmatic energy-performance problem, we propose a
balanced detector driven by energy using discovered low-energy components named
\textit{FemtoDet}.
  In addition to the novel construction, we improve FemtoDet by considering
convolutions and training strategy optimizations.
  Specifically, we develop a new instance boundary enhancement (IBE) module for
convolution optimization to overcome the contradiction between the limited
capacity of CNNs and detection tasks in diverse spatial representations, and
propose a recursive warm-restart (RecWR) for optimizing training strategy to
escape the sub-optimization of light-weight detectors by considering the data
shift produced in popular augmentations.
  As a result, FemtoDet with only 68.77k parameters achieves a competitive
score of 46.3 AP50 on PASCAL VOC and 1.11 W $\&$ 64.47 FPS on Qualcomm
Snapdragon 865 CPU platforms.
  Extensive experiments on COCO and TJU-DHD datasets indicate that the proposed
method achieves competitive results in diverse scenes.
