  Dynamic neural network is an emerging research topic in deep learning. With
adaptive inference, dynamic models can achieve remarkable accuracy and
computational efficiency. However, it is challenging to design a powerful
dynamic detector, because of no suitable dynamic architecture and exiting
criterion for object detection. To tackle these difficulties, we propose a
dynamic framework for object detection, named DynamicDet. Firstly, we carefully
design a dynamic architecture based on the nature of the object detection task.
Then, we propose an adaptive router to analyze the multi-scale information and
to decide the inference route automatically. We also present a novel
optimization strategy with an exiting criterion based on the detection losses
for our dynamic detectors. Last, we present a variable-speed inference
strategy, which helps to realize a wide range of accuracy-speed trade-offs with
only one dynamic detector. Extensive experiments conducted on the COCO
benchmark demonstrate that the proposed DynamicDet achieves new
state-of-the-art accuracy-speed trade-offs. For instance, with comparable
accuracy, the inference speed of our dynamic detector Dy-YOLOv7-W6 surpasses
YOLOv7-E6 by 12%, YOLOv7-D6 by 17%, and YOLOv7-E6E by 39%. The code is
available at https://github.com/VDIGPKU/DynamicDet.
