The state-of-the-art object detection method is complicated with various
modules such as backbone, feature fusion neck, RPN and RCNN head, where each
module may have different designs and structures. How to leverage the
computational cost and accuracy trade-off for the structural combination as
well as the modular selection of multiple modules? Neural architecture search
(NAS) has shown great potential in finding an optimal solution. Existing NAS
works for object detection only focus on searching better design of a single
module such as backbone or feature fusion neck, while neglecting the balance of
the whole system. In this paper, we present a two-stage coarse-to-fine
searching strategy named Structural-to-Modular NAS (SM-NAS) for searching a
GPU-friendly design of both an efficient combination of modules and better
modular-level architecture for object detection. Specifically, Structural-level
searching stage first aims to find an efficient combination of different
modules; Modular-level searching stage then evolves each specific module and
pushes the Pareto front forward to a faster task-specific network. We consider
a multi-objective search where the search space covers many popular designs of
detection methods. We directly search a detection backbone without pre-trained
models or any proxy task by exploring a fast training from scratch strategy.
The resulting architectures dominate state-of-the-art object detection systems
in both inference time and accuracy and demonstrate the effectiveness on
multiple detection datasets, e.g. halving the inference time with additional 1%
mAP improvement compared to FPN and reaching 46% mAP with the similar inference
time of MaskRCNN.

本篇论文提出了一种名为 SM-NAS 的两阶段粗到细搜索策略，用于搜索既具有高效的模块组合也具有更好的模块级架构的 GPU 友好设计，优于现有的目标检测系统，为多种检测数据集提供更快的推理时间和更高的准确性。