  Recently, neural architecture search (NAS) has been exploited to design
feature pyramid networks (FPNs) and achieved promising results for visual
object detection. Encouraged by the success, we propose a novel One-Shot Path
Aggregation Network Architecture Search (OPANAS) algorithm, which significantly
improves both searching efficiency and detection accuracy. Specifically, we
first introduce six heterogeneous information paths to build our search space,
namely top-down, bottom-up, fusing-splitting, scale-equalizing, skip-connect
and none. Second, we propose a novel search space of FPNs, in which each FPN
candidate is represented by a densely-connected directed acyclic graph (each
node is a feature pyramid and each edge is one of the six heterogeneous
information paths). Third, we propose an efficient one-shot search method to
find the optimal path aggregation architecture, that is, we first train a
super-net and then find the optimal candidate with an evolutionary algorithm.
Experimental results demonstrate the efficacy of the proposed OPANAS for object
detection: (1) OPANAS is more efficient than state-of-the-art methods (e.g.,
NAS-FPN and Auto-FPN), at significantly smaller searching cost (e.g., only 4
GPU days on MS-COCO); (2) the optimal architecture found by OPANAS
significantly improves main-stream detectors including RetinaNet, Faster R-CNN
and Cascade R-CNN, by 2.3-3.2 % mAP comparing to their FPN counterparts; and
(3) a new state-of-the-art accuracy-speed trade-off (52.2 % mAP at 7.6 FPS) at
smaller training costs than comparable state-of-the-arts. Code will be released
at https://github.com/VDIGPKU/OPANAS.
