OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection

TL;DR

OPANAS introduces an efficient neural architecture search method for feature pyramid networks, significantly enhancing object detection accuracy and speed while reducing search costs, outperforming existing NAS-based FPN designs.

Contribution

The paper proposes a novel one-shot NAS approach with a new search space and an efficient evolutionary search method for FPNs, improving detection performance and search efficiency.

Findings

OPANAS reduces search cost to 4 GPU days on MS-COCO.

It improves detection accuracy by 2.3-3.2% mAP over baseline FPNs.

Achieves a new state-of-the-art trade-off with 52.2% mAP at 7.6 FPS.

Abstract

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.