NAS-FCOS: Fast Neural Architecture Search for Object Detection

TL;DR

This paper introduces NAS-FCOS, a fast neural architecture search method that efficiently designs object detection networks, achieving superior accuracy with limited computational resources.

Contribution

It presents a tailored NAS approach for object detection that searches for optimal FPN and prediction head structures within 4 days using 8 GPUs.

Findings

Discovered architecture outperforms state-of-the-art models in AP on COCO.

Achieves high detection accuracy with comparable complexity and memory footprint.

Reduces search time to 4 days on 8 GPUs.

Abstract

The success of deep neural networks relies on significant architecture engineering. Recently neural architecture search (NAS) has emerged as a promise to greatly reduce manual effort in network design by automatically searching for optimal architectures, although typically such algorithms need an excessive amount of computational resources, e.g., a few thousand GPU-days. To date, on challenging vision tasks such as object detection, NAS, especially fast versions of NAS, is less studied. Here we propose to search for the decoder structure of object detectors with search efficiency being taken into consideration. To be more specific, we aim to efficiently search for the feature pyramid network (FPN) as well as the prediction head of a simple anchor-free object detector, namely FCOS, using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms and strategies for evaluating network quality, we are able to efficiently search a top-performing detection architecture within 4 days using 8 V100 GPUs. The discovered architecture surpasses state-of-the-art object detection models (such as Faster R-CNN, RetinaNet and FCOS) by 1.5 to 3.5 points in AP on the COCO dataset, with comparable computation complexity and memory footprint, demonstrating the efficacy of the proposed NAS for object detection.