Current state-of-the-art convolutional architectures for object detection are manually designed. Here we aim to learn a better architecture of feature pyramid network for object detection. We adopt Neural Architecture Search and discover a new feature pyramid architecture in a novel scalable search space covering all cross-scale connections. The discovered architecture, named NAS-FPN, consists of a combination of top-down and bottom-up connections to fuse features across scales. NAS-FPN, combined with various backbone models in the RetinaNet framework, achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models. NAS-FPN improves mobile detection accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in [32] and achieves 48.3 AP which surpasses Mask R-CNN [10] detection accuracy with less computation time.
@article{arxiv.1904.07392,
title = {NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection},
author = {Golnaz Ghiasi and Tsung-Yi Lin and Ruoming Pang and Quoc V. Le},
journal= {arXiv preprint arXiv:1904.07392},
year = {2019}
}