papers AI Learner
The Github is limit! Click to go to the new site.

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

2019-04-16
Golnaz Ghiasi, Tsung-Yi Lin, Ruoming Pang, Quoc V. Le

Abstract

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.

Abstract (translated by Google)
URL

http://arxiv.org/abs/1904.07392

PDF

http://arxiv.org/pdf/1904.07392


Similar Posts

Comments