NAS FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
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.
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)