  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.
