  Model efficiency has become increasingly important in computer vision. In
this paper, we systematically study neural network architecture design choices
for object detection and propose several key optimizations to improve
efficiency. First, we propose a weighted bi-directional feature pyramid network
(BiFPN), which allows easy and fast multiscale feature fusion; Second, we
propose a compound scaling method that uniformly scales the resolution, depth,
and width for all backbone, feature network, and box/class prediction networks
at the same time. Based on these optimizations and better backbones, we have
developed a new family of object detectors, called EfficientDet, which
consistently achieve much better efficiency than prior art across a wide
spectrum of resource constraints. In particular, with single model and
single-scale, our EfficientDet-D7 achieves state-of-the-art 55.1 AP on COCO
test-dev with 77M parameters and 410B FLOPs, being 4x - 9x smaller and using
13x - 42x fewer FLOPs than previous detectors. Code is available at
https://github.com/google/automl/tree/master/efficientdet.
