Feature Pyramid Networks for Object Detection Tsung-Yi Lin1,2, Piotr Dollár1, Ross Girshick1, Kaiming He1, Bharath Hariharan1, and Serge Belongie2 1Facebook AI Research (FAIR) 2Cornell University and Cornell Tech Abstract Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But recent deep learning object detectors have avoided pyramid rep- resentations, in part because they are compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to con- struct feature pyramids with marginal extra cost. A top- down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extrac- tor in several applications. Using FPN in a basic Faster R-CNN system, our method achieves state-of-the-art single
