  Feature pyramids are widely exploited in many detectors to solve the scale
variation problem for object detection. In this paper, we first investigate the
Feature Pyramid Network (FPN) architectures and briefly categorize them into
three typical fashions: top-down, bottom-up and fusing-splitting, which have
their own merits for detecting small objects, large objects, and medium-sized
objects, respectively. Further, we design three FPNs of different architectures
and propose a novel Mixture Feature Pyramid Network (MFPN) which inherits the
merits of all these three kinds of FPNs, by assembling the three kinds of FPNs
in a parallel multi-branch architecture and mixing the features. MFPN can
significantly enhance both one-stage and two-stage FPN-based detectors with
about 2 percent Average Precision(AP) increment on the MS-COCO benchmark, at
little sacrifice in running time latency. By simply assembling MFPN with the
one-stage and two-stage baseline detectors, we achieve competitive single-model
detection results on the COCO detection benchmark without bells and whistles.
