  We present an object detection framework based on PaddlePaddle. We put all
the strategies together (multi-scale training, FPN, Cascade, Dcnv2, Non-local,
libra loss) based on ResNet200-vd backbone. Our model score on public
leaderboard comes to 0.6269 with single scale test. We proposed a new voting
method called top-k voting-nms, based on the SoftNMS detection results. The
voting method helps us merge all the models' results more easily and achieve
2nd place in the Google AI Open Images Object Detection Track 2019.
