  This report details our solution to the Google AI Open Images Challenge 2019
Object Detection Track. Based on our detailed analysis on the Open Images
dataset, it is found that there are four typical features: large-scale,
hierarchical tag system, severe annotation incompleteness and data imbalance.
Considering these characteristics, many strategies are employed, including
larger backbone, distributed softmax loss, class-aware sampling, expert model,
and heavier classifier. In virtue of these effective strategies, our best
single model could achieve a mAP of 61.90. After ensemble, the final mAP is
boosted to 67.17 in the public leaderboard and 64.21 in the private
leaderboard, which earns 3rd place in the Open Images Challenge 2019.
