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This report demonstrates our solution for the Open Images 2018 Challenge.Based on our detailed analysis on the Open Images Datasets (OID), it is foundthat there are four typical features: large-scale, hierarchical tag system,severe annotation incompleteness and data imbalance. Considering thesecharacteristics, an amount of strategies are employed, including SNIPER, softsampling, class-aware sampling (CAS), hierarchical non-maximum suppression(HNMS) and so on. In virtue of these effective strategies, and further usingthe powerful SENet154 armed with feature pyramid module and deformable ROIalignas the backbone, our best single model could achieve a mAP of 56.9%. After afurther ensemble with 9 models, the final mAP is boosted to 62.2% in the publicleaderboard (ranked the 2nd place) and 58.6% in the private leaderboard (rankedthe 3rd place, slightly inferior to the 1st place by only 0.04 point).

Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalance