  Training with more data has always been the most stable and effective way of
improving performance in deep learning era. As the largest object detection
dataset so far, Open Images brings great opportunities and challenges for
object detection in general and sophisticated scenarios. However, owing to its
semi-automatic collecting and labeling pipeline to deal with the huge data
scale, Open Images dataset suffers from label-related problems that objects may
explicitly or implicitly have multiple labels and the label distribution is
extremely imbalanced. In this work, we quantitatively analyze these label
problems and provide a simple but effective solution. We design a concurrent
softmax to handle the multi-label problems in object detection and propose a
soft-sampling methods with hybrid training scheduler to deal with the label
imbalance. Overall, our method yields a dramatic improvement of 3.34 points,
leading to the best single model with 60.90 mAP on the public object detection
test set of Open Images. And our ensembling result achieves 67.17 mAP, which is
4.29 points higher than the best result of Open Images public test 2018.
