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Training with more data has always been the most stable and effective way ofimproving performance in deep learning era. As the largest object detectiondataset so far, Open Images brings great opportunities and challenges forobject detection in general and sophisticated scenarios. However, owing to itssemi-automatic collecting and labeling pipeline to deal with the huge datascale, Open Images dataset suffers from label-related problems that objects mayexplicitly or implicitly have multiple labels and the label distribution isextremely imbalanced. In this work, we quantitatively analyze these labelproblems and provide a simple but effective solution. We design a concurrentsoftmax to handle the multi-label problems in object detection and propose asoft-sampling methods with hybrid training scheduler to deal with the labelimbalance. 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 detectiontest set of Open Images. And our ensembling result achieves 67.17 mAP, which is4.29 points higher than the best result of Open Images public test 2018.

Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels