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We introduce MixTraining, a new training paradigm for object detection thatcan improve the performance of existing detectors for free. MixTrainingenhances data augmentation by utilizing augmentations of different strengthswhile excluding the strong augmentations of certain training samples that maybe detrimental to training. In addition, it addresses localization noise andmissing labels in human annotations by incorporating pseudo boxes that cancompensate for these errors. Both of these MixTraining capabilities are madepossible through bootstrapping on the detector, which can be used to predictthe difficulty of training on a strong augmentation, as well as to generatereliable pseudo boxes thanks to the robustness of neural networks to labelingerror. MixTraining is found to bring consistent improvements across variousdetectors on the COCO dataset. In particular, the performance of Faster R-CNN\cite{ren2015faster} with a ResNet-50 \cite{he2016deep} backbone is improvedfrom 41.7 mAP to 44.0 mAP, and the accuracy of Cascade-RCNN\cite{cai2018cascade} with a Swin-Small \cite{liu2021swin} backbone is raisedfrom 50.9 mAP to 52.8 mAP. The code and models will be made publicly availableat \url{https://github.com/MendelXu/MixTraining}.

Bootstrap Your Object Detector via Mixed Training