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Recent self-supervised pretraining methods for object detection largely focuson pretraining the backbone of the object detector, neglecting key parts ofdetection architecture. Instead, we introduce DETReg, a new self-supervisedmethod that pretrains the entire object detection network, including the objectlocalization and embedding components. During pretraining, DETReg predictsobject localizations to match the localizations from an unsupervised regionproposal generator and simultaneously aligns the corresponding featureembeddings with embeddings from a self-supervised image encoder. We implementDETReg using the DETR family of detectors and show that it improves overcompetitive baselines when finetuned on COCO, PASCAL VOC, and Airbus Shipbenchmarks. In low-data regimes DETReg achieves improved performance, e.g.,when training with only 1% of the labels and in the few-shot learning settings.

DETReg: Unsupervised Pretraining with Region Priors for Object Detection