  Recently two-stage detectors have surged ahead of single-shot detectors in
the accuracy-vs-speed trade-off. Nevertheless single-shot detectors are
immensely popular in embedded vision applications. This paper brings
single-shot detectors up to the same level as current two-stage techniques. We
do this by improving training for the state-of-the-art single-shot detector,
RetinaNet, in three ways: integrating instance mask prediction for the first
time, making the loss function adaptive and more stable, and including
additional hard examples in training. We call the resulting augmented network
RetinaMask. The detection component of RetinaMask has the same computational
cost as the original RetinaNet, but is more accurate. COCO test-dev results are
up to 41.4 mAP for RetinaMask-101 vs 39.1mAP for RetinaNet-101, while the
runtime is the same during evaluation. Adding Group Normalization increases the
performance of RetinaMask-101 to 41.7 mAP. Code is
at:https://github.com/chengyangfu/retinamask
