  We present consistent optimization for single stage object detection.
Previous works of single stage object detectors usually rely on the regular,
dense sampled anchors to generate hypothesis for the optimization of the model.
Through an examination of the behavior of the detector, we observe that the
misalignment between the optimization target and inference configurations has
hindered the performance improvement. We propose to bride this gap by
consistent optimization, which is an extension of the traditional single stage
detector's optimization strategy. Consistent optimization focuses on matching
the training hypotheses and the inference quality by utilizing of the refined
anchors during training. To evaluate its effectiveness, we conduct various
design choices based on the state-of-the-art RetinaNet detector. We demonstrate
it is the consistent optimization, not the architecture design, that yields the
performance boosts. Consistent optimization is nearly cost-free, and achieves
stable performance gains independent of the model capacities or input scales.
Specifically, utilizing consistent optimization improves RetinaNet from 39.1 AP
to 40.1 AP on COCO dataset without any bells or whistles, which surpasses the
accuracy of all existing state-of-the-art one-stage detectors when adopting
ResNet-101 as backbone. The code will be made available.
