  Do you want to improve 1.0 AP for your object detector without any inference
cost and any change to your detector? Let us tell you such a recipe. It is
surprisingly simple: train your detector for an extra 12 epochs using cyclical
learning rates and then average these 12 checkpoints as your final detection
model}. This potent recipe is inspired by Stochastic Weights Averaging (SWA),
which is proposed in arXiv:1803.05407 for improving generalization in deep
neural networks. We found it also very effective in object detection. In this
technique report, we systematically investigate the effects of applying SWA to
object detection as well as instance segmentation. Through extensive
experiments, we discover the aforementioned workable policy of performing SWA
in object detection, and we consistently achieve $\sim$1.0 AP improvement over
various popular detectors on the challenging COCO benchmark, including Mask
RCNN, Faster RCNN, RetinaNet, FCOS, YOLOv3 and VFNet. We hope this work will
make more researchers in object detection know this technique and help them
train better object detectors. Code is available at:
https://github.com/hyz-xmaster/swa_object_detection .
