  Grid R-CNN is a well-performed objection detection framework. It transforms
the traditional box offset regression problem into a grid point estimation
problem. With the guidance of the grid points, it can obtain high-quality
localization results. However, the speed of Grid R-CNN is not so satisfactory.
In this technical report we present Grid R-CNN Plus, a better and faster
version of Grid R-CNN. We have made several updates that significantly speed up
the framework and simultaneously improve the accuracy. On COCO dataset, the
Res50-FPN based Grid R-CNN Plus detector achieves an mAP of 40.4%,
outperforming the baseline on the same model by 3.0 points with similar
inference time. Code is available at https://github.com/STVIR/Grid-R-CNN .
