# Grid R-CNN Plus: Faster and Better

Source: [Grid R-CNN Plus: Faster and Better](<https://www.sota2.com/research/paper/68fa65e31e209131cea0d37f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:38:30.146Z

Authors: Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan

Published: 2019-06-13T13:52:28.000Z

## Abstract

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 .

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.05688>)

[PDF](<https://arxiv.org/pdf/1906.05688.pdf>)