Grid R-CNN

Introduction

@inproceedings{lu2019grid,
  title={Grid r-cnn},
  author={Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2019}
}

@article{lu2019grid,
  title={Grid R-CNN Plus: Faster and Better},
  author={Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie},
  journal={arXiv preprint arXiv:1906.05688},
  year={2019}
}

Results and Models

Backbone	Lr schd	Mem (GB)	Train time (s/iter)	Inf time (fps)	box AP	Download
R-50	2x	4.8	1.172	10.9	40.3	model
R-101	2x	6.7	1.214	10.0	41.7	model
X-101-32x4d	2x	8.0	1.335	8.5	43.0	model
X-101-64x4d	2x	10.9	1.753	6.4	43.1	model

Notes:

• All models are trained with 8 GPUs instead of 32 GPUs in the original paper.
• The warming up lasts for 1 epoch and 2x here indicates 25 epochs.