Mask Scoring R-CNN
Introduction
@inproceedings{huang2019msrcnn,
title={Mask Scoring R-CNN},
author={Zhaojin Huang and Lichao Huang and Yongchao Gong and Chang Huang and Xinggang Wang},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2019},
}
Results and Models
| Backbone |
style |
Lr schd |
Mem (GB) |
Train time (s/iter) |
Inf time (fps) |
box AP |
mask AP |
Download |
| R-50-FPN |
caffe |
1x |
4.3 |
0.537 |
10.1 |
37.4 |
35.5 |
model |
| R-50-FPN |
caffe |
2x |
- |
- |
- |
38.2 |
35.9 |
model |
| R-101-FPN |
caffe |
1x |
6.2 |
0.682 |
9.1 |
39.8 |
37.2 |
model |
| R-101-FPN |
caffe |
2x |
- |
- |
- |
40.7 |
37.8 |
model |
| R-X101-32x4d |
pytorch |
2x |
7.6 |
0.844 |
8.0 |
41.7 |
38.5 |
model |
| R-X101-64x4d |
pytorch |
1x |
10.5 |
1.214 |
6.4 |
42.0 |
39.1 |
model |
| R-X101-64x4d |
pytorch |
2x |
- |
- |
- |
42.2 |
38.9 |
model |