CCNet: Criss-Cross Attention for Semantic Segmentation
Introduction
[ALGORITHM]
@article{huang2018ccnet,
title={CCNet: Criss-Cross Attention for Semantic Segmentation},
author={Huang, Zilong and Wang, Xinggang and Huang, Lichao and Huang, Chang and Wei, Yunchao and Liu, Wenyu},
booktitle={ICCV},
year={2019}
}
Results and models
Cityscapes
Method |
Backbone |
Crop Size |
Lr schd |
Mem (GB) |
Inf time (fps) |
mIoU |
mIoU(ms+flip) |
download |
CCNet |
R-50-D8 |
512x1024 |
40000 |
6 |
3.32 |
77.76 |
78.87 |
model | log |
CCNet |
R-101-D8 |
512x1024 |
40000 |
9.5 |
2.31 |
76.35 |
78.19 |
model | log |
CCNet |
R-50-D8 |
769x769 |
40000 |
6.8 |
1.43 |
78.46 |
79.93 |
model | log |
CCNet |
R-101-D8 |
769x769 |
40000 |
10.7 |
1.01 |
76.94 |
78.62 |
model | log |
CCNet |
R-50-D8 |
512x1024 |
80000 |
- |
- |
79.03 |
80.16 |
model | log |
CCNet |
R-101-D8 |
512x1024 |
80000 |
- |
- |
78.87 |
79.90 |
model | log |
CCNet |
R-50-D8 |
769x769 |
80000 |
- |
- |
79.29 |
81.08 |
model | log |
CCNet |
R-101-D8 |
769x769 |
80000 |
- |
- |
79.45 |
80.66 |
model | log |
ADE20K
Method |
Backbone |
Crop Size |
Lr schd |
Mem (GB) |
Inf time (fps) |
mIoU |
mIoU(ms+flip) |
download |
CCNet |
R-50-D8 |
512x512 |
80000 |
8.8 |
20.89 |
41.78 |
42.98 |
model | log |
CCNet |
R-101-D8 |
512x512 |
80000 |
12.2 |
14.11 |
43.97 |
45.13 |
model | log |
CCNet |
R-50-D8 |
512x512 |
160000 |
- |
- |
42.08 |
43.13 |
model | log |
CCNet |
R-101-D8 |
512x512 |
160000 |
- |
- |
43.71 |
45.04 |
model | log |
Pascal VOC 2012 + Aug
Method |
Backbone |
Crop Size |
Lr schd |
Mem (GB) |
Inf time (fps) |
mIoU |
mIoU(ms+flip) |
download |
CCNet |
R-50-D8 |
512x512 |
20000 |
6 |
20.45 |
76.17 |
77.51 |
model | log |
CCNet |
R-101-D8 |
512x512 |
20000 |
9.5 |
13.64 |
77.27 |
79.02 |
model | log |
CCNet |
R-50-D8 |
512x512 |
40000 |
- |
- |
75.96 |
77.04 |
model | log |
CCNet |
R-101-D8 |
512x512 |
40000 |
- |
- |
77.87 |
78.90 |
model | log |