Adaptive Pyramid Context Network for Semantic Segmentation
Introduction
[ALGORITHM]
@InProceedings{He_2019_CVPR,
author = {He, Junjun and Deng, Zhongying and Zhou, Lei and Wang, Yali and Qiao, Yu},
title = {Adaptive Pyramid Context Network for Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
Results and models
Cityscapes
| Method |
Backbone |
Crop Size |
Lr schd |
Mem (GB) |
Inf time (fps) |
mIoU |
mIoU(ms+flip) |
download |
| APCNet |
R-50-D8 |
512x1024 |
40000 |
7.7 |
3.57 |
78.02 |
79.26 |
model | log |
| APCNet |
R-101-D8 |
512x1024 |
40000 |
11.2 |
2.15 |
79.08 |
80.34 |
model | log |
| APCNet |
R-50-D8 |
769x769 |
40000 |
8.7 |
1.52 |
77.89 |
79.75 |
model | log |
| APCNet |
R-101-D8 |
769x769 |
40000 |
12.7 |
1.03 |
77.96 |
79.24 |
model | log |
| APCNet |
R-50-D8 |
512x1024 |
80000 |
- |
- |
78.96 |
79.94 |
model | log |
| APCNet |
R-101-D8 |
512x1024 |
80000 |
- |
- |
79.64 |
80.61 |
model | log |
| APCNet |
R-50-D8 |
769x769 |
80000 |
- |
- |
78.79 |
80.35 |
model | log |
| APCNet |
R-101-D8 |
769x769 |
80000 |
- |
- |
78.45 |
79.91 |
model | log |
ADE20K
| Method |
Backbone |
Crop Size |
Lr schd |
Mem (GB) |
Inf time (fps) |
mIoU |
mIoU(ms+flip) |
download |
| APCNet |
R-50-D8 |
512x512 |
80000 |
10.1 |
19.61 |
42.20 |
43.30 |
model | log |
| APCNet |
R-101-D8 |
512x512 |
80000 |
13.6 |
13.10 |
45.54 |
46.65 |
model | log |
| APCNet |
R-50-D8 |
512x512 |
160000 |
- |
- |
43.40 |
43.94 |
model | log |
| APCNet |
R-101-D8 |
512x512 |
160000 |
- |
- |
45.41 |
46.63 |
model | log |
