ResNeSt: Split-Attention Networks
Introduction
[ALGORITHM]
@article{zhang2020resnest,
title={ResNeSt: Split-Attention Networks},
author={Zhang, Hang and Wu, Chongruo and Zhang, Zhongyue and Zhu, Yi and Zhang, Zhi and Lin, Haibin and Sun, Yue and He, Tong and Muller, Jonas and Manmatha, R. and Li, Mu and Smola, Alexander},
journal={arXiv preprint arXiv:2004.08955},
year={2020}
}
Results and models
Cityscapes
| Method |
Backbone |
Crop Size |
Lr schd |
Mem (GB) |
Inf time (fps) |
mIoU |
mIoU(ms+flip) |
download |
| FCN |
S-101-D8 |
512x1024 |
80000 |
11.4 |
2.39 |
77.56 |
78.98 |
model | log |
| PSPNet |
S-101-D8 |
512x1024 |
80000 |
11.8 |
2.52 |
78.57 |
79.19 |
model | log |
| DeepLabV3 |
S-101-D8 |
512x1024 |
80000 |
11.9 |
1.88 |
79.67 |
80.51 |
model | log |
| DeepLabV3+ |
S-101-D8 |
512x1024 |
80000 |
13.2 |
2.36 |
79.62 |
80.27 |
model | log |
ADE20k
| Method |
Backbone |
Crop Size |
Lr schd |
Mem (GB) |
Inf time (fps) |
mIoU |
mIoU(ms+flip) |
download |
| FCN |
S-101-D8 |
512x512 |
160000 |
14.2 |
12.86 |
45.62 |
46.16 |
model | log |
| PSPNet |
S-101-D8 |
512x512 |
160000 |
14.2 |
13.02 |
45.44 |
46.28 |
model | log |
| DeepLabV3 |
S-101-D8 |
512x512 |
160000 |
14.6 |
9.28 |
45.71 |
46.59 |
model | log |
| DeepLabV3+ |
S-101-D8 |
512x512 |
160000 |
16.2 |
11.96 |
46.47 |
47.27 |
model | log |
