Xilinx / DSRL

DSRL: Dual Super-Resolution Learning for Semantic Segmentation

The code is inspired by EdgeNet in pytorch, you can follow the procedure in it to prepare the datasets and model directory files.

Unzip the repo folder

# first download and unzip the repo folder
unzip dsrl_released-master.zip
cd dsrl_released-master

Testing

• The first step aims to save the gray prediction mask
• The second step aims to evaluate the mIoU with prediction mask and groundtruth

# To evaluate ESPNetv2_DSRL, use below command:
sh run_eval_256x512.sh   
# sh run_eval_512x1024.sh

Main results

Method	s	Image Size	FLOPs	Params	mIOU (class-wise)	Link
ESPNetv2	2.0	512x256	674.78M	0.79M	54.83% (val)	N/A
ESPNetv2 + DSRL	2.0	512x256	674.78M	0.79M	60.61% (val)	here
ESPNetv2	2.0	1024x512	2.7G	0.79M	64.44 (val)	N/A
ESPNetv2 + DSRL	2.0	1024x512	2.7G	0.79M	66.50% (val)	here

Citation

If you find this repository helpful, please feel free to cite below work:

@InProceedings{Wang_2020_CVPR,
author = {Wang, Li and Li, Dong and Zhu, Yousong and Tian, Lu and Shan, Yi},
title = {Dual Super-Resolution Learning for Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}