The Code is modified from EdgeNet in pytorch, you can follow the procedure in it to prepare the datasets and model directory files.
- 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
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 |
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}
}