Inter-Region Affinity Distillation for Road Marking Segmentation (CVPR 2020)
Codes for "Inter-Region Affinity Distillation for Road Marking Segmentation"
Please follow list to put ApolloScape in the desired folder. We'll call the directory that you cloned Codes-for-IntRA-KD as `$IntRA_KD_ROOT .
Download the trained ResNet-101 and ERFNet, and put them in the folder trained_model
.
cd $IntRA_KD_ROOT
sh test_pspnet_multi_scale.sh # sh test_erfnet_multi_scale.sh
The output predictions will be saved to road05_tmp
by default.
python road_npy2img.py
The outputs will be stored in road05
by default.
mkdir test
mv road05 test/
zip -r test.zip test
Now, just upload test.zip to ApolloScape online server. The trained ResNet-101 can achieve 46.63% mIoU and trained ERFNet can achieve 43.48% mIoU.
python trainId2color.py
Please use the script to perform the visualization.
cd $IntRA_KD_ROOT
sh train_pspnet.sh # sh train_erfnet_vanilla.sh
Please make sure that you have 8 GPUs and each GPU has least 11 GB memory if you want to train ResNet-101.
If you use the codes, please cite the following publication:
@inproceedings{hou2020interregion,
title = {Inter-Region Affinity Distillation for Road Marking Segmentation},
author = {Yuenan Hou, Zheng Ma, Chunxiao Liu, Tak-Wai Hui, and Chen Change Loy},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2020},
}
This repo is built upon ERFNet-CULane-PyTorch.
If you have any problems in reproducing the results, just raise an issue in this repo.