This repo contains the the implementation of Our ECCV-2020 work: Improving Semantic Segmentation via Decoupled Body and Edge Supervision.

This is the join work of Peking University, University of Oxford and Sensetime Research. (Much thanks for Sensetimes' GPU server)

Any Suggestions/Questions/Pull Requests are welcome.

It also contains reimplementation of our previous AAAI-2020 work (oral) . GFFNet:Gated Fully Fusion for semantic segmentation which also achieves the state-of-the-art results on CityScapes:

Dataloaders for Cityscapes, Mapillary, Camvid ,BDD and Kitti are available in datasets. Details of preparing each dataset can be found at PREPARE_DATASETS.md

pytorch >= 1.2.0 apex opencv-python

Baidu Pan Link: https://pan.baidu.com/s/1MWzpkI3PwtnEl1LSOyLrLw 4lwf

Wider-ResNet-Imagenet Link: https://drive.google.com/file/d/1dGfPvzf4fS0aaSDnw2uahQpnBrUJfRDt/view?usp=sharing

You can use these ckpts for training decouple models or doing the evaluations for saving both time and computation.

DecoupleSegNet: Baidu Pan Link: link: https://pan.baidu.com/s/191joLpHxSByVKnJu8_w4_Q password:yg4c

GFFNet_Betst: Google Drive: link: https://drive.google.com/file/d/1wPF49PEdYHIvVLIAO5AsiEfc8ZmNkDY5/view?usp=sharing

To be note that, Our best models(Wider-ResNet-38) are trained on 8 V-100 GPUs with 32GB memory. It is hard to reproduce such best results if you do not have such resources. However, our resnet-based methods including fcn, deeplabv3+, pspnet can be trained by 8-1080-TI gpus with batchsize 8. Our training contains two steps(Here I give the ):

We found 70-80 epoch is good enough for warm up traning.

sh ./scripts/train/train_cityscapes_ResNet50_deeplab.sh

You can use the pretrained ckpt in previous step.

sh ./scripts/train/train_ciytscapes_W38_decouple.

sh ./scripts/train/train_ciytscapes_ResNet50_deeplab_decouple.sh

sh ./scripts/evaluate_val/eval_cityscapes_deeplab_r101_decouple.sh 

sh ./scripts/evaluate_val/eval_cityscapes_deeplab_r101_decouple_ms.sh 

sh ./scripts/evaluate_boundary_fscore/evaluate_cityscapes_deeplabv3_r101_decouple

You can submit the results using our checkpoint by running

sh ./scripts/submit_tes/submit_cityscapes_WideResNet38_decouple Your_Model_Path Model_Output_Path

Here we give some demo scripts for using our checkpoints. You can change the scripts according to your needs.

python ./demo/demo_folder_decouple.py

If you find this repo is helpful to your research Or our models are useful for your research. Please consider cite our work.

@inproceedings{xiangtl_decouple
  title     = {Improving Semantic Segmentation via Decoupled Body and Edge Supervision},
  author    = {Li, Xiangtai and Li, Xia and Zhang, Li and Cheng Guangliang and Shi, Jianping and 
    Lin, Zhouchen and Tong, Yunhai and Tan, Shaohua},
  booktitle = {ECCV},
  year = {2020}
}

@inproceedings{xiangtl_gff
  title     = {GFF: Gated Fully Fusion for semantic segmentation},
  author    = {Li, Xiangtai and  Zhao Houlong and Han Lei and Tong Yunhai and Yang Kuiyuan},
  booktitle = {AAAI},
  year = {2020}
}

This repo is based on NVIDIA segmentation repo. We fully thank their open-sourced code.

MIT License