Code for our paper Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation ICCV21
Please create and activate the following conda envrionment. To reproduce our results, please kindly create and use this environment.
# It may take several minutes for conda to solve the environment
conda update conda
conda env create -f environment.yml
conda activate corda
Code was tested on a V100 with 16G Memory.
# Train for the SYNTHIA2Cityscapes task
bash run_synthia_stereo.sh
# Train for the GTA2Cityscapes task
bash run_gta.sh
bash shells/eval_syn2city.sh
bash shells/eval_gta2city.sh
Pre-trained models are provided (Google Drive). Please put them in ./checkpoint
.
- The provided SYNTHIA2Cityscapes model achieves 56.3 mIoU (16 classes) at the end of the training.
- The provided GTA2Cityscapes model achieves 57.7 mIoU (19 classes) at the end of the training.
Reported Results on SYNTHIA2Cityscapes (The reported results are based on 5 runs instead of the best run.)
Method | mIoU*(13) | mIoU(16) |
---|---|---|
CBST | 48.9 | 42.6 |
FDA | 52.5 | - |
DADA | 49.8 | 42.6 |
DACS | 54.8 | 48.3 |
CorDA | 62.8 | 55.0 |
Please cite our work if you find it useful.
@inproceedings{wang2021domain,
title={Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation},
author={Wang, Qin and Dai, Dengxin and Hoyer, Lukas and Van Gool, Luc and Fink, Olga},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
year={2021}
}
- DACS is used as our codebase and our DA baseline official
- SFSU as the source of stereo Cityscapes depth estimation Official
- Download links
- Stereo Depth Estimation for Cityscapes
- Mono Depth Estimation for GTA
- SYNTHIA Depth and images SYNTHIA-RAND-CITYSCAPES (CVPR16)
- Dataset Folder Structure Tree
For questions regarding the code, please contact [email protected] .