DSP

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Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation"

DSP 최신버젼 다운로드

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# DSP
Official implementation of "DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation". Accepted by ACM Multimedia 2021.

> Authors: Li Gao, Jing Zhang, Lefei Zhang, Dacheng Tao.

# Prerequisite

> - CUDA/CUDNN
> - Python3
> - PyTorch==1.7
> - Packages found in requirements.txt
1. Creat a new conda environment
```
conda create -n dsp_env python=3.7
conda activate dsp_env
conda install pytorch=1.7 torchvision torchaudio cudatoolkit -c pytorch
pip install -r requirements.txt
```
2. Download the code from github and change the directory

```
git clone https://github.com/GaoLii/DSP/
cd DSP
```
3. Prepare dataset

Download Cityscapes, GTA5 and SYNTHIA dataset, then organize the folder as follows:

# Training and Evaluation example

> Training and evaluation are on a single Tesla V100 GPU.

### Train with unsupervised domain adaptation

```
python train.py
```
### Evaluation

```
python evaluateUDA.py --model-path checkpoint.pth
```

# Pretrained models
- [Pretrained model for GTA5->Cityscapes](Link: https://pan.baidu.com/s/10adjjSXarJOvat-ibzfoLg  Code: wv28).
- [Pretrained model for SYNTHIA->Cityscapes](Link: https://pan.baidu.com/s/1FOXwfkihUZeWmEvh3RM15g Code: a9pj).

This model should be unzipped in the '../saved' folder.

# License

The code is heavily borrowed from [DACS](https://github.com/vikolss/DACS).

If you use this code in your research please consider citing

```
@article{Gao_2021,
   title={DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation},
   url={https://arxiv.org/abs/2107.09600},
   DOI={10.1145/3474085.3475186},
   journal={Proceedings of the 29th ACM International Conference on Multimedia},
   publisher={ACM},
   author={Gao, Li and Zhang, Jing and Zhang, Lefei and Tao, Dacheng},
   year={2021},
   month={Oct}
}
  
```

DSP

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