prenet.md

# PReNet

## 1 Introduction
"Progressive Image Deraining Networks: A Better and Simpler Baseline" provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions.

<div align="center">
    <img src="https://github.com/simonsLiang/PReNet_paddle/blob/main/data/net.jpg" width=800">
</div>

## 2 How to use

### 2.1 Prepare dataset

  The dataset(RainH.zip) used by PReNet can be downloaded from [here](https://pan.baidu.com/s/1_vxCatOV3sOA6Vkx1l23eA?pwd=vitu),uncompress it and get two folders(RainTrainH、Rain100H).

  The structure of dataset is as following:

```
    ├── data
        ├── RainTrainH
            ├── rain
                ├── 1.png
                └── 2.png
                    .
                    .
            └── norain
                ├── 1.png
                └── 2.png
                    .
                    .
        └── Rain100H
            ├── rain
                ├── 001.png
                └── 002.png
                    .
                    .
            └── norain
                ├── 001.png
                └── 002.png
                    .
                    .
```


### 2.2 Train/Test


  train model:
  ```
     python -u tools/main.py --config-file configs/prenet.yaml
  ```

  test model:
  ```
     python tools/main.py --config-file configs/prenet.yaml --evaluate-only --load ${PATH_OF_WEIGHT}
  ```

## 3 Results

Input:

<div align="center">
    <img src="https://github.com/simonsLiang/PReNet_paddle/blob/main/data/rain-001.png" width=300">
</div>

Output:

<div align="center">
    <img src="https://github.com/simonsLiang/PReNet_paddle/blob/main/data/derain-rain-001.png" width=300">
</div>

## 4 Model Download
| model | dataset |
|---|---|
| [PReNet](https://paddlegan.bj.bcebos.com/models/PReNet.pdparams)  | [RainH.zip](https://pan.baidu.com/s/1_vxCatOV3sOA6Vkx1l23eA?pwd=vitu) |




# References

- 1. [Progressive Image Deraining Networks: A Better and Simpler Baseline](https://arxiv.org/pdf/1901.09221v3.pdf)


```
@inproceedings{ren2019progressive,
   title={Progressive Image Deraining Networks: A Better and Simpler Baseline},
   author={Ren, Dongwei and Zuo, Wangmeng and Hu, Qinghua and Zhu, Pengfei and Meng, Deyu},
   booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
   year={2019},
 }
```