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https://github.com/xinntao/EDVR

Winning Solution in NTIRE19 Challenges on Video Restoration and Enhancement (CVPR19 Workshops) - Video Restoration with Enhanced Deformable Convolutional Networks. EDVR has been merged into BasicSR and this repo is a mirror of BasicSR.
https://github.com/xinntao/EDVR

basicsr edvr pytorch

Last synced: about 11 hours ago
JSON representation

Host: GitHub
URL: https://github.com/xinntao/EDVR
Owner: xinntao
Created: 2019-04-09T14:03:10.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2021-08-09T14:20:10.000Z (about 3 years ago)
Last Synced: 2024-10-21T12:37:17.692Z (13 days ago)
Topics: basicsr, edvr, pytorch
Language: Python
Homepage: https://github.com/xinntao/BasicSR
Size: 2.44 MB
Stars: 1,490
Watchers: 57
Forks: 318
Open Issues: 120
Metadata Files:
- Readme: README.md
- License: LICENSE/LICENSE

Awesome Lists containing this project

awesome-image-denoising-state-of-the-art - [Web

README

        #### EDVR has been merged into [BasicSR](https://github.com/xinntao/BasicSR). This GitHub repo is a mirror of [BasicSR](https://github.com/xinntao/BasicSR). Recommend to use [BasicSR](https://github.com/xinntao/BasicSR), and open issues, pull requests, etc in [BasicSR](https://github.com/xinntao/BasicSR).

Note that this version is not compatible with previous versions. If you want to use previous ones, please refer to the `old_version` branch.

---

# :rocket: [BasicSR](https://github.com/xinntao/BasicSR)

[English](README.md) **|** [简体中文](README_CN.md)   [GitHub](https://github.com/xinntao/BasicSR) **|** [Gitee码云](https://gitee.com/xinntao/BasicSR)

 Google Colab: [GitHub Link](colab) **|** [Google Drive Link](https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing) 


:m: [Model Zoo](docs/ModelZoo.md) :arrow_double_down: Google Drive: [Pretrained Models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing) **|** [Reproduced Experiments](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing)

:arrow_double_down: 百度网盘: [预训练模型](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g) **|** [复现实验](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ) 


:file_folder: [Datasets](docs/DatasetPreparation.md) :arrow_double_down: [Google Drive](https://drive.google.com/drive/folders/1gt5eT293esqY0yr1Anbm36EdnxWW_5oH?usp=sharing) :arrow_double_down: [百度网盘](https://pan.baidu.com/s/1AZDcEAFwwc1OC3KCd7EDnQ) (提取码:basr)


:chart_with_upwards_trend: [Training curves in wandb](https://app.wandb.ai/xintao/basicsr) 


:computer: [Commands for training and testing](docs/TrainTest.md) 


:zap: [HOWTOs](#zap-howtos)

---

BasicSR (**Basic** **S**uper **R**estoration) is an open source **image and video restoration** toolbox based on PyTorch, such as super-resolution, denoise, deblurring, JPEG artifacts removal, *etc*.


_{([ESRGAN](https://github.com/xinntao/ESRGAN), [EDVR](https://github.com/xinntao/EDVR), [DNI](https://github.com/xinntao/DNI), [SFTGAN](https://github.com/xinntao/SFTGAN))}

_{([HandyView](https://github.com/xinntao/HandyView), [HandyFigure](https://github.com/xinntao/HandyFigure), [HandyCrawler](https://github.com/xinntao/HandyCrawler), [HandyWriting](https://github.com/xinntao/HandyWriting))}

## :sparkles: New Features

- Nov 29, 2020. Add **ESRGAN** and **DFDNet** [colab demo](colab).

- Sep 8, 2020. Add **blind face restoration** inference codes: [DFDNet](https://github.com/csxmli2016/DFDNet).

- Aug 27, 2020. Add **StyleGAN2 training and testing** codes: [StyleGAN2](https://github.com/rosinality/stylegan2-pytorch).

  More



   Sep 8, 2020. Add blind face restoration inference codes: DFDNet. 
 ECCV20: Blind Face Restoration via Deep Multi-scale Component Dictionaries 
 Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo and Lei Zhang 

   Aug 27, 2020. Add StyleGAN2 training and testing codes. 
 CVPR20: Analyzing and Improving the Image Quality of StyleGAN 
 Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen and Timo Aila 

  Aug 19, 2020. A brand-new BasicSR v1.0.0 online.



## :zap: HOWTOs

We provides simple pipelines to train/test/inference models for quick start.

These pipelines/commands cannot cover all the cases and more details are in the following sections.

| GAN |  |  |  | | |

| :--- | :---:        |     :---:      | :--- | :---:        |     :---:      |

| StyleGAN2   | [Train](docs/HOWTOs.md#How-to-train-StyleGAN2) | [Inference](docs/HOWTOs.md#How-to-inference-StyleGAN2) | | | |

| **Face Restoration** |  |  |  | | |

| DFDNet | - | [Inference](docs/HOWTOs.md#How-to-inference-DFDNet) | | | |

| **Super Resolution** |  |  |  | | |

| ESRGAN | *TODO* | *TODO* | SRGAN | *TODO* | *TODO*|

| EDSR | *TODO* | *TODO* | SRResNet | *TODO* | *TODO*|

| RCAN | *TODO* | *TODO* |  |  | |

| EDVR | *TODO* | *TODO* | DUF | - | *TODO* |

| BasicVSR | *TODO* | *TODO* | TOF | - | *TODO* |

| **Deblurring** |  |  |  | | |

| DeblurGANv2 | - | *TODO* |  | | |

| **Denoise** |  |  |  | | |

| RIDNet | - | *TODO* | CBDNet | - | *TODO*|

## :wrench: Dependencies and Installation

- Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))

- [PyTorch >= 1.3](https://pytorch.org/)

- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)

1. Clone repo

    ```bash

    git clone https://github.com/xinntao/BasicSR.git

    ```

1. Install dependent packages

    ```bash

    cd BasicSR

    pip install -r requirements.txt

    ```

1. Install BasicSR

    Please run the following commands in the **BasicSR root path** to install BasicSR:


    (Make sure that your GCC version: gcc >= 5) 


    If you do not need the cuda extensions: 


     [*dcn* for EDVR](basicsr/models/ops)


     [*upfirdn2d* and *fused_act* for StyleGAN2](basicsr/models/ops)


    please add `--no_cuda_ext` when installing

    ```bash

    python setup.py develop --no_cuda_ext

    ```

    If you use the EDVR and StyleGAN2 model, the above cuda extensions are necessary.

    ```bash

    python setup.py develop

    ```

    You may also want to specify the CUDA paths:

      ```bash

      CUDA_HOME=/usr/local/cuda \

      CUDNN_INCLUDE_DIR=/usr/local/cuda \

      CUDNN_LIB_DIR=/usr/local/cuda \

      python setup.py develop

      ```

Note that BasicSR is only tested in Ubuntu, and may be not suitable for Windows. You may try [Windows WSL with CUDA supports](https://docs.microsoft.com/en-us/windows/win32/direct3d12/gpu-cuda-in-wsl) :-) (It is now only available for insider build with Fast ring).

## :hourglass_flowing_sand: TODO List

Please see [project boards](https://github.com/xinntao/BasicSR/projects).

## :turtle: Dataset Preparation

- Please refer to **[DatasetPreparation.md](docs/DatasetPreparation.md)** for more details.

- The descriptions of currently supported datasets (`torch.utils.data.Dataset` classes) are in [Datasets.md](docs/Datasets.md).

## :computer: Train and Test

- **Training and testing commands**: Please see **[TrainTest.md](docs/TrainTest.md)** for the basic usage.

- **Options/Configs**: Please refer to [Config.md](docs/Config.md).

- **Logging**: Please refer to [Logging.md](docs/Logging.md).

## :european_castle: Model Zoo and Baselines

- The descriptions of currently supported models are in [Models.md](docs/Models.md).

- **Pre-trained models and log examples** are available in **[ModelZoo.md](docs/ModelZoo.md)**.

- We also provide **training curves** in [wandb](https://app.wandb.ai/xintao/basicsr):





   



## :memo: Codebase Designs and Conventions

Please see [DesignConvention.md](docs/DesignConvention.md) for the designs and conventions of the BasicSR codebase.


The figure below shows the overall framework. More descriptions for each component: 


**[Datasets.md](docs/Datasets.md)** | **[Models.md](docs/Models.md)** | **[Config.md](Config.md)** | **[Logging.md](docs/Logging.md)**

![overall_structure](./assets/overall_structure.png)

## :scroll: License and Acknowledgement

This project is released under the Apache 2.0 license.


More details about **license** and **acknowledgement** are in [LICENSE](LICENSE/README.md).

## :earth_asia: Citations

If BasicSR helps your research or work, please consider citing BasicSR.


The following is a BibTeX reference. The BibTeX entry requires the `url` LaTeX package.

``` latex

@misc{wang2020basicsr,

  author =       {Xintao Wang and Ke Yu and Kelvin C.K. Chan and

                  Chao Dong and Chen Change Loy},

  title =        {BasicSR},

  howpublished = {\url{https://github.com/xinntao/BasicSR}},

  year =         {2020}

}

```

> Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong and Chen Change Loy. BasicSR. https://github.com/xinntao/BasicSR, 2020.

## :e-mail: Contact

If you have any question, please email `[email protected]`.