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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: 3 days ago
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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.
- 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 (over 3 years ago)
- Last Synced: 2024-12-21T18:04:59.495Z (3 days ago)
- Topics: basicsr, edvr, pytorch
- Language: Python
- Homepage: https://github.com/xinntao/BasicSR
- Size: 2.44 MB
- Stars: 1,503
- Watchers: 57
- Forks: 314
- Open Issues: 120
-
Metadata Files:
- Readme: README.md
- License: LICENSE/LICENSE
Awesome Lists containing this project
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)**
## :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]`.