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https://github.com/bitzpy/Blind-Face-Restoration-Benchmark-Datasets-and-a-Baseline-Model
https://github.com/bitzpy/Blind-Face-Restoration-Benchmark-Datasets-and-a-Baseline-Model
Last synced: 29 days ago
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- Host: GitHub
- URL: https://github.com/bitzpy/Blind-Face-Restoration-Benchmark-Datasets-and-a-Baseline-Model
- Owner: bitzpy
- License: mit
- Created: 2022-03-26T06:59:21.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-09-14T10:57:14.000Z (about 2 years ago)
- Last Synced: 2024-08-04T00:12:11.362Z (4 months ago)
- Language: Python
- Size: 3.91 MB
- Stars: 33
- Watchers: 2
- Forks: 6
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Face-Restoration - Code
README
# **Contents**
1. [STUNet](#STUNet)
2. [Blind Face Restoration Methods](#Blind-Face-Restoration-Methods)
3. [Pre-trained Models](#Pre-trained-Models)
4. [Benchmarking Results](#Benchmarking-Results)
# **STUNet**
### **Environment**
- Ubuntu
- Pytorch 1.9
- CUDA 11.3
- Python_packages: torchvision, torch, numpy, opencv-python, Pillow, timm, ipython
- System_packages: libgl1-mesa-glx, libglib2.0-0
### **Dataset Preparation**
Download [BFR128](https://github.com/HDCVLab/EDFace-Celeb-1M#edface-celeb-1m-bfr128--blind-face-restoration-hq-lq-blur-jpeg-artifact-noise-sr-full-full_x2-full_x4-full_x8) and [BFR512](https://github.com/HDCVLab/EDFace-Celeb-1M#edface-celeb-150k-bfr512-blind-face-restoration-hq-lq-blur-jpeg-artifact-noise-sr-full-full_x2-full_x4-full_x8), and put them in the appropriate directory according to your needs. The two datasets provide
### **Testing**
Download the [pre-trained models](#Pre-trained-Models) of STUNet and put them in `STUNet/check_points/`. Next, modify the path of datasets and pre-trained models in `test.py` . Last, use `python test.py` to generate results.
### Training
You can also train our STUNet by yourself. The training configurations is stored in `STUNet/options/opt.json`. Modify the configuration file according to your needs. Then use `python train.py` to train the STUNet.
### Evaluation
# **Blind Face Restoration Methods**
```
DFDNet: https://github.com/csxmli2016/DFDNet
HiFaceGAN: https://github.com/chaofengc/PSFRGAN
PSFRGAN: https://github.com/Lotayou/Face-Renovation
GFPGAN: https://github.com/TencentARC/GFPGAN
GPEN: https://github.com/yangxy/GPEN
```
# **Pre-trained Models**
```
DFDNet: https://github.com/csxmli2016/DFDNet (We use the pre-train model provided by the author.)
HiFaceGAN: https://pan.baidu.com/s/1Yiof155wF1GUNOuw2wUWXw (4kya)
PSFRGAN: https://pan.baidu.com/s/1GgTOc1FMF34b1Nf0_9rlGg (4kya)
GFPGAN: https://pan.baidu.com/s/1E7yt_FLLZghMJFYO8cSj9g (4kya)
GPEN: https://pan.baidu.com/s/1mySckCwIKIUGJbyhW28Idw (4kya)
STUNet: https://pan.baidu.com/s/16E4cqM7pbnh9w236l7_79Q (4kya)
```
# **Benchmark Results**
```
DFDNet: https://pan.baidu.com/s/1I2UysJ5vDfwGMvRgVVSqzg (exb4)
HiFaceGAN: https://pan.baidu.com/s/17tTcEYdy6AGNh9UZCQ4CiA (exb4)
PSFRGAN: https://pan.baidu.com/s/1fzcnJote018v6g3Tn1-URw (exb4)
GFPGAN: https://pan.baidu.com/s/1_P8OMwGaDyCe7H0G2a9fjA (exb4)
GPEN: https://pan.baidu.com/s/19OaTwlqvJlgOc_sIFESxpw (exb4)
STUNet: https://pan.baidu.com/s/1Mi-TlCmnFXgvY_jk17Tzeg (exb4)
```
# **Acknowledgement**
Our codes are heavily based on [SwinIR](https://github.com/JingyunLiang/SwinIR). We also borrow some codes from [HiFaceGAN](https://github.com/Lotayou/Face-Renovation) and
[Restormer](https://github.com/swz30/Restormer).
# **Citation**
If you think this work is useful for your research, please cite the following papers.
```
@inproceedings{zhang2022blind,
title={Blind Face Restoration: Benchmark Datasets and a Baseline Model},
author={Zhang, Puyang and Zhang, Kaihao and Luo, Wenhan and Li, Changsheng and Wang, Guoren},
booktitle={arXiv:2206.03697},
year={2022}
}
@inproceedings{zhang2022edface,
title={EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset},
author={Zhang, Kaihao and Li, Dongxu and Luo, Wenhan and Liu, Jingyu and Deng, Jiankang and Liu, Wei and Stefanos Zafeiriou},
booktitle={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year={2022}
}
```