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

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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: 2024-07-15T19:01:39.901Z (2 months ago)
• Language: Python
• Size: 3.91 MB
• Stars: 32
• Watchers: 2
• Forks: 6
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• 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}

}

```