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Fast-AgingGAN

(★ 265)

A deep learning model to age faces in the wild, currently runs at 60+ fps on GPUs

파일 탐색기

최종 버전 다운로드 (.zip)

- aging_gan.yaml
- __init__.py
- preprocess_cacd.py
- preprocess_utk.py
- state_dict.pth
.gitignore
dataset.py
gan_module.py
infer.py
LICENSE
main.py
models.py
README.md
requirements.txt
timing.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/HasnainRaz/Fast-AgingGAN

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd Fast-AgingGAN

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

python main.py

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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# Fast-AgingGAN
This repository holds code for a face aging deep learning model. It is based on the CycleGAN, where we translate young faces to old and vice versa.

# Samples
Top row is input image, bottom row is aged output from the GAN.
![Sample](https://user-images.githubusercontent.com/4294680/86517626-b4d54100-be2a-11ea-8cf1-7e4e088f96a3.png)
![Second-Sample](https://user-images.githubusercontent.com/4294680/86517663-f5cd5580-be2a-11ea-9e39-51ddf8be2084.png)
# Timing
The model executes at 66fps on a GTX1080 with an image size of 512x512. Because of the way it is trained, a face detection pipeline is not needed. As long as the image of spatial dims 512x512 contains a face of size 256x256, this will work fine.

# Demo
To try out the pretrained model on your images, use the following command:
```bash
python infer.py --image_dir 'path/to/your/image/directory'
```

# Training
To train your own model on CACD or UTK faces datasets, you can use the provided preprocessing scripts in the preprocessing directory to prepare the dataset.
If you are going to use CACD, use the following command:
```bash
python preprocessing/preprocess_cacd.py --image_dir '/path/to/cacd/images' --metadata '/path/to/the/cacd/metadata/file' --output_dir 'path/to/save/processed/data'
```
If using UTK faces, use the following:
```bash
python preprocessing/preprocess_utk.py --data_dir '/path/to/cacd/images' --output_dir 'path/to/save/processed/data'
```

Once the dataset is processed, you should go into ``` configs/aging_gan.yaml``` and modify the paths to point to the processed dataset you just created. Change any other hyperparameters if you wish, then run training with:
```bash
python main.py
```

# Tensorboard
While training is running, you can observe the losses and the gan generated images in tensorboard, just point it to the 'lightning_logs' directory like so:
```bash
tensorboard --logdir=lightning_logs --bind_all
```