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Fast-AgingGAN

(★ 265)

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

File Explorer

Download Latest Version (.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

# Installation Guide

1. Get the code

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

Downloads the entire project code from GitHub to your computer.

cd Fast-AgingGAN

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

python main.py

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// 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
```