https://github.com/ajithvcoder/caae-age_progression_regression_pytorch

Code for Age progression/Regression, http://web.eecs.utk.edu/~zzhang61/docs/papers/2017_CVPR_Age.pdf
https://github.com/ajithvcoder/caae-age_progression_regression_pytorch

aging gan pytorch utkface

Last synced: 4 days ago
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Code for Age progression/Regression, http://web.eecs.utk.edu/~zzhang61/docs/papers/2017_CVPR_Age.pdf

• Host: GitHub
• URL: https://github.com/ajithvcoder/caae-age_progression_regression_pytorch
• Owner: ajithvcoder
• Created: 2018-09-01T17:29:56.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2021-01-31T19:03:57.000Z (almost 4 years ago)
• Last Synced: 2024-01-05T18:28:12.996Z (11 months ago)
• Topics: aging, gan, pytorch, utkface
• Language: Jupyter Notebook
• Homepage:
• Size: 2.03 MB
• Stars: 60
• Watchers: 8
• Forks: 23
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
## Face Aging CAAE 

## Requirements

> pip install -r requirements.txt 

* torchvision 0.4.0

* torch 1.2.0

* [UTKFace Aligned&Cropped](https://drive.google.com/drive/folders/0BxYys69jI14kU0I1YUQyY1ZDRUE) dataset

## Colab Notebook

Training - [Colab Notebook](CAAE_Age_Progression_Regression_UTK_Face_dataset.ipynb)

Inference - [Colab Notebook]() - pending

**Snapshot**

![Training Snapshot](assests/snapshot_CAAE.PNG)

## Usage

* git clone or download zip file of this repository

* download Aligned & Cropped version of UTKFace from [here](https://drive.google.com/drive/folders/0BxYys69jI14kU0I1YUQyY1ZDRUE)

* Install requirements

* execute main.py 

> python main.py

## Results

**UTKFace**

> rows: years of  0 ~ 5, 5 ~ 10, 10 ~ 15, 16 ~ 20, 21 ~ 30, 31 ~ 40, 41 ~ 50, 51 ~ 60, 61 ~ 70, over 70  

```

epoch:50, step:86

EG_L1_loss:0.075875           |G_img_loss:5.226651           

G_tv_loss:0.003358            |Ez_loss:0.851948

D_img:0.998970                |D_reconst:0.015672            |D_loss:0.017007               

D_z:0.435863                  |D_z_prior:0.606904            |Dz_loss:1.133016 

```

output with 1.7k images trained for 50 epochs

![Epochs](assests/reconst_epoch050.png)




## To-do:

- [ ] Check the corretness of model 

- [ ] Write inference code with trained weights

- [ ] Release pretrained weights for this repo

## Credits

- [Age Progression/Regression by Conditional Adversarial Autoencoder](http://web.eecs.utk.edu/~zzhang61/docs/papers/2017_CVPR_Age.pdf)

- [Face-Aging-CAAE-Pytorch](https://github.com/Jooong/Face-Aging-CAAE-Pytorch)

## Other creations

- [AgeProgression-Pytorch](https://github.com/mattans/AgeProgression)

- [AgeProgression-tensorflow](https://github.com/ZZUTK/Face-Aging-CAAE)