https://github.com/interdigitalinc/hrfae

Official implementation for paper High Resolution Face Age Editing
https://github.com/interdigitalinc/hrfae

Last synced: 6 days ago
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Official implementation for paper High Resolution Face Age Editing

• Host: GitHub
• URL: https://github.com/interdigitalinc/hrfae
• Owner: InterDigitalInc
• License: other
• Created: 2020-04-08T13:16:35.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-01-12T08:26:12.000Z (almost 2 years ago)
• Last Synced: 2024-12-08T10:10:52.202Z (15 days ago)
• Language: Python
• Homepage: https://arxiv.org/abs/2005.04410
• Size: 2.74 MB
• Stars: 301
• Watchers: 6
• Forks: 65
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
## HRFAE: High Resolution Face Age Editing

Official implementation for paper [High Resolution Face Age Editing](https://arxiv.org/pdf/2005.04410.pdf).

![Teaser image](./arch.png)

## Dependencies

* Python 3.7

* Pytorch 1.1

* Numpy

* Opencv

* TensorboardX

* Tensorboard_logger

You can also create a new environment for this repo by running

```

conda env create -f env.yml

```

## Load and test pretrained network 

1. You can download the pretrained model by running:

    ```

    cd ./logs/001

    ./download.sh

    ```

2. Upload test images in the folder `/test/input` and run the test file. The output images will be saved in the folder `/test/output`. You can change the desired target age with `--target_age`.

    ```

    python test.py --config 001 --target_age 65

    ```

## Train a new model

1. Pretrained age classifier

    To get age information, we use an age classifier pretrained on [IMDB-WIKI](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/) dataset. We use the model released from paper [Deep expectation of real and apparent age from a single image without facial landmarks](https://data.vision.ee.ethz.ch/cvl/publications/papers/articles/eth_biwi_01299.pdf) by Rothe et al. 

    To prepare the model, you need to download the original [caffe model](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/dex_imdb_wiki.caffemodel) and convert it to PyTorch format. We use the converter [caffemodel2pytorch](https://github.com/vadimkantorov/caffemodel2pytorch) released by Vadim Kantorov. Then name the PyTorch model as `dex_imdb_wiki.caffemodel.pt` and put it in the folder `/models`.

2. Preparing your dataset

    Download [FFHQ](https://github.com/NVlabs/ffhq-dataset) dataset and unzip it to the `/data/ffhq` directory. 

    

    Download [age label](https://partage.imt.fr/index.php/s/DbSk4HzFkeCYXDt) to the `/data` directory.

    You can also train the model with your own dataset. Put your images in the `/data` directory. With the pretrained classifier, you can create a new label file with the age of each image. 

3. Training

    

    You can modify the training options of the config file in `configs` directory.

    ```

    python train.py --config 001 

    ```

## Google Colab

We also provide a colab version for quick test. To run it using Google Colab, please click [here](https://colab.research.google.com/github/InterDigitalInc/HRFAE/blob/master/test.ipynb). 

## Citation

```

@article{yao2020high,

    title   = {High Resolution Face Age Editing},

    author  = {Xu Yao and Gilles Puy and Alasdair Newson and Yann Gousseau and Pierre Hellier},

    journal = {CoRR},

    volume  = {abs/2005.04410},

    year    = {2020},

}

```

## License

Copyright © 2020, InterDigital R&D France. All rights reserved.

This source code is made available under the license found in the LICENSE.txt in the root directory of this source tree.