Skip to content

Navigation Menu

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Appearance settings

An implement of Disentangled Representation Learning GAN for Pose-Invariant Face Recognition

Notifications You must be signed in to change notification settings

zhangjunh/DR-GAN-by-pytorch

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

14 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DR-GAN-by-pytorch

  • Authors: Luan Tran, Xi Yin, Xiaoming Liu
  • CVPR2017: http://cvlab.cse.msu.edu/pdfs/Tran_Yin_Liu_CVPR2017.pdf
  • Pytorch implimentation of DR-GAN (updated version in "Representation Learning by Rotating Your Faces")
  • Added a pretrained ResNet18 to offer a feature loss in order to improve Generator's performance. (Only in Multi_DRGAN)

Requirements

  • python 3.x
  • pytorch 0.2
  • torchvision
  • numpy
  • scipy
  • matplotlib
  • pillow
  • tensorboardX

How to use

Single-Image DR-GAN

  1. Modify model function at base_options.py to define single model.

    • Data needs to have ID and pose lables corresponds to each image.
    • If you don't have, default dataset is CFP_dataset. Modify dataroot function at base_options.py.
  2. Run train.py to train models

    • Trained models and Loss_log will be saved at "checkpoints" by default. Generated pictures will be saved at "result".

    python train.py

    • You can also use tensorboard to watch the loss graphs in real-time. (Install tensorboard before doing it.)

    tensorboard --logdir=/home/zhangjunhao/logs (Or the address of dir 'logs' in your folder.)

  3. Generate Image with arbitrary pose

    • Change the "save_path" in base_model.py.
    • Specify leaned model's filename by "--pretrained_G" option in base_options.py.
    • Generated images will be saved at specified result directory.

    python test.py

Multi-Image DR-GAN

  1. Modify model function at base_options.py to define multi model.

    • Data needs to have ID and pose lables corresponds to each image.
    • If you don't have, default dataset is CFP_dataset. Modify dataroot function at base_options.py.
  2. Run train.py to train models

    • Trained models and Loss_log will be saved at "checkpoints" by default. Generated pictures will be saved at "result".

    python train.py

    • You can also use tensorboard to watch the loss graphs in real-time. (Install tensorboard before doing it.)

    tensorboard --logdir=/home/zhangjunhao/logs (Or the address of dir 'logs' in your folder.)

  3. Generate Image with arbitrary pose

    • Change the "save_path" in base_model.py.
    • Specify leaned model's filename by "--pretrained_G" option in base_options.py.
    • Generated images will be saved at specified result directory.

    python test.py

About

An implement of Disentangled Representation Learning GAN for Pose-Invariant Face Recognition

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 3

  •  
  •  
  •  

Languages

Morty Proxy This is a proxified and sanitized view of the page, visit original site.