Deep Differentiable Random Forests for Age Estimation

Code accompanying the paper Deep Differentiable Random Forests for Age Estimation.

How to use

1. Download the Morph dataset. The Morph dataset is not free availabel, but you can request for it from here.

2. Download pre-trained VGG model VGG_ILSVRC_16_layers.caffemodel .

3. Create a symbolic link to the Morph dataset with the name 'data/morph'

   ln -s 'the directory for Morph dataset' data/morph

   or change the dir in scripts.

4. Create the train set list and test set list.

   python split_setting*.py

5. Start to train.

   python run.py

   You can choose DRF or DLDLF by argument --method (and morph2lmdb.py is used to create LMDB for DLDLF)

Please cite the following paper if it helps your research:

  @article{ShenTPAMI2019,
  author = {Wei Shen and Yilu Guo and Yan Wang and Kai Zhao and Bo Wang and Alan Yuille},
  title     = {Deep Differentiable Random Forests for Age Estimation},
  journal   = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
  year = {2019}
}

If you have any issues using the code please email us at shenwei1231@gmail.com, gyl.luan0@gmail.com