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Keras-MORPH2-age-estimation

Keras implementation for MORPH2 dataset age estimation.

This project contains Mobilenet and Densenet with regression and DEX framework.

Update (2017/12/1)

  • Fix inconsistent label problem.
  • Add face align in preprocessing.

Update(2017/11/27)

  • Change training default epoch to 90
  • Decay learning rate at epoch [30,60]

How to run?

You have to apply for the dataset. No easy way to download it unfortunately :(

  • Step.3 Preprocess the dataset (change isPlot inside TYY_MORPH_create_db.py to True if you want to see the process)
python TYY_MORPH_create_db.py --output morph_db.npz
  • Step.4 Run the training and evalutation (change netType inside TYY_train_MORPH.py for different networks)
KERAS_BACKEND=tensorflow python TYY_train_MORPH.py --input ./morph_db.npz

Training and evaluation

  • Training ratio: 0.8

  • Validation ratio: 0.2

  • Evaluation metric: Mean-absoluate-error (MAE) -> name: val_pred_a_mean_absolute_error

  • Output example:

pred_a_softmax_loss: 2.4073 - pred_a_loss: 9.4221 - pred_a_softmax_acc: 0.1183 - pred_a_mean_absolute_error: 9.4221 - val_loss: 2.4423 - val_pred_a_softmax_loss: 2.4423 - val_pred_a_loss: 9.4864 - val_pred_a_softmax_acc: 0.1339 - val_pred_a_mean_absolute_error: 9.4864

Parameters

  • DEX: num_neu is the output dimension of the classfication training part. Range of num_neu: [1~101]
  • Mobilenet: alpha is the paramters to control the network size. Recommended value of alpha: 1, 0.5, 0.25
  • Densenet: densenet_depth is the depth of the network (Obviously~~)

Dependencies

  • Keras
  • Tensorflow
  • anaconda
  • python3
  • opencv3
  • dlib
  • moviepy
  • pytables

References

keras-morph2-age-estimation's People

Contributors

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