Inference python script for several age and gender estimation neural network models.
- python3.6
- numpy==1.13.0
- scikit_image==0.13.1
- caffe2==0.8.1
- skimage==0.0
./download_models.shThe script will download the LAP age and gender prediction models in caffe format.
For example converting the LAP age model:
cd models/lap
python -m caffe2.python.caffe_translator \
age.prototxt \
dex_chalearn_iccv2015.caffemodelThis will create the needed init_net.pb and predict_net.pb
needed for inference.
Similarly for the gender model:
cd models/gender
python -m caffe2.python.caffe_translator \
gender.prototxt \
gender.caffemodel python run.pyIf you are using this codebase or the provided train models please cite the authors:
@article{Rothe-IJCV-2016,
author = {Rasmus Rothe and Radu Timofte and Luc Van Gool},
title = {Deep expectation of real and apparent age from a single image without facial landmarks},
journal = {International Journal of Computer Vision (IJCV)},
year = {2016},
month = {July},
}
@InProceedings{Rothe-ICCVW-2015,
author = {Rasmus Rothe and Radu Timofte and Luc Van Gool},
title = {DEX: Deep EXpectation of apparent age from a single image},
booktitle = {IEEE International Conference on Computer Vision Workshops (ICCVW)},
year = {2015},
month = {December},
}
- https://www.vision.ee.ethz.ch/en/publications/papers/articles/eth_biwi_01299.pdf
- https://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_01229.pdf