caffemodel: age_net.caffemodel
caffemodel_url: https://github.com/GilLevi/AgeGenderDeepLearning/raw/master/models/age_net.caffemodel
caffemodel: gender_net.caffemodel
caffemodel_url: https://github.com/GilLevi/AgeGenderDeepLearning/raw/master/models/gender_net.caffemodel
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mean_file_proto: https://github.com/GilLevi/AgeGenderDeepLearning/raw/master/models/mean.binaryproto
gist_id: c9e99062283c719c03de
Convolutional neural networks for age and gender classification as described in the following work:
Gil Levi and Tal Hassner, Age and Gender Classification Using Convolutional Neural Networks, IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG), at the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, June 2015
Project page: http://www.openu.ac.il/home/hassner/projects/cnn_agegender/
If you find our models useful, please add suitable reference to our paper in your work.
Copyright 2015, Gil Levi and Tal Hassner
The SOFTWARE provided in this page is provided "as is", without any guarantee made as to its suitability or fitness for any particular use. It may contain bugs, so use of this tool is at your own risk. We take no responsibility for any damage of any sort that may unintentionally be caused through its use.
Hi @GilLevi,
I've tested your Caffe models in the OpenCV DNN module on a live camera preview, and it's taking 1.6-1.7 seconds to process each frame. Are there any possible further optimizations I can use on the model, without sacrificing accuracy or with a minimum accuracy tradeoff, to make it faster? Because I've gotten another model that could do it in 11 ms (though with very low accuracy - most female faces are guessed male) that was only 2.5 MB in size (maybe trained with 510 images only).