Gender and Age Classification Using Caffe Network

The availability of labelled data at scale has contributed to the rapid expansion of Deep Learning. This results show that training deep-cnn learning representations can lead to huge increases in performance on specific tasks. The caffe net model surpasses existing modernization approaches for gender and age approximation when tested against the Adience benchmark. It is suggested convolutional net architecture is simple and accurate to 95%.