Python Deep Learning Projects

Feature extraction

Now that we've segmented and aligned the data, we'll generate vector embeddings of each identity. These embeddings can then be used as input to a classification, regression, or clustering task. 

This process of training a CNN to output face embeddings requires a lot of data and computer power. However, once the network has been trained, it can generate measurements for any face, even ones it has never seen before! So this step only needs to be done once.

For convenience, we have provided a model that has been pre-trained on Inception-Resnet-v1, which you can run over any face image to get the 128 dimension feature vectors. We downloaded this file in the Setup environment section, and it's located in the /pre-model/Resnet-185253.pb ...