Training a model for face detection

In this section, you will use a pre-trained model to build your own model for detecting a human face in a picture. This may be a simple example but we have chosen it for a reason. Our aim is to show you how different components work together in such a system while being able to test it from any laptop with a webcam. You can enhance and rebuild the model for more complicated use cases if needed.

You will use Google’s EfficientNet, a highly efficient convolutional neural network, as the base pre-trained model. With pre-trained models, you do not need a huge amount of data to train the model for your use case. This will save you both time and compute resources. This method of reusing pre-trained models is also called transfer learning.

Because this model is specifically designed for image classification, in this example, we will be using it to classify whether an image contains a human face, a human finger, or something else. As a result...