Blood Face Detector in Python (Part-1)

First, we initialize the parameters for the model: learning rate, number of epochs to train for and batch size. To train the model we have used the concept of Transfer Learning. We will fine-tune the MobileNet V2 architecture which is pre-trained on the ImageNet weights by leaving the head fully connected layer of the based model and then constructing our own head Fully Connected layer and place it on top of the base model. During training, we freeze all the layers of the base model so that they don't get updated during the first training process. Then we compile the model using adam optimizer and binary cross-entropy loss function as it is a binary classification problem.