To implement the car damage prediction system, we are going to build our own TensorFlow-based machine learning (ML) model for the vehicle datasets. Millions of parameters are needed with modern recognition models. We need a lot of time and data to train a new model from scratch, as well as hundreds of Graphical Processing Units (GPUs) or Tensor Processing Units (TPUs) that run for hours.
Transfer learning makes this task easier by taking an existing model that is already trained and reusing it on a new model. In our example, we will use the feature extraction capabilities from the MobileNet model and train our own classifiers on top of it. Even if we don't get 100% accuracy, this works best in a lot of cases, especially on a mobile phone where we don't have heavy resources. We can easily train this model on a typical laptop for a few hours, even...
