In this chapter, we will build a system that detects the level of damage  that's been done to a vehicle by analyzing photographs using transfer learning. A solution like this will be helpful in reducing the cost of insurance claims, as well as making the process simpler for vehicle owners. If the system is implemented properly, in an ideal scenario, the user will upload a bunch of photographs of the damaged vehicle, the photos will go through damage assessment, and the insurance claim will be processed automatically.

There are a lot of risks and challenges involved in implementing a perfect solution for this use case. To start with, there are multiple unknown conditions that could have caused damage to the car. We are not aware of the outdoor environment, surrounding objects, light in the area, and the quality of the vehicle before the incident. Passing through all these hurdles and figuring out a common solution for the problem is challenging. This is a common problem across any computer vision-based scenario.

In this chapter, we will cover the following topics:

• Transfer learning basics
• Image dataset collections
• Setting up a web application
• Training our own TensorFlow model
• Building a web app that consumes the model