Summary

This chapter covered the necessary background knowledge and standard hyperparameters to help you fine-tune Detectron2’s solvers. Specifically, it showed you how to use TensorBoard to analyze training results and find insights. Then, we utilized the code and visualization approach to illustrate and find appropriate hyperparameters for Detectron2’s solver (optimizer). This chapter also provided a set of hyperparameters deemed suitable for the Detectron2 object detection model, which was trained on a brain tumor dataset. As an exercise, use all the configurations produced in this chapter, perform training experiments, load the results into TensorBoard, and analyze the differences and how these configurations improve accuracy.

This chapter covered the standard set of techniques and hyperparameters since they can be used to fine-tune machine learning in general. The following three chapters will cover fine-tuning techniques for fine-tuning object detection models...