Summary

This chapter discussed the steps to explore, process, and prepare a custom dataset for training object detection models using Detectron2. After processing the dataset, it is relatively easy to register the train, test, and evaluation data (if there is any) with Detectron2 and start training object detection models using the default trainer. The training process may result in many models. Therefore, this chapter provided the standard evaluation metrics and approaches for selecting the best model. The default trainer may meet the most common training requirements. However, in several cases, a custom trainer may be necessary to incorporate more customizations into the training process. This chapter provided code snippets to build a custom trainer that incorporates evaluations on the test set during training. It also provided a code snippet for a custom hook that extracts the evaluation metrics and stores the best model during training.

The next chapter, Chapter 6, uses TensorBoard...