Inspecting Training Results and Fine-Tuning Detectron2’s Solvers

This chapter covers how to use TensorBoard to inspect training histories. It utilizes the code and visualization approach to explain the concepts behind Detectron2’s solvers and their hyperparameters. The related concepts include gradient descent, stochastic gradient descent, momentum, and variable learning rate optimizers. This chapter also provides code to help you find the standard hyperparameters for Detectron2’s solvers.

By the end of this chapter, you will be able to use TensorBoard to analyze training results and find insights. You will also have a deep understanding of the essential hyperparameters for Detectron2’s solvers. Additionally, you will be able to use code to ...

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