This chapter isObject detection models dedicated to important concepts for training and fine-tuningFine-tuning object detection modelsObject detection models. This chapter stresses the data preparation stage, which spans the real-world task of annotating and augmenting your data. This chapter also emphasizes the importance of transfer learningTransfer learning (especially for models that are limited in their training data) as there are many model architectures available, it is possible they could have been trained on a large dataset that is irrelevant to the task you are exploring. This chapter discusses the important hyperparameters such as learning rate and batch size, and how to automate their settings. The chapter explains the creation of multiple task loss functionsLoss functions, such as focal lossFocal loss and Smooth L1 loss that need to be balanced between classificationClassification and localizationLocalization. The chapter explains potential strategies for dealing with overfittingOverfitting and underfitting—regularization, early stopping, and class imbalance. Overall, this chapter compiles a selection of best practices in the specificity of the geographical location to build a frameworkFrameworks for model development that yields performance.