Analyzing the Hyperparameters

Hyperparameters are the specific variables we use to change the training strategy of a model. The final step to complete the entire process involves understanding the hyperparameter configuration. This knowledge enables us to make specific adjustments during the training of our model, potentially impacting its performance.

Let’s start examining the default hyperparameter configuration file provided by the official code and understand the role and function of each parameter

Learning rate

As we know, learning rate is the coefficient that determines the step size to update the weights during training. YOLOv7 uses the one-cycle learning rate as default. This process initiates the learning rate at a minimal starting point, gradually elevates it to a peak, and then progressively reduces it back to the minimal endpoint.