Analyzing the Hyperparameters
Learn and analyze the hyperparameter configurations of YOLOv7.
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.
lr0: It is the hyperparameter to determine the initial minimum point.lrf: It is the hyperparameter to determine the final minimum point. The learning rate starts fromlr0, it increases through the maximum point, and decreases untillrflr0. For example, in the default configuration file, we see thatlr0is0.01andlrfis0.1, therefore the learning rate starts from0.01, increases until a maximum point, and then decreases until0.010.1, which results in0.001