The Role of Hyperparameters in YOLO
Explore the significance of hyperparameters in training and fine-tuning YOLO for object detection. Learn how settings like learning rate, momentum, warmup epochs, and loss weights influence model performance and stability during training.
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We'll cover the following...
Hyperparameters play a pivotal role in the training and fine-tuning of machine learning models, including object detection systems like YOLO. While fine-tuning adjusts the internal weights of a pretrained model for a specific task, hyperparameters guide this adjustment process to ensure optimal performance.
lr0(initial learning rate): This is the learning rate at the start of the training process. A common value for the initial learning rate is 0.01. ...