Understanding Learning Rate (LR) Schedulers
Learn how the learning rate scheduler in YOLO enhances convergence and the model’s performance.
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Training a deep neural network is an iterative process that involves several hyperparameter tuning. One of the most crucial hyperparameters is the learning rate of optimization algorithms such as SGD, which is difficult to optimize.
Note: The learning rate only controls the rate at which weights are updated.
Why do we need learning rate schedulers?
Learning rate schedulers are algorithms that adjust the learning rate during the training process to achieve faster convergence and better overall model performance. A well-tuned learning rate scheduler can significantly improve the training dynamics, avoiding problems such as overshooting or getting stuck in local minima. Here are a few advantages of using a learning rate scheduler
Avoiding overshooting: A high learning rate can cause the model to exceed the optimal solution.
Escaping local minima: A low learning rate can cause the model to converge too slowly or get stuck in local minima.
Faster convergence: By adjusting the learning rate to adapt to the current state of the optimization landscape, learning rate schedulers can help the model converge to the optimal solution more quickly.
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