Optimizers in YOLO
Learn about the various optimizers in YOLO to minimize the prediction error.
Why is optimization needed in deep learning?
We know that developing deep learning models can be a challenging task due to the high dimensionality of the data and the complexity of the models. Optimization is a crucial aspect of deep learning that helps to improve the accuracy and efficiency of models.
The primary objective of optimization is to minimize a cost or loss function. The cost function represents the difference between the predicted and actual output.
How does optimization work in YOLO?
During the training, the YOLO model learns to update the anchor boxes to fit the objects in an image. Here are the steps in which it does that:
Initialization: The weights and biases of the YOLO model are initialized randomly before the training begins.
Forward pass: During the forward pass, the image is processed, and features are extracted. The predictions are made for each anchor box.
Loss computation: In this step, the predictions are compared to the GT annotations to compute the localization and classification loss.
Localization loss: We determine if we have predicted the accurate bounding box.
Classification loss: We determine if we have predicted the correct class.
Objectness loss: We determine if the object is present.
Backward pass: PyTorch computes the gradients of the model weights with respect to a loss value.
Weight update: The weights of the NN are updated using the gradient calculated in the previous step.
Repeat: Steps 2–5 are repeated for a fixed number of iterations or until the loss function converges.
Optimizers in YOLO
Optimizers play a crucial role in training a neural network like YOLO by adjusting the parameters of the neural network to minimize the loss function. Several optimization algorithms are available, each with its strengths and weaknesses. ...