One of the essential aspects of developing an object detection model is understanding how to measure its performance. Loss functions help us quantify the difference between the model’s predictions and the ground truth. They are essential for optimizing the model’s performance and guiding the learning process.

Loss calculation in YOLO

The YOLO algorithm uses a custom loss function to calculate the prediction error for bounding boxes and object classes in object detection tasks. The loss function in YOLO is a sum of the localization (for bounding box coordinates), classification (for object classes), and confidence losses (for objectness score).

Types of loss functions

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