YOLOv1 Loss Function Walkthrough: Regression for All

The YOLOv1 loss function is a multi-part equation designed to handle both object localization and classification tasks simultaneously. It breaks down the total loss into five components: midpoint loss for coordinate accuracy, size loss for bounding box dimensions, object loss for confidence when an object is present, no-object loss for when a cell is empty, and classification loss. A notable feature is the use of square roots for the width and height loss, which more heavily penalizes errors on small bounding boxes compared to large ones. The confidence score, or objectness, is trained to reflect both the probability of an object being present and the Intersection over Union (IoU) of the predicted box.