box loss obj loss

"Box loss" and "object loss" are two terms commonly used in object detection tasks.

Box loss refers to the loss function used to optimize the predicted bounding box coordinates of an object. The goal of box loss is to minimize the difference between the predicted bounding box and the ground truth bounding box. The most commonly used box loss function is the Smooth L1 loss.

Object loss, on the other hand, refers to the loss function used to optimize the predicted class probabilities of an object. The goal of object loss is to minimize the difference between the predicted class probabilities and the ground truth class probabilities. The most commonly used object loss function is the Cross-Entropy loss.

In summary, box loss is used to optimize the predicted bounding box coordinates, while object loss is used to optimize the predicted class probabilities of an object in object detection tasks.