Yolo losses

1. What is loss in Yolo?
2. What are the limitations of Yolo?
3. What is objectness loss?
4. What is box loss and object loss?

What is loss in Yolo?

YOLO uses sum-squared error between the predictions and the ground truth to calculate loss. The loss function composes of: the classification loss. the localization loss (errors between the predicted boundary box and the ground truth). the confidence loss (the objectness of the box).

What are the limitations of Yolo?

YOLO struggles to detect and segregate small objects in images that appear in groups, as each grid is constrained to detect only a single object. Small objects that naturally come in groups, such as a line of ants, are therefore hard for YOLO to detect and localize.

What is objectness loss?

Objectness loss measures the probability that an object exists in a proposed region of interest. If the objectness is high, this means that the image window is likely to contain an object. The box loss points to how well the model can locate the centre of an ...

What is box loss and object loss?

The box loss represents how well the algorithm can locate the centre of an object and how well the predicted bounding box covers an object. ...