Mis on eesmärk detekteerimise ülesandes ja kuidas kujuneb kaotuse funktsioon?

In the object detection task, the goal (target) is information about the position and class of objects in the image. Usually, this is a set of annotations that include:

• Coordinates of bounding boxes, for example in the format (x, y, width, height) or (x_min, y_min, x_max, y_max).
• Object classes corresponding to each box.

The loss function in detection typically combines several components:

1. Localization loss — measures the error in predicting the coordinates of the boxes (e.g., Smooth L1 loss).
2. Classification loss — evaluates the accuracy of predicting the object class (e.g., cross-entropy).
3. Sometimes additional components are added, such as for confidence estimation (confidence loss).

The total loss function is a weighted sum of these components, which is optimized during model training to better predict both the location and class of objects.