Hands-On Convolutional Neural Networks with TensorFlow

Detector Loss function (YOLO loss)

As the localizer, the YOLO loss function is broken into three parts: the one responsible for finding the bounding-box coordinates, the bounding-box score prediction, and the class-score prediction. All of them are Mean-Squared error losses and are modulated by some scalar meta-parameter or IoU score between the prediction and ground truth:

The member 1ij obj member is used to modulate the loss based on the presence of an object on a particular cell i, j:

• If an object is present in grid cell i and the jth bounding box having the highest IoU: 1
• Otherwise: 0

Also, 1ij noobj is just the opposite.