

DETR uses a bipartite matching loss during training to compare a fixed number of predictions with a fixed set of ground truth labels. If there are fewer ground truth labels in the set of N labels, then they're padded with a no object class. This loss function encourages DETR to find a one-to-one assignment between the predictions and ground truth labels. If either the bounding boxes or class labels aren't correct, a loss is incurred. Likewise, if DETR predicts an object that doesn't exist, it is penalized. This encourages DETR to find other objects in an image instead of focusing on one really prominent object.