  We introduce MOD-CL, a multi-label object detection framework that utilizes
constrained loss in the training process to produce outputs that better satisfy
the given requirements. In this paper, we use $\mathrm{MOD_{YOLO}}$, a
multi-label object detection model built upon the state-of-the-art object
detection model YOLOv8, which has been published in recent years. In Task 1, we
introduce the Corrector Model and Blender Model, two new models that follow
after the object detection process, aiming to generate a more constrained
output. For Task 2, constrained losses have been incorporated into the
$\mathrm{MOD_{YOLO}}$ architecture using Product T-Norm. The results show that
these implementations are instrumental to improving the scores for both Task 1
and Task 2.
