# MOD-CL: Multi-label Object Detection with Constrained Loss

Source: [MOD-CL: Multi-label Object Detection with Constrained Loss](<https://www.sota2.com/research/paper/68fa72821e209131cea56e5b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:27:53.383Z

Authors: Sota Moriyama, Koji Watanabe, Katsumi Inoue, Akihiro Takemura

Published: 2024-01-31T23:13:20.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.07885>)

[PDF](<https://arxiv.org/pdf/2403.07885.pdf>)