A Smart Dress Code Monitoring System Leveraging Transfer Learning With YOLOV11

Upholding appropriate attire in workplaces and other environments is essential. It promotes discipline and shows the personality and professionalism of people. Many organizations, including schools and companies, require people to follow specific dress codes. Manually checking if everyone follows these rules can be time-consuming and prone to mistakes. An automated system can save time and reduce errors. This plan of action aims to build a model that automatically verifies the dress code. conformity in educational institutions and workplaces. The model focuses on detecting key items like ID cards and shoes using Convolutional Neural Networks (CNNs) and YOLOv11 for object detection. YOLOv11 was selected because it offers better accuracy compared to other models. The model performs well in correctly detecting dress code conformity, with a Mean Average Precision of roughly 85 %.