status

Completed

year

2026

category

Applied AI

Overview

Face Mask Detection is an image-based detection project built to identify face masks in selected image inputs using a locally trained YOLOv5 model. The project focuses on the practical detection workflow: preparing the environment, loading the trained model weights, running inference on images, and producing visual results that show where masks are detected. It includes a Python-based execution flow for testing images and a lightweight local upload interface for trying the model in a simple way. The project is not meant to be a full production system; it is a focused applied AI implementation that shows how a trained detection model can move from model files and test images into clear, visible output. Through this work, image processing, model inference, and detection results come together around one direct task: recognizing face masks inside images.