real-object detection

This project is a computer vision application that can detect and classify objects in real-time from images or live video streams. It uses deep learning models (such as YOLO and Faster R-CNN) to identify multiple objects simultaneously, draw bounding boxes around them, and provide confidence scores.

I implemented the system using Python, OpenCV, and PyTorch, and it can be adapted to different datasets for custom object detection tasks. The project includes a simple interface for testing, clear documentation, and can be deployed on desktop or edge devices.

This project demonstrates my skills in machine learning, deep learning, and computer vision, as well as practical experience with training, testing, and deploying object detection models.