This repository contains an implementation of two popular object detection algorithms: YOLO (You Only Look Once) and Faster R-CNN (Region-based Convolutional Neural Networks). These algorithms are widely used in computer vision tasks, specifically for real-time object detection. YOLO (You Only Look Once)
is a state-of-the-art real-time object detection algorithm. It divides the input image into a grid and predicts bounding boxes and class probabilities for objects within each grid cell. Unlike other object detection methods that use region proposal techniques, YOLO performs detection in a single pass, making it extremely fast. This implementation includes the YOLO model architecture, training code, and inference scripts. Faster R-CNN (Region-based Convolutional Neural Networks)
is another popular object detection algorithm that achieves high accuracy. It consists of two main components: a region proposal network (RPN) and a region classification network. The RPN generates potential bounding box proposals, which are then refined and classified by the classification network. This implementation includes the Faster R-CNN model architecture, training code, and inference scripts.