Object Detection with Pretrained CNNs

Object detection is at the heart of modern computer vision, powering everything from autonomous driving to automated image tagging. This text-based course provides a clear, step-by-step pathway to understanding and using pretrained Convolutional Neural Networks (CNNs) for detecting objects in images. You will transition from basic theory to confidently selecting, running, and evaluating established model architectures. What you'll learn: Understand the foundational concepts of CNNs, bounding boxes, and object localization; Explore prominent pretrained architectures including YOLO, SSD, and Faster R-CNN; Implement pretrained models using modern deep learning libraries; Evaluate detection accuracy using key metrics like Intersection over Union (IoU) and mean Average Precision (mAP); Apply transfer learning principles to leverage existing models for specific tasks; Test your understanding with written conceptual quizzes and code-analysis exercises. Starting with core terminology and neural network basics, the course guides you through model selection, inference pipelines, and performance evaluation. This course is built for beginners eager to enter the field of computer vision, requiring only basic programming familiarity. Start reading today to build your computer vision skills.