Real-Time Object Detection: Build and Evaluate Custom Models

Object detection is at the heart of modern AI, powering everything from autonomous vehicles to smart security systems. Understanding how to build and assess these models is an essential skill for anyone entering the field of computer vision. This text-based course guides you from foundational machine learning concepts to deploying functional real-time object detectors. You will gain a clear, conceptual understanding of how neural networks locate and label objects within images, and how to measure their accuracy using industry-standard metrics. In this course, you will: Understand the fundamental concepts of image classification versus object detection; Configure modern, lightweight object detection architectures optimized for real-time performance; Prepare and annotate custom image datasets for training computer vision models; Evaluate model performance using critical metrics like Intersection over Union (IoU) and mean Average Precision (mAP); Apply optimization techniques to improve inference speed and detection accuracy. You will start with core definitions and terminology before moving step-by-step through dataset preparation, model configuration, training, and rigorous evaluation. The written explanations and structured code walkthroughs ensure you understand the logic behind every parameter and metric. This course is designed for beginners interested in AI and computer vision, with no prior deep learning experience required. Start reading today to build your first real-time object detection system from scratch.