Object Detection with TensorFlow 2: Train Faster R-CNN, SSD, and YOLO

Object detection is a cornerstone of modern computer vision, powering everything from autonomous vehicles to intelligent retail systems. If you want to build systems that can locate and classify multiple objects within an image, understanding the core architectures is essential. This text-based course guides you through the foundational concepts and practical workflows needed to train, evaluate, and deploy deep learning models. You will gain a clear conceptual understanding of key object detection architectures and learn how to implement them using TensorFlow 2, transitioning smoothly from local development to scalable cloud-based training. What you'll learn: - Understand the fundamental mechanics of Faster R-CNN, SSD, and YOLO architectures. - Configure and prepare custom datasets specifically for object detection tasks. - Train deep learning models using TensorFlow 2 and modern transfer learning techniques. - Evaluate model performance using key metrics like Intersection over Union (IoU) and mean Average Precision (mAP). - Scale your training workflows by leveraging cloud-based GPU acceleration on Cloud AI Platform. - Apply best practices for debugging and optimizing object detection training pipelines. You will start by exploring the essential terminology and theoretical foundations of computer vision before moving into practical implementation. From there, you will progress through structured written explanations and code snippets to build, train, and evaluate your own custom models. This course is designed for aspiring computer vision engineers, data scientists, and developers who are new to object detection. No prior experience with deep learning architectures is required, though a basic familiarity with Python is recommended. Begin reading today to build your first professional-grade object detection pipeline.