Object Detection and Segmentation: Building Computer Vision Systems

Computer vision is transforming industries, from autonomous driving to medical imaging, by allowing machines to see and understand the world. If you want to move beyond basic image classification and build systems that can locate and outline specific objects, mastering detection and segmentation is the next essential step. This text-based course guides you from foundational computer vision concepts to deploying functional detection and segmentation models. You will learn how to prepare datasets, configure modern architectures, evaluate model performance with industry-standard metrics, and prepare your models for real-world deployment. What you'll learn: - Understand the fundamental differences between object detection, semantic segmentation, and instance segmentation. - Prepare and annotate custom image datasets using modern labeling standards and formats. - Implement popular object detection architectures using PyTorch and Hugging Face Transformers. - Evaluate model performance using key metrics such as Intersection over Union (IoU) and mean Average Precision (mAP). - Apply post-processing techniques like Non-Maximum Suppression (NMS) to refine model predictions. - Deploy trained models as lightweight inference services using modern web frameworks. You will start by exploring core terminology and classical computer vision concepts before moving into deep learning architectures. Through detailed written explanations and step-by-step code walkthroughs, you will gain the practical skills needed to train and optimize your own vision models. This course is designed for aspiring AI developers, data scientists, and software engineers who are new to computer vision. A basic understanding of Python programming is helpful, but no prior experience with deep learning or image processing is required. Start reading today and learn how to build intelligent systems that can see and interpret the physical world.