Image Segmentation with Vision Transformers

Many real-world applications, from autonomous driving to medical imaging, rely on accurately identifying and delineating objects within images. Image segmentation is the crucial technique that makes this possible, and the advent of Transformer architectures has revolutionized how we approach these complex visual recognition challenges. This course will transform you from a beginner into someone who understands the fundamental principles of image segmentation and how to apply cutting-edge Vision Transformer architectures. What you'll learn: * Understand the fundamental principles of image segmentation and its role in computer vision. * Learn how Transformer architectures, including self-attention, are adapted for visual data processing. * Apply Vision Transformer models like SETR and Segmenter to perform semantic segmentation. * Practice preparing datasets and implementing basic training routines for segmentation tasks. * Evaluate the performance of segmentation models using relevant metrics and interpret their outputs. * Explore strategies for data augmentation and transfer learning to enhance model performance and efficiency. The course begins with core definitions and gradually introduces the architecture of Vision Transformers, detailing their application to image segmentation. You will then progress to practical implementation strategies and model evaluation techniques. This course is designed for absolute beginners in deep learning and computer vision. No prior experience with Transformers or advanced machine learning is required. Start your journey into advanced computer vision today by mastering image segmentation with Transformers.