Self-Supervised Computer Vision: Predicting Image Patch Positions

How can computers learn to understand the visual world without millions of manually labeled images? Self-supervised learning allows neural networks to find their own labels by solving clever pretext tasks, like figuring out how parts of an image fit together. In this course, you will learn how to implement a classic self-supervised learning task: predicting the relative position of image patches. By training a convolutional neural network (CNN) to recognize spatial relationships, you will help the model develop a deep, foundational understanding of visual structures. What you'll learn: - Understand the core concepts of self-supervised learning and pretext tasks - Extract and preprocess image patches using modern PyTorch data pipelines - Design a dual-path Convolutional Neural Network (CNN) architecture to process patch pairs - Generate pseudo-labels to train models without manual human annotations - Train and evaluate the network's ability to predict spatial relationships - Analyze how pretext training helps downstream computer vision tasks You will start with the fundamental terminology of self-supervised representation learning before moving step-by-step through data preparation, neural network design, and training loops. The material guides you through reading and writing clean, modern PyTorch code to construct and evaluate your spatial prediction model. This text-based course is designed for beginners in deep learning and computer vision who have basic Python knowledge and want to explore self-supervised learning. No prior experience with advanced computer vision architectures is required. Start reading today to build your first self-supervised vision model from scratch.