Vision Systems

Part II

Vision systems transform spatial structure into increasingly abstract feature maps, predictions, and decisions.

Building on the training foundations of Part I, this Part examines how architecture encodes assumptions about images. Chapter 4 follows the development of modern convolutional networks, residual learning, efficient scaling, transfer learning, and Vision Transformers. Chapter 5 moves beyond classification to object detection and segmentation, where a model must preserve spatial information rather than produce only a label. Chapter 6 completes the vision pipeline through evaluation, error analysis, dataset discipline, representation inspection, experiment tracking, and auditing.

The transition is from training a model to engineering a defensible visual system. Readers learn to connect feature hierarchy and spatial structure with task requirements, evaluation design, and deployment constraints.