Image Models

This part is about seeing: applying convolutional and attention-based architectures to real computer-vision problems. It starts with the techniques that make image models train and generalize in practice — data augmentation and fine-tuning from pretrained backbones — and then works through the major vision tasks beyond classification: object detection and bounding-box regression, anchor boxes and multiscale detectors, semantic segmentation, and neural style transfer.

The emphasis throughout is on the practical pipeline: preparing image datasets, transferring learned features, and assembling the specialized heads each task requires, using the CIFAR-10 and ImageNet-style workflows introduced earlier in the book.