Chapter 4

Image Classification

The history of CNN architectures is a series of solved problems: LeNet proved CNNs work, AlexNet proved scale matters, VGGNet revealed accuracy saturation, ResNet solved it with residual connections, Inception showed multi-scale parallel convolutions outperform single-filter designs, and DenseNet/SqueezeNet pushed efficiency to its limits. Each breakthrough reflects a fundamental insight about what makes deep networks trainable and deployable.

• 1. LeNet to AlexNet: From Proof of Concept to Revolution→
• 2. VGGNet, Accuracy Saturation, and the ResNet Breakthrough→
• 3. Inception, DenseNet, and the Efficiency Era→