Chapter 7. Classifying Images with Residual Networks
This chapter presents state-of-the-art deep networks for image classification.
Residual networks have become the latest architecture, with a huge improvement in accuracy and greater simplicity.
Before residual networks, there had been a long history of architectures, such as AlexNet, VGG, Inception (GoogLeNet), Inception v2,v3, and v4. Researchers were searching for different concepts and discovered some underlying rules with which to design better architectures.
This chapter will address the following topics:
- Main datasets for image classification evaluation
- Network architectures for image classification
- Batch normalization
- Global average pooling
- Residual connections
- Stochastic depth
- Dense connections
- Multi-GPU
- Data augmentation techniques
