Running a pre-trained VGG model

We have already discussed LeNet and AlexNet, two of the foundational CNN architectures. As we progress in the chapter, we will explore increasingly complex CNN models. Although, the key principles in building these model architectures will be the same. We will see a modular model-building approach in putting together convolutional layers, pooling layers, and fully connected layers into blocks/modules and then stacking these blocks sequentially or in a branched manner. In this section, we look at the successor to AlexNet – VGGNet.

The name VGG is derived from the Visual Geometry Group of Oxford University, where this model was invented. Compared to the 8 layers and 60 million parameters of AlexNet, VGG consists of 13 layers (10 convolutional layers and 3 fully connected layers) and 138 million parameters. VGG basically stacks more layers onto the AlexNet architecture with smaller size convolution kernels (2x2 or 3x3). Hence, VGG's novelty lies...