Fine-tuning the AlexNet model
In this section, we will first take a quick look at the AlexNet architecture and how to build one by using PyTorch. Then we will explore PyTorch's pre-trained CNN models repository, and finally, use a pre-trained AlexNet model for fine-tuning on an image classification task, as well as making predictions.
AlexNet is a successor of LeNet with incremental changes in the architecture, such as 8 layers (5 convolutional and 3 fully connected) instead of 5, and 60 million model parameters instead of 60,000, as well as using MaxPool instead of AvgPool. Moreover, AlexNet was trained and tested on a much bigger dataset – ImageNet, which is over 100 GB in size, as opposed to the MNIST dataset (on which LeNet was trained), which amounts to a few MBs. AlexNet truly revolutionized CNNs as it emerged as a significantly more powerful class of models on image-related tasks than the other classical machine learning models, such as SVMs. Figure 3.14 shows...
