AlexNet

Overview

AlexNet is a pioneering neural network architecture that achieved a significant milestone in the field of computer vision by winning the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) with a top-5 test error rate of 15.4%. This achievement marked a breakthrough in demonstrating the potential of deep learning techniques for image classification tasks. Although AlexNet is relatively small compared to modern architectures, it remains a valuable model for understanding the application of pretrained models to new image datasets.

Architecture

The structure of AlexNet is depicted in Figure 2.3. The architecture consists of a series of layers, each performing specific operations on the input data. The network processes input images through five stacks of filters, each stack producing a set of output images. As the data progresses through these layers, the size of the images is reduced, as indicated in the figure. The final output from the last stack of filters is a 4,096-element one-dimensional vector, which is then classified to produce 1,000 output probabilities, corresponding to different output classes.

Figure 2.3 The AlexNet architecture (numbers denote outputs at each layer).

Implementation

To run the AlexNet architecture on an input image, one can create an instance of the AlexNet class. This is achieved using the following code snippet:

# [In[3]:](https://livebook.manning.com/book/deep-learning-with-pytorch-second-edition/chapter-2/32)
alexnet = models.AlexNet()

Once instantiated, the alexnet object can execute the AlexNet architecture. Although understanding the intricate details of the architecture is not immediately necessary, it is important to note that AlexNet functions as an opaque object that can be invoked like a function. By providing alexnet with appropriately sized input data, a forward pass through the network can be executed. This involves the input data traversing through successive layers of neurons, ultimately producing the final output. Assuming the presence of an input object of the correct type, the forward pass can be performed with the following command:

output = alexnet(input)

Model Availability

AlexNet is part of a broader collection of models available in the PyTorch library. The models.list_models() function provides a list of available models, including AlexNet, which can be instantiated and used for various tasks. The lowercase names in the list serve as convenience functions that return models instantiated from their respective classes, sometimes with different parameter configurations. For example, densenet121 returns an instance of DenseNet with 121 layers, while densenet201 has 201 layers.