Unlock Computer Vision with AlexNet: Step-by-Step Tutorial

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Overview

Are you curious about implementing one of computer vision's most impactful and pioneering deep learning models? Look no further! In this article, we will guide you on how to build the AlexNet model, a convolutional neural network that won the 2012 ImageNet challenge and transformed the world of deep learning. We will use Keras, a high-level deep-learning API, to create and train the AlexNet model in Python.

The AlexNet model has five convolutional layers, three dense layers, and other features such as max pooling, dropout, and batch normalization.

The original AlexNet model used two parallel GPUs to train the network faster and overcome memory limitations. However, we will stick to a single GPU to keep the model simple. We will apply the model to classify cat and dog images.

In this article, you will learn the following:

• A brief overview of the AlexNet network

• How to preprocess the dataset involving cat and dog images

• How to develop and compile the AlexNet model using Keras

• How to train and evaluate the AlexNet model 

• How to visualize and interpret the results of the AlexNet model 

The Birth Of AlexNet

Do you know that the year 2012 is considered a significant turning point in the field of artificial intelligence? This year,  Alex Krizhevsky and his research group proposed AlexNet, a milestone in computer vision, particularly image classification.

Before the invention of AlexNet, traditional machine-learning algorithms such as support vector machines and random forest algorithms were popular for image classification tasks. However, classifying images with the help of traditional machine-learning algorithms was a formidable challenge as they often found it difficult to extract essential features from raw pixel data for datasets like ImageNet, which included millions of images across thousands of categories.

With the limitations of traditional machine-learning algorithms in mind, Krizhevsky and his team developed a new neural network architecture that broke records in the ImageNet challenge. AlexNet won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012 by a massive margin while achieving the top-5 error rate of 15.3%.

Understanding Alexnet Architecture

The AlexNet is a deep architecture consisting of five convolutional layers, three fully connected layers and one SoftMax output layer. The size of the input is 227x227x3, representing an RGB image. The output of the AlexNet model is a SoftMax layer that produces a probability distribution over 1000 categories, indicating the likelihood of the input image belonging to each category.

We can visualize the different layers and connections in AlexNet in the figure below. In the table below, we further provided the key details of each layer in AlexNet, including the type of layer, output size, kernel size, stride, and activation function used.

In the table below, we further provided the key details of each layer in AlexNet, including the type of layer, output size, kernel size, stride, and activation function used. In this blog post, we won’t discuss each layer one by one. Interested readers can follow our previous blog post for more detailed discussions on Alexnet architecture and its important features. Instead of going deep into the AlexNet architecture, we will focus on the implementation details of the AlexNet model to classify the cat and dog images using TensorFlow and Keras.  

Practical Implementation

Here, we will develop a model based on AlexNet architecture to classify a dataset containing images of dogs and cats. You can also download the dataset from this link. You can also check the whole code here.

Import Dependencies

Import the necessary modules for data preprocessing, model building, and image processing

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