AlexNet is a deep learning model designed for image classification, introduced by Alex Krizhevsky and his colleagues in 2012. It consists of multiple layers, including convolutional layers, pooling layers, and fully connected layers, which help the model learn to recognize patterns in images. The architecture of AlexNet significantly improved the accuracy of image classification tasks, winning the ImageNet Large Scale Visual Recognition Challenge that year. Its success popularized the use of deep convolutional neural networks in computer vision, paving the way for further advancements in the field.