Over the years, variants of CNN architectures have been developed, leading to amazing advances in the field of deep learning. A good measure of this progress is the error rates in competitions such as the ILSVRC ImageNet challenge. In this competition, the top-5 error rate for image classification fell from over 26% to less than 3%.
In this article, we will look at some of the popular CNN architectures that stood out in their approach and significantly improved on the error rates as compared to their predecessors. These are LeNet-5,AlexNet,VGG,andResNet.
Source: topbots.com