4 CNN Networks Every Machine Learning Engineer Should Know!

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, and ResNet.

AlexNet, VGG, and ResNet are ILSVRC challenge winners in 2012, 2014 and 2015

If you are not familiar with the Convolutional neural network, please read our Beginner intro to CNN's

We will explain LeNet-5 in detail until we feel familiar with calculating network inputs/outputs showing which makes it easy to understand how a CNN works from only seeing the architecture. This also helps when trying to implement your CNN network using a low-level framework such as PyTorch and TensorFlow ( other high-level frameworks will do the calculation for you)

LeNet-5

LeNet-5 architecture is perhaps the most widely known CNN architecture. It was created by Yann LeCun in 1998 and widely used for written digits recognition(MNIST).

Here is the LeNet-5 architecture.