‫Now, in this section, we are going to discuss the architecture of some popular CNN models, which
‫one image classification competitions in the past.
‫There are two reasons for doing this.
‫First is we want to understand these architectures because this will build our intuition as to what
‫is a good CNN model architecture.
‫And the second reason is that these train models can be used by us in our software.
‫Without retraining, these models with the architecture and the trained weights can be downloaded as part
‫of the keras Library only.
‫But how will these train models, which are trained on other set, how can these be used for our classification
‫problem?
‫We will see the answer to this question in the coming lectures.
‫Before we discuss these architectures, I briefly tell you about the image net competition which as
‫given us these popular architectures.
‫So imageNet competition, which is known as ILSVRC
‫for short.
‫Stand for image net
‫Large scale visual recognition challenge.
‫This challenge was held between 2010 and 2017.
‫Every year, participants in this challenge were given a data set of images and they had to classify
‫those images into several labels
‫In some of the competitions, the dataset had over a million observations and classes to be identified
‫were in thousands
‫So it was a very large scale competition.
‫The goal of this challenge was to promote the development of better computer vision techniques.
‫And also to benchmark the state of the art.
‫So the winner of each year, that convolutional network was the benchmark network.
‫It was considered as the best of its time.
‫So here are some popular CNN architectures.
‫Some of these also won the ILSVRC challenge.
‫The first one here is LeNet.
‫This was the oldest and most popular CNN architecture.
‫It has only 60000 parameters.
‫And if you look at it, it was made in nineteen ninety eight.
‫So even in 1998, convolutional neural networks were there and they were gaining popularity.
‫But a major breakthrough came in 2012 when Alex Net was able to achieve very high accuracy on previous
‫image classification problems.
‫This raise the interest of people in CNN and in 2013, ZF Net won the ILSVRC challenge
‫and it was a convolutional network.
‫It was able to achieve accuracy rate of nearly 85 percent.
‫In 2014, we got two very popular architectures.
‫One is GoogleNet and the other is VGGNet
‫GoogleNetwas the winner.
‫VGGNet
‫Net was runnerup. GoogleNet
‫had four million parameters as compared to VGGNet, which had 138 million parameters in
‫2005
‫The winner was ResNet and in 2016 and 17.
‫Also, there were two other architectures which won.
‫But these are the most popular architectures that you should know about.
‫We look at LeNet.
‫GoogleNet and VGGNet in more detail.