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 green models can be used by us in our software.
Without retraining, these models with the architecture and the trained wait can be downloaded as part
of the Kiraz 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 has
given us these popular architectures.
So image that competition, which is known as eyeless.
We are see 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 labeled.
In some of the competitions, the dataset had over a million observations and glasses to be identified
but intolerant.
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 A-list VRC challenge.
The first one here is Leonard.
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 dead 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 raised the interest of people in CNN and in 2013, ZF Net won the eyeless we are see 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 Google led and the other is reading it.
Google it was the winner.
We did it.
Net was runnerup Google.
It had four million parameters as compared to Reginette, which had 138 million parameters and does
zone five.
The winner was arrest net 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 Linnet.
Google it and we in it in more detail.