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 weight can be downloaded as part of the Get Us library
only.
But how will these train models which are trained on other data set.
How can these be used.
What 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 Eilis 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 the classes to be identified were intelligent.
So it was a very large scale competition.
The goal of this challenge was to promote the mind of better computer vision techniques and also to
benchmark the state of the art.
So the winner of each year that conventional network was the benchmark network it was considered as
the best of it time
so here are some popular CNN architectures.
Some of these also won the idealist 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 finalist no neural networks were dead and they were gaining popularity but a major breakthrough
came in 2012 when Alex Nate was able to achieve very high accuracy on previous image classification
problems.
This raised the interest of people in CNN and in 2013 the chef Nate won the Eilis.
We asked challenge and it was a conversational network.
It was able to achieve accuracy rate of nearly 85 percent in 2014.
We got two very popular architectures.
One is Google it and the other is we did it google it was the winner.
We did it net was runner up google it had four million parameters as compared to we did in it which
had 138 million parameters in 2005.
The winner was resonate 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 Leonard Google it.
And we did it in more detail.