Hi and welcome back that, of course, in this section, we take a look at video unit, which is one
of my favorite scenes.
I'll explain why in this section, because I mean, generally I'll tell you why.
Prior to diving into vogue, it's merely because Fiji is such a reliable, straightforward, simple
network that could get quite good results.
However, it comes at a cost which we'll see shortly.
So let's take a look at avidity architecture.
I wouldn't go into this architecture diagram just yet.
Let's talk about talk a bit about the network first.
Fiji was introduced by Oxford researchers, Oxford University researchers Karen Simonian and Andrew
Eisenman in 2014.
So it's about seven years old right now and in the Chiefs, ninety two point seven percent in the top
five accuracy on the image note dataset.
Now, we haven't discussed what top five accuracy is, but I'll explain it to you a bit now, but we'll
go into this.
We'll go into more detail about top five accuracy or top inaccuracies later on in this course.
But what it is is if the predicted class for an image is in the top five results I can remember.
Remember, we get probability outputs of of of an artwork.
So if the five highest probability classes were the five classes with the highest probability scores,
if one of those five were with belong to the class, the correct class we considered correct.
So that's what top five accuracy means.
And you can see with a thousand classes on image dataset, ninety two point seven percent is actually
quite good.
It's quite remarkable.
That means it's doing fairly well.
And just to elaborate, video has 16 to 18 conveyors and three F Celia's to basically give us six layers
in total.
However, there's also Vejjajiva 19, which has 16 conclaves and three fully connected layers.
So let's take a look at the architecture that bit more detail.
So you can see basically, let's alter the bottom, because that's where the input is considered to
have these conversations here with 64 filters, then 128 then is Mark spoonley between those?
Then there's two more countries, two more and then a pooling layer.
Then on Virgin 19, we have four layers instead of three.
And they all have 512 filters then is four more as opposed to more compared to when you comparing Fiji
19 to 16.
Then there's less pooling layer and then the fully connected layers with four thousand ninety six nodes
in each one, then output.
It outputs two thousand nodes, does a final output classes and then we have the off max on top to get
the probability scores.
So, as you can see, visually follows what we will now be calling the classical CNN approach.
That's this design here, where we have multiple layers that are pooling columns, pool and so on,
and then the fully connected layers at the end.
Although you wouldn't notice in this classical scene in architectures that we have feature maps of increasing
size or filters, no filters and increasing size.
So we go 64 128 256GB, 512GB at this level of the top of at 512 here.
So that's effectively it.
This is another slide and that image from the visiting that paper that was published in 2014.
What I want to show you here, I mean, this describes the architecture we can see.
There's different flavors of the widgets that we're busy, which is the Widget 19 D, which is Figure
16 is another C, which is also 16 layers, 13 11.
And they're just variations of the widget network.
You can make it one one.
In my previous course, I made one call.
I think it was Minifig was the name of it, and it basically just had it just cut it, just cut the
top players out and left these Italy as I left Italy is here a six and six layers, and then this series
of nine layers and total is what I left.
So going back to here, what it wanted to show you was take a look at this.
This is 144 million parameters and between BGT nineteen.
That is quite a bit which is often taken with big is that it's often very, very slow to train.
So while you do get generally good results, it's reliable.
You will always get a good network out of gig.
However, it's just so slow the trim and an inference becomes a problem.
The model size is also a problem if you want to have a small model or an embedded system, which is
not going to work.
In that case, it's too big to slow.
I mean, it will work is just too slow at that point to be practical sometimes.
So that's it for big will now take a look at resonance and resonance of my current favorite model.
For good reason, they're just so good, and it solves so many problems with deep networks.
So we'll take a look at Raisinets in the next section.