Hi and welcome back.
We're about to take a look at Cuisinart, which is another very good small CNN that contains very good
accuracy similar to mobile it.
So it was developed in 2016 by researchers at the University of California at Berkeley and Stanford,
as well as a company called Deep Skill Where.
The aim was to make a very highly accurate but small CNN analyst.
They listed their reasons in the paper less communication across servers during distributed filling
process because back then, trading on multiple multiple GPUs was quite difficult.
Now, it's not that difficult at all with PyTorch.
Also, the smaller size of the network allowed it to be used on embedded systems like mobile phones,
as well as devices with FPGA, as does a custom architecture, CPUs or GPUs, whatever you want to call
them, custom processes.
And it was also faster to update these models by the cloud of less bandwidth, which is good if you
want to update like a model.
That's an embedded camera that's deployed somewhere and you only have like a 2G or 3G connection.
This having a small model is hugely advantages in that situation, and it had a 50 times less parameters
and Alex that while performing, treat him as faster.
So that's that's quite good.
So two key takeaways of the squeeze net architecture is that in many cases, they replace the true battery
filters with one by one filters.
Other filters do left at a tree by tree filters.
And this just so you know, having a one way one filter is nine times less parameters than free battery
filter.
And then also, we don't sample it in the network so that the convolution layers still have large activation
or feature maps to work with.
And that may not explain everything to you.
Yet you may not make sense, so let's take a look at this.
This is the fire module, which is a basically a squeeze and expand block in the architecture of squeezing
it.
So what's what's going on here?
Well, the fire module module has a squeeze convolutional LEO.
That's what these one by one convolutional builders are that feed into and expand layers and brings
it back up to the size that if we get that, it may have had if it was a tree by tree or a larger filter.
And that basically took basically a combination of those that we're using to squeeze in architecture.
We use it five five modules.
You can see them here.
We have a convoluted beginning, just one and then we have to fire, fire, fire all the way down to
fire.
It actually has five nine and then some of them.
Maybe that's because it can't do the physics of a one subducting.
If I want some night Leo, that's beside the point just in nomenclature terminology.
So you can see I don't know why it is.
Labrador is wearing a mask.
He doesn't look too happy about it, but it still looks in this case.
This is the example the researchers used in this paper.
So let's take a look at squeezing its performance squeeze that was actually able to outperform Alex.
Think it actually can see its fifty seven point five, whereas Alex Net was fifty seven point two.
What image not, and the top five accuracy was the same.
However, the reduction in model size was drastic.
You can see we got up to 500 and 10x smaller model that we can.
They applied some data compression with instead of the could, instead of cutting the data's free to
the tooth to use it better than six bit, which I've actually never really seen that much in in reality.
But it's pretty cool that they tested it out and tried it, and the model size went down to zero point
four seven megs, which is tiny.
So to achieve this type of performance on image net with such a small model is actually quite remarkable.
Right now, the best model is give you an 80s and the percentage and the percentage scores, but fifty
seven point five is actually quite good.
And you can see you can compare it to Alex Net.
How much bigger Alex that was here.
So you can see there are different things you can do to compress Alex Net, the smallest you will get
to do, able to get it down to a six point nine megs.
And even then, that was still pretty much like not even comparable to this.
This is actually smaller.
So we'll stop there for now.
And then we'll take a look at a very good network, which is Google's efficient.
That solves a lot of problems for researchers and something I actually use in my day to day practice
sometimes.
So I'll see you in the next section.
Thank you.