Hi, welcome back.
In this section will discuss image net, which is extremely important to the computer vision community.
And I'll tell you why, because it's the largest dataset of labeled images that exist out there.
You can see it right now.
It's entirely if you go to the image that website, the entirety of the dataset has 14 million images
and it actually is constantly growing.
However, they are the different visions of image net, so we don't always use a 14 million image of
vision.
In fact, the one we used, it houses an object class image and that which is what a lot of those prior
scenes that we discussed earlier.
I'll benchmark one.
They've got to tell us in classes, which I mentioned here.
So let's talk a bit about what image net is used for.
Basically, it's used to train our CNN models.
And then by having such string, well-trained, seen and models, we can benchmark different architectures
and see which is performing best on such a hot dataset.
It spans 1000 object classes, which I said, and the dataset, the image net vision that's used for
benchmarking.
These things consist of 1.2 million training images, 50000 validation images and 100000 test images.
This subset, because the entire dataset is 14 million, is available on Kaggle.
So if you wanted to experiment and train and benchmark your models on these, you can.
It's all, though it's going to take very, very long.
So as I said, it's the most common benchmark when used when evaluating new CNN architectures, and
you can see how well cited it as the two different flavors of images that are cited when you combine
them just over 7000 times, which is quite a bit.
And it's probably grew a lot since this image was taken and shown on the image that website.
However, just so you know, it's hardly the first image image that.
So just so, you know, imaging, it isn't the first big image dataset.
In fact, obviously you've seen the amnesty, the said, which we have worked with before.
However, we haven't taken a look at many of these.
This is one we may experimental on a sign language.
One is of use a couple of years.
Caltech Sofar dataset is quite useful as well.
To benchmark that works on Lotus Hill is a good segmentation model of seeing the Pascal dataset, which
is used when benchmarking object detectors as well.
So a lot of these are quite useful.
But let's take a look at this.
What makes imaging that's so good?
Well, it's size 15 million images, basically, it's almost up to right now.
And you can see how that compares if if you were to size, use this type of representation to size the
image, the other datasets available.
You can see how tiny they are and how vast emission at this in comparison.
So there's also imaging that wouldn't.
It wouldn't.
It is basically a semantic mapping and an ontological structure based on the relationships of the images
and words.
So you can see things like German Shepherd belongs to a dog, which belongs to animal models already,
which belongs to a different entity, which is probably like models of four legged creatures.
So you can see all of these.
All of these images are mapped onto this into this logical structure.
And this gives us the ability to bring a human level understanding to images because we can take an
image like this when a guy standing on a scale and take all the basically get all these observations
out of it.
So it's quite this.
This is going to be quite a good stepping stone for AI to be developed further and get better analysis
and understanding from images.
So you've seen what emission it is and you've seen that we use it commonly to train and benchmark CNN's
now who is doing the best on imaging it?
Well, in late 2021, when I recorded this video, you can see meter through two levels the sufficient
net, a vision of an efficient net efficient that dash L2 is currently getting the best performance.
This is around King Hill and papers which could go to this to get up more up to date information.
And it's ninety point two percent accuracy, which is incredible.
However, take a look at a number of parameters.
It is massive 480 million parameters and the network is huge, and it's probably not going to ever be
deployed, at least not right now in real world applications.
It's purely research and experimental.
All of these are huge networks, as you can see.
And they do do get extremely good results.
You can see the top five accuracy is close to 100 percent, which is remarkable.
That's very, very good generalization.
So we'll stop there for now and we'll go into the code and start using some of these networks.
We'll start with Alex, Nate and Leonard.
We'll learn how to to build them and pay to watch and carrots.
Then we'll take a look at offloading some of these pre-trained networks like Viji and Raisinets and
experimenting and inferencing images on them.
So that's it for now, and I'll see you in the next section where we start building Lynnette and Alex
net interests, so I'll see you shortly.
But.