Welcome back.
This is a lecture on YOLO.
This is an overview, a high level overview of YOLO, which is my favorite object detector, so let's
get started.
So your stands for you only look once and was developed by two brilliant researchers, Joseph Redmond
and Ali Farad in 2015.
And this is basically a diagram that has become famous for YOLO because its use illustrates how good
and how simple it was.
So YOLO as a single stage detector, hence the name you only look once single stage, meaning that it
actually saves a lot of time compared to our CNN's and even faster CNN's.
Because the biggest drawback with faster our CNN's was that it was quite slow.
It wasn't practical to run this on my video because frame rate on even powerful GPUs use back then with
single digit frame rates, it was a seven usually unloved or faster and video super GPUs at that time.
So since these two did dramatically improve that and they were also using a single stage technique,
however, they wouldn't as nearly as accurate as our CNN's.
And that's where YOLO is so important.
YOLO.
And now you look for and you live five attempted to solve both of those problems and strike a balance
between speed and accuracy.
And I actually would say no YOLO which and four and five and your X surpasses all other option factors
in not just performance, but I mean, not not at speed, but in accuracy as well.
So there are many flavors of YOLO.
YOLO has had tremendous success in real world applications and have many, many different visions right
now.
Some examples are Tinio your motivation to live within Shreeve, which is four or five you'll scale
the YOLO YOLO with various backhands is very customizable, and the research world and research community
has really taken advantage of that and has explored so many different visions of YOLO.
Right now, the most popular flavor fuel will use an industry at least right.
Maybe before 2019 was Univision tree.
However, no, it's the ultimate x implementation of YOLO five.
YOLO five, which we'll talk about later on in a few lectures, is technically YOLO four.
However, it's been YOLO for ported to PyTorch, with a number of optimizations that make training quite
quick inference testing.
It's really, really well done by automatics, and actually, I encourage you to use that can go to
the GitHub and check it out.
I'll actually be teaching a lecture on that later on in the course anyway.
And because of how common YOLO is, is a number of pre-trained your models on the data said that are
readily available out there that you can use in your code.
Right now, it's quite easy to use, and these are the easy classes that it's been trained on on the
cocoa dataset.
So how does your little compare to other objects adapters?
Well, let's take a look.
So currently, the best performing YOLO models are YOLO four.
Technically, you'll have four and five are the same, but your five is a little.
That's a generally and you'll X.
Then you can see this was a from the publication.
This image here, the top right one from the YOLO four people.
And you can see the speeds here, and the V 100 CPU was magnificent.
It was like 110, and it was still getting very high at accuracies.
You can see the efficient detect model with the backends we're getting better Macnab scores.
However,
however, they were quite slow.
And then you can see in this diagram here this is the bottom left program.
Yellow five.
This is the diagram we used to compare to efficient with text.
You can see it's actually beating a lot of the efficient tech models in terms of faster inference and
better maps go as well, if it's quite remarkable.
And this diagram here was from the Yellow X research paper that again showed it speeding efficient detect
light, though not the efficient detect, not non light visions.
You can see this is a small vision here.
This is a larger vision here as well, with using the full efficient detector model YOLO Vision five
a different backhands as well, and large and small models.
And you can see your Excel has achieved quite good map score as well.
So how does your work?
Well, in prior object detection systems, they repurposed classify as a localized localizes to perform
detection, and then they applied the model to an image at multiple locations and skills.
So high scoring regions are considered image considered optic detections.
So instead, yellower plays a single neural network.
The full image, this network divides the image into regions and predicts bounding boxes and probabilities
for each region probabilities of different classes to be specific.
These bounding boxes are then weighted by the predicted probabilities, and this method has several
advantages over classifier based systems because it looks at the whole image.
Test time So its predictions are informed by the global context of the image, and also it makes predictions
with a single network evaluation.
Unlike RC events, which require 2000 for a single image.
So this makes it extremely fast more than a thousand times faster than the original or CNN and 100x
faster and faster as the events and that coming to the end of this lesson.
However, I really do encourage you to read the your vision three people.
It's actually quite hilarious.
The researchers definitely don't take themselves too seriously, and it's quite an entertaining read
to be thought to be us.
So we'll stop there.
And in the next section, we'll take a deeper look on how exactly does your work?
So I'll see you in the next section.
Thank you.