Hi and welcome back.
So in this lecture, we'll take a look at the new architecture and evolution that yoga took all the
way up to Vision five.
So let's get started.
So firstly, most conventional object detectors consist of two to three main parts.
OK, we have the backbone, which is typically a classified network that has been trained on image that
unpopular ones are resonant, big, dense net and darknet.
Darknet was one that was used for.
It's a CSP network that's been used for you.
We then have to head for loss calculations and predictions and inference.
And then the NEC, which was introduced in recent detectors.
It is directly leveraged into the backbones for enhancing the richness and semantic representation of
the extracted features for objects of different shapes and sizes.
So let's take a look at the initial Eurovision one and use Eurovision to networks.
So in Eurovision one, which came out in 2015, it was basically the first single stage detector, along
with estimates around the time it was developed simultaneously.
So the researchers probably didn't have much knowledge of what was going on in the SSD research world
of it utilized by some musician and Leekie.
We knew activations as well.
And this is a diagram of the architecture here.
Now you listen to what is implemented, several changes, such as removing the fully connected layer
at the end, facilitating resolution of independence and a few new visions of the other two, such as
Tiny Button two will also released and tiny.
The reason they made a tiny its vision, too, was because it was easy to deploy and embedded systems,
and that was a big need for optimal action on the edge.
So you can have all of these Android cameras or little Raspberry Pi type cameras running on the edge
with a low computational power, but were able to run an object the model object detector models like
leggy tiny little yellow.
So let's take a look at it a little bit and treat.
So using Vision Tree was actually quite good.
It actually had a number of features that actually improved the network they are.
It was inspired by Raisinets, and they had feature pyramid networks inside of it.
The researchers utilized a new feature extractive backend called Darknet 53, which had skipped connections
similar to resonate and tree prediction heads like the FPN, which were available to use, and it actually
performed very, very well for a few years, maybe from 2016 to 2019.
It was the industry standard using your artificial tree for object detection, but in 2020 you're looking
for came out and basically it's shortlisted three different backend.
So you can see the back into the views here.
And these basically were tested extensively in the research paper and provided different speeds and
accuracy, according to what you want to use.
However, Darknet 53, was generally the best choice for most datasets, and that was the one they continued
with most of the experiments in the research paper, and one of the big features of you're looking for
was a modified pat aggregation network called Pan.
It uses spatial pyramid pooling tightly, coupled with the darknet 5G model.
So this aided increasingly receptive field of the model.
So you had a lot better bounding box predictions at that point.
And this is an overview of the very complicated.
Your model has a lot of good features that basically implemented something called a bag of tricks,
which allowed us to get a lot of the performance out of it would basically minimal computational penalties.
And you can see this is a summary of some optimizations that were made here in the model.
The bug of special they call it bag of tricks.
This bag of freebies and bug specializes in inference time, so a training time.
They are things like class labeled smoothening, different data augmentation techniques such as mosaic
and cut mics.
We have dropped the block regulator regularisation.
We had a self adversarial trading.
We had something called Claw Hugh loss across many batch normalization and then doing testing and had
a bunch of different things that optimized performance as well.
So this bag of freebies and Baggott specials were introduced by the researchers, and it got a lot of
benefits out of them.
Now there's the other five by ultra-Orthodox.
It's a heavily optimized PyTorch machine, a field of four that has been open sourced by this company
called Control Ethics.
And you definitely should check out the GitHub because it's a very, very good implementation of YOLO.
Well, basically YOLO Vision for but they call it you, Eurovision five because it meets so many different
optimizations.
Did you get very, very good performance out of the box on your own data sets with you a little bit
in faith, and it's quite easy to use, extensively developed and is a number of ways to configure it
if you needed to.
I've made a number of customizations to your version five of on that on their model, and it's quite
fun to work with, very efficient and so easy to train and multiple GPUs as well.
So we'll stop there for now on YOLO, and in the next section, we'll take a look at Eficiente Attempt,
which is a different obliquity action model coming out from Google.
So stay tuned for that lesson.
Thank you.