Welcome back in this section, we'll take a look at how we actually Trina YOLO model.
So let's get started.
So firstly, in all training processes, you have the training data, which includes the ground truth
labels as well as a test data.
But for now, we'll be focusing on how we use the training data in your.
So initially, we have a human who annotate these these images here.
So we have a ground truth green box here with the class being dug.
So during training, we have to be basically tried to get the model to basically learn a function that
can propose a box that is as close to this box as possible with the precise class.
So let's take a look what happens during training?
So during training, the model attempts to match the example to the right.
So.
So initially, we have the mapping being here and we have the plus class probability being like this
here.
So one being taught and the other classes being zero.
Next, we get two bounding box predictions for that.
So.
So we get these two boxes here, as you can see in this example.
However, we need to adjust these bounding boxes because they both can't be right.
So you can see that is one the larger box is closer to the ground truth box here.
So what we do, we increase the confidence of that box and then decrease the confidence of this box
of smaller box.
So by doing that, you can see we just made this larger here.
Sorry, we made the box larger here on this one.
And then simultaneously, we make that the middle box smaller, so we lower the confidence.
So this is what we get here.
So no cells with no ground to it because you will have cells that predict that basically bounding boxes
over nothing.
The background we call it those basically, we decrease the confidence of those and we don't adjust
class probabilities or coordinates of these boxes.
So that's essentially how Eula tries to fit these boxes to the data.
But how do we how does it actually work?
Well, we need a combination of three lost functions to do this.
Firstly, we need a classification loss.
So if an object is detected, it is a squared error loss of the class conditional probabilities for
each class.
Then we have the localization of loss, which measures the performance for the predicted both bounding
box to the ground it.
And then we have the confidence loss.
That's the confidence that the box has an object.
So that's basically a short summary of how the training process in Yuma works, and you can see just
a summary slide after you lose performance.
This is the a little bit and treat, to be precise.
You can see how much faster it was 45 frames per second compared to the same 50 yards at the time,
which was faster RC and just getting a better, much better map score by at least 10 points.
However, it was a lot slower than usual, and you can see you're going does generalize and generalize
quite well, even on other paintings like this than defies bottles dining table Pearson's cat?
So that's quite cool.
And by the way, the original Mona Lisa painting did not have a cut.
In case you were wondering also, you know, compared to past our CNN's, you can see that actually
fast approaching ends, at least compared to your vision tree did perform better.
You can see 71 percent correct as opposed to 65.
However, Eula was much faster.
So the key takeaways from this your lesson is that you lose fast and you lose vision for Vision five
and except perhaps the best in accuracy right now in 2001, still surpassing other models which include
our CNN's detector on to which we'll talk about shortly.
And if we shouldn't detect the other provides end to end training, which we've seen there, and it
gives us very little background error and even doing the illusion treat him up wasn't as good as our
CNN's.
It was a lot faster.
When you look does tend to have more localization errors at times.
However, in the little regions of your world, which is you mentioned four or five in X, they actually
have minimized that quite a bit, so it will stop them from now.
And in the next section, we'll take a look at the architecture and evolution from region three to version
five.
So stay tuned for that.
Thank you for watching.