Hi and welcome back.
So in this section, we'll be taking a look at intersection with a union, which is one of the ways
we can assess how well our object detector is performing.
So let's take a look at this example here.
So imagine we have a data set and humans living dataset.
So the ground truth for this car, for the bounding box that humans have labeled is this green box here.
So that's a ground truth.
So what if a contractor proposes this red box hill?
Oh, that's not too bad, but isn't it?
But how do we assess the goodness of this proposal?
So we'll take a look at that.
So as you can see here from a glance, it looks like the red box covers 90 percent of the ground should
original box, which is great.
It looks pretty good, doesn't it?
Of it is this coverage concept.
Is that a good metric to use?
It's actually not, because take a look at this.
This box also covers 90 percent of the original box, and by extension, you can just draw a box over
the entire image and tend to always cover what the ground truth boxes because overlapping with everything.
So basically.
So we solve this by using the intersection per union metric.
So let's take a closer look at that.
So how much of the correct area is covered by our predicted bounding box to do that?
Let's take a look at this formula here.
So what is the size of a union?
The size of the union is this region here, the shaded region here?
And what's the size of the prediction?
That's this entire red box here.
So as you can see, the shaded region occupies what looks like roughly to be 50 percent of the predicted
box.
So that's borderline acceptable, but not great, to be honest.
So what?
Let's get to let's take a look at what you basically is.
Are you considers the ratio of the overlap to the size of the proposed box.
That's what we just did here.
And generally, an IOU over point five is considered acceptable.
However, like I said, it's good, not great.
We would want this box to be a lot closer to the car in this case here.
So the higher the IOU, the better the bungling box, because that would mean the union that means do
overlap between the original ground truth and the proposed box is quite high compared to the size of
the prediction.
So that's why we get a high value value.
So I would use a very simple concept.
Cooper posted a clip across clearly.
Next, we'll take a look at the mean average precision, which is another metric that we can use to
evaluate optical factors, and it's actually the mean metric we do use.
So I'll talk about this in the next section.
Thank you.