Welcome back to the course in this lecture.
We'll take a closer look on how you'll actually works.
So let's dive in.
So firstly, your splits the image into a seven by seven grid.
This is important and this is also configurable.
But for now, we'll use seven by seven for teaching purposes.
So not we have a seven by seven grid.
What does that do?
Well, that splits the image up into different cells.
We have 49 different cells here and what we get.
We run a model, a neural network over it and itself gives us, predicts B bounding boxes.
And we also get the confidence of the probability score for each bounding box here having an object.
So for each cell here, we can be found in boxes and you can see the bounding boxes are given by this
center coordinate x way and then their width and their height.
And then we get the confidence for each box having an object.
We don't get the confidence for the object being a dog or a bicycle or call a tree.
Yet we just get the confidence.
I don't eat an object is there or not there?
It's p of object.
Next, we generate all the bowling boxes so you can see we will have tons of fun then boxes for an image
like this, as well as we get the probabilities for each box having an image.
So that's fine.
Next, we take a look at getting to class probability per cell.
So for each, so we run that sure classifier and we get a positive score out of it.
So as you can see for an image like this, we're going to get bicycle, bicycle, bicycle in this pink
region here in the green region.
We're going to get a dog because it can see a dog was in that period there.
Then over here, you're going to get caught on over here, you're going to get dining table.
Apparently, I don't know why, but I guess it's because of the wood.
So moving on, you can see how this is.
The probability class probably scores for each cell, how they actually look.
But what do we have to do next?
Well, next before remember, we had the probability of the bounding boxes that we proposed for each
region.
We have the probability of it being having an object or not.
And then we also have the probability, the class probability piso.
So now we can basically get we can combine them to get our class predictions.
So that's so we can also filter out one predictions that are low.
So if you had a low object score or low confidence, go for that image.
You can drop that box so you can immediately clean up some boxes here.
However, this is actually a cleaned up version right here.
This isn't all the dummy boxes initially that we got.
So what we have to do next is that we have to actually use null maximum suppression and also trash all
the detections which I mentioned before.
So actually, when I said we, we we didn't actually threshold these predictions here.
Yet actually, you can go back and see these are the exact same bungling boxes we propose initially
and now we have the class probabilities combined with it.
But then we drop the ones that are below a threshold after we dropped it off.
We calculate this here.
And we also simultaneously use non maximum suppression, which you've seen before, and aims to basically
get the final bounding box positions here.
So this is what the final output looks like.
So actually, this is what the real output looks like here because we have a seven by seven grid.
We also have the probability of an object here.
That's a p of the object.
We have x y which height and we have that for all the different bounding boxes here.
So you can see this is what the output looks like.
A space is seven by seven.
So we can take a look at the data and understand deeper what the other output is just giving us.
And this here is the inference overview of you can see how it goes in the image comes in here.
This is in different sizes as it passes through the network.
And this is the output block we get right here.
So that's it for this quick overview of how these little works that will help you understand the next
section where we talk about training over your role models.
So stay tuned for that lesson.
Thank you very much for watching.