Hi and welcome to the lecture on Efficient Detect.
This is an object detection model from Google.
So let's get started.
So recall that efficient net was an effective scaling methodology for coming up with a very good model
for different CNN's.
Depending on the computational requirements we had with scaling up scaling resolution scaling as well
as component scaling.
And a combination of these methods produced a number of efficient net networks that provided very good
accuracy, as well as very good performance.
So they were computationally quite fast, quick to train and quick to inference.
So what the researchers at Google, who are the ones who were behind efficient detect what they wanted
to see was was it possible to build a scalable object detection architecture that offered both high
accuracy and better efficiency across a wide variety of performance constraints?
So firstly, I know it's efficient that that's the official name.
However, it seems quite morbid, so I'm going to see if it should detect for this presentation.
I hope you don't mind.
So Efficient Detect is also a single shot detector, just like Estes or resident internet, which we
haven't discussed much and use of.
It utilizes philosophies from efficient that and has made several improvements, especially to model
scaling multi-skilled features of feature fusion.
So efficient detect solves two main problems the researchers at Google sort to solve the problems.
The first one they wanted to solve was efficient multi-skilled feature fusion.
So in most typical object detectors, feature pyramid networks have become probably the most popular
method of using multi-skilled features together.
However, they simply sum them up without any distinction.
And we don't know, and we all know that not all the features contribute equally to the outfit.
So we needed a better strategy.
Also, one of the other problems was that they wanted to solve model scaling, so most detectors relied
on improving the backbone for improving the accuracy, which in my experience works quite well.
However, the researchers noted that scaling up to the feature network and box class prediction networks
are also quite important because you need to consider the overall network model.
So it also was quite important for accuracy and efficiency concerns.
So efficient detect solve this by using something called a by FPN.
That's a bi feature pyramid network.
And basically it offered multiscale fusion, which aggregated features at several different resolutions.
And you can see this is what future permits do the architecture for it.
This is another one planet.
This is nurse paeans, which launched this on the fly and this system by FPN here.
So you can see.
Let's talk a bit more about this.
So why by FPN?
One of the feature Pyramid Network's limitations was that it was limited to a top down flow of information,
and you can see that here now.
Pundit did it, but did it the top down and a bottom up network aggregation that worked to be precise
enough snipped, which is also the predecessor to efficient detect, basically did a network, a grid
search to find an irregular featured network topology and then repeatedly applied the same block like
this here.
However, inefficient detect they used it to buy FP and network to grid success, which improved better,
which improved accuracy and got better efficiency.
So this is what this is basically an overview of the full efficient detect architecture.
And you can see this is basically two different features that have been aggregated here to the by FP
and layer.
This is one block of it and you have multiple concurrent blocks as well.
And then it goes to the conflicts that do the box prediction as well as the class prediction.
And let's take a look at some of the architecture designs.
You can see the number of layers, number of channels, the backbone C used in efficient net, the use
d zero to D7 to call it.
So it'd be zero to be seven to be six.
But an efficient detect there has to have to call a D as well for detection, so you can see that there.
And now let's take a look at the performance and you can see they don't have you here, unfortunately,
to compare it.
But efficient detect is actually quite good is probably second best to your in my experience, and you
can see it was definitely getting extremely good performance with very low flops even up to the tree
and D4 is quite good.
You can see how much better look at this here.
D4 compares all the way to a Meebo net amiibo that now.
Have been +y and had so much more flops involved in processing that model.
So you can see how good efficient that act really is.
It lives up to its name, efficient.
So this is basically a summary of all the performance here.
You can see the different speed ups when you compare it to similar networks, and you can see basically
how impressive the map scores are, especially for efficient detect D7.
They got 51 on a map score, which is quite good, actually.
So in summary, I'll say efficient detect is a very good, very useful model that in my experience,
works almost or probably even better than yellow in some situations.
Next, we'll take a look at the tech, too, which is basically entire framework from Facebook and offers
a number of different features, not just object detection.
So we'll take a look at detection too shortly.
So I'll see you in the next section.
Thank you.