Welcome to our lecture on Deteccion, too, which is an entire moral platform library for not just object
detection, but a number of other capabilities.
So let's get started.
So firstly, detection too is a research platform.
It's not a model, it's an entire platform and production library for object detection and a number
of other things that you'll see shortly.
It was developed an open sourced by Facebook's research team called Fair.
And it takes over from the original detection model.
That model was actually made in caffey to the new detection to is developing PyTorch and makes it much
faster, much more modular and more accurate as well because it has improvements all across the board
detection to features a number of capabilities.
As I mentioned, things such as Cascade, our CNN's Mask, our CNN's Point Trend then puts deep lab
for segmentation and many more.
And you can see an example of detection to object detection right here.
So let's take a look at a demo video that Facebook Facebook's A-Team uses to promote detection, too.
And you can see -- and you can see its vast capabilities here, so you can see, firstly, those object
detection quite well.
Next, you can do body pose estimation as well as dense pools here.
Then you can do some semantic segmentation, which you're seeing there.
And this is pent up tech segmentation now.
So you can see it's quite amazing, isn't it?
So this is the timeline for object detectors, however, doesn't include many of them, including you.
I believe a lot of these research teams leave out the oil because they don't see YOLO as being an academic
type network because, yes, the original books were published.
I don't think they were published in peer reviewed journals, unlike these others here.
So for some reason, a lot of them leave them out and their comparisons, but the one don't.
But don't let that get you down.
What a yellow, because yellow is actually quite good and quite popular in the industry, so you can
see here firstly, they were first.
Our CNN's and first hour of CNN's All Those and Cafe.
Then there was the tech trend here in 2018, my report to the company that actually pioneered mass Garcia
and models, and they used TensorFlow intensive tech for the RC and ends.
Then there was the Textron PyTorch in 2018.
Later on in March, then they had Musk.
Our CNN's and pipe torches will then different networks, Simple Network of Simple Detect, which I've
never used, actually.
And then 2019, late 2019 there was the on two.
And I mean, to be honest, I don't think it.
I don't find it actually became as popular as it should have because it's quite good.
However, I think you five and you look was actually much more popular on the time.
So a lot of people in a complete division will actually didn't know about detection, too, but they
haven't actually used it.
And this is some improvements are made with training time.
We can see it's actually quite fast to train and in my experience, it is quite fast to train.
And now let's talk about these segmentation models they offer.
So this is a normal image here, and this is how it looks at semantic segmentation, where all the all
the cores blend into one core color here and people as well.
Then there's instant segmentation, where each core is not individually separated into different colors
and panopticon segmentation, which combines both semantic and instant segmentation to give you basically
the best of all the worlds right now, and it's probably the most challenging type of segmentation to
do right now.
So detection to, as I mentioned initially in the first slide, also made quite a bit of improvements
in the deployment sphere.
This detection to go, which is a library that the additional software layer that the engineers introduced
that allowed it to do modern deployments quite easily.
It made it it had offered a number of things, such as the network quantization, which is shrinking
the network size as well as well.
Technically, it's decreasing to the floating point that storage values of the widths so that the models
take up less space and also a much faster to execute.
And you do lose some accuracy, but actually it's not that much in the real world.
Model conversion to different model formats, depending on the different deployment platforms, can
use some of those model formats and deploy them quite easily on mobile phones as well.
So the Section two does offer a wide range of services that can be used in industry as well in research
and detection to talk a bit about some of their network designs in here.
So you can see it's just not actually that complicated for diagram here, but it just shows you that
there's a backbone, gives you proposals, crop and warp, and then you can get different things here
so you can get the semantic segmentation.
And then from that, you can even get panopticon.
Segmentation can get key points as it can body pools.
Estimation is dense because regular mask our CNN's as well as normal bounding boxes, which we were
talking about merely in this section.
So it does offer quite a bit, doesn't it?
And it's also quite customizable because in the framework it allows for custom models, the datasets,
data loaders, augmentation, various metrics and tests.
Remember the entire framework similar to how you will find was made by Ultra X, as well as training
logic.
So it does offer a lot of flexibility, and a lot of this is good for researchers, especially because
they would want to tweak things, try different augmentation strategies, try different techniques in
the data loading, as well as optimize for different metrics as well.
So it's quite good.
So that's it for detection, too.
We'll stop there now.
You might be quite bored of these terry slides, so now we'll go into the code again on call up and
we're implementing 10 different object detection projects, all in different datasets and using all
different networks will touch on a few different visions of yellow detection to obviously officially
detect.
I believe the two different models here mobile net SSD, as well as faster or CNN, is the only one
we don't touch on, is written in that.
But that's OK because these are these models are capable of much better performance and much better
efficiency anyway.
And these are by far the most popular models that are deployed in the real world as well as academically
as well.
So that's it, and I hope you enjoyed it, and I'll see you now in the cool lessons.
Thank you.