Hello.

I'm Martin Crawford.

And this is the IoT Developers Show

where we look at IoT technology, share learning

opportunities, and showcase cool demos and the creators

behind them.

In this episode, we talk with Stuart Christie, an IoT

evangelist here at Intel, as he walks us

through two demos showcasing the Movidius Neural Compute Stick.

Stuart, thank you so much for joining us today.

Do you want to tell us a little bit more about yourself?

Sure.

I'm an IoT evangelist, like yourself, Martin.

And I'm the hardware guy in this software group.

On a personal side, I'm interested in robotics

and photography.

And this is sort of a photography example

here we're going to be showing.

Fantastic.

Well, I'm really excited in this demo.

Do you want to tell us a bit more about it?

Sure.

It's a follow on from last month's example

where we were doing the unboxing of the Upsquared Board.

This time, we are showing some demos

that you can run on this board using these Movidius Compute

Sticks.

These are also development devices from Intel, much

smaller, as you can see here.

They're USB devices.

There's an SDK available if you want

to write your own programs.

But today, we're just going to demo

some of the prepackaged applications

that you can find on GitHub and the Movidius Application Zoo.

Sure.

So kind of like monkeys?

Yes, zoo as in animals in a zoo.

That's correct.

In fact, they call it a park-like area

in which deep learning models are housed for exhibition.

I'm not sure why they call it a zoo.

But a quick search for GitHub on zoo in Python

shows a lot of deep learning examples.

Excellent.

So what kind of applications are housed in the zoo then?

We don't have any monkeys.

But we do have some of birds.

But today, we're going to demo two of them.

We'll feature you and me in the zoo

using a USB camera to do object recognition.

And then the second example shows the scalability

of these USB sticks.

And we'll run the same demo side by side,

one running on one stick and one on the second,

so you can see the speed difference.

And so all of these demos are running on pretrained models?

Exactly.

The first one using the webcam has been trained

on a selection of objects.

It's fun to get it confused.

There are Windsor ties, lab coats,

and other seemingly random objects that show up.

This demo doesn't do any background removal,

processes the whole video frame.

So we'll likely see some odd guesses.

One of my favorites I've been trying to get

is to recognize my face.

And then I cover it up so it looks like a ski mask.

It thinks it's a ski mask.

Well, let's see what it recognizes here right now.

I've got a bulletproof vest on.

It sees me as a person, that's good.

Oh, I'm there as a person.

Interesting.

So I'm assuming that you can also

tweak the threshold levels to kind of get

it to be more exacting.

Exactly.

This one's actually running a threaded example.

So it's running TinyYOLO on one of these USB sticks.

And then the second one is really GoogLeNet.

And you can tweak the thresholds for it for both of those things

using the keyword.

OK.

And YOLO is you only look once.

And that is being used for object detection.

And then the GoogLeNet is being used as an object

classification essentially.

Exactly.

It refines the models, yes.

There are 20 things that the YOLONet can detect.

OK.

Well, do you want to take a look at the next demo?

Yes.

We'll close this down.

And then we'll start it up and see it

with these two sticks versus the one stick.

So this is the parallel compute demo.

And you're saying that it's using

three of these Movidius sticks?

Exactly, two sticks here versus one stick.

And it shows how the system is scalable.

This demo process is a system of directory of images.

There's about 65 images.

And it scans an image, uses Open CB to print the object name

that it's finding of the image.

And then it also says running one stick, running two sticks.

And you can see this one, the frame rate's a lot faster.

The scrolling here, the text, says stick one and stick two

there.

Again, one stick, two.

And then this one's running as stick zero.

So this shows the scalability.

You can add as many sticks as you really

need for your application.

OK.

And can I add any photos to this database,

or is it only trained to recognize these 65?

And

No.

You can add as many photographs as you want.

I've run it on my own directory of photographs.

So that works fine.

But obviously, they're trained to recognize certain devices.

So if I've got a cathedral photograph

and they haven't been trained to find a cathedral,

it's not going to find it.

It may say that it's a porcupine or something.

Porcupine or a dog, yes, because it does do probabilities.

This is what it thinks it is.

So how is it actually doing the parallel processing?

It's simply doing a ping pong, alternating between the sticks,

as all three sticks are loaded with the same detection

algorithms.

I've seen other demos where the controlling program

pulls the sticks and looks for a free stick to use.

That's a better algorithm to use, for example, if the time

to recognize something is indeterminate,

from, say example, a live video feed.

OK.

Is there a limit to how many sticks you can use in parallel?

Not really.

A USB-3 port can support for.

I believe that's the bandwidth limit on the port.

We've got to photograph we can show you on the screen

right now. it's got eight.

It's a bit of a problem to move.

It has an external PCI to USB card running on an extender.

So it's unwieldy, but you can use it.

OK.

So you mentioned that this is a development platform.

Now, are there any kind of production versions

of this thing out there?

There are.

It's shipping in the Google Clip camera and the DJI drone.

So there's the chip itself.

There's also a hardware developer kit,

which is a bit more like a regular PCB for doing

some software development.

I think this is a great platform for developers that want

to get into computer vision.

And thank you so much for coming down here and showing it

off to us.

Well, I've enjoyed showing it off.

Thanks very much for inviting me.

In February, Intel Technologies will

help fans experience the Olympic Winter Games, Pyongyang,

2018 with the latest innovations in virtual reality,

5G, drones, and gaming.

Check out the links to learn about Intel

at the Olympic games.

Thanks for watching the IoT Developers Show.

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Thanks, guys.

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