Hi and welcome back in this lesson, we'll take a look at running an inference using Arcane Go, which
is a very cool gun that has just been released in late 2021.
So go ahead and open notebook three six and we can start messing with this scanner.
So this gun auto auto to created a called Alex Burn.
And you can check him out here and his Twitter, as well as donate to him here.
And you can check out his GitHub as well here for the resource.
So this is a license for this notebook here.
You can view it if you need to and know this is a could here this.
This cohered basically gets dark and gun code and model from its GitHub, and we also install fears
that play torch and it's a package that we've been using in this notebook.
So that's it takes about six seconds to get.
And now this is a code here that allows us to basically load an image that we want to infer, you know,
akin gump.
And let's run this.
There we go, so it took about 15 seconds to run and no, not a lot that we have to call it in the background.
Now we have all of this around here.
We can no, just run this process here that allows us now to upload an image here so we can choose any
image we want.
So I'm going to choose.
Let's take a picture of my wife and I.
This was taken at my cousin's wedding about a year ago.
And no, we can just run this here.
It's quite fast.
The inference to this model?
And then we go.
So we got the output here.
So it makes us look a bit gothic kind of evil, to be fair.
But it's a very, very cool model.
And again, which I hope you can experiment and have some fun with it.
So that's it for the all Kim gang model.
I do encourage you to check out what is work here and his GitHub.
You can see some of the cool creations he's done with our Kim to celebration original 1.2 here, so
you can see it's all quite good.
It's very stylish, isn't it?
And if you've watched the Netflix series Arcane, it's quite good as well, and it does definitely resemble
the artistic style of that series.
So you can see how guns can be used for very cool off generation.
All right, thank you.
And that's it for this lesson.
I'll see you in the next section where we start looking at Siamese networks, so I'll see you then.
But.