And welcome back to Lesson 45, we will build a pothole detector, which could be a very cool thing
if you're thinking about if you're thinking about deploying self-driving cars and that type of robotic
vehicle type stuff.
And we're going to use the tiny yellow V model.
Tiny UAV for is basically a smaller, lightweight version of the of the 44 model.
It's meant for deployment in embedded devices and mobile phones and that type of low computational requirement
or low power device.
So let's take a look.
Let's open up this notebook notebook 45, and we'll get started.
So again, this is a rebel floor notebook, and this is what the dataset looks like.
This is actually an output of the object attached to inference because you can see this level this twice.
So maybe we need to adjust the animus.
That's not a maximal suppression settings, but nevertheless you can see how it actually works and you
can see it's quite cool.
It's still going to possible, even though it counted, it has to.
So let's take a look.
So I've run all this before, and this model is actually being trained right now.
You can see, firstly, we need to configure oh, cool DNA, which is a CUDA library for deep learning
neural networks.
It also depends on which video card you get in call up here, which would be the card you're using.
So you can see those have some environments, things that changes depending on the video card or CPU
you have.
So let's run off that.
Then we install darknet because darknet is a framework where you're going to be using to train in attaining
your lawful model.
So we clone there, get triple the darknet one honorable, flawless AI.
Then we set up all the environment variables here for to make files.
Then we also load the cat pre-trained widths, as well as the configuration file for the tiny.
And then what do we do next?
We take a look.
We get the data here.
This is the dataset.
We'll be using the portal dataset and let's scroll down now.
So now we need to set up our directories and point to folders in the correct places and load different
files.
So we do that, dear.
Next, we're going to write a custom training configuration for your V4.
So this is going to be this isn't actually about how we just run this here.
Make sure you get a number of classes as correct here, because that's that's important.
We only have one class in this portable training file, which you can see it printed out here just in
case.
And no, we have a custom CFG, which is a configuration file.
You can see it's quite long, actually, it has some moral architecture here.
A lot of the model training settings, as well as some of the data.
Well, basically, it's a small architecture mainly.
And some of the actually these are some of the data augmentation settings and other criterion.
They're in school and back to the top.
You can see we have that size, image size channels, momentum for letting read the key for that twinning
rates as well.
So you can see there's a lot of different things here.
This is actually some of the data augmentation settings as well.
So it's pretty good.
So we're ready to train this network with this well.
So let's talk to training so you can see we just run the darknet.
We just pointed this file here, and the document detector starts the training process right here.
And you can see it's training.
It's training pretty quick.
Actually, I believe this model doesn't take that long to train.
We already have a fairly good map score at point zero, five, six or seven percent.
That's pretty good.
Time left started this.
Let's see how long this is taking.
I didn't start this that long ago.
To be fair, the school up to the top because it's printing out a lot of different lines.
Whoops.
So you can see it's probably going to train this entire network in maybe about a half full, roughly.
So, yeah, so the 20 minutes initially had left.
So that's pretty good.
So I don't actually have the inference screenshots for the moment that you'll be training here.
So you just have to wait for this to be finished training and then you can see it.
But that's it.
Actually, it for this lesson is quite short.
It's a lot going on, obviously, but we aren't going to get into the nitty-gritty of training this
one when these notebooks are meant for.
Is that you can put your own datasets in them once you can host them on rebel floor and quite easily
and put them here and then start training all these different models on your data and create a bunch
of different models that you can use and deploy.
Different spaces.
So that's it for this lesson.
We'll stop there, and the next lesson, we'll take a look at a mushroom type detector, which is a
very cool one, and we're going to use detection too from Facebook to create that model.
So stay tuned for that.
Thank you.