Hi, guys.
Welcome back in this lesson, we'll be treating a object detector to detect different chess pieces,
which is actually a really cool project.
I will be using PyTorch vision of your vision tree.
So let's get started.
So I've already set up this notebook here.
I already run.
Some of these files can see how long they each take to run six seconds one second.
Here we just clue into robots the way gilovich and tree ripple.
But then we're done with our dataset here.
Let me just check our directory as make sure all images up there.
Then we just move around images to the correct directories.
Then we just check to make sure terrible, flawed text is all right.
Then again, we just do the same thing with the trees and the other validation directories, and now
we can set up our model config here.
So you just navigate into this directory display labels.
These are the labels we'll be looking at here.
This is a Bishop Black Bishop.
Well, you can see a bulletin here.
Standard chess pieces that we all know.
And now we can convert all labels to the automatic specification Ultralight X, by the way.
Maybe I mentioned that in previous lessons, but they're a group.
I guess they're a company that has open sourced some of their your pie torch implementations, and it's
quite well done.
It has a lot of features and a lot of bells and whistles and some fine tuning.
It's a really, really well done package.
I encourage you to check out the Get Up report, and you can explore it there.
You can take a look at the YOLO Vision five models I did.
I did mention to you in the previous section that there's a reason sometimes you would want to train
your fish tree model, and it's not because it's faster.
It's because in some OpenSea the implementations, it's actually quite easy to get a view of which entry
model up and running using the DNN module in OpenCV.
So if you wanted to do that, I mean open, see if we probably will support your vision five.
Maybe, maybe if it does right now and haven't checked, but it didn't.
Maybe a couple of months ago when I last checked, but we can see that there's a lot of reasons why
you would want to use it.
You'll love which industry still has good, very good performance, and it's quite easy to create prototype
prototypes using the elevation tree.
So anyway, let's get back to the notebook.
So once you've run all of these blocks of code, you can start beginning the training model and that's
what's going on here.
So you can see this is from beginning output.
It's what it looks like, and you can see the trading summary here.
You can see we've only completed 60 books, and that was pretty quick, actually, because I started
this maybe about 10 minutes ago, and you can see some of the losses here, as well as some other details.
So this is quite good.
I can actually see what it is your memory usage here, which is also a good thing to note.
So this is quite good here.
Now let's take a look and see some of the performance metrics we get from an ultra that's x open source
to you report.
Did they provide similar metrics to for the all of which in five models actually
can also see some of the inferences here?
No, I actually did run some inferences here which were on the previous model I trained.
And you can see this is how it looks right here.
You can see all of the pieces I've been doing to fight correctly, which is quite good.
Amazing.
And you can see it actually works quite well.
Now, one thing you will notice here is that this model works quite well on this chessboard, and that's
because it's actually just what it's trained on.
If you were to try this model on a different chessboard, a different looking chess pieces, it may
have some issues.
Definitely.
However, if you wanted to combine several different data sets, they could get a group of like maybe
20 of the most popular styles of chess board, just boards and different pieces.
And to get them different lighting conditions, different angles, then I would say you would you would
have a fairly good object detector that can recognize all these chess pieces quite accurately.
So that's it for this chess piece model.
I hope you enjoyed it.
And in the next lesson, we'll take a look at a hard hat detector.
So stay tuned for that.
This is useful for construction sites, by the way, which I mentioned here.
All right, thank you.
And I'll see you in the next lesson.
But.