Hi, guys, welcome back to Lesson 46.
We will train a mushroom type detector, which is pretty cool.
Might be useful for some people.
You know, if you're in the forest and you have this mushroom type detector on your mobile phone, you
can use it and see what mushroom it is.
I'm not sure why, but yeah, they actually are magic mushrooms, poisonous mushrooms.
So that's a useful tool.
Anyway, let me stop talking now, so let's open Notebook 46 and we'll begin to lesson so you can see
this is another room of the notebook here.
I have links on some of the accompanying blogs where they talk about the data set as well, so you can
see this is what the labels look like.
Every mushroom, all these mushrooms of the same type here that shinta really mushroom mushroom soup.
I pronounce that right.
So, so now let's set up detector on two.
So this takes a little while, takes about two minutes.
So we have to install the correct torch vision that detector on two uses and a few of the libraries
as well.
And that's not to say no to that, so it takes about two minutes to run.
Then we can install detectors to the specific vision right here.
So that's located in this URL as well, and that takes about five seconds as well.
Pretty quick.
Then we just import to libraries that we're going to use that we've done with our data set here and
says the mushroom data so that you can see has quite a few different categories actually believe.
Take a look at some of these here.
I actually, I think, is just two categories, or it's not that make categories of mushroom.
I should probably know this.
We actually can see.
We can investigate this a bit if we just take a look at this data sets here.
You can see it should tell us how many different mushrooms, chicken of the woods and central elicit
just to a species of mushroom, actually.
So let's go back to the notebook.
We were sorry about scrolling.
It's going to make you a bit dizzy.
You can imagine so this function here, we pointed to this that files this one is encoded as the cuckoo
Jason so file, which you can take a look at here if you wanted to.
Let's see if it's in this folder.
Here it is.
You can see the annotations are like this where you have the assets, as did actually the file.
And for the categories, this is what annotations look like here.
So you can see the height imitates.
Actually, this doesn't have to be on the books, does it?
Yes, it is.
To be in the box corresponding to each of those IDs is here as well in the file format.
So now we can move on to setting up and sorting out training.
So firstly, with visualize some of the labels here, you can see this is the color of go with its standard
stood for Chicken of the woods, apparently.
And this is a chance to really mushroom here.
So now we can just start training or custom detection to detect them.
So we create this Katrina class here that is going to build which in the cuckoo evaluator set here and
from that.
So from our detection to libraries here and notices to file that actually start the starts, Katrina.
You can see we can set some settings here, some woman by corrections, some different configurations
of things.
If you don't know what these are.
I suggest you leave them alone.
Either way, it's probably going to get decent results with the text one, two and no control.
You can see it starts to train right here, and it's nice and neat display.
Up until you can see how much memory is occupying this CPU memory.
I believe you can see lots different lost functions here.
You can see it going down south of that point nine point ninety one and starts to go down gradually
as the box loss.
Then there's different losses here.
Total loss as well.
And you can see it's turning each iteration here displays the output right here, every 20, it looks
like.
And you can also visualize the results later on in the tens of board here.
And while the you can see how we can test a model so we can test it right now because that's training.
But you can inference, which does seem to be its here, and you can see the inference we get here is
these are the bounding box.
This is what our model is predicting.
This is quite good, quite accurate because these definitely do look like the correct classes and not
a mushroom expert, but I believe these are correct.
So that's pretty good.
So that's it for this lesson.
Thank you for watching.
And in the next.
Listen, we'll take a look at website screenshot region detection using your look for darknet.
So stay tuned for that lesson.
Thank you.