Hello, everyone, welcome back.
In this lecture, we will see how to train an object detection model.
So in the previous lecture, we already see how to combine the model.
And now let's see how to train the object reduction models.
So first, what we will do, let's important are support.
So from the front lines at low dot us.
Dark callbacks.
Input the entire vote.
Now, the scary variable to be equal to the entire vote.
All right.
So what we need to provide here is that we need to provide the log directory.
So the log directly that I'm going to provide here is object detection.
So what are the log files that is being stored in the object detection folder executed?
Now let's fit our model.
So let me start all the variables in the history history equal model, since this is the model where
I have compiled some model don't fit.
What we need to provide here is X.
X is nothing but extreme.
Y is nothing but Y and bad signs.
So what is the size of the batch that is going to provide and a box of verbose and callbacks obviously
to provide that support and validation data?
So let's provide one by one exercycle to exercycle to extreme.
Why call to.
Why train and in require the backsides you can also provide what are the bad sides you want?
It really depends upon the bad side.
So what we can do is that like a..
You can provide the bad sides at something between less, could be 10 or 20.
It depends upon the speed of your computer.
And after that, you can provide a box set box, let's provide a hundred apples and after that, validation
data.
So this time I'm providing the validation data is what my best and whitest X test come, whitest test.
And finally we will provide the callbacks.
So callbacks is that so?
Let's let we put that in the list.
And it is a dense board.
So that's it.
So what we'll do let executed.
All right, my training has been started, usually it takes the at least a three to four hours depending
upon the speed of your computer, and if you got some issue like a memory error or something like this,
so what do you do?
You try to reduce the bad side instead of 10 to probably some five or six and try it.
So we will continue in the next lecture once we're done with our training process.
Thank you very much.