Welcome back.
Congratulations.
We created our model inside this folder called runs.
So open runs detect train.
And here we have the results.
Also we have a folder called weights best and last.
This is our best trained model.
Now let's add the validate our model.
So in this video we're gonna validate the model.
Create a new code here from ultra import YOLO.
And we need to load the model.
What model.
We need to load this model that created one.
So copy the path of this best model and paste it here inside this cell okay.
Like this.
And let's let's run.
Here we go.
It's running safe with no errors.
Now let's validate the model.
So validate the model.
How to validate.
We're going to allow the model to use the validate the validate images and use them metrics equals to
model dot val.
Let's run.
And here we go.
We are validating our model results saved to content runs.
So again guys, let me go to the runs.
Here.
We have runs detect validate.
So here is used for training and here used for validation.
Open the val and let me see that the box F1 curve.
You see guys this is the F1 confidence curve.
You can you can open the box PR curve.
This is the second one.
We are reaching one.
And the battery is 0.995.
It's detecting and working fine here.
The precision confidence curve.
It's increased to reach one all classes and the battery.
You see the battery curve box R curve.
This is the error recall confidence curve confusion matrix normalized and other predictions okay we
are interested in the box PR curve the precision recall curve.
It's detecting all the the batteries and the precisions precision confidence curve.
It's showing that all the images that contains battery are being detected with a precision confidence
curve okay.
So this is how to validate the model.