Welcome back.
We finished training our models and we get the hard disk and the airports.
We created the two classes hard disk and airports.
Now let's train the model.
You can click Advanced Apex.
Apex.
As I told you, one epoch means that each and every sample in the training dataset has been fed through
the training model at least once.
If your apex are set to 50, for example, it means that the model you are training will work through
the entire training data set 50 times.
Generally, the larger the number, the better your model will learn and predict data.
As we did in the previous sections, I'm going to use 100.
The batch size a batch is a set of samples used in one iteration of training.
Keep it 16 learning rate.
Be careful of tweaking this number.
Even small differences can have huge effects on how well your model learns.
Okay, click train the model and you see preparing training data.
You get this note.
You must leave this tab open to train your model.
Click okay.
And here we are getting training progress in the next sections, we're going to learn how to train the
models, how to detect objects and and work with images using the Google Colab.
And we'll go into create our custom model in the same way, but without using teachable machine, we're
going to use our code.
So we're going to create codes as we did in the previous sections, but we're going to use it with the
images for object detection models.
Now we have to export your model.
We have the input a webcam.
So go and rerun the webcam.
And we see guys that in this preview we have this is the AirPods is 100.
This is the hard disk is 100 and it's well trained.
So change the position with the keyboard.
You get the hard disk and you see guys the percentage of the hard disk increases and you see guys that
the hard disk is well detected in our model.
Let me go to the AirPods.
You see guys how fast is detection.
Because we will train our model with hundreds of images and we make 100 epochs for each class.