- (Instructor) Hello. Today I'm going to show
you how to train a custom computer vision model
using Amazon Rekognition Custom Labels.
This tutorial assumes
that you have already created a project and a data set.
If you haven't already created a project and a data set
please check out our other videos to show you how.
So let's get started.
From the Amazon Rekognition product page.
We'll select use Custom Labels.
From there we'll select the hamburger icon
on the top left and click on projects.
(beep)
Here we'll select the home classified project
which was created in the previous video.
(beep) (beep)
From the previous video.
You should already be familiar with this console
which provides a step-by-step direction
on how to train and evaluate your model.
As part of the previous video, we created a single data set
which was automatically labeled
by Amazon Rekognition Custom Labels.
In this video, we'll go to the next stage
which is to train a model.
We will choose train model,
(beep)
and this takes us to the train Model Console
we will scroll down
and click on
train model.
(beep) (beep)
Since we used a single data set,
Amazon Rekonition Custom Labels when training the model
for the first time,
will automatically keep aside 20% of the data set,
as test data set and will train on the remaining 80%.
Once the model is trained,
it'll use the 20% test data set
to evaluate the model's performance.
(beep) (beep)
Once the model training is triggered,
it'll, that's the first step.
It'll basically split the single data set,
for you into a training and a test data set,
(mouse clicking)
and after that it will take you back
to the home classified project console.
We can see that our model has moved
to the training in progress status
under the model step.
The time for training a model depends on multiple factors
like amount of training data, size of images
and the number of types of labels.
While the model training is in progress,
we can explore the data sets.
As I mentioned earlier,
since we started with the single data set,
for the first time when the model is trained.
Amazon Rekognition Custom Label will split the data set
into training and test data sets.
To explore that, we will choose data set
under the home classified project.
(beep) (beep)
This will take us to the data sets console page
of the home classified project, which displays the training
data set under the training tab
and the test data set
under the test tab.
This console also provides your capability to
add images to the existing training
and the test data set,
using the action button.
(beep) (beep)
You can add images to an existing training data set.
You can also add images to an existing test data set.
So since we've explored the training
and the test data sets for this model
you can go back to the home classified project console
and we can see the model is still has a status
of training in progress.
I'm going to wait a bit for this model to train
and then I will show you the results.
Now that our model has finished training,
you can see it under the model step
that the status for that model
has changed to training completed.
You can also see the model performance score,
which is a 0.917.
We can get more details about the model
by choosing that model,
and it takes us to the model detail page.
Where it provides details
around how much time it took to train the models.
In this case, it took around 1.3 hours
to train the model.
It provides evaluation metrics like the F one score
the average precision, and the overall recall.
In the later videos we'll explore these performance numbers,
as well as show you how to deploy your model
and perform inference.
But for now, that shows you how to train a model
using Amazon Rekognition Custom Labels.