SARA ROBINSON: Hi there.
In this video, I'll show you some tips and tricks
to help you prepare your image data in Cloud AutoML Vision.
Although I'll be showing AutoML Vision here,
the concepts I'll be showing apply to AutoML Natural
Language as well.
For this walkthrough, we'll be building a model
to detect the type of cloud in an image,
because there's no better place to analyze clouds
than the Cloud.
I've got five possible labels, each corresponding
with a different type of cloud.
There are many ways to add labeled images to your AutoML
Vision data set.
In this video, I'll show you how to do the following.
First, we'll upload labeled images directly
from our computer.
Then, we'll review image labels and add additional images
in the AutoML Vision UI.
The cloud images on my computer are organized
into folders for each label.
Using this approach, I can create
a zip file of these folders.
And when I upload the zip to AutoML,
it will use the folder names to automatically assign labels
to each image.
For example, this image of a cirrus cloud
will get the cirrus label assigned,
since it's in that folder.
I'll create the zip now.
I've already enabled AutoML Vision for this project
and created my data set.
So next, let's upload the zip file of labeled images.
Note that AutoML Vision has a 30 megabyte file size limit.
For larger data sets, you can either
create multiple zip files, or upload your images
to Google Cloud Storage and create a labeled CSV.
With all of my labeled images uploaded,
it's time to review them in the AutoML Vision UI.
Here, I can look at all of the images assigned
to a specific label to make sure they're labeled correctly.
Looking at the cirrus images, it seems like this one
is incorrectly labeled.
I can click on it and switch it to the correct label,
cumulonimbus.
Since I created my label data set, some of my friends
heard about this project and sent
over a few of their own cloud images to be used in training.
Let's add them.
From the All Images tab, I can select Add Images, and then
Upload From Your Computer.
Notice that because I uploaded these from the All Images tab,
they went into the Unlabeled category.
I can click on Unlabelled to go in and add labels
to these images.
One of my friends is a cloud expert
and sent me images specifically of cirrus clouds.
I can apply that label to them directly by clicking on Cirrus,
and then adding the images.
Now that I've got all of my images uploaded,
let's explore what I can do with the image selection tool.
I can click the check mark here to select all images on a page,
or I can select individual images
by clicking on the checkbox on the top left of an image.
With images selected, I can move them to the Unlabeled category
by clicking on Label, and then Unlabel.
These images will still be in my data
set and will remain in Cloud storage,
but because they don't have a label,
they won't be used in training.
To delete an image from my data set entirely
and from Google Cloud Storage, I can select it, and then
press Delete.
There are also some handy label ordering tools on the left.
If I click on the menu bar here, I
can choose how I want to order my labels.
I can order them alphabetically, or if I
want to see where I need to add more images,
I can sort from fewest to most.
Remember that you need at least 10 images per label
to train a model in AutoML Vision.
To start gathering images of a new cloud type,
I can add the label here and start importing images for it.
Now that I've uploaded all my images
and labeled them correctly, I can
export the data set to a cloud storage directory.
This will create a CSV file with details
on whether an image was included in the training, test,
or validation data set, its Google Cloud Storage URL,
and its corresponding label.
This is useful if I want to share the data set with others
on my team, or see how my data set was
split for training and testing.
With all my images uploaded and labeled,
I'm ready to train my model.
The great thing about AutoML is that I
don't need to worry about writing
any of the code for my model.
All I need to do is click a button to train it.
That's all for now.
Stay tuned for future videos to cover more AutoML features.