Classify Images of Clouds in the Cloud with AutoML Vision

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Classify Images of Clouds in the Cloud with AutoML Vision

2 hours 5 Credits


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AutoML Vision helps developers with limited ML expertise train high quality image recognition models. Once you upload images to the AutoML UI, you can train a model that will be immediately available on Google Cloud for generating predictions via an easy to use REST API.

In this lab, you will upload images to Cloud Storage and use them to train a custom model to recognize different types of clouds (cumulus, cumulonimbus, etc.).

What you'll learn

  • Uploading a labeled dataset to Cloud Storage and connecting it to AutoML Vision with a CSV label file.

  • Training a model with AutoML Vision and evaluating its accuracy.

  • Generating predictions on your trained model.

Setup and requirements

Qwiklabs setup

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

  • Access to a standard internet browser (Chrome browser recommended).
Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.
  • Time to complete the lab---remember, once you start, you cannot pause a lab.
Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

How to start your lab and sign in to the Google Cloud Console

  1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

    • The Open Google Console button
    • Time remaining
    • The temporary credentials that you must use for this lab
    • Other information, if needed, to step through this lab
  2. Click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.

    Tip: Arrange the tabs in separate windows, side-by-side.

    Note: If you see the Choose an account dialog, click Use Another Account.
  3. If necessary, copy the Username from the Lab Details panel and paste it into the Sign in dialog. Click Next.

  4. Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.

    Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.
  5. Click through the subsequent pages:

    • Accept the terms and conditions.
    • Do not add recovery options or two-factor authentication (because this is a temporary account).
    • Do not sign up for free trials.

After a few moments, the Cloud Console opens in this tab.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left. Navigation menu icon

Set up AutoML Vision

AutoML Vision provides an interface for all the steps in training an image classification model and generating predictions on it. Start by enabling the Cloud AutoML API.

From the Navigation menu, select APIs & Services > Library.

In the search bar type in "Cloud AutoML". Click on the Cloud AutoML API result and then click Enable.

This will take a minute to set up.

Now open this AutoML UI link in a new browser.

Click Check my progress to verify the objective.

Enable the AutoML API

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

  1. In the Cloud Console, in the top right toolbar, click the Activate Cloud Shell button.

Cloud Shell icon

  1. Click Continue.

It takes a few moments to provision and connect to the environment. When you are connected, you are already authenticated, and the project is set to your PROJECT_ID. The output contains a line that declares the PROJECT_ID for this session:

Your Cloud Platform project in this session is set to YOUR_PROJECT_ID

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

  1. (Optional) You can list the active account name with this command:

gcloud auth list


ACTIVE: * ACCOUNT: To set the active account, run: $ gcloud config set account `ACCOUNT`
  1. (Optional) You can list the project ID with this command:

gcloud config list project


[core] project = <project_ID>

(Example output)

[core] project = qwiklabs-gcp-44776a13dea667a6 For full documentation of gcloud, in Google Cloud, Cloud SDK documentation, see the gcloud command-line tool overview.

In Cloud Shell, use the following commands to create environment variables for your Project ID and Username, replacing <USERNAME> with the User Name you logged into the lab with:


Run the following command to give AutoML permissions:

gcloud projects add-iam-policy-binding $PROJECT_ID \ --member="user:$QWIKLABS_USERNAME" \ --role="roles/automl.admin"

Now create a storage bucket by running the following:

gsutil mb -p $PROJECT_ID \ -c standard \ -l us-central1 \ gs://$PROJECT_ID-vcm/

In the Google Cloud console, open the Navigation menu and click on Cloud Storage to see it.


Click Check my progress to verify the objective.

Create a Cloud Storage Bucket

Upload training images to Cloud Storage

In order to train a model to classify images of clouds, you need to provide labeled training data so the model can develop an understanding of the image features associated with different types of clouds. In this example your model will learn to classify three different types of clouds: cirrus, cumulus, and cumulonimbus. To use AutoML Vision you need to put your training images in Cloud Storage.

Before adding the cloud images, create an environment variable with the name of your bucket.

Run the following command in Cloud Shell:


The training images are publicly available in a Cloud Storage bucket.

Use the gsutil command line utility for Cloud Storage to copy the training images into your bucket:

gsutil -m cp -r gs://spls/gsp223/images/* gs://${BUCKET}

When the images finish copying, click the Refresh button at the top of the Storage browser, then click on your bucket name. You should see 3 folders of photos for each of the 3 different cloud types to be classified:


If you click on the individual image files in each folder you can see the photos you'll be using to train your model for each type of cloud.

Create a dataset

Now that your training data is in Cloud Storage, you need a way for AutoML Vision to access it. You'll create a CSV file where each row contains a URL to a training image and the associated label for that image. This CSV file has been created for you; you just need to update it with your bucket name.

Run the following command to copy the file to your Cloud Shell instance:

gsutil cp gs://spls/gsp223/data.csv .

Then update the CSV with the files in your project:

sed -i -e "s/placeholder/${BUCKET}/g" ./data.csv

Now upload this file to your Cloud Storage bucket:

gsutil cp ./data.csv gs://${BUCKET}

Once that command completes, click the Refresh button at the top of the Storage browser. Confirm that you see the data.csv file in your bucket.

Navigate back to the AutoML Vision tab. Your page should now resemble the following:


At the top of the console, click + NEW DATASET.

Type "clouds" for the Dataset name.

Select "Single-Label Classification".



Choose Select a CSV file on Cloud Storage and add the file name to the URL for the file you just uploaded - gs://your-bucket-name/data.csv

An easy way to get this link is to go back to the Cloud Console, click on the data.csv file. Click on the copy icon in the URI field.



It will take 2 - 5 minutes for your images to import. Once the import has completed, you'll be brought to a page with all the images in your dataset.

Click Check my progress to verify the objective.

Create a Dataset

Inspect images

After the import completes, click on the Images tab to see the images you uploaded.


Try filtering by different labels in the left menu (i.e. click cumulus) to review the training images:

If any images are labeled incorrectly you can click on the image to switch the label:


To see a summary of how many images you have for each label, click on LABEL STATS at the top of the page. You should see the following show up on the right side of your browser.


Train your model

You're ready to start training your model! AutoML Vision handles this for you automatically, without requiring you to write any of the model code.

To train your clouds model, go to the Train tab and click Start Training.

Enter a name for your model, or use the default auto-generated name.

Leave Cloud-hosted selected, then click Continue.

Set the node hours to 8.


Click Start Training.

Since this is a small dataset, it will only take around 25-30 minutes to complete.

While you're waiting, you can watch this YouTube video on preparing an image data in AutoML - the images should look familiar!

Evaluate your model

In the Evaluate tab, you'll see information about Precision and Recall of the model.


You can also play around with Confidence threshold.

Finally, scroll down to take a look at the Confusion matrix.


All of this provides some common machine learning metrics to evaluate your model accuracy and see where you can improve your training data. Since the focus for this lab was not on accuracy, move on to the next section about predictions section. Feel free to browse the accuracy metrics on your own.

Deploy your model

Now it's time for the most important part: generating predictions on your trained model using data it hasn't seen before.

Navigate to the Test & Use tab in the AutoML UI:


Click Deploy model and then click Deploy.

This will take around 20 minutes to deploy.

Generate predictions

There are a few ways to generate predictions. In this lab you'll use the UI to upload images. You'll see how your model does classifying these two images (the first is a cirrus cloud, the second is a cumulonimbus).

Download these images to your local machine by right-clicking on each of them:



Return to the AutoML Vision UI, click Upload Images and upload the clouds to the online prediction UI. When the prediction request completes you should see something like the following:


Click Check my progress to verify the objective.

Run the predictions

Pretty cool - the model classified each type of cloud correctly!


You've learned how to train your own custom machine learning model and generate predictions on it through the web UI. Now you've got what it takes to train a model on your own image dataset.

What was covered

  • Uploading training images to Cloud Storage and creating a CSV for AutoML Vision to find these images.

  • Reviewing labels and training a model in the AutoML Vision UI.

  • Generating predictions on new cloud images.

Finish your Quest

ml_quest_icon.png ML-Image-Processing-badge.png

This self-paced lab is part of the Qwiklabs Machine Learning APIs and Intro to ML: Image Processing Quests. A Quest is a series of related labs that form a learning path. Completing a Quest earns you a badge to recognize your achievement. You can make your badge (or badges) public and link to them in your online resume or social media account. Enroll in these Quests and get immediate completion credit if you've taken this lab. See other available Qwiklabs Quests.

Take your next lab

Continue your Quest with Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API, or check out these suggestions:

Next steps / learn more

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Manual Last Updated April 20, 2022
Lab Last Tested April 20, 2022

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