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Label images by using AutoML Vision

This quickstart walks you through the process of:

  • Copying a set of images into Cloud Storage.
  • Creating a CSV listing the images and their classification labels.
  • Using AutoML Vision to create your dataset, and train and deploy your model.
  • Classifying an image using your model trained with custom labels.

Before you begin

Set up your project

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
  2. Install and initialize the Google Cloud CLI.
  3. Create or select a Google Cloud project.

    • Create a Cloud project:

      gcloud projects create PROJECT_ID
    • Select the Cloud project that you created:

      gcloud config set project PROJECT_ID
  4. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

  5. Enable the AutoML and Cloud Storage APIs:

    gcloud services enable storage-component.googleapis.com automl.googleapis.com storage-api.googleapis.com
  6. Install and initialize the Google Cloud CLI.
  7. Create or select a Google Cloud project.

    • Create a Cloud project:

      gcloud projects create PROJECT_ID
    • Select the Cloud project that you created:

      gcloud config set project PROJECT_ID
  8. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

  9. Enable the AutoML and Cloud Storage APIs:

    gcloud services enable storage-component.googleapis.com automl.googleapis.com storage-api.googleapis.com
  10. Set the PROJECT_ID environment variable to your Project ID.
    export PROJECT_ID=PROJECT_ID
    The AutoML API calls and resource names include your Project ID in them. The PROJECT_ID environment variable provides a convenient way to specify the ID.

Create a Cloud Storage bucket

Use Cloud Shell, a browser-based Linux command line connected to your Google Cloud console project, to create your Cloud Storage bucket:

  1. Open Cloud Shell.

  2. Create a Cloud Storage bucket. The bucket name must be in the format: project-id-vcm.

    The following command creates a storage bucket in the us-central1 region named project-id-vcm. For a complete list of available regions, see the Bucket Locations page.

    gsutil mb -p ${PROJECT_ID} -c regional -l us-central1 gs://${PROJECT_ID}-vcm/

  3. Set the BUCKET variable.

    export BUCKET=${PROJECT_ID}-vcm

Copy the sample images into your bucket

Next, copy the flower dataset used in this Tensorflow blog post. The images are stored in a public Cloud Storage bucket, so you can copy them directly from there to your own bucket.

  1. In your Cloud Shell session, enter:

    gsutil -m cp -R gs://cloud-samples-data/ai-platform/flowers/ gs://${BUCKET}/img/
    

    The file copying takes about 20 minutes to complete.

Create the image classification CSV file

The sample dataset contains a CSV file with all of the image locations and the labels for each image. You'll use that to create your own CSV file:

  1. Update the CSV file to point to the files in your own bucket:

    gsutil cat gs://${BUCKET}/img/flowers/all_data.csv | sed "s:cloud-ml-data/img/flower_photos/:${BUCKET}/img/flowers/:" > all_data.csv
    
  2. Copy the CSV file into your bucket:

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

Create your image classification dataset

Visit the AutoML Vision UI to begin the process of creating your dataset and training your model.

When prompted, make sure to select the project that you used for your Cloud Storage bucket.

  1. From the AutoML Vision page, click New Dataset:

    New dataset button in console

  2. Specify a name for this dataset. Click the + sign to continue.

    New dataset name field

  3. In the Select files to import screen choose the Select a CSV file on Cloud Storage radio option. Specify the Cloud Storage URI of your CSV file. For this quickstart, the CSV file is at:

    • gs://${PROJECT_ID}-vcm/csv/all_data.csv

    Replace PROJECT_ID with your specific project ID.

    Select file import window

  4. Click Continue to begin image import. The import process takes a few minutes. When it completes, you are taken to the next page which has details on all of the images identified for your dataset, both labeled and unlabeled images.

    Images listed after import finishes

Train your Cloud-hosted AutoML Vision model

  1. Once your dataset has been created and processed, select the Train tab to initiate model training.

    select train tab

  2. Select Start training to continue. This will open a Train new model window with training options.

  3. In the Define your model section of the new model training window, change the model name (optional) and select the Cloud hosted model radio option. Select Continue to move to the following section.

    define your model section for training

  4. In the Set a node hour budget section accept the suggested node budget (32 node hours).

    Train Edge model

  5. Select "Start training" to begin model training.

    Training is initiated for your model, and should take about an hour. The training might stop earlier than the node hour you selected. The service will email you once training has completed, or if any errors occur.

Deploy your AutoML Vision classification model

After your model has been successfully trained, you can use it to label images using your custom model. Select the Test and Use tab.

If you didn't opt-in for auto-deploy you will be prompted to deploy your model before you can make predictions.

Label an image using your AutoML Vision model

Click the Predict tab for instructions on sending an image to your model to get image content labels. You can also refer to Making an online prediction or Making batch predictions for examples.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, delete the Cloud project with the resources.

If you no longer need your custom model or dataset, you can delete them.

To avoid unnecessary Google Cloud Platform charges, use the GCP Console to delete your project if you do not need it.

Undeploy your model

Your model incurs charges while it is deployed.

  1. Select the Test & Use tab just below the title bar.
  2. Select Remove deployment from the banner beneath your model name to open the undeploy option window.

    undeploy popup menu

  3. Select Remove deployment to undeploy the model.

    model deploying

  4. You will receive an email when model undeployment has completed.

Delete your project (optional)

To avoid unnecessary Google Cloud Platform charges, use the Google Cloud console to delete your project if you do not need it.

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