Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Image Classification (Multi-label)

Use an Amazon SageMaker Ground Truth multi-label image classification labeling task when you need workers to classify multiple objects in an image. For example, the following image features a dog and a cat. You can use multi-label image classification to associate the labels "dog" and "cat" with this image.

When working on a multi-label image classification task, workers should choose all applicable labels, but must choose at least one. When creating a job using this task type, you can provide up to 50 label-categories.

When creating a labeling job in the console, Ground Truth doesn't provide a "none" category for when none of the labels applies to an image. To provide this option to workers, include a label similar to "none" or "other" when you create a multi-label image classification job.

To restrict workers to choosing a single label for each image, use the Image Classification (Single Label) task type.

Create a Multi-Label Image Classification Labeling Job (Console)

You can follow the instructions Create a Labeling Job (Console) to learn how to create a multi-label image classification labeling job in the SageMaker console. In Step 10, choose Image from the Task category drop down menu, and choose Image Classification (Multi-label) as the task type.

Ground Truth provides a worker UI similar to the following for labeling tasks. When you create a labeling job in the console, you specify instructions to help workers complete the job and labels that workers can choose from.

Create a Multi-Label Image Classification Labeling Job (API)

To create a multi-label image classification labeling job, use the SageMaker API operation CreateLabelingJob. This API defines this operation for all Amazon SDKs. To see a list of language-specific SDKs supported for this operation, review the See Also section of CreateLabelingJob.

Follow the instructions on Create a Labeling Job (API) and do the following while you configure your request:

The following is an example of an Amazon Python SDK (Boto3) request to create a labeling job in the US East (N. Virginia) Region. All parameters in red should be replaced with your specifications and resources.

response = client.create_labeling_job(
    LabelingJobName='example-multi-label-image-classification-labeling-job,
    LabelAttributeName='label',
    InputConfig={
        'DataSource': {
            'S3DataSource': {
                'ManifestS3Uri': 's3://bucket/path/manifest-with-input-data.json'
            }
        },
        'DataAttributes': {
            'ContentClassifiers': [
                'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent',
            ]
        }
    },
    OutputConfig={
        'S3OutputPath': 's3://bucket/path/file-to-store-output-data',
        'KmsKeyId': 'string'
    },
    RoleArn='arn:aws:iam::*:role/*,
    LabelCategoryConfigS3Uri='s3://bucket/path/label-categories.json',
    StoppingConditions={
        'MaxHumanLabeledObjectCount': 123,
        'MaxPercentageOfInputDatasetLabeled': 123
    },
    HumanTaskConfig={
        'WorkteamArn': 'arn:aws:sagemaker:region:*:workteam/private-crowd/*',
        'UiConfig': {
            'UiTemplateS3Uri': 's3://bucket/path/worker-task-template.html'
        },
        'PreHumanTaskLambdaArn': 'arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel',
        'TaskKeywords': [
            'Image Classification',
        ],
        'TaskTitle': 'Multi-label image classification task',
        'TaskDescription': 'Select all labels that apply to the images shown',
        'NumberOfHumanWorkersPerDataObject': 123,
        'TaskTimeLimitInSeconds': 123,
        'TaskAvailabilityLifetimeInSeconds': 123,
        'MaxConcurrentTaskCount': 123,
        'AnnotationConsolidationConfig': {
            'AnnotationConsolidationLambdaArn': 'arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabel'
        },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)

Provide a Template for Multi-label Image Classification

If you create a labeling job using the API, you must supply a worker task template in UiTemplateS3Uri. Copy and modify the following template. Only modify the short-instructions, full-instructions, and header.

Upload this template to S3, and provide the S3 URI for this file in UiTemplateS3Uri.

Multi-label Image Classification Output Data

Once you have created a multi-label image classification labeling job, your output data will be located in the Amazon S3 bucket specified in the S3OutputPath parameter when using the API or in the Output dataset location field of the Job overview section of the console.

To learn more about the output manifest file generated by Ground Truth and the file structure the Ground Truth uses to store your output data, see Output Data.

To see an example of output manifest files for multi-label image classification labeling job, see Multi-label Classification Job Output.