Image Classification (Multi-label) - Amazon SageMaker

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.

            Photo by Anusha Barwa on Unsplash

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.


For this task type, if you create your own manifest file, use "source-ref" to identify the location of each image file in Amazon S3 that you want labeled. For more information, see Input Data.

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 AWS 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:

  • Pre-annotation Lambda functions for this task type end with PRE-ImageMultiClassMultiLabel. To find the pre-annotation Lambda ARN for your Region, see PreHumanTaskLambdaArn .

  • Annotation-consolidation Lambda functions for this task type end with ACS-ImageMultiClassMultiLabel. To find the annotation-consolidation Lambda ARN for your Region, see AnnotationConsolidationLambdaArn.

The following is an example of an AWS 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.

<script src=""></script> <crowd-form> <crowd-image-classifier-multi-select name="crowd-image-classifier-multi-select" src="{{ task.input.taskObject | grant_read_access }}" header="Please identify all classes in image" categories="{{ task.input.labels | to_json | escape }}" > <full-instructions header="Multi Label Image classification instructions"> <ol><li><strong>Read</strong> the task carefully and inspect the image.</li> <li><strong>Read</strong> the options and review the examples provided to understand more about the labels.</li> <li><strong>Choose</strong> the appropriate labels that best suit the image.</li></ol> </full-instructions> <short-instructions> <h3><span style="color: rgb(0, 138, 0);">Good example</span></h3> <p>Enter description to explain the correct label to the workers</p> <h3><span style="color: rgb(230, 0, 0);">Bad example</span></h3> <p>Enter description of an incorrect label</p> </short-instructions> </crowd-image-classifier-multi-select> </crowd-form>

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.