create-labeling-job¶

Description¶

Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.

You can select your workforce from one of three providers:

A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
One or more vendors that you select from the AWS Marketplace. Vendors provide expertise in specific areas.
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.

You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling .

The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data .

The output can be used as the manifest file for another labeling job or as training data for your machine learning models.

Synopsis¶

  create-labeling-job
--labeling-job-name <value>
--label-attribute-name <value>
--input-config <value>
--output-config <value>
--role-arn <value>
[--label-category-config-s3-uri <value>]
[--stopping-conditions <value>]
[--labeling-job-algorithms-config <value>]
--human-task-config <value>
[--tags <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--cli-auto-prompt <value>]

Options¶

--labeling-job-name (string)

The name of the labeling job. This name is used to identify the job in a list of labeling jobs.

--label-attribute-name (string)

The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The name can’t end with “-metadata”. If you are running a semantic segmentation labeling job, the attribute name must end with “-ref”. If you are running any other kind of labeling job, the attribute name must not end with “-ref”.

--input-config (structure)

Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.

DataSource -> (structure)

The location of the input data.

S3DataSource -> (structure)

The Amazon S3 location of the input data objects.

ManifestS3Uri -> (string)

The Amazon S3 location of the manifest file that describes the input data objects.

DataAttributes -> (structure)

Attributes of the data specified by the customer.

ContentClassifiers -> (list)

Declares that your content is free of personally identifiable information or adult content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.

(string)

Shorthand Syntax:

DataSource={S3DataSource={ManifestS3Uri=string}},DataAttributes={ContentClassifiers=[string,string]}

JSON Syntax:

{
  "DataSource": {
    "S3DataSource": {
      "ManifestS3Uri": "string"
    }
  },
  "DataAttributes": {
    "ContentClassifiers": ["FreeOfPersonallyIdentifiableInformation"|"FreeOfAdultContent", ...]
  }
}

--output-config (structure)

The location of the output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.

S3OutputPath -> (string)

The Amazon S3 location to write output data.

KmsKeyId -> (string)

The AWS Key Management Service ID of the key used to encrypt the output data, if any.

If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don’t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role’s account. Amazon SageMaker uses server-side encryption with KMS-managed keys for LabelingJobOutputConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateLabelingJob request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide .

Shorthand Syntax:

S3OutputPath=string,KmsKeyId=string

JSON Syntax:

{
  "S3OutputPath": "string",
  "KmsKeyId": "string"
}

--role-arn (string)

The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.

--label-category-config-s3-uri (string)

The S3 URL of the file that defines the categories used to label the data objects.

For 3D point cloud task types, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs .

For all other built-in task types and custom tasks , your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing label_1 , label_2 ,``…`` ,``label_n`` with your label categories.

{

"document-version": "2018-11-28"

"labels": [

{

"label": "*label_1* "

},

{

"label": "*label_2* "

},

...

{

"label": "*label_n* "

}

]

}

--stopping-conditions (structure)

A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.

MaxHumanLabeledObjectCount -> (integer)

The maximum number of objects that can be labeled by human workers.

MaxPercentageOfInputDatasetLabeled -> (integer)

The maximum number of input data objects that should be labeled.

Shorthand Syntax:

MaxHumanLabeledObjectCount=integer,MaxPercentageOfInputDatasetLabeled=integer

JSON Syntax:

{
  "MaxHumanLabeledObjectCount": integer,
  "MaxPercentageOfInputDatasetLabeled": integer
}

--labeling-job-algorithms-config (structure)

Configures the information required to perform automated data labeling.

LabelingJobAlgorithmSpecificationArn -> (string)

Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:

Image classification arn:aws:sagemaker:*region* :027400017018:labeling-job-algorithm-specification/image-classification

Text classification arn:aws:sagemaker:*region* :027400017018:labeling-job-algorithm-specification/text-classification

Object detection arn:aws:sagemaker:*region* :027400017018:labeling-job-algorithm-specification/object-detection

Semantic Segmentation arn:aws:sagemaker:*region* :027400017018:labeling-job-algorithm-specification/semantic-segmentation

InitialActiveLearningModelArn -> (string)

At the end of an auto-label job Amazon SageMaker Ground Truth sends the Amazon Resource Nam (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.

LabelingJobResourceConfig -> (structure)

Provides configuration information for a labeling job.

VolumeKmsKeyId -> (string)

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job. The VolumeKmsKeyId can be any of the following formats:

// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"

// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

Shorthand Syntax:

LabelingJobAlgorithmSpecificationArn=string,InitialActiveLearningModelArn=string,LabelingJobResourceConfig={VolumeKmsKeyId=string}

JSON Syntax:

{
  "LabelingJobAlgorithmSpecificationArn": "string",
  "InitialActiveLearningModelArn": "string",
  "LabelingJobResourceConfig": {
    "VolumeKmsKeyId": "string"
  }
}

--human-task-config (structure)

Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).

WorkteamArn -> (string)

The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.

UiConfig -> (structure)

Information about the user interface that workers use to complete the labeling task.

UiTemplateS3Uri -> (string)

The Amazon S3 bucket location of the UI template, or worker task template. This is the template used to render the worker UI and tools for labeling job tasks. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template .

HumanTaskUiArn -> (string)

The ARN of the worker task template used to render the worker UI and tools for labeling job tasks.

Use this parameter when you are creating a labeling job for 3D point cloud labeling modalities. Use your labeling job task type to select one of the following ARN’s and use it with this parameter when you create a labeling job. Replace aws-region with the AWS region you are creating your labeling job in.

Use this HumanTaskUiArn for 3D point cloud object detection and 3D point cloud object detection adjustment labeling jobs.

arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectDetection

Use this HumanTaskUiArn for 3D point cloud object tracking and 3D point cloud object tracking adjustment labeling jobs.

arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectTracking

Use this HumanTaskUiArn for 3D point cloud semantic segmentation and 3D point cloud semantic segmentation adjustment labeling jobs.

arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudSemanticSegmentation

PreHumanTaskLambdaArn -> (string)

The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.

For built-in task types , use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for PreHumanTaskLambdaArn . For custom labeling workflows, see Pre-annotation Lambda .

Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.

arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox

arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox

arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox

arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox

arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox

arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox

arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox

arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox

Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.

arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass

arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass

arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass

arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass

arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass

arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass

arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass

arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass

Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.

arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel

arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabel

arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabel

arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabel

arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabel

arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabel

arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabel

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabel

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabel

arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabel

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabel

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabel

Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as “votes” for the correct label.

arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation

arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation

arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation

arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation

arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation

arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation

arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation

arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation

Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.

arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass

arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass

arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass

arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass

arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass

arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass

arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass

arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass

Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.

arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabel

arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabel

arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabel

arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabel

arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabel

arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabel

arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabel

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabel

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabel

arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabel

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabel

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabel

Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.

arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition

arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition

arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition

arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition

arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition

arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition

arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition

arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition

3D Point Cloud Modalities

Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks. See 3D Point Cloud Task types to learn more.

3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.

arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetection

arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetection

arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetection

arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetection

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetection

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetection

arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetection

arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetection

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetection

arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetection

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetection

arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection

3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.

arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTracking

arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTracking

arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTracking

arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTracking

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTracking

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTracking

arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTracking

arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTracking

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTracking

arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTracking

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTracking

arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking

3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.

arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation

arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentation

arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentation

arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentation

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentation

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentation

arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentation

arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentation

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentation

arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentation

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentation

arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentation

Use the following ARNs for Label Verification and Adjustment Jobs

Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .

Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.

arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking

Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.

arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox

arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox

arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox

arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox

arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox

arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox

arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox

arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox

Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.

arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation

arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation

arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation

arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation

arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation

arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation

arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation

arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation

Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as “votes” for the correct label.

arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation

arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation

arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation

arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation

arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation

arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation

arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation

arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation

3D point cloud object detection adjustment - Adjust 3D cuboids in a point cloud frame.

arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetection

arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetection

arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetection

arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetection

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetection

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetection

arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetection

arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetection

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetection

arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetection

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetection

arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection

3D point cloud object tracking adjustment - Adjust 3D cuboids across a sequence of point cloud frames.

arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking

arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking

3D point cloud semantic segmentation adjustment - Adjust semantic segmentation masks in a 3D point cloud.

arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentation

arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentation

arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentation

arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentation

arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentation

arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentation

arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentation

arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentation

arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentation

arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentation

arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentation

arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentation

TaskKeywords -> (list)

Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.

(string)

TaskTitle -> (string)

A title for the task for your human workers.

TaskDescription -> (string)

A description of the task for your human workers.

NumberOfHumanWorkersPerDataObject -> (integer)

The number of human workers that will label an object.

TaskTimeLimitInSeconds -> (integer)

The amount of time that a worker has to complete a task.

TaskAvailabilityLifetimeInSeconds -> (integer)

The length of time that a task remains available for labeling by human workers. If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours (43200) . The default value is 864000 seconds (10 days). For private and vendor workforces, the maximum is as listed.

MaxConcurrentTaskCount -> (integer)

Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects.

AnnotationConsolidationConfig -> (structure)

Configures how labels are consolidated across human workers.

AnnotationConsolidationLambdaArn -> (string)

The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data.

This parameter is required for all labeling jobs. For built-in task types , use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for AnnotationConsolidationLambdaArn . For custom labeling workflows, see Post-annotation Lambda .

Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.

arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox arn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBox arn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBox arn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBox arn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBox

Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.

arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClass arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClass arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClass arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClass

Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.

arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMultiLabel

Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as “votes” for the correct label.

arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentation

Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.

arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClass arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClass arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClass arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClass

Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.

arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMultiLabel

Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.

arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition arn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition

3D point cloud object detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.

arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectDetection

3D point cloud object tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.

arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectTracking

3D point cloud semantic segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.

arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudSemanticSegmentation

Use the following ARNs for Label Verification and Adjustment Jobs

Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .

Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as “votes” for the correct label.

arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentation

Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.

arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation

Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.

arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBox arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBox arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBox arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBox arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBox arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBox arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBox

Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.

arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBox arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBox arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBox arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBox arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBox arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox

3D point cloud object detection adjustment - Use this task type when you want workers to adjust 3D cuboids around objects in a 3D point cloud.

arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectDetection

3D point cloud object tracking adjustment - Use this task type when you want workers to adjust 3D cuboids around objects that appear in a sequence of 3D point cloud frames.

arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectTracking

3D point cloud semantic segmentation adjustment - Use this task type when you want workers to adjust a point-level semantic segmentation masks using a paint tool.

arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudSemanticSegmentation

PublicWorkforceTaskPrice -> (structure)

The price that you pay for each task performed by an Amazon Mechanical Turk worker.

AmountInUsd -> (structure)

Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.

Dollars -> (integer)

The whole number of dollars in the amount.

Cents -> (integer)

The fractional portion, in cents, of the amount.

TenthFractionsOfACent -> (integer)

Fractions of a cent, in tenths.

Shorthand Syntax:

WorkteamArn=string,UiConfig={UiTemplateS3Uri=string,HumanTaskUiArn=string},PreHumanTaskLambdaArn=string,TaskKeywords=string,string,TaskTitle=string,TaskDescription=string,NumberOfHumanWorkersPerDataObject=integer,TaskTimeLimitInSeconds=integer,TaskAvailabilityLifetimeInSeconds=integer,MaxConcurrentTaskCount=integer,AnnotationConsolidationConfig={AnnotationConsolidationLambdaArn=string},PublicWorkforceTaskPrice={AmountInUsd={Dollars=integer,Cents=integer,TenthFractionsOfACent=integer}}

JSON Syntax:

{
  "WorkteamArn": "string",
  "UiConfig": {
    "UiTemplateS3Uri": "string",
    "HumanTaskUiArn": "string"
  },
  "PreHumanTaskLambdaArn": "string",
  "TaskKeywords": ["string", ...],
  "TaskTitle": "string",
  "TaskDescription": "string",
  "NumberOfHumanWorkersPerDataObject": integer,
  "TaskTimeLimitInSeconds": integer,
  "TaskAvailabilityLifetimeInSeconds": integer,
  "MaxConcurrentTaskCount": integer,
  "AnnotationConsolidationConfig": {
    "AnnotationConsolidationLambdaArn": "string"
  },
  "PublicWorkforceTaskPrice": {
    "AmountInUsd": {
      "Dollars": integer,
      "Cents": integer,
      "TenthFractionsOfACent": integer
    }
  }
}

--tags (list)

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .

(structure)

Describes a tag.

Key -> (string)

The tag key.

Value -> (string)

The tag value.

Shorthand Syntax:

Key=string,Value=string ...

JSON Syntax:

[
  {
    "Key": "string",
    "Value": "string"
  }
  ...
]

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

--cli-auto-prompt (boolean) Automatically prompt for CLI input parameters.

See ‘aws help’ for descriptions of global parameters.

Output¶

LabelingJobArn -> (string)

The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.

Table of Contents

Feedback

User Guide

create-labeling-job¶

Description¶

Synopsis¶

Options¶

Output¶