# Create an image classification job (Multi-label)
**Note**
Amazon SageMaker Ground Truth is no longer open to new customers. Existing customers can continue to use the service as normal. AWS continues to invest in security and availability improvements for Ground Truth, but we do not plan to introduce new features.
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. The following page gives information about creating an image classification job.
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 [Create an image classification job (Single Label)](sms-image-classification.md) task type.
**Important**
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](sms-data-input.md).
## Create a Multi-Label Image Classification Labeling Job (Console)
You can follow the instructions [Create a Labeling Job (Console)](sms-create-labeling-job-console.md) to learn how to create a multi-label image classification labeling job in the SageMaker AI 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`](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html).
Follow the instructions on [Create a Labeling Job (API)](sms-create-labeling-job-api.md) 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](https://docs.aws.amazon.com/sagemaker/latest/dg/API_HumanTaskConfig.html#SageMaker-Type-HumanTaskConfig-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](https://docs.aws.amazon.com/sagemaker/latest/dg/API_AnnotationConsolidationConfig.html#SageMaker-Type-AnnotationConsolidationConfig-AnnotationConsolidationLambdaArn).
The following is an example of an [AWS Python SDK (Boto3) request](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_labeling_job) 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`](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-creating-instruction-pages.html#sms-creating-quick-instructions), [`full-instructions`](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-creating-instruction-pages.html#sms-creating-full-instructions), and `header`.
Upload this template to S3, and provide the S3 URI for this file in `UiTemplateS3Uri`.
```