# Importing image-level labels in manifest files
<a name="md-create-manifest-file-classification"></a>

To import image-level labels (images labeled with scenes, concepts, or objects that don't require localization information), you add SageMaker AI Ground Truth [Classification Job Output](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-data-output.html#sms-output-class) format JSON lines to a manifest file. A manifest file is made of one or more JSON lines, one for each image that you want to import. 

**Tip**  
To simplify creation of a manifest file, we provide a Python script that creates a manifest file from a CSV file. For more information, see [Creating a manifest file from a CSV file](ex-csv-manifest.md).

**To create a manifest file for image-level labels**

1. Create an empty text file.

1. Add a JSON line for each image the that you want to import. Each JSON line should look similar to the following.

   ```
   {"source-ref":"s3://custom-labels-console-us-east-1-nnnnnnnnnn/gt-job/manifest/IMG_1133.png","TestCLConsoleBucket":0,"TestCLConsoleBucket-metadata":{"confidence":0.95,"job-name":"labeling-job/testclconsolebucket","class-name":"Echo Dot","human-annotated":"yes","creation-date":"2020-04-15T20:17:23.433061","type":"groundtruth/image-classification"}}
   ```

1. Save the file. You can use the extension `.manifest`, but it is not required. 

1. Create a dataset using the manifest file that you created. For more information, see [To create a dataset using a SageMaker AI Ground Truth format manifest file (console)](md-create-manifest-file.md#create-dataset-procedure-manifest-file). 

 

## Image-Level JSON Lines
<a name="md-manifest-classification-json"></a>

In this section, we show you how to create a JSON line for a single image. Consider the following image. A scene for the following image might be called *Sunrise*.

![\[Sunset over a lake with a dock and small boats, surrounded by mountains.\]](http://docs.aws.amazon.com/rekognition/latest/customlabels-dg/images/sunrise.png)


The JSON line for the preceding image, with the scene *Sunrise*, might be the following. 

```
{
    "source-ref": "s3://bucket/images/sunrise.png",
    "testdataset-classification_Sunrise": 1,
    "testdataset-classification_Sunrise-metadata": {
        "confidence": 1,
        "job-name": "labeling-job/testdataset-classification_Sunrise",
        "class-name": "Sunrise",
        "human-annotated": "yes",
        "creation-date": "2020-03-06T17:46:39.176",
        "type": "groundtruth/image-classification"
    }
}
```

Note the following information.

### source-ref
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(Required) The Amazon S3 location of the image. The format is `"s3://BUCKET/OBJECT_PATH"`. Images in an imported dataset must be stored in the same Amazon S3 bucket. 

### *testdataset-classification\$1Sunrise*
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(Required) The label attribute. You choose the field name. The field value (1 in the preceding example) is a label attribute identifier. It is not used by Amazon Rekognition Custom Labels and can be any integer value. There must be corresponding metadata identified by the field name with *-metadata* appended. For example, `"testdataset-classification_Sunrise-metadata"`. 

### *testdataset-classification\$1Sunrise*-metadata
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(Required) Metadata about the label attribute. The field name must be the same as the label attribute with *-metadata* appended. 

*confidence*  
(Required) Currently not used by Amazon Rekognition Custom Labels but a value between 0 and 1 must be supplied. 

*job-name*  
(Optional) A name that you choose for the job that processes the image. 

*class-name*  
(Required) A class name that you choose for the scene or concept that applies to the image. For example, `"Sunrise"`. 

*human-annotated*  
(Required) Specify `"yes"`, if the annotation was completed by a human. Otherwise `"no"`. 

*creation-date*   
(Required) The Coordinated Universal Time (UTC) date and time that the label was created. 

*type*  
(Required) The type of processing that should be applied to the image. For image-level labels, the value is `"groundtruth/image-classification"`. 

### Adding multiple image-level labels to an image
<a name="md-dataset-purpose-classification-multiple-labels"></a>

You can add multiple labels to an image. For example, the follow JSON adds two labels, *football* and *ball* to a single image. 

```
{
    "source-ref": "S3 bucket location", 
    "sport0":0, # FIRST label
    "sport0-metadata": { 
        "class-name": "football", 
        "confidence": 0.8, 
        "type":"groundtruth/image-classification", 
        "job-name": "identify-sport", 
        "human-annotated": "yes", 
        "creation-date": "2018-10-18T22:18:13.527256" 
    },
    "sport1":1, # SECOND label
    "sport1-metadata": { 
        "class-name": "ball", 
        "confidence": 0.8, 
        "type":"groundtruth/image-classification", 
        "job-name": "identify-sport", 
        "human-annotated": "yes", 
        "creation-date": "2018-10-18T22:18:13.527256" 
    }
}  # end of annotations for 1 image
```