# Step 4: Analyze an image with your model


You analyze an image by calling the [DetectCustomLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectCustomLabels) API. In this step, you use the `detect-custom-labels` AWS Command Line Interface (AWS CLI) command to analyze an example image. You get the AWS CLI command from the Amazon Rekognition Custom Labels console. The console configures the AWS CLI command to use your model. You only need to supply an image that's stored in an Amazon S3 bucket. This topic provides an image that you can use for each example project. 

**Note**  
The console also provides Python example code.

The output from `detect-custom-labels` includes a list of labels found in the image, bounding boxes (if the model finds object locations), and the confidence that the model has in the accuracy of the predictions.

For more information, see [Analyzing an image with a trained model](detecting-custom-labels.md).

**To analyze an image (console)**

1. <textobject><phrase>Model status showing as Running, with Stop button to stop the running model.</phrase></textobject>

   If you haven't already, set up the AWS CLI. For instructions, see [Step 4: Set up the AWS CLI and AWS SDKs](su-awscli-sdk.md).

1. If you haven't already, start running your model. For more information, see [Step 3: Start your model](gs-step-start-model.md).

1. Choose the **Use Model** tab and then choose **API code**. The model status panel shown below shows the model as Running, with a Stop button to stop the running model, and an option to display the API.  
![\[Model status showing as Running, with Stop button to stop the running model.\]](http://docs.aws.amazon.com/rekognition/latest/customlabels-dg/images/get-started-use-model-api-code.png)

1. Choose **AWS CLI command**.

1. In the **Analyze image** section, copy the AWS CLI command that calls `detect-custom-labels`. The following image of the Rekognition console shows the "Analyze Image" section with the AWS CLI command to detect custom labels on an image using a machine learning model, and instructions to start the model and provide image details.  
![\[Console screenshot with the AWS CLI command to detect custom labels on an image using a machine learning model, and instructions to start the model and provide image details.\]](http://docs.aws.amazon.com/rekognition/latest/customlabels-dg/images/get-started-cli-code-analyze.png)

1. Upload an example image to an Amazon S3 bucket. For instructions, see [Getting an example image](#gs-example-images).

1. At the command prompt, enter the AWS CLI command that you copied in the previous step. It should look like the following example. 

   The value of `--project-version-arn` should be Amazon Resource Name (ARN) of your model. The value of `--region` should be the AWS Region in which you created the model.

   Change `MY_BUCKET` and `PATH_TO_MY_IMAGE` to the Amazon S3 bucket and image that you used in the previous step. 

   If you are using the [custom-labels-access](su-sdk-programmatic-access.md#su-sdk-programmatic-access-customlabels-examples) profile to get credentials, add the `--profile custom-labels-access` parameter.

   ```
   aws rekognition detect-custom-labels \
     --project-version-arn "model_arn" \
     --image '{"S3Object": {"Bucket": "MY_BUCKET","Name": "PATH_TO_MY_IMAGE"}}' \
     --region us-east-1 \
     --profile custom-labels-access
   ```

   If the model finds objects, scenes, and concepts, the JSON output from the AWS CLI command should look similar to the following. `Name` is the name of the image-level label that the model found. `Confidence` (0-100) is the model's confidence in the accuracy of the prediction.

   ```
   {
       "CustomLabels": [
           {
               "Name": "living_space",
               "Confidence": 83.41299819946289
           }
       ]
   }
   ```

   If the model finds object locations or finds brand, labeled bounding boxes are returned. `BoundingBox` contains the location of a box that surrounds the object. `Name` is the object that the model found in the bounding box. `Confidence` is the model's confidence that the bounding box contains the object. 

   ```
   {
       "CustomLabels": [
           {
               "Name": "textract",
               "Confidence": 87.7729721069336,
               "Geometry": {
                   "BoundingBox": {
                       "Width": 0.198987677693367,
                       "Height": 0.31296101212501526,
                       "Left": 0.07924537360668182,
                       "Top": 0.4037395715713501
                   }
               }
           }
       ]
   }
   ```

1. Continue to use the model to analyze other images. Stop the model if you are no longer using it. For more information, see [Step 5: Stop your model](gs-step-stop-model.md).

## Getting an example image


You can use the following images with the `DetectCustomLabels` operation. There is one image for each project. To use the images, you upload them to an S3 bucket. 

**To use an example image**

1. Right-click the following image that matches the example project that you are using. Then choose **Save image** to save the image to your computer. The menu option might be different, depending on which browser you are using.

1. Upload the image to an Amazon S3 bucket that's owned by your AWS account and is in the same AWS region in which you are using Amazon Rekognition Custom Labels.

   For instructions, see [Uploading Objects into Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UploadingObjectsintoAmazonS3.html) in the *Amazon Simple Storage Service User Guide*.

### Image classification


![\[Living room with fireplace, couch, armchair, end tables, lamps, and large windows.\]](http://docs.aws.amazon.com/rekognition/latest/customlabels-dg/images/image-classification.jpg)


### Multi-label classification


![\[Spherical green flower head composed of densely packed overlapping petals or bracts forming a ball-like shape.\]](http://docs.aws.amazon.com/rekognition/latest/customlabels-dg/images/multi-label-classification.jpg)


### Brand detection


![\[Diagram showing user activity data flowing from Lambda to Amazon Personalize for recommendations, and to Amazon Pinpoint for recommendations.\]](http://docs.aws.amazon.com/rekognition/latest/customlabels-dg/images/brand-detection.png)


### Object localization


![\[Small circuit with various electronic components, and connector pins.\]](http://docs.aws.amazon.com/rekognition/latest/customlabels-dg/images/object-localization.jpg)