Label images with an AutoML-trained model on Android

After you train your own model using AutoML Vision Edge , you can use it in your app to label images. There are two ways to integrate models trained from AutoML Vision Edge: You can bundle the model by putting it inside your app’s asset folder, or you can dynamically download it from Firebase.

Model bundling options

Bundled in your app

The model is part of your app's APK
The model is available immediately, even when the Android device is offline
No need for a Firebase project

Hosted with Firebase

Host the model by uploading it to Firebase Machine Learning
Reduces APK size
The model is downloaded on demand
Push model updates without republishing your app
Easy A/B testing with Firebase Remote Config
Requires a Firebase project

Try it out

Play around with the sample app to see an example usage of this API.

Before you begin

1. In your project-level build.gradle file, make sure to include Google's Maven repository in both your buildscript and allprojects sections.

2. Add the dependencies for the ML Kit Android libraries to your module's app-level gradle file, which is usually app/build.gradle : For bundling a model with your app:

dependencies {
      // ...
      // Image labeling feature with bundled automl model
      implementation 'com.google.mlkit:image-labeling-automl:16.2.1'
    }

For dynamically downloading a model from Firebase, add the linkFirebase dependency:

dependencies {
      // ...
      // Image labeling feature with automl model downloaded
      // from firebase
      implementation 'com.google.mlkit:image-labeling-automl:16.2.1'
      implementation 'com.google.mlkit:linkfirebase:16.0.1'
    }

3. If you want to download a model , make sure you add Firebase to your Android project , if you have not already done so. This is not required when you bundle the model.

1. Load the model

Configure a local model source

To bundle the model with your app:

1. Extract the model and its metadata from the zip archive you downloaded from Firebase console. We recommend you use the files as you downloaded them, without modification (including the file names).

2. Include your model and its metadata files in your app package:

a. If you don't have an assets folder in your project, create one by right-clicking the app/ folder, then clicking New > Folder > Assets Folder .

b. Create a sub-folder under the assets folder to contain the model files.

c. Copy the files model.tflite , dict.txt , and manifest.json to the sub-folder (all three files must be in the same folder).

3. Add the following to your app's build.gradle file to ensure Gradle doesn’t compress the model file when building the app:

android {
        // ...
        aaptOptions {
            noCompress "tflite"
        }
    }

The model file will be included in the app package and available to ML Kit as a raw asset.

Note: starting from version 4.1 of the Android Gradle plugin, .tflite will be added to the noCompress list by default and the above is not needed anymore.

4. Create LocalModel object, specifying the path to the model manifest file:

Kotlin

 val 
  
 localModel 
  
 = 
  
 AutoMLImageLabelerLocalModel 
 . 
 Builder 
 () 
  
 . 
 setAssetFilePath 
 ( 
 "manifest.json" 
 ) 
  
 // or .setAbsoluteFilePath(absolute file path to manifest file) 
  
 . 
 build 
 ()

Java

 AutoMLImageLabelerLocalModel 
  
 localModel 
  
 = 
  
 new 
  
 AutoMLImageLabelerLocalModel 
 . 
 Builder 
 () 
  
 . 
 setAssetFilePath 
 ( 
 "manifest.json" 
 ) 
  
 // or .setAbsoluteFilePath(absolute file path to manifest file) 
  
 . 
 build 
 ();

Configure a Firebase-hosted model source

To use the remotely-hosted model, create a RemoteModel object, specifying the name you assigned the model when you published it:

Kotlin

 // Specify the name you assigned in the Firebase console. 
 val 
  
 remoteModel 
  
 = 
  
 AutoMLImageLabelerRemoteModel 
 . 
 Builder 
 ( 
 "your_model_name" 
 ). 
 build 
 ()

Java

 // Specify the name you assigned in the Firebase console. 
 AutoMLImageLabelerRemoteModel 
  
 remoteModel 
  
 = 
  
 new 
  
 AutoMLImageLabelerRemoteModel 
 . 
 Builder 
 ( 
 "your_model_name" 
 ). 
 build 
 ();

Then, start the model download task, specifying the conditions under which you want to allow downloading. If the model isn't on the device, or if a newer version of the model is available, the task will asynchronously download the model from Firebase:

Kotlin

 val 
  
 downloadConditions 
  
 = 
  
 DownloadConditions 
 . 
 Builder 
 () 
  
 . 
 requireWifi 
 () 
  
 . 
 build 
 () 
 RemoteModelManager 
 . 
 getInstance 
 (). 
 download 
 ( 
 remoteModel 
 , 
  
 downloadConditions 
 ) 
  
 . 
 addOnSuccessListener 
  
 { 
  
 // Success. 
  
 }

Java

 DownloadConditions 
  
 downloadConditions 
  
 = 
  
 new 
  
 DownloadConditions 
 . 
 Builder 
 () 
  
 . 
 requireWifi 
 () 
  
 . 
 build 
 (); 
 RemoteModelManager 
 . 
 getInstance 
 (). 
 download 
 ( 
 remoteModel 
 , 
  
 downloadConditions 
 ) 
  
 . 
 addOnSuccessListener 
 ( 
 new 
  
 OnSuccessListener 
  () 
  
 { 
  
 @Override 
  
 public 
  
 void 
  
 onSuccess 
 ( 
 @NonNull 
  
 Task 
   
 task 
 ) 
  
 { 
  
 // Success. 
  
 } 
  
 });

Many apps start the download task in their initialization code, but you can do so at any point before you need to use the model.

Create an image labeler from your model

After you configure your model sources, create a ImageLabeler object from one of them.

If you only have a locally-bundled model, just create a labeler from your AutoMLImageLabelerLocalModel object and configure the confidence score threshold you want to require (see Evaluate your model ):

Kotlin

 val 
  
 autoMLImageLabelerOptions 
  
 = 
  
 AutoMLImageLabelerOptions 
 . 
 Builder 
 ( 
 localModel 
 ) 
  
 . 
 setConfidenceThreshold 
 ( 
 0 
 ) 
  
 // Evaluate your model in the Firebase console 
  
 // to determine an appropriate value. 
  
 . 
 build 
 () 
 val 
  
 labeler 
  
 = 
  
 ImageLabeling 
 . 
 getClient 
 ( 
 autoMLImageLabelerOptions 
 )

Java

 AutoMLImageLabelerOptions 
  
 autoMLImageLabelerOptions 
  
 = 
  
 new 
  
 AutoMLImageLabelerOptions 
 . 
 Builder 
 ( 
 localModel 
 ) 
  
 . 
 setConfidenceThreshold 
 ( 
 0.0f 
 ) 
  
 // Evaluate your model in the Firebase console 
  
 // to determine an appropriate value. 
  
 . 
 build 
 (); 
 ImageLabeler 
  
 labeler 
  
 = 
  
 ImageLabeling 
 . 
 getClient 
 ( 
 autoMLImageLabelerOptions 
 )

If you have a remotely-hosted model, you will have to check that it has been downloaded before you run it. You can check the status of the model download task using the model manager's isModelDownloaded() method.

Although you only have to confirm this before running the labeler, if you have both a remotely-hosted model and a locally-bundled model, it might make sense to perform this check when instantiating the image labeler: create a labeler from the remote model if it's been downloaded, and from the local model otherwise.

Kotlin

 RemoteModelManager 
 . 
 getInstance 
 (). 
 isModelDownloaded 
 ( 
 remoteModel 
 ) 
  
 . 
 addOnSuccessListener 
  
 { 
  
 isDownloaded 
  
 -> 
  
  
 val 
  
 optionsBuilder 
  
 = 
  
 if 
  
 ( 
 isDownloaded 
 ) 
  
 { 
  
 AutoMLImageLabelerOptions 
 . 
 Builder 
 ( 
 remoteModel 
 ) 
  
 } 
  
 else 
  
 { 
  
 AutoMLImageLabelerOptions 
 . 
 Builder 
 ( 
 localModel 
 ) 
  
 } 
  
 // Evaluate your model in the Firebase console to determine an appropriate threshold. 
  
 val 
  
 options 
  
 = 
  
 optionsBuilder 
 . 
 setConfidenceThreshold 
 ( 
 0.0f 
 ). 
 build 
 () 
  
 val 
  
 labeler 
  
 = 
  
 ImageLabeling 
 . 
 getClient 
 ( 
 options 
 ) 
 }

Java

 RemoteModelManager 
 . 
 getInstance 
 (). 
 isModelDownloaded 
 ( 
 remoteModel 
 ) 
  
 . 
 addOnSuccessListener 
 ( 
 new 
  
 OnSuccessListener 
  () 
  
 { 
  
 @Override 
  
 public 
  
 void 
  
 onSuccess 
 ( 
 Boolean 
  
 isDownloaded 
 ) 
  
 { 
  
 AutoMLImageLabelerOptions 
 . 
 Builder 
  
 optionsBuilder 
 ; 
  
 if 
  
 ( 
 isDownloaded 
 ) 
  
 { 
  
 optionsBuilder 
  
 = 
  
 new 
  
 AutoMLImageLabelerOptions 
 . 
 Builder 
 ( 
 remoteModel 
 ); 
  
 } 
  
 else 
  
 { 
  
 optionsBuilder 
  
 = 
  
 new 
  
 AutoMLImageLabelerOptions 
 . 
 Builder 
 ( 
 localModel 
 ); 
  
 } 
  
 AutoMLImageLabelerOptions 
  
 options 
  
 = 
  
 optionsBuilder 
  
 . 
 setConfidenceThreshold 
 ( 
 0.0f 
 ) 
  
 // Evaluate your model in the Firebase console 
  
 // to determine an appropriate threshold. 
  
 . 
 build 
 (); 
  
 ImageLabeler 
  
 labeler 
  
 = 
  
 ImageLabeling 
 . 
 getClient 
 ( 
 options 
 ); 
  
 } 
  
 });

If you only have a remotely-hosted model, you should disable model-related functionality—for example, grey-out or hide part of your UI—until you confirm the model has been downloaded. You can do so by attaching a listener to the model manager's download() method:

Kotlin

 RemoteModelManager 
 . 
 getInstance 
 (). 
 download 
 ( 
 remoteModel 
 , 
  
 conditions 
 ) 
  
 . 
 addOnSuccessListener 
  
 { 
  
 // Download complete. Depending on your app, you could enable the ML 
  
 // feature, or switch from the local model to the remote model, etc. 
  
 }

Java

 RemoteModelManager 
 . 
 getInstance 
 (). 
 download 
 ( 
 remoteModel 
 , 
  
 conditions 
 ) 
  
 . 
 addOnSuccessListener 
 ( 
 new 
  
 OnSuccessListener 
  () 
  
 { 
  
 @Override 
  
 public 
  
 void 
  
 onSuccess 
 ( 
 Void 
  
 v 
 ) 
  
 { 
  
 // Download complete. Depending on your app, you could enable 
  
 // the ML feature, or switch from the local model to the remote 
  
 // model, etc. 
  
 } 
  
 });

2. Prepare the input image

Then, for each image you want to label, create an InputImage object from your image. The image labeler runs fastest when you use a Bitmap or, if you use the camera2 API, a YUV_420_888 media.Image , which are recommended when possible.

You can create an InputImage object from different sources, each is explained below.

Using a `media.Image`

To create an InputImage object from a media.Image object, such as when you capture an image from a device's camera, pass the media.Image object and the image's rotation to InputImage.fromMediaImage() .

If you use the CameraX library, the OnImageCapturedListener and ImageAnalysis.Analyzer classes calculate the rotation value for you.

Kotlin

 private 
  
 class 
  
 YourImageAnalyzer 
  
 : 
  
 ImageAnalysis 
 . 
 Analyzer 
  
 { 
  
 override 
  
 fun 
  
 analyze 
 ( 
 imageProxy 
 : 
  
 ImageProxy 
 ) 
  
 { 
  
 val 
  
 mediaImage 
  
 = 
  
 imageProxy 
 . 
 image 
  
 if 
  
 ( 
 mediaImage 
  
 != 
  
 null 
 ) 
  
 { 
  
 val 
  
 image 
  
 = 
  
 InputImage 
 . 
 fromMediaImage 
 ( 
 mediaImage 
 , 
  
 imageProxy 
 . 
 imageInfo 
 . 
 rotationDegrees 
 ) 
  
 // Pass image to an ML Kit Vision API 
  
 // ... 
  
 } 
  
 } 
 }

Java

 private 
  
 class 
 YourAnalyzer 
  
 implements 
  
 ImageAnalysis 
 . 
 Analyzer 
  
 { 
  
 @Override 
  
 public 
  
 void 
  
 analyze 
 ( 
 ImageProxy 
  
 imageProxy 
 ) 
  
 { 
  
 Image 
  
 mediaImage 
  
 = 
  
 imageProxy 
 . 
 getImage 
 (); 
  
 if 
  
 ( 
 mediaImage 
  
 != 
  
 null 
 ) 
  
 { 
  
 InputImage 
  
 image 
  
 = 
  
 InputImage 
 . 
 fromMediaImage 
 ( 
 mediaImage 
 , 
  
 imageProxy 
 . 
 getImageInfo 
 (). 
 getRotationDegrees 
 ()); 
  
 // Pass image to an ML Kit Vision API 
  
 // ... 
  
 } 
  
 } 
 }

If you don't use a camera library that gives you the image's rotation degree, you can calculate it from the device's rotation degree and the orientation of camera sensor in the device:

Kotlin

 private 
  
 val 
  
 ORIENTATIONS 
  
 = 
  
 SparseIntArray 
 () 
 init 
  
 { 
  
 ORIENTATIONS 
 . 
 append 
 ( 
 Surface 
 . 
 ROTATION_0 
 , 
  
 0 
 ) 
  
 ORIENTATIONS 
 . 
 append 
 ( 
 Surface 
 . 
 ROTATION_90 
 , 
  
 90 
 ) 
  
 ORIENTATIONS 
 . 
 append 
 ( 
 Surface 
 . 
 ROTATION_180 
 , 
  
 180 
 ) 
  
 ORIENTATIONS 
 . 
 append 
 ( 
 Surface 
 . 
 ROTATION_270 
 , 
  
 270 
 ) 
 } 
 /** 
 * Get the angle by which an image must be rotated given the device's current 
 * orientation. 
 */ 
 @RequiresApi 
 ( 
 api 
  
 = 
  
 Build 
 . 
 VERSION_CODES 
 . 
 LOLLIPOP 
 ) 
 @Throws 
 ( 
 CameraAccessException 
 :: 
 class 
 ) 
 private 
  
 fun 
  
 getRotationCompensation 
 ( 
 cameraId 
 : 
  
 String 
 , 
  
 activity 
 : 
  
 Activity 
 , 
  
 isFrontFacing 
 : 
  
 Boolean 
 ): 
  
 Int 
  
 { 
  
 // Get the device's current rotation relative to its "native" orientation. 
  
 // Then, from the ORIENTATIONS table, look up the angle the image must be 
  
 // rotated to compensate for the device's rotation. 
  
 val 
  
 deviceRotation 
  
 = 
  
 activity 
 . 
 windowManager 
 . 
 defaultDisplay 
 . 
 rotation 
  
 var 
  
 rotationCompensation 
  
 = 
  
 ORIENTATIONS 
 . 
 get 
 ( 
 deviceRotation 
 ) 
  
 // Get the device's sensor orientation. 
  
 val 
  
 cameraManager 
  
 = 
  
 activity 
 . 
 getSystemService 
 ( 
 CAMERA_SERVICE 
 ) 
  
 as 
  
 CameraManager 
  
 val 
  
 sensorOrientation 
  
 = 
  
 cameraManager 
  
 . 
 getCameraCharacteristics 
 ( 
 cameraId 
 ) 
  
 . 
 get 
 ( 
 CameraCharacteristics 
 . 
 SENSOR_ORIENTATION 
 ) 
 !! 
  
 if 
  
 ( 
 isFrontFacing 
 ) 
  
 { 
  
 rotationCompensation 
  
 = 
  
 ( 
 sensorOrientation 
  
 + 
  
 rotationCompensation 
 ) 
  
 % 
  
 360 
  
 } 
  
 else 
  
 { 
  
 // back-facing 
  
 rotationCompensation 
  
 = 
  
 ( 
 sensorOrientation 
  
 - 
  
 rotationCompensation 
  
 + 
  
 360 
 ) 
  
 % 
  
 360 
  
 } 
  
 return 
  
 rotationCompensation 
 } 
   MLKitVisionImage 
 . 
 kt

Java

 private 
  
 static 
  
 final 
  
 SparseIntArray 
  
 ORIENTATIONS 
  
 = 
  
 new 
  
 SparseIntArray 
 (); 
 static 
  
 { 
  
 ORIENTATIONS 
 . 
 append 
 ( 
 Surface 
 . 
 ROTATION_0 
 , 
  
 0 
 ); 
  
 ORIENTATIONS 
 . 
 append 
 ( 
 Surface 
 . 
 ROTATION_90 
 , 
  
 90 
 ); 
  
 ORIENTATIONS 
 . 
 append 
 ( 
 Surface 
 . 
 ROTATION_180 
 , 
  
 180 
 ); 
  
 ORIENTATIONS 
 . 
 append 
 ( 
 Surface 
 . 
 ROTATION_270 
 , 
  
 270 
 ); 
 } 
 /** 
 * Get the angle by which an image must be rotated given the device's current 
 * orientation. 
 */ 
 @RequiresApi 
 ( 
 api 
  
 = 
  
 Build 
 . 
 VERSION_CODES 
 . 
 LOLLIPOP 
 ) 
 private 
  
 int 
  
 getRotationCompensation 
 ( 
 String 
  
 cameraId 
 , 
  
 Activity 
  
 activity 
 , 
  
 boolean 
  
 isFrontFacing 
 ) 
  
 throws 
  
 CameraAccessException 
  
 { 
  
 // Get the device's current rotation relative to its "native" orientation. 
  
 // Then, from the ORIENTATIONS table, look up the angle the image must be 
  
 // rotated to compensate for the device's rotation. 
  
 int 
  
 deviceRotation 
  
 = 
  
 activity 
 . 
 getWindowManager 
 (). 
 getDefaultDisplay 
 (). 
 getRotation 
 (); 
  
 int 
  
 rotationCompensation 
  
 = 
  
 ORIENTATIONS 
 . 
 get 
 ( 
 deviceRotation 
 ); 
  
 // Get the device's sensor orientation. 
  
 CameraManager 
  
 cameraManager 
  
 = 
  
 ( 
 CameraManager 
 ) 
  
 activity 
 . 
 getSystemService 
 ( 
 CAMERA_SERVICE 
 ); 
  
 int 
  
 sensorOrientation 
  
 = 
  
 cameraManager 
  
 . 
 getCameraCharacteristics 
 ( 
 cameraId 
 ) 
  
 . 
 get 
 ( 
 CameraCharacteristics 
 . 
 SENSOR_ORIENTATION 
 ); 
  
 if 
  
 ( 
 isFrontFacing 
 ) 
  
 { 
  
 rotationCompensation 
  
 = 
  
 ( 
 sensorOrientation 
  
 + 
  
 rotationCompensation 
 ) 
  
 % 
  
 360 
 ; 
  
 } 
  
 else 
  
 { 
  
 // back-facing 
  
 rotationCompensation 
  
 = 
  
 ( 
 sensorOrientation 
  
 - 
  
 rotationCompensation 
  
 + 
  
 360 
 ) 
  
 % 
  
 360 
 ; 
  
 } 
  
 return 
  
 rotationCompensation 
 ; 
 }

Then, pass the media.Image object and the rotation degree value to InputImage.fromMediaImage() :

Kotlin

 val 
  
 image 
  
 = 
  
 InputImage 
 . 
 fromMediaImage 
 ( 
 mediaImage 
 , 
  
 rotation 
 ) 
   MLKitVisionImage 
 . 
 kt

Java

 InputImage 
  
 image 
  
 = 
  
 InputImage 
 . 
 fromMediaImage 
 ( 
 mediaImage 
 , 
  
 rotation 
 );

Using a file URI

To create an InputImage object from a file URI, pass the app context and file URI to InputImage.fromFilePath() . This is useful when you use an ACTION_GET_CONTENT intent to prompt the user to select an image from their gallery app.

Kotlin

 val 
  
 image 
 : 
  
 InputImage 
 try 
  
 { 
  
 image 
  
 = 
  
 InputImage 
 . 
 fromFilePath 
 ( 
 context 
 , 
  
 uri 
 ) 
 } 
  
 catch 
  
 ( 
 e 
 : 
  
 IOException 
 ) 
  
 { 
  
 e 
 . 
 printStackTrace 
 () 
 } 
   MLKitVisionImage 
 . 
 kt

Java

 InputImage 
  
 image 
 ; 
 try 
  
 { 
  
 image 
  
 = 
  
 InputImage 
 . 
 fromFilePath 
 ( 
 context 
 , 
  
 uri 
 ); 
 } 
  
 catch 
  
 ( 
 IOException 
  
 e 
 ) 
  
 { 
  
 e 
 . 
 printStackTrace 
 (); 
 }

Using a `ByteBuffer` or `ByteArray`

To create an InputImage object from a ByteBuffer or a ByteArray , first calculate the image rotation degree as previously described for media.Image input. Then, create the InputImage object with the buffer or array, together with image's height, width, color encoding format, and rotation degree:

Kotlin

 val 
  
 image 
  
 = 
  
 InputImage 
 . 
 fromByteBuffer 
 ( 
  
 byteBuffer 
 , 
  
 /* image width */ 
  
 480 
 , 
  
 /* image height */ 
  
 360 
 , 
  
 rotationDegrees 
 , 
  
 InputImage 
 . 
 IMAGE_FORMAT_NV21 
  
 // or IMAGE_FORMAT_YV12 
 ) 
   MLKitVisionImage 
 . 
 kt 
 
 
 // Or: 
 val 
  
 image 
  
 = 
  
 InputImage 
 . 
 fromByteArray 
 ( 
  
 byteArray 
 , 
  
 /* image width */ 
  
 480 
 , 
  
 /* image height */ 
  
 360 
 , 
  
 rotationDegrees 
 , 
  
 InputImage 
 . 
 IMAGE_FORMAT_NV21 
  
 // or IMAGE_FORMAT_YV12 
 ) 
   MLKitVisionImage 
 . 
 kt

Java

 InputImage 
  
 image 
  
 = 
  
 InputImage 
 . 
 fromByteBuffer 
 ( 
 byteBuffer 
 , 
  
 /* image width */ 
  
 480 
 , 
  
 /* image height */ 
  
 360 
 , 
  
 rotationDegrees 
 , 
  
 InputImage 
 . 
 IMAGE_FORMAT_NV21 
  
 // or IMAGE_FORMAT_YV12 
 ); 
   MLKitVisionImage 
 . 
 java 
 
 
 // Or: 
 InputImage 
  
 image 
  
 = 
  
 InputImage 
 . 
 fromByteArray 
 ( 
  
 byteArray 
 , 
  
 /* image width */ 
 480 
 , 
  
 /* image height */ 
 360 
 , 
  
 rotation 
 , 
  
 InputImage 
 . 
 IMAGE_FORMAT_NV21 
  
 // or IMAGE_FORMAT_YV12 
 ); 
   MLKitVisionImage 
 . 
 java

Using a `Bitmap`

To create an InputImage object from a Bitmap object, make the following declaration:

Kotlin

 val 
  
 image 
  
 = 
  
 InputImage 
 . 
 fromBitmap 
 ( 
 bitmap 
 , 
  
 0 
 ) 
   MLKitVisionImage 
 . 
 kt

Java

 InputImage 
  
 image 
  
 = 
  
 InputImage 
 . 
 fromBitmap 
 ( 
 bitmap 
 , 
  
 rotationDegree 
 ); 
   MLKitVisionImage 
 . 
 java

The image is represented by a Bitmap object together with rotation degrees.

3. Run the image labeler

To label objects in an image, pass the image object to the ImageLabeler 's process() method.

Kotlin

 labeler 
 . 
 process 
 ( 
 image 
 ) 
  
 . 
 addOnSuccessListener 
  
 { 
  
 labels 
  
 - 
>  
 // Task completed successfully 
  
 // ... 
  
 } 
  
 . 
 addOnFailureListener 
  
 { 
  
 e 
  
 - 
>  
 // Task failed with an exception 
  
 // ... 
  
 } 
   ImageLabelingActivity 
 . 
 kt

Java

 labeler 
 . 
 process 
 ( 
 image 
 ) 
  
 . 
 addOnSuccessListener 
 ( 
 new 
  
 OnSuccessListener<List<ImageLabel> 
> () 
  
 { 
  
 @Override 
  
 public 
  
 void 
  
 onSuccess 
 ( 
 List<ImageLabel> 
  
 labels 
 ) 
  
 { 
  
 // Task completed successfully 
  
 // ... 
  
 } 
  
 }) 
  
 . 
 addOnFailureListener 
 ( 
 new 
  
 OnFailureListener 
 () 
  
 { 
  
 @Override 
  
 public 
  
 void 
  
 onFailure 
 ( 
 @NonNull 
  
 Exception 
  
 e 
 ) 
  
 { 
  
 // Task failed with an exception 
  
 // ... 
  
 } 
  
 }); 
   ImageLabelingActivity 
 . 
 java

Note: If you are using the CameraX API, make sure to close the ImageProxy when finish using it, e.g., by adding an OnCompleteListener to the Task returned from the process method. See the VisionProcessorBase class in the quickstart sample app for an example.

4. Get information about labeled objects

If the image labeling operation succeeds, a list of ImageLabel objects is passed to the success listener. Each ImageLabel object represents something that was labeled in the image. You can get each label's text description, the confidence score of the match and the index of the match. For example:

Kotlin

 for 
  
 ( 
 label 
  
 in 
  
 labels 
 ) 
  
 { 
  
 val 
  
 text 
  
 = 
  
 label 
 . 
 text 
  
 val 
  
 confidence 
  
 = 
  
 label 
 . 
 confidence 
  
 val 
  
 index 
  
 = 
  
 label 
 . 
 index 
 } 
   ImageLabelingActivity 
 . 
 kt

Java

 for 
  
 ( 
 ImageLabel 
  
 label 
  
 : 
  
 labels 
 ) 
  
 { 
  
 String 
  
 text 
  
 = 
  
 label 
 . 
 getText 
 (); 
  
 float 
  
 confidence 
  
 = 
  
 label 
 . 
 getConfidence 
 (); 
  
 int 
  
 index 
  
 = 
  
 label 
 . 
 getIndex 
 (); 
 } 
   ImageLabelingActivity 
 . 
 java

Tips to improve real-time performance

If you want to label images in a real-time application, follow these guidelines to achieve the best framerates:

If you use the Camera or camera2 API, throttle calls to the image labeler. If a new video frame becomes available while the image labeler is running, drop the frame. See the VisionProcessorBase class in the quickstart sample app for an example.
If you use the CameraX API, be sure that backpressure strategy is set to its default value ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST . This guarantees only one image will be delivered for analysis at a time. If more images are produced when the analyzer is busy, they will be dropped automatically and not queued for delivery. Once the image being analyzed is closed by calling ImageProxy.close(), the next latest image will be delivered.
If you use the output of the image labeler to overlay graphics on the input image, first get the result from ML Kit, then render the image and overlay in a single step. This renders to the display surface only once for each input frame. See the CameraSourcePreview and GraphicOverlay classes in the quickstart sample app for an example.
If you use the Camera2 API, capture images in ImageFormat.YUV_420_888 format. If you use the older Camera API, capture images in ImageFormat.NV21 format.

Label images with an AutoML-trained model on Android

Try it out

Before you begin

1. Load the model

Configure a local model source

Kotlin

Java

Configure a Firebase-hosted model source

Kotlin

Java

Kotlin

Java

Create an image labeler from your model

Kotlin

Java

Kotlin

Java

Kotlin

Java

2. Prepare the input image

Using a media.Image

Kotlin

Java

Kotlin

Java

Kotlin

Java

Using a file URI

Kotlin

Java

Using a ByteBuffer or ByteArray

Kotlin

Java

Using a Bitmap

Kotlin

Java

3. Run the image labeler

Kotlin

Java

4. Get information about labeled objects

Kotlin

Java

Tips to improve real-time performance

Using a `media.Image`

Using a `ByteBuffer` or `ByteArray`

Using a `Bitmap`