You can use ML Kit to recognize entities in an image and label them. This API supports a wide range of custom image classification models. Please refer to Custom models with ML Kit for guidance on model compatibility requirements, where to find pre-trained models, and how to train your own models.
There are two ways to integrate a custom model. You can bundle the model by putting it inside your app’s asset folder, or you can dynamically download it from Firebase. The following table compares the two options.
| Bundled Model | Hosted Model |
|---|---|
| The model is part of your app's APK, which increases its size. | The model is not part your APK. It is hosted by uploading to Firebase Machine Learning . |
| The model is available immediately, even when the Android device is offline | The model is downloaded on demand |
| No need for a Firebase project | Requires a Firebase project |
| You must republish your app to update the model | Push model updates without republishing your app |
| No built-in A/B testing | Easy A/B testing with Firebase Remote Config |
Try it out
- See the vision quickstart app for an example usage of the bundled model and the automl quickstart app for an example usage of the hosted model.
Before you begin
-
Include the ML Kit libraries in your Podfile:
For bundling a model with your app:
pod 'GoogleMLKit/ImageLabelingCustom', '8.0.0'For dynamically downloading a model from Firebase, add the
LinkFirebasedependency:pod 'GoogleMLKit/ImageLabelingCustom', '8.0.0' pod 'GoogleMLKit/LinkFirebase', '8.0.0' -
After you install or update your project's Pods, open your Xcode project using its
.xcworkspace. ML Kit is supported in Xcode version 13.2.1 or higher. -
If you want to download a model, make sure you add Firebase to your iOS 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:
-
Copy the model file (usually ending in
.tfliteor.lite) to your Xcode project, taking care to selectCopy bundle resourceswhen you do so. The model file will be included in the app bundle and available to ML Kit. -
Create
LocalModelobject, specifying the path to the model file:Swift
let localModel = LocalModel ( path : localModelFilePath )
Objective-C
MLKLocalModel * localModel = [[ MLKLocalModel alloc ] initWithPath : localModelFilePath ];
Configure a Firebase-hosted model source
To use the remotely-hosted model, create an RemoteModel
object, specifying the
name you assigned the model when you published it:
Swift
let firebaseModelSource = FirebaseModelSource ( name : "your_remote_model" ) // The name you assigned in // the Firebase console. let remoteModel = CustomRemoteModel ( remoteModelSource : firebaseModelSource )
Objective-C
MLKFirebaseModelSource * firebaseModelSource = [[ MLKFirebaseModelSource alloc ] initWithName : @"your_remote_model" ]; // The name you assigned in // the Firebase console. MLKCustomRemoteModel * remoteModel = [[ MLKCustomRemoteModel alloc ] initWithRemoteModelSource : firebaseModelSource ];
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:
Swift
let downloadConditions = ModelDownloadConditions ( allowsCellularAccess : true , allowsBackgroundDownloading : true ) let downloadProgress = ModelManager . modelManager (). download ( remoteModel , conditions : downloadConditions )
Objective-C
MLKModelDownloadConditions * downloadConditions = [[ MLKModelDownloadConditions alloc ] initWithAllowsCellularAccess : YES allowsBackgroundDownloading : YES ]; NSProgress * downloadProgress = [[ MLKModelManager modelManager ] downloadModel : remoteModel conditions : downloadConditions ];
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.
Configure the image labeler
After you configure your model sources, create an ImageLabeler
object from one
of them.
The following options are available:
confidenceThreshold
Minimum confidence score of detected labels. If not set, any classifier threshold specified by the model’s metadata will be used. If the model does not contain any metadata or the metadata does not specify a classifier threshold, a default threshold of 0.0 will be used.
maxResultCount
Maximum number of labels to return. If not set, the default value of 10 will be used.
If you only have a locally-bundled model, just create a labeler from your LocalModel
object:
Swift
let options = CustomImageLabelerOptions ( localModel : localModel ) options . confidenceThreshold = NSNumber ( value : 0.0 ) let imageLabeler = ImageLabeler . imageLabeler ( options : options )
Objective-C
MLKCustomImageLabelerOptions * options = [[ MLKCustomImageLabelerOptions alloc ] initWithLocalModel : localModel ]; options . confidenceThreshold = @( 0.0 ) ; MLKImageLabeler * imageLabeler = [ MLKImageLabeler imageLabelerWithOptions : options ];
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(remoteModel:)
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 ImageLabeler
: create a
labeler from the remote model if it's been downloaded, and from the local model
otherwise.
Swift
var options : CustomImageLabelerOptions ! if ( ModelManager . modelManager (). isModelDownloaded ( remoteModel )) { options = CustomImageLabelerOptions ( remoteModel : remoteModel ) } else { options = CustomImageLabelerOptions ( localModel : localModel ) } options . confidenceThreshold = NSNumber ( value : 0.0 ) let imageLabeler = ImageLabeler . imageLabeler ( options : options )
Objective-C
MLKCustomImageLabelerOptions * options ; if ([[ MLKModelManager modelManager ] isModelDownloaded : remoteModel ]) { options = [[ MLKCustomImageLabelerOptions alloc ] initWithRemoteModel : remoteModel ]; } else { options = [[ MLKCustomImageLabelerOptions alloc ] initWithLocalModel : localModel ]; } options . confidenceThreshold = @( 0.0 ) ; MLKImageLabeler * imageLabeler = [ MLKImageLabeler imageLabelerWithOptions : options ];
If you only have a remotely-hosted model, you should disable model-related functionality—for example, gray-out or hide part of your UI—until you confirm the model has been downloaded.
You can get the model download status by attaching observers to the default
Notification Center. Be sure to use a weak reference to self
in the observer
block, since downloads can take some time, and the originating object can be
freed by the time the download finishes. For example:
Swift
NotificationCenter . default . addObserver ( forName : . mlkitModelDownloadDidSucceed , object : nil , queue : nil ) { [ weak self ] notification in guard let strongSelf = self , let userInfo = notification . userInfo , let model = userInfo [ ModelDownloadUserInfoKey . remoteModel . rawValue ] as ? RemoteModel , model . name == "your_remote_model" else { return } // The model was downloaded and is available on the device } NotificationCenter . default . addObserver ( forName : . mlkitModelDownloadDidFail , object : nil , queue : nil ) { [ weak self ] notification in guard let strongSelf = self , let userInfo = notification . userInfo , let model = userInfo [ ModelDownloadUserInfoKey . remoteModel . rawValue ] as ? RemoteModel else { return } let error = userInfo [ ModelDownloadUserInfoKey . error . rawValue ] // ... }
Objective-C
__weak typeof ( self ) weakSelf = self ; [ NSNotificationCenter . defaultCenter addObserverForName : MLKModelDownloadDidSucceedNotification object : nil queue : nil usingBlock : ^ ( NSNotification * _Nonnull note ) { if ( weakSelf == nil | note . userInfo == nil ) { return ; } __strong typeof ( self ) strongSelf = weakSelf ; MLKRemoteModel * model = note . userInfo [ MLKModelDownloadUserInfoKeyRemoteModel ]; if ([ model . name isEqualToString : @"your_remote_model" ]) { // The model was downloaded and is available on the device } }]; [ NSNotificationCenter . defaultCenter addObserverForName : MLKModelDownloadDidFailNotification object : nil queue : nil usingBlock : ^ ( NSNotification * _Nonnull note ) { if ( weakSelf == nil | note . userInfo == nil ) { return ; } __strong typeof ( self ) strongSelf = weakSelf ; NSError * error = note . userInfo [ MLKModelDownloadUserInfoKeyError ]; }];
2. Prepare the input image
Create a VisionImage
object using a UIImage
or a CMSampleBuffer
.
If you use a UIImage
, follow these steps:
- Create a
VisionImageobject with theUIImage. Make sure to specify the correct.orientation.Swift
let image = VisionImage(image: UIImage) visionImage.orientation = image.imageOrientation
Objective-C
MLKVisionImage * visionImage = [[ MLKVisionImage alloc ] initWithImage : image ]; visionImage . orientation = image . imageOrientation ;
If you use a CMSampleBuffer
, follow these steps:
-
Specify the orientation of the image data contained in the
CMSampleBuffer.To get the image orientation:
Swift
func imageOrientation ( deviceOrientation : UIDeviceOrientation , cameraPosition : AVCaptureDevice . Position ) -> UIImage . Orientation { switch deviceOrientation { case . portrait : return cameraPosition == . front ? . leftMirrored : . right case . landscapeLeft : return cameraPosition == . front ? . downMirrored : . up case . portraitUpsideDown : return cameraPosition == . front ? . rightMirrored : . left case . landscapeRight : return cameraPosition == . front ? . upMirrored : . down case . faceDown , . faceUp , . unknown : return . up } }
Objective-C
- ( UIImageOrientation ) imageOrientationFromDeviceOrientation :( UIDeviceOrientation ) deviceOrientation cameraPosition :( AVCaptureDevicePosition ) cameraPosition { switch (deviceOrientation) { case UIDeviceOrientationPortrait : return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationLeftMirrored : UIImageOrientationRight ; case UIDeviceOrientationLandscapeLeft : return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationDownMirrored : UIImageOrientationUp ; case UIDeviceOrientationPortraitUpsideDown : return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationRightMirrored : UIImageOrientationLeft ; case UIDeviceOrientationLandscapeRight : return cameraPosition == AVCaptureDevicePositionFront ? UIImageOrientationUpMirrored : UIImageOrientationDown ; case UIDeviceOrientationUnknown : case UIDeviceOrientationFaceUp : case UIDeviceOrientationFaceDown : return UIImageOrientationUp ; } }
- Create a
VisionImageobject using theCMSampleBufferobject and orientation:Swift
let image = VisionImage ( buffer : sampleBuffer ) image . orientation = imageOrientation ( deviceOrientation : UIDevice . current . orientation , cameraPosition : cameraPosition )
Objective-C
MLKVisionImage * image = [[ MLKVisionImage alloc ] initWithBuffer : sampleBuffer ]; image . orientation = [ self imageOrientationFromDeviceOrientation : UIDevice . currentDevice . orientation cameraPosition : cameraPosition ];
3. Run the image labeler
To label objects in an image, pass the image
object to the ImageLabeler
's process()
method.
Asynchronously:
Swift
imageLabeler . process ( image ) { labels , error in guard error == nil , let labels = labels , ! labels . isEmpty else { // Handle the error. return } // Show results. }
Objective-C
[ imageLabeler processImage : image completion : ^ ( NSArray* _Nullable labels , NSError * _Nullable error ) { if ( label . count == 0 ) { // Handle the error. return ; } // Show results. }];
Synchronously:
Swift
var labels : [ ImageLabel ] do { labels = try imageLabeler . results ( in : image ) } catch let error { // Handle the error. return } // Show results.
Objective-C
NSError * error ; NSArray* labels = [ imageLabeler resultsInImage : image error :& error ]; // Show results or handle the error.
4. Get information about labeled entities
If the image labeling operation succeeds, it returns an array ofImageLabel
. Each ImageLabel
represents something that was
labeled in the image. You can get each label's text description (if available in
the metadata of the TensorFlow Lite model file), confidence score, and index.
For example: Swift
for label in labels { let labelText = label . text let confidence = label . confidence let index = label . index }
Objective-C
for ( MLKImageLabel * label in labels ) { NSString * labelText = label . text ; float confidence = label . confidence ; NSInteger index = label . index ; }
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:
- For processing video frames, use the
results(in:)synchronous API of the detector. Call this method from theAVCaptureVideoDataOutputSampleBufferDelegate'scaptureOutput(_, didOutput:from:)function to synchronously get results from the given video frame. KeepAVCaptureVideoDataOutput'salwaysDiscardsLateVideoFramesastrueto throttle calls to the detector. If a new video frame becomes available while the detector is running, it will be dropped. - If you use the output of the detector to overlay graphics on the input image, first get the result from ML Kit, then render the image and overlay in a single step. By doing so, you render to the display surface only once for each processed input frame. See the updatePreviewOverlayViewWithLastFrame in the ML Kit quickstart sample for an example.
