Detect, track and classify objects with a custom classification model on iOS

You can use ML Kit to detect and track objects in successive video frames.

When you pass an image to ML Kit, it detects up to five objects in the image along with the position of each object in the image. When detecting objects in video streams, each object has a unique ID that you can use to track the object from frame to frame.

You can use a custom image classification model to classify the objects that are detected. 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 `.ipa` file, which increases its size.	The model is not part of your app's `.ipa` file. 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.
See the Material Design showcase app for an end-to-end implementation of this API.

Before you begin

Include the ML Kit libraries in your Podfile:

For bundling a model with your app:
```
pod 'GoogleMLKit/ObjectDetectionCustom', '7.0.0'
```
For dynamically downloading a model from Firebase, add the LinkFirebase dependency:
```
pod 'GoogleMLKit/ObjectDetectionCustom', '7.0.0'
pod 'GoogleMLKit/LinkFirebase', '7.0.0'
```
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 .tflite or .lite) to your Xcode project, taking care to select Copy bundle resources when you do so. The model file will be included in the app bundle and available to ML Kit.

Create LocalModel object, 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 CustomRemoteModel 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.

2. Configure the object detector

After you configure your model sources, configure the object detector for your use case with a CustomObjectDetectorOptions object. You can change the following settings:

Object Detector Settings
Detection mode	`STREAM_MODE` (default) \| `SINGLE_IMAGE_MODE` In `STREAM_MODE` (default), the object detector runs with low latency, but might produce incomplete results (such as unspecified bounding boxes or category labels) on the first few invocations of the detector. Also, in `STREAM_MODE`, the detector assigns tracking IDs to objects, which you can use to track objects across frames. Use this mode when you want to track objects, or when low latency is important, such as when processing video streams in real time. In `SINGLE_IMAGE_MODE`, the object detector returns the result after the object's bounding box is determined. If you also enable classification it returns the result after the bounding box and category label are both available. As a consequence, detection latency is potentially higher. Also, in `SINGLE_IMAGE_MODE`, tracking IDs are not assigned. Use this mode if latency isn't critical and you don't want to deal with partial results.
Detect and track multiple objects	`false` (default) \| `true` Whether to detect and track up to five objects or only the most prominent object (default).
Classify objects	`false` (default) \| `true` Whether or not to classify detected objects by using the provided custom classifier model. To use your custom classification model, you need to set this to `true`.
Classification confidence threshold	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.
Maximum labels per object	Maximum number of labels per object that the detector will return. If not set, the default value of 10 will be used.

If you only have a locally-bundled model, just create an object detector from your LocalModel object:

Swift

let options = CustomObjectDetectorOptions(localModel: localModel)
options.detectorMode = .singleImage
options.shouldEnableClassification = true
options.shouldEnableMultipleObjects = true
options.classificationConfidenceThreshold = NSNumber(value: 0.5)
options.maxPerObjectLabelCount = 3

Objective-C

MLKCustomObjectDetectorOptions *options =
    [[MLKCustomObjectDetectorOptions alloc] initWithLocalModel:localModel];
options.detectorMode = MLKObjectDetectorModeSingleImage;
options.shouldEnableClassification = YES;
options.shouldEnableMultipleObjects = YES;
options.classificationConfidenceThreshold = @(0.5);
options.maxPerObjectLabelCount = 3;

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 object detector, if you have both a remotely-hosted model and a locally-bundled model, it might make sense to perform this check when instantiating the ObjectDetector: create a detector from the remote model if it's been downloaded, and from the local model otherwise.

Swift

var options: CustomObjectDetectorOptions!
if (ModelManager.modelManager().isModelDownloaded(remoteModel)) {
  options = CustomObjectDetectorOptions(remoteModel: remoteModel)
} else {
  options = CustomObjectDetectorOptions(localModel: localModel)
}
options.detectorMode = .singleImage
options.shouldEnableClassification = true
options.shouldEnableMultipleObjects = true
options.classificationConfidenceThreshold = NSNumber(value: 0.5)
options.maxPerObjectLabelCount = 3

Objective-C

MLKCustomObjectDetectorOptions *options;
if ([[MLKModelManager modelManager] isModelDownloaded:remoteModel]) {
  options = [[MLKCustomObjectDetectorOptions alloc] initWithRemoteModel:remoteModel];
} else {
  options = [[MLKCustomObjectDetectorOptions alloc] initWithLocalModel:localModel];
}
options.detectorMode = MLKObjectDetectorModeSingleImage;
options.shouldEnableClassification = YES;
options.shouldEnableMultipleObjects = YES;
options.classificationConfidenceThreshold = @(0.5);
options.maxPerObjectLabelCount = 3;

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];
            }];

The object detection and tracking API is optimized for these two core use cases:

Live detection and tracking of the most prominent object in the camera viewfinder.
The detection of multiple objects from a static image.

To configure the API for these use cases:

Swift

// Live detection and tracking
let options = CustomObjectDetectorOptions(localModel: localModel)
options.shouldEnableClassification = true
options.maxPerObjectLabelCount = 3

// Multiple object detection in static images
let options = CustomObjectDetectorOptions(localModel: localModel)
options.detectorMode = .singleImage
options.shouldEnableMultipleObjects = true
options.shouldEnableClassification = true
options.maxPerObjectLabelCount = 3

Objective-C

// Live detection and tracking
MLKCustomObjectDetectorOptions *options =
    [[MLKCustomObjectDetectorOptions alloc] initWithLocalModel:localModel];
options.shouldEnableClassification = YES;
options.maxPerObjectLabelCount = 3;

// Multiple object detection in static images
MLKCustomObjectDetectorOptions *options =
    [[MLKCustomObjectDetectorOptions alloc] initWithLocalModel:localModel];
options.detectorMode = MLKObjectDetectorModeSingleImage;
options.shouldEnableMultipleObjects = YES;
options.shouldEnableClassification = YES;
options.maxPerObjectLabelCount = 3;

3. Prepare the input image

Create a VisionImage object using a UIImage or a CMSampleBuffer.

If you use a UIImage, follow these steps:

Create a VisionImage object with the UIImage. 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 VisionImage object using the CMSampleBuffer object 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];

4. Create and run the object detector

Create a new object detector:

Swift

let objectDetector = ObjectDetector.objectDetector(options: options)

Objective-C

MLKObjectDetector *objectDetector = [MLKObjectDetector objectDetectorWithOptions:options];

Then, use the detector:

Asynchronously:

Swift

objectDetector.process(image) { objects, error in
    guard error == nil, let objects = objects, !objects.isEmpty else {
        // Handle the error.
        return
    }
    // Show results.
}

Objective-C

[objectDetector
    processImage:image
      completion:^(NSArray *_Nullable objects,
                   NSError *_Nullable error) {
        if (objects.count == 0) {
            // Handle the error.
            return;
        }
        // Show results.
     }];

Synchronously:

Swift

var objects: [Object]
do {
    objects = try objectDetector.results(in: image)
} catch let error {
    // Handle the error.
    return
}
// Show results.

Objective-C

NSError *error;
NSArray *objects =
    [objectDetector resultsInImage:image error:&error];
// Show results or handle the error.

5. Get information about labeled objects

If the call to the image processor succeeds, it either passes a list of Objects to the completion handler or returns the list, depending on whether you called the asynchronous or synchronous method.

Each Object contains the following properties:

frame A CGRect indicating the position of the object in the image.

trackingID An integer that identifies the object across images, or `nil` in SINGLE_IMAGE_MODE.

labels

`label.text`	The label's text description. Only returned if the TensorFlow Lite model's metadata contains label descriptions.
`label.index`	The label's index among all the labels supported by the classifier.
`label.confidence`	The confidence value of the object classification.

Swift

// objects contains one item if multiple object detection wasn't enabled.
for object in objects {
  let frame = object.frame
  let trackingID = object.trackingID
  let description = object.labels.enumerated().map { (index, label) in
    "Label \(index): \(label.text), \(label.confidence), \(label.index)"
  }.joined(separator: "\n")
}

Objective-C

// The list of detected objects contains one item if multiple object detection
// wasn't enabled.
for (MLKObject *object in objects) {
  CGRect frame = object.frame;
  NSNumber *trackingID = object.trackingID;
  for (MLKObjectLabel *label in object.labels) {
    NSString *labelString =
        [NSString stringWithFormat:@"%@, %f, %lu",
                                   label.text,
                                   label.confidence,
                                   (unsigned long)label.index];
  }
}

Ensuring a great user experience

For the best user experience, follow these guidelines in your app:

Successful object detection depends on the object's visual complexity. In order to be detected, objects with a small number of visual features might need to take up a larger part of the image. You should provide users with guidance on capturing input that works well with the kind of objects you want to detect.
When you use classification, if you want to detect objects that don't fall cleanly into the supported categories, implement special handling for unknown objects.

Also, check out the [ML Kit Material Design showcase app][showcase-link]{: .external } and the Material Design Patterns for machine learning-powered features collection.

Improving performance

If you want to use object detection in a real-time application, follow these guidelines to achieve the best framerates:

When you use streaming mode in a real-time application, don't use multiple object detection, as most devices won't be able to produce adequate framerates.
For processing video frames, use the results(in:) synchronous API of the detector. Call this method from the AVCaptureVideoDataOutputSampleBufferDelegate's captureOutput(_, didOutput:from:) function to synchronously get results from the given video frame. Keep AVCaptureVideoDataOutput's alwaysDiscardsLateVideoFrames as true to 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.