Image Labeling on iOS


If you haven’t set up the SDK yet, make sure to go through those directions first. You’ll need to add the Core library to the app before using the specific feature API or custom model. Follow iOS setup or Android setup directions.

You can use the FritzVisionLabelModel to label the contents of images. Fritz provides a variety of options to configure predictions.

1. Build the FritzVisionLabelModel

To create the label model, you can either include the model in your bundle or download it over the air once the user installs your app.

Include the model in your application bundle

Add the model to your Podfile

Include Fritz/VisionLabelModel in your Podfile. This will include the model file in your app bundle.

pod 'Fritz/VisionLabelModel/Fast'

Make sure to install the recent addition.

pod install


If you’ve built the app with just the core Fritz pod and add a new submodule for the model, you may encounter an error “Cannot invoke initializer for type”. To fix this, run a pod update and clean your XCode build to resolve the issue.

Define FritzVisionLabelModelFast

Define the instance of the FritzVisionLabelModelFast in your code. There should only be one instance that is reused for each prediction.

import Fritz

let labelModel = FritzVisionLabelModelFast()
@import Fritz;

FritzVisionLabelModelFast *labelModel = [FritzVisionLabelModelFastObjc model];


Model initialization

It’s important to intialize one instance of the model so you are not loading the entire model into memory on each model execution. Usually this is a property on a ViewController. When loading the model in a ViewController, the following ways are recommended:

Lazy-load the model

By lazy-loading model, you won’t load the model until the first prediction. This has the benefit of not prematurely loading the model, but it may make the first prediction take slghtly longer.

class MyViewController: UIViewController {
  lazy var model = FritzVisionHumanPoseModelFast()

Load model in viewDidLoad

By loading the model in viewDidLoad, you’ll ensure that you’re not loading the model before the view controller is loaded. The model will be ready to go for the first prediction.

class MyViewController: UIViewController {
  let model: FritzVisionHumanPoseModelFast!

  override func viewDidAppear(_ animated: Bool) {
    model = FritzVisionHumanPoseModelFast()

Alternatively, you can initialize the model property directly. However, if the ViewController is instantiated by a Storyboard and is the Initial View Controller, the properties will be initialized before the appDelegate function is called. This can cause the app to crash if the model is loaded before FritzCore.configure() is called.

Download the model over the air


Over-the-air model downloads are not included on certain subscription plans. For more information on plans and pricing, visit our website.

Add FritzVision to your Podfile

Include Fritz/Vision in your Podfile.

pod 'Fritz/Vision'

Make sure to run a pod install with the latest changes.

pod install

Download Model

import Fritz

var labelModel: FritzVisionLabelModelFast?

FritzVisionLabelModelFast.fetchModel { model, error in
   guard let downloadedModel = model, error == nil else { return }

   labelModel = downloadedModel
@import Fritz;

[FritzVisionLabelModelFast fetchModelWithCompletionHandler:^(FritzVisionLabelModelFast * _Nullable model, NSError * _Nullable error) {
    // Use downloaded label model

2. Create FritzVisionImage

FritzVisionImage supports different image formats.

  • Using a CMSampleBuffer

    If you are using a CMSampleBuffer from the built-in camera, first create the FritzVisionImage instance:

    let image = FritzVisionImage(buffer: sampleBuffer)
    FritzVisionImage *visionImage = [[FritzVisionImage alloc] initWithBuffer: sampleBuffer];
    // or
    FritzVisionImage *visionImage = [[FritzVisionImage alloc] initWithImage: uiImage];

    The image orientation data needs to be properly set for predictions to work. Use FritzImageMetadata to customize orientation for an image. By default, if you specify FritzVisionImageMetadata the orientation will be .right:

    image.metadata = FritzVisionImageMetadata()
    image.metadata?.orientation = .left
    // Add metdata
    visionImage.metadata = [FritzVisionImageMetadata new];
    visionImage.metadata.orientation = FritzImageOrientationLeft;


    Data passed in from the camera will generally need the orientation set. When using a CMSampleBuffer to create a FritzVisionImage the orientation will change depending on which camera and device orientation you are using.

    When using the back camera in the portrait Device Orientation, the orientation should be .right (the default if you specify FritzVisionImageMetadata on the image). When using the front facing camera in portrait Device Orientation, the orientation should be .left.

    You can initialize the FritzImageOrientation with the AVCaptureConnection to infer orientation (if the Device Orientation is portrait):

    func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) {
        let image = FritzVisionImage(sampleBuffer: sampleBuffer, connection: connection)
  • Using an UIImage

    If you are using an UIImage, create the FritzVisionImage instance:

    let image = FritzVisionImage(image: uiImage)

    The image orientation data needs to be properly set for predictions to work. Use FritzImageMetadata to customize orientation for an image:

    image.metadata = FritzVisionImageMetadata()
    image.metadata?.orientation = .right


    UIImage can have associated UIImageOrientation data (for example when capturing a photo from the camera). To make sure the model is correctly handling the orientation data, initialize the FritzImageOrientation with the image’s image orientation:

    image.metadata?.orientation = FritzImageOrientation(image.imageOrientation)

3. Run image labeling

Run Image Labeling Model

Use the labelModel instance you created earlier to run predictions:

guard let results = try? labelModel.predict(image) else { return }
FritzVisionLabelModelOptions* options = [FritzVisionLabelModelOptions new];
[labelModel predictWithImage:visionImage options:options completion:^(NSArray<FritzVisionLabel* > * _Nullable result, NSError *error) {

  // Code to work with labels here

Configure Label Prediction

Before running image labeling, you can configure the prediction with a FritzVisionLabelModelOptions object.


.scaleFit (default)

Crop and Scale option for how to resize and crop the image for the model


0.6 (default)

Confidence threshold for prediction results in the range of [0, 1].


15 (default)

Maxiumum number of results to return from prediction.

For example, to build a more lenient FritzVisionLabelModelOptions object:

let options = FritzVisionLabelModelOptions()
options.threshold = 0.3
options.numResults = 2

guard let results = try? labelModel.predict(image, options: options) else { return }
FritzVisionLabelModelOptions* options = [FritzVisionLabelModelOptions new];
options.threshold = 0.3;
options.numResults = 2;

[labelModel predictWithImage:image options:options completion:^(NSArray<FritzVisionLabel* > * _Nullable result, NSError *error) {
  // Code to work with labels here

4. Get labels in image

Once you have an array of FritzVisionLabel you can use them to access the image classifications.

 // Created from model prediction.
let labels: [FritzVisionLabel]

// Print highest confidence result
NSArray<FritzVisionLabel*> * labels;

// Access highest confidence result

5. Use the record method on the predictor to collect data

The FritzVisionLabelPredictor used to make predictions has a record method allowing you to send an image, a model-predicted annotation, and a user-generated annotation back to your Fritz AI account.

guard let results = try? labelModel.predict(image, options: options),

// Implement your own custom UX for users to label an image and store
// that as a list of [FritzVisionLabel].
labelModel.record(image, predicted: results, modified: modifiedLabels)