Migrating for iOS

Page Summary

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• Migrate your iOS app to the standalone ML Kit SDK by updating CocoaPods dependencies, renaming classes and methods, and addressing API-specific changes.

• Ensure your project meets the prerequisites, including Xcode 13.2.1 or later, iOS 15.5 or later, 64-bit architecture, and CocoaPods usage.

• Consult the provided tables for specific class, enum, type, and method name changes in Swift and Objective-C.

• Consider removing Firebase dependencies if you solely utilize on-device ML Kit APIs and don't use model serving or other Firebase components.

• Refer to the Community page for support and troubleshooting assistance during the migration process.

This guide explains how to migrate away from ML Kit for Firebase for iOS.

Prerequisites

Before you start to migrate your code, be sure you meet these requirements:

• ML Kit supports Xcode 13.2.1 or greater.
• ML Kit supports iOS version 15.5 or greater.
• ML Kit does not support 32-bit architectures (i386 and armv7). ML Kit does support 64-bit architectures (x86_64 and arm64).
• The ML Kit library is only provided as cocoapods. You can't mix frameworks and cocoapods, so in order to use this library you need to first migrate to use cocoapods.

Update Cocoapods

Update the dependencies for the ML Kit iOS cocoapods in your app's Podfile:

Update names of classes, enums, and types

In general, classes, enums, and types need to be renamed as follows:

• Swift: Remove the Vision prefix from class names and enums
• Objective-C: Replace both FIRVision and FIR class name and enum prefixes by MLK

For some class names and types this general rule does not apply:

Update method names

Update method names according to these rules:

• Domain entry point classes (Vision, NaturalLanguage) no longer exist. They have been replaced by task specific classes. Replace calls to their various factory methods for getting detectors with direct calls to each detector's factory method.

• The VisionImageMetadata class has been removed, along with the VisionDetectorImageOrientation enum. Use the orientation property of VisionImage to specify the display orientation of an image.

• The onDeviceTextRecognizer method that gets a new TextRecognizer instance has been renamed to textRecognizer.

• The confidence property has been removed from text recognition result classes, including TextElement, TextLine, and TextBlock.

• The onDeviceImageLabeler and onDeviceImageLabeler(options:) methods to get a new ImageLabeler instance have been merged and renamed to imageLabeler(options:).

• The objectDetector method to get a new ObjectDetector instance has been removed. Use objectDetector(options:) instead.

• The type property has been removed from ImageLabeler and the entityID property has been removed from the image labeling result class, ImageLabel.

• The barcode scanning API detect(in _:, completion:) has been renamed to process(_:, completion:) to be consistent with other vision APIs.

• The Natural Language APIs now use the term "language tag" (as defined by the BCP-47 standard) instead of "language code".

• TranslateLanguage now uses readable names (like .english) for its constants instead of language tags (like .en).

Here are some examples of old and new Swift methods:

let options = VisionOnDeviceImageLabelerOptions()
options.confidenceThreshold = 0.75
let labeler = Vision.vision().onDeviceImageLabeler(options: options)

let detector = Vision.vision().faceDetector(options: options)

let localModel = AutoMLLocalModel(manifestPath: "automl/manifest.json")
let options = VisionOnDeviceAutoMLImageLabelerOptions(localModel: localModel)
options.confidenceThreshold = 0.75
let labeler = vision.onDeviceAutoMLImageLabeler(options: options)

let detector = Vision.vision().objectDetector()
    

let options = ImageLabelerOptions()
options.confidenceThreshold = NSNumber(value:0.75)
let labeler = ImageLabeler.imageLabeler(options: options)

let detector = FaceDetector.faceDetector(options: options)

let localModel = LocalModel(manifestPath: "automl/manifest.json")
let options = CustomImageLabelerOptions(localModel: localModel)
options.confidenceThreshold = NSNumber(value:0.75)
let labeler = ImageLabeler.imageLabeler(options: options)

let detector = ObjectDetector.objectDetector(options: ObjectDetectorOptions())
    

Here are some examples of old and new Objective-C methods:

FIRVisionOnDeviceImageLabelerOptions *options =
    [[FIRVisionOnDeviceImageLabelerOptions alloc] init];
options.confidenceThreshold = 0.75;
FIRVisionImageLabeler *labeler =
    [[FIRVision vision] onDeviceImageLabelerWithOptions:options];

FIRVisionFaceDetector *detector =
    [[FIRVision vision] faceDetectorWithOptions: options];

FIRAutoMLLocalModel *localModel =
    [[FIRAutoMLLocalModel alloc] initWithManifestPath:@"automl/manifest.json"];
FIRVisionOnDeviceAutoMLImageLabelerOptions *options =
    [[FIRVisionOnDeviceAutoMLImageLabelerOptions alloc]
        initWithLocalModel: localModel];
options.confidenceThreshold = 0.75
FIRVisionImageLabeler *labeler =
    [[FIRVision vision] onDeviceAutoMLImageLabelerWithOptions:options];

FIRVisionObjectDetector *detector =
    [[FIRVision vision] objectDetector];
    

MLKImageLabelerOptions *options =
    [[MLKImageLabelerOptions alloc] init];
options.confidenceThreshold = @(0.75);
MLKImageLabeler *labeler =
    [MLKImageLabeler imageLabelerWithOptions:options];

MLKFaceDetector *detector =
    [MLKFaceDetector faceDetectorWithOptions:options];

MLKLocalModel *localModel =
    [[MLKLocalModel alloc]
        initWithManifestPath:@"automl/manifest.json"];
MLKCustomImageLabelerOptions *options =
    [[MLKCustomImageLabelerOptions alloc] initWithLocalModel:localModel];
options.confidenceThreshold = @(0.75)
MLKImageLabeler *labeler =
    [MLKImageLabeler imageLabelerWithOptions:options];

MLKObjectDetectorOptions *options = [[MLKObjectDetectorOptions alloc] init];
MLKObjectDetector *detector = [MLKObjectDetector objectDetectorWithOptions:options];
    

API-specific changes

Object detection and tracking

If your app uses object classification, be aware that the new SDK has changed the way it returns the classification category for detected objects.

VisionObjectCategory in VisionObject is returned as text in an ObjectLabel object, instead of an integer. All possible string categories are included in the DetectedObjectLabel enum.

Note that the .unknown category has been removed. When the confidence of classifying an object is low, the classifier returns no label at all.

Here is an example of the old and new Swift code:

if (object.classificationCategory == .food) {
    ...
}

if let label = object.labels.first {
  if (label.text == DetectedObjectLabel.food.rawValue) {
    ...
  }
}
// or
if let label = object.labels.first {
  if (label.index == DetectedObjectLabelIndex.food.rawValue) {
    ...
  }
}

Here is an example of the old and new Objective-C code:

if (object.classificationCategory == FIRVisionObjectCategoryFood) {
    ...
}

if ([object.labels[0].text isEqualToString:MLKDetectedObjectLabelFood]) {
  ...
}
// or
if ([object.labels[0].index == MLKDetectedObjectLabelIndexFood]) {
  ...
}

Remove Firebase dependencies

Remove Firebase dependencies after migration. Follow these steps:

• Remove the Firebase configuration file by deleting the GoogleService-Info.plist file from your app's directory and your Xcode project.
• Remove any Firebase cocoapod, such as pod 'Firebase/Analytics', from your Podfile.
• Remove any FirebaseApp initialization, such as FirebaseApp.configure() from your code.
• Delete your Firebase app at the Firebase console according to the instructions on the Firebase support site.

Getting Help

If you run into any issues, please check out our Community page where we outline the channels available for getting in touch with us.