Page Summary
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The AutoML Vision Edge image labeling API is deprecated and replaced by the custom model APIs, supporting AutoML-trained models for image labeling, object detection, and tracking.
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Existing AutoML Vision Edge implementations need to migrate to the new custom model API, which offers simplified integration and expanded functionalities.
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The migration involves updating dependencies, renaming classes and methods, and adjusting model initialization for both Android and iOS.
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Developers can continue to bundle models within their app or host them on Firebase Console as custom models.
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The new custom model API offers benefits such as the ability to use AutoML-trained models for object detection and tracking and support for models with embedded label maps.
You can pass an AutoML-trained image classification model to the custom model APIs. You can continue to either bundle the model inside your app or host it on Firebase Console as a custom model. The AutoML image labeling API has been removed from ML Kit since it's fully replaced by the Custom Model Image Labeling API.
| API | What is changing? |
|---|---|
| AutoML Vision Edge image labeling API | It's fully replaced by the Custom Model image labeling API. Existing AutoML Vision Edge image labeling API is removed. |
If you use the AutoML Vision Edge API, follow the migration instructions for Android and iOS.
Frequently Asked Questions
Why this change?
It helps simplify ML Kit APIs, and make it easier to integrate ML Kit into your app. With this change, you can use an AutoML-trained model in the exact same way as a custom model. It also lets you use AutoML-trained models for Object Detection and Tracking, in addition to Image Labeling which we support. Furthermore, the custom model API supports both models with label map embedded in its metadata, and models with separate manifest and label files.
What benefits do I get from migrating to the new SDK?
- New features: Ability to use AutoML-trained models for both Image Labeling and Object Detection & Tracking and ability to use models with label map embedded in its metadata.
Migration Guide for Android
Step 1: Update Gradle Imports
Update the dependencies for the ML Kit Android libraries in your module
(app-level) Gradle file (usually app/build.gradle.kts) according to the
following table:
| Feature | Old Artifacts | New Artifact |
|---|---|---|
| Image labeling AutoML without remote model downloading | com.google.mlkit:image-labeling-automl:16.2.1 | com.google.mlkit:image-labeling-custom:16.0.0-beta5 |
| Image labeling AutoML with remote model downloading |
com.google.mlkit:image-labeling-automl:16.2.1 com.google.mlkit:linkfirebase:16.0.1 |
com.google.mlkit:image-labeling-custom:16.0.0-beta5 To host and download custom models, move your models to Cloud Storage and add download logic in your app to load them using LocalModel. For details, see the [Firebase ML to Cloud Storage migration guide][migrate-storage]. |
Step 2: Update class names
If your class appears in this table, make the indicated change:
| Old class | New class |
|---|---|
| com.google.mlkit.vision.label.automl.AutoMLImageLabelerLocalModel | com.google.mlkit.common.model.LocalModel |
| com.google.mlkit.vision.label.automl.AutoMLImageLabelerRemoteModel |
com.google.mlkit.common.model.LocalModel Your app must include logic to download remotely-hosted models and initialize them using LocalModel. |
| com.google.mlkit.vision.label.automl.AutoMLImageLabelerOptions | com.google.mlkit.vision.label.custom.CustomImageLabelerOptions |
Step 3: Update method names
There are minimal code changes:
LocalModelcan now be initialized with either a model path (if the model has metadata containing the label map) or a model manifest path (if the manifest, model, and labels are in separate files).- Add logic to your app to download remotely-hosted models to local storage and load them using
LocalModel.
Here are some examples of old and new Kotlin methods:
Old
val localModel = AutoMLImageLabelerLocalModel.Builder() .setAssetFilePath("automl/manifest.json") // or .setAbsoluteFilePath(absolute path to manifest file) .build() val optionsWithLocalModel = AutoMLImageLabelerOptions.Builder(localModel) .setConfidenceThreshold(0.5f) .build() val remoteModel = AutoMLImageLabelerRemoteModel.Builder("automl_remote_model") .build() val optionsWithRemoteModel = AutoMLImageLabelerOptions.Builder(remoteModel) .build()
New
// Similar process for both local and remotely-hosted models (that have been downloaded) val localModel = LocalModel.Builder() .setAssetManifestFilePath("automl/manifest.json") // or .setAbsoluteManifestFilePath(absolute path to manifest file) .build() val optionsWithLocalModel = CustomImageLabelerOptions.Builder(localModel) .setConfidenceThreshold(0.5f) .build()
Here are some examples of old and new Java methods:
Old
AutoMLImageLabelerLocalModel localModel = new AutoMLImageLabelerLocalModel.Builder() .setAssetFilePath("automl/manifest.json") // or .setAbsoluteFilePath(absolute path to manifest file) .build(); AutoMLImageLabelerOptions optionsWithLocalModel = new AutoMLImageLabelerOptions.Builder(localModel) .setConfidenceThreshold(0.5f) .build(); AutoMLImageLabelerRemoteModel remoteModel = new AutoMLImageLabelerRemoteModel.Builder("automl_remote_model").build(); AutoMLImageLabelerOptions optionsWithRemoteModel = new AutoMLImageLabelerOptions.Builder(remoteModel) .build();
New
// Similar process for local models and remotely-hosted models (that have been downloaded) LocalModel localModel = new LocalModel.Builder() .setAssetManifestFilePath("automl/manifest.json") // or .setAbsoluteManifestFilePath(absolute path to manifest file) .build(); CustomImageLabelerOptions optionsWithLocalModel = new CustomImageLabelerOptions.Builder(localModel) .setConfidenceThreshold(0.5f) .build();
Migration Guide for iOS
Prerequisites
- Xcode 13.2.1 or greater is required.
Step 1: Update Cocoapods
Update the dependencies for the ML Kit iOS cocoapods in your app's Podfile:
| Feature | Old pod name(s) | New pod name(s) |
|---|---|---|
| Image labeling AutoML without remote model downloading | GoogleMLKit/ImageLabelingAutoML | GoogleMLKit/ImageLabelingCustom |
| Image labeling AutoML with remote model downloading |
GoogleMLKit/ImageLabelingAutoML GoogleMLKit/LinkFirebase |
GoogleMLKit/ImageLabelingCustom To host and download custom models, move your models to Cloud Storage and add download logic in your app to load them as local models. For details, see the [Firebase ML to Cloud Storage migration guide][migrate-storage]. |
Step 2: Update class names
If your class appears in this table, make the indicated change:
Swift
| Old class | New class |
|---|---|
| AutoMLImageLabelerLocalModel | LocalModel |
| AutoMLImageLabelerRemoteModel |
LocalModel To host and download custom models, move your models to Cloud Storage and add download logic in your app to load them as local models. For details, see the [Firebase ML to Cloud Storage migration guide][migrate-storage]. |
| AutoMLImageLabelerOptions | CustomImageLabelerOptions |
Objective-C
| Old class | New class |
|---|---|
| MLKAutoMLImageLabelerLocalModel | MLKLocalModel |
| MLKAutoMLImageLabelerRemoteModel |
MLKLocalModel To host and download custom models, move your models to Cloud Storage and add download logic in your app to load them as local models. For details, see the [Firebase ML to Cloud Storage migration guide][migrate-storage]. |
| MLKAutoMLImageLabelerOptions | MLKCustomImageLabelerOptions |
Step 3: Update method names
There are minimal code changes:
LocalModelcan now be initialized with either a model path (if the model has metadata containing the label map) or a model manifest path (if the manifest, model, and labels are in separate files).- Add logic to your app to download remotely-hosted models to local storage and load them using
LocalModel.
Here are some examples of old and new Swift methods:
Old
let localModel =
AutoMLImageLabelerLocalModel(manifestPath: "automl/manifest.json")
let optionsWithLocalModel = AutoMLImageLabelerOptions(localModel: localModel)
let remoteModel = AutoMLImageLabelerRemoteModel(name: "automl_remote_model")
let optionsWithRemoteModel = AutoMLImageLabelerOptions(remoteModel: remoteModel)New
// Similar process for local models and remotely-hosted models (that have been downloaded) guard let localModel = LocalModel(manifestPath: "automl/manifest.json") else { return } let optionsWithLocalModel = CustomImageLabelerOptions(localModel: localModel)
Here are some examples of old and new Objective-C methods:
Old
MLKAutoMLImageLabelerLocalModel *localModel = [[MLKAutoMLImageLabelerLocalModel alloc] initWithManifestPath:"automl/manifest.json"]; MLKAutoMLImageLabelerOptions *optionsWithLocalModel = [[MLKAutoMLImageLabelerOptions alloc] initWithLocalModel:localModel]; MLKAutoMLImageLabelerRemoteModel *remoteModel = [[MLKAutoMLImageLabelerRemoteModel alloc] initWithManifestPath:"automl/manifest.json"]; MLKAutoMLImageLabelerOptions *optionsWithRemoteModel = [[MLKAutoMLImageLabelerOptions alloc] initWithRemoteModel:remoteModel];
New
// Similar process for local models and remotely-hosted models (that have been downloaded) MLKLocalModel *localModel = [[MLKLocalModel alloc] initWithManifestPath:"automl/manifest.json"]; MLKCustomImageLabelerOptions *optionsWithLocalModel = [[MLKCustomImageLabelerOptions alloc] initWithLocalModel:localModel];