Machine Learning Vision for Firebase

This package is now discontinued since these APIs are no longer available in the latest Firebase SDKs. As an alternative you can switch to Google's standalone ML Kit library via google_ml_kit for on-device vision APIs. For calling the Cloud Vision API from your app the recommended approach is using Firebase Authentication and Functions, which gives you a managed, serverless gateway to Google Cloud Vision APIs. For an example Functions project see the vision-annotate-images sample project.

pub package

A Flutter plugin to use the capabilities of Firebase ML, which includes all of Firebase's cloud-based ML features, and ML Kit, a standalone library for on-device ML, which can be used with or without Firebase.

For Flutter plugins for other Firebase products, see


To use this plugin, add firebase_ml_vision as a dependency in your pubspec.yaml file. You must also configure Firebase for each platform project: Android and iOS (see the example folder).


If you're using the on-device ImageLabeler, include the latest matching ML Kit: Image Labeling dependency in your app-level build.gradle file.

android {
    dependencies {
        // ...

        api ''

If you're using the on-device Face Contour Detection, include the latest matching ML Kit: Face Detection Model dependency in your app-level build.gradle file.

android {
    dependencies {
        // ...

        api ''

If you receive compilation errors, try an earlier version of ML Kit: Image Labeling.

Optional but recommended: If you use the on-device API, configure your app to automatically download the ML model to the device after your app is installed from the Play Store. To do so, add the following declaration to your app's AndroidManifest.xml file:

<application ...>
    android:value="ocr" />
  <!-- To use multiple models: android:value="ocr,label,barcode,face" -->


Versions 0.7.0+ use the latest ML Kit for Firebase version which requires a minimum deployment target of 9.0. You can add the line platform :ios, '9.0' in your iOS project Podfile.

You may also need to update your app's deployment target to 9.0 using Xcode. Otherwise, you may see compilation errors.

If you're using one of the on-device APIs, include the corresponding ML Kit library model in your Podfile as indicated below. Then run pod update in a terminal within the same directory as your Podfile.

# ...
target 'Runner' do
  flutter_install_all_ios_pods File.dirname(File.realpath(__FILE__))
  # ...
  # Include any of the following for the APIs you are using:
  pod 'Firebase/MLVisionBarcodeModel'
  pod 'Firebase/MLVisionFaceModel'
  pod 'Firebase/MLVisionLabelModel'
  pod 'Firebase/MLVisionTextModel'
  # ...

Using an ML Vision Detector

1. Create a FirebaseVisionImage.

Create a FirebaseVisionImage object from your image. To create a FirebaseVisionImage from an image File object:

final File imageFile = getImageFile();
final FirebaseVisionImage visionImage = FirebaseVisionImage.fromFile(imageFile);

2. Create an instance of a detector.

final BarcodeDetector barcodeDetector = FirebaseVision.instance.barcodeDetector();
final FaceDetector faceDetector = FirebaseVision.instance.faceDetector();
final ImageLabeler labeler = FirebaseVision.instance.imageLabeler();
final ImageLabeler cloudLabeler = FirebaseVision.instance.cloudImageLabeler();
final TextRecognizer textRecognizer = FirebaseVision.instance.textRecognizer();
final TextRecognizer cloudTextRecognizer = FirebaseVision.instance.cloudTextRecognizer();
final DocumentTextRecognizer cloudDocumentTextRecognizer = FirebaseVision.instance.cloudDocumentTextRecognizer();

You can also configure all detectors, except on-device TextRecognizer, with desired options.

final ImageLabeler labeler = FirebaseVision.instance.imageLabeler(
  ImageLabelerOptions(confidenceThreshold: 0.75),

3. Call detectInImage() or processImage() with visionImage.

final List<Barcode> barcodes = await barcodeDetector.detectInImage(visionImage);
final List<Face> faces = await faceDetector.processImage(visionImage);
final List<ImageLabel> labels = await labeler.processImage(visionImage);
final List<ImageLabel> cloudLabels = await cloudLabeler.processImage(visionImage);
final VisionText visionText = await textRecognizer.processImage(visionImage);
final VisionText visionText = await cloudTextRecognizer.processImage(visionImage);
final VisionDocumentText visionDocumentText = await cloudDocumentTextRecognizer.processImage(visionImage);

4. Extract data.

a. Extract barcodes.

for (Barcode barcode in barcodes) {
  final Rectangle<int> boundingBox = barcode.boundingBox;
  final List<Point<int>> cornerPoints = barcode.cornerPoints;

  final String rawValue = barcode.rawValue;

  final BarcodeValueType valueType = barcode.valueType;

  // See API reference for complete list of supported types
  switch (valueType) {
    case BarcodeValueType.wifi:
      final String ssid = barcode.wifi.ssid;
      final String password = barcode.wifi.password;
      final BarcodeWiFiEncryptionType type = barcode.wifi.encryptionType;
    case BarcodeValueType.url:
      final String title = barcode.url.title;
      final String url = barcode.url.url;

b. Extract faces.

for (Face face in faces) {
  final Rectangle<int> boundingBox = face.boundingBox;

  final double rotY = face.headEulerAngleY; // Head is rotated to the right rotY degrees
  final double rotZ = face.headEulerAngleZ; // Head is tilted sideways rotZ degrees

  // If landmark detection was enabled with FaceDetectorOptions (mouth, ears,
  // eyes, cheeks, and nose available):
  final FaceLandmark leftEar = face.getLandmark(FaceLandmarkType.leftEar);
  if (leftEar != null) {
    final Point<double> leftEarPos = leftEar.position;

  // If classification was enabled with FaceDetectorOptions:
  if (face.smilingProbability != null) {
    final double smileProb = face.smilingProbability;

  // If face tracking was enabled with FaceDetectorOptions:
  if (face.trackingId != null) {
    final int id = face.trackingId;

c. Extract labels.

for (ImageLabel label in labels) {
  final String text = label.text;
  final String entityId = label.entityId;
  final double confidence = label.confidence;

d. Extract text.

String text = visionText.text;
for (TextBlock block in visionText.blocks) {
  final Rect boundingBox = block.boundingBox;
  final List<Offset> cornerPoints = block.cornerPoints;
  final String text = block.text;
  final List<RecognizedLanguage> languages = block.recognizedLanguages;

  for (TextLine line in block.lines) {
    // Same getters as TextBlock
    for (TextElement element in line.elements) {
      // Same getters as TextBlock

d. Extract document text.

String text = visionDocumentText.text;
for (DocumentTextBlock block in visionDocumentText.blocks) {
  final Rect boundingBox = block.boundingBox;
  final String text = block.text;
  final List<RecognizedLanguage> languages = block.recognizedLanguages;
  final DocumentTextRecognizedBreak = block.recognizedBreak;

  for (DocumentTextParagraph paragraph in block.paragraphs) {
    // Same getters as DocumentTextBlock
    for (DocumentTextWord word in paragraph.words) {
      // Same getters as DocumentTextBlock
      for (DocumentTextSymbol symbol in word.symbols) {
        // Same getters as DocumentTextBlock

5. Release resources with close().


Getting Started

See the example directory for a complete sample app using Machine Learning Vision for Firebase.

Issues and feedback

Please file FlutterFire specific issues, bugs, or feature requests in our issue tracker.

Plugin issues that are not specific to Flutterfire can be filed in the Flutter issue tracker.

To contribute a change to this plugin, please review our contribution guide and open a pull request.