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MediaPipe Android Archive

  1. Steps to build a MediaPipe AAR
  2. Steps to use a MediaPipe AAR in Android Studio with Gradle

Experimental Only

The MediaPipe Android Archive (AAR) library is a convenient way to use MediaPipe with Android Studio and Gradle. MediaPipe doesn’t publish a general AAR that can be used by all projects. Instead, developers need to add a mediapipe_aar() target to generate a custom AAR file for their own projects. This is necessary in order to include specific resources such as MediaPipe calculators needed for each project.

Steps to build a MediaPipe AAR

  1. Create a mediapipe_aar() target.

    In the MediaPipe directory, create a new mediapipe_aar() target in a BUILD file. You need to figure out what calculators are used in the graph and provide the calculator dependencies to the mediapipe_aar(). For example, to build an AAR for MediaPipe Face Detection, you can put the following code into mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/BUILD.

    load("//mediapipe/java/com/google/mediapipe:mediapipe_aar.bzl", "mediapipe_aar")
        name = "mediapipe_face_detection",
        calculators = ["//mediapipe/graphs/face_detection:mobile_calculators"],
  2. Run the Bazel build command to generate the AAR.

    bazel build -c opt --strip=ALWAYS \
        --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
        --fat_apk_cpu=arm64-v8a,armeabi-v7a \

    For the face detection AAR target we made in step 1, run:

    bazel build -c opt --strip=ALWAYS \
        --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
        --fat_apk_cpu=arm64-v8a,armeabi-v7a \
    # It should print:
    # Target //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mediapipe_face_detection.aar up-to-date:
    # bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
  3. (Optional) Save the AAR to your preferred location.

    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar

Steps to use a MediaPipe AAR in Android Studio with Gradle

  1. Start Android Studio and go to your project.

  2. Copy the AAR into app/libs.

    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar


  3. Make app/src/main/assets and copy assets (graph, model, and etc) into app/src/main/assets.

    Build the MediaPipe binary graph and copy the assets into app/src/main/assets, e.g., for the face detection graph, you need to build and copy the binary graph and the face detection tflite model.

    bazel build -c opt mediapipe/graphs/face_detection:face_detection_mobile_gpu_binary_graph
    cp bazel-bin/mediapipe/graphs/face_detection/face_detection_mobile_gpu.binarypb /path/to/your/app/src/main/assets/
    cp mediapipe/modules/face_detection/face_detection_short_range.tflite /path/to/your/app/src/main/assets/


  4. Modify app/build.gradle to add MediaPipe dependencies and MediaPipe AAR.

    dependencies {
        implementation fileTree(dir: 'libs', include: ['*.jar', '*.aar'])
        implementation 'androidx.appcompat:appcompat:1.0.2'
        implementation 'androidx.constraintlayout:constraintlayout:1.1.3'
        testImplementation 'junit:junit:4.12'
        androidTestImplementation 'androidx.test.ext:junit:1.1.0'
        androidTestImplementation 'androidx.test.espresso:espresso-core:3.1.1'
        // MediaPipe deps
        implementation ''
        implementation ''
        implementation ''
        implementation ''
        implementation ''
        // CameraX core library
        def camerax_version = "1.0.0-beta10"
        implementation "$camerax_version"
        implementation "$camerax_version"
        implementation "$camerax_version"
        // AutoValue
        def auto_value_version = "1.8.1"
        implementation "$auto_value_version"
        annotationProcessor "$auto_value_version"
  5. Follow our Android app examples to use MediaPipe in Android Studio for your use case. If you are looking for an example, a face detection example can be found here and a multi-hand tracking example can be found here.