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Community Blog MNN Kit: An Out-of-the-Box Algorithms Kit from Alibaba

MNN Kit: An Out-of-the-Box Algorithms Kit from Alibaba

Learn about what an MNN kit is and how it has simplified the process of using general-purpose models in mobile development.

By the MNN team, from Taobao Technology Department, Alibaba New Retail

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In recent years, On-Device AI has developed gradually from an interesting attempt at mobile development to a core tool for mobile development engineers. AI is ubiquitous in today's mobile development with key additions, such as Pailitao in Taobao Mobile, commodity identification in Taobao Live, and personalized recommendations in TouTiao. AI is playing an increasingly important role in mobile apps. It has also gradually evolved from some amusing features, such as face stickers in Snapchat and other social media software to the application scenarios in Taobao that can truly empower businesses.

Under such a background, the MNN team from Taobao Technology Department, recently released an out-of-the-box tool kit, MNN Kit.

What Is MNN Kit?

MNN Kit is a series of easy-to-use SDKs that were obtained by Alibaba by encapsulating the models that came from frequently used business scenarios. They have been battle-tested in many campaigns. Its underlying technology cornerstone is the inference engine, which MNN made open-source in May 2019.

If you are eager to try out the MNN Kit, visit this link for the MNN Kit documentation and open-source demo projects.

These models are backed by technology accumulated over many years from the algorithm teams within the Taobao Technology Department. For example, a series of models used in MNN Kit's face detection are provided by the PixelAI team. This team is from Taobao Technology Department and is focusing on client-side deep-learning computer vision algorithms.

A Brief Introduction to MNN Kit Models

Gesture Recognition

Gesture recognition is MNN Kit's AI solution SDK for mobile clients. It provides real-time gesture detection and recognition capabilities for mobile clients. Apps based on it can develop a wealth of business scenarios and usage methods. For example, you can develop a finger-guessing game based on the gesture detection model, or you can use the model as the basis for developing an app applied with and controlled by gestures, which allows users to swipe their mobile phone screens remotely when touching the screen with their fingers is inconvenient (such as during a meal.) Only your imagination limits how this technology could be used.

Portrait Segmentation

Portrait segmentation is MNN Kit's AI solution SDK for mobile clients. It provides a real-time portrait segmentation capability on mobile clients. It is also a basic model capability, which can be applied to scenarios, such as automatic matting on ID photos and assisting PS.

The effect is shown below:

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Extremely Simple Access Process

MNN Kit was created in the Alibaba Taobao Technology Department, which has an environment where launching quick online businesses was required in promotional campaigns, such as Double 11, Double 12, and the New Year's Sale. It is an essential feature of the MNN Kit to quickly and simply integrate models into apps.

There are only three basic APIs in MNN Kit: create an instance, make an inference, and release an instance. The process is shown below:

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Let's take the face detection on Android as an example to briefly introduce the access code.

Create an Instance

We create a FaceDetector instance asynchronously, which will call back onSucceeded in the main thread:

void createKitInstance() {
    // 定义Detector Config
    FaceDetector.FaceDetectorCreateConfig createConfig = new FaceDetector.FaceDetectorCreateConfig();
    createConfig.mode = FaceDetector.FaceDetectMode.MOBILE_DETECT_MODE_VIDEO;
    FaceDetector.createInstanceAsync(this, createConfig, new InstanceCreatedListener<FaceDetector>() {
        @Override
        public void onSucceeded(FaceDetector faceDetector) {
            // 获取回调实例
            mFaceDetector = faceDetector;
        }

        @Override
        public void onFailed(int i, Error error) {
            Log.e(Common.TAG, "create face detetector failed: " + error);
        }
    });
}

Make an Inference

We can get the camera frame data from Android Camera API's onPreviewFrame callback. Define the face actions needed in the video stream (such as blinking and mouth opening), and the face detection can be performed on the current frame.

// 从相机API回调中获取视频流的一帧
byte[] data = ....;

// 定义哪些人脸的动作需要在视频流中进行检测
long detectConfig =
        FaceDetectConfig.ACTIONTYPE_EYE_BLINK |
                FaceDetectConfig.ACTIONTYPE_MOUTH_AH |
                FaceDetectConfig.ACTIONTYPE_HEAD_YAW |
                FaceDetectConfig.ACTIONTYPE_HEAD_PITCH |
                FaceDetectConfig.ACTIONTYPE_BROW_JUMP;

// 进行推理
FaceDetectionReport[] results = mFaceDetector.inference(data, width, height,
        MNNCVImageFormat.YUV_NV21, detectConfig, inAngle, outAngle,
        mCameraView.isFrontCamera() ? MNNFlipType.FLIP_Y : MNNFlipType.FLIP_NONE);

Release an Instance

Apps need to actively free up the memory occupied by the model according to its application scenario. Take our demo as an example. The memory occupied by the model is released at onDestroy on the Activity. You can decide when to release according to the process of your app.

protected void onDestroy() {
    super.onDestroy();

    if (mFaceDetector! =null) {
        mFaceDetector.release();
    }
}

Best Practices for Pre-Processing and Post-Processing

In the implementation of a scenario, in addition to the three steps above, there are some pre-processing and post-processing for camera video streams. In many cases, these processing actions are similar. For example, when it comes to pre-processing and how to correctly process camera frames, we need to consider whether the camera is pre- or post-positioned as well as the camera rotation. For the post-processing, we need to map the output of the model (such as the coordinates of the key face points) to the original image for rendering. MNN Kit puts these best engineering practices into the demo for reference. The code for the demo is open-source in the GitHub repository for MNN Kit.

Future

The MNN Kit has greatly simplified the process of using general-purpose models in mobile development. What if your application scenario doesn't exactly fit this general-purpose scenario? In many cases, with an excellent basic model as the starting point, and through transfer learning, you can get a model that is suitable for your scenario. The MNN team will release products later to help you continue to transfer learning more easily and efficiently.

Furthermore, MNN Kit will gradually cover other models for general-purpose scenarios. Keep tuned to learn about upcoming announcements!

User Agreement

MNN Kit has its own user agreement. Here are some points that should be noted:

  1. MNN Kit itself is not open-source and the models are not provided separately. The ability to use these models requires APIs from the MNN Kit. Reverse engineering on MNN Kit is prohibited in the user agreement.
  2. The secondary encapsulation on the MNN Kit for profit or commercial purposes is not allowed. For example, encapsulation on the MNN Kit as other SDKs for commercial or profitable purposes.
  3. On the premise of complying with the MNN Kit license, integrating MNN Kit SDK with an application is allowed as well as any commercial value or benefit that is generated.
Artificial Intelligence Mobile development Mobile Neural Network Gesture Recognition Alibaba MNN MNN Kit
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