Get Image and Object recognition with ML Kit on Android


ML Kit is a mobile SDK that allows Android and iOS apps to use Google’s on-device machine learning expertise. However, you may utilize the sophisticated yet simple to use Vision and Natural Language APIs to address common difficulties in your apps or create brand-new user experiences. All are free and driven by Google’s best-in-class machine learning models. The APIs of the ML Kit operates entirely on the device, enabling real-time use cases such as processing a live camera video. Moreover, it also implies that the functionality is available when the device is offline. So, if you want to add functionalities like image detection, hire android developer who has experience in Machine learning.

The professionals who have knowledge of Machine learning can quickly develop the functionality in the android app with only a few lines of code. Moreover, they do not require a lot of experience or any model optimization. So, in this blog, we will discuss the steps how to integrate the ML kit in the Android app to enjoy the new functionalities like object recognition and image detection.

Before we proceed with the steps, let us discuss how the Machine Learning kit works.

How does ML Kit work on Android Apps?

By combining Google’s ML technologies, such as the Google Cloud Vision API, Mobile Vision, and TensorFlow Lite, in a single SDK, ML Kit makes it simple to implement machine learning techniques in your projects. 

Moreover, with only a few lines of code, you can avail the real-time capabilities of the mobile vision on-device models, cloud-based processing, and flexibility of custom TensorFlow lite Models. So, why are you delaying in availing of this feature in your android mobile app? Contact an android app development company to add ML kit features to your Android App.  

Steps to detect and recognize objects with the ML Kit on the Android Apps

We got to know what ML Kit can do and how its works on Android mobile apps. Below we will discuss steps of how to add the features of the image and object recognition on the Android Application with the ML kit.

Image classification and labeling

Do you know that image categorization is a well-known concept in Machine learning circles? Indeed it is a cornerstone of computer vision. 

However, image classification occurs when you show an image to your device, and it tells you what it includes. For example, if you display an image of a cat to your device, it will recognize it as a cat.

Perhaps, you can hire an android developer instead of downloading the complete setup for the object detection. The professionals can help you set up the ML kit on your Android app.

Also Read -: How To Design A Website That Appeals To The Customers

First of all, the professionals will add the ML kit object Detection and Tracking API to the project.

Import the App into Android Studio

The Android specialists will open Android Studio and select Import Project(Gradle, ADT, etc.). Further, the developer will select the starter folder from the source code.

After it, add the dependencies for the ML KIt Android libraries to the module’s app-level Gradle file for object detection and tracking. 

You might do not know, but ML kit dependencies enable you to integrate the ML Kit ODT SDK in your Android App.

So, the developers will add the below code to the end of the app/build. The gradle file of the project.

dependencies {

  // …

  implementation ‘’


Sync your project with gradle files

The android developer will sync the project with the Gradle files to make sure all the dependencies are available in the android app.

So, you open the Android Studio toolbar to sync your project with the Gradle Files.

Run the starter app.

Now that the specialists have added the dependencies for the ML kit into the Android studio. For the first time, you can run your app. And to run the app, connect the android device to the Host through the USB and click on the Run option in the Android Studio toolbar.

The Android application will get established on your Android device. However, the app has some boilerplate code that will allow you to capture an image, select a preset image and feed it to the object detection. Moreover, if you want to explore the app you can also do that.

Add on-device object detection.

Here, you can add the features to the starter app to recognize the objects in the images. As we mentioned in the previous step, it contains boilerplate code. When you take a photo with the camera, the code will decode that image into a Bitmap instance and shows it on the screen, and further calls the runObjectDetection process with the image.

And to do the object detection, you need to add code to the runObjectDetection.

Set up and Run object detection on an image

You can set up the ML Kit ODT with the 3 simple steps:

  • prepare an image: InputImage
  • create a detector object: ObjectDetection.getClient(options)
  • connect the 2 objects above: process(image)

So, to achieve these functions inside the app runObjectDetection(bitmap: Bitmap) in file MainActivity.kt.


 * ML Kit Object Detection Function


private fun runObjectDetection(bitmap: Bitmap) {


To implement the ML Kit, you need to add the necessary imports in the Android Studio.


 Create an InputImage

To create an InputImage from a Bitmap, ML Kit will provide a simple API. After it, you can feed the InputImage in the ML Kits APIs.

// Step 1: create ML Kit’s InputImage object

val image = InputImage.from Bitmap(bitmap, 0)

The professionals will add the above code to the top of the runObjectDetection(bitmap: Bitmap).

Configure the Object detector

First of all, create an instance of the ObjectDetector to detect and track the objects. Further, specify any detector settings that you want to change from the default. You will 3 options to configure.

  • detector mode
  • detection mode (single or multiple object detection)
  • classification mode (on or off)

Feed image(s) to the detector.

Object detection and classification is async processing:

  • Through the process, you can send an image to the detector.
  • The Detector works pretty hard on it.
  • Through a callback, the Detector reports the result back to you.

Once the process gets complete, it will notify you with the trackingId, boundingBox, labels, index, text, and confidence.

Hence, the boilerplate code will help you in visualizing the detection result. With the help of these visualization utilities, you can draw the ML Kit object detection result on the top of the input image. At last, run the app to see the results.


On the whole, we hope this blog helps you understand the steps to add ML Kit for image and object detection in the Android App. In fact, you can hire an android developer who can add these functionalities to your android app.



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