Harnessing Deep Learning on Android with the Depth Library

Robert Harris -

The realm of mobile applications is rapidly evolving, and with the emergence of the Depth library, a groundbreaking project open-sourced by Google, Android developers can now effortlessly integrate deep learning functionalities into their applications. Powered by TensorFlow Lite, Depth is not only a powerhouse of capabilities but is also designed to be user-friendly, making the implementation of deep learning on Android a straightforward endeavor.

Key Features of Depth:

  1. A myriad of deep learning functionalities including image recognition, image classification, image segmentation, speech recognition, and natural language processing, providing a comprehensive suite of tools for developers.
  2. The ability to incorporate custom deep learning models, offering flexibility to cater to specific project requirements.
  3. Support for online model updates, ensuring the deployment of the latest and most accurate models.

Getting started with Depth is a breeze. Simply add the following dependency to your Android project:

dependencies {
    implementation 'com.google.android.gms:depth:1.2.0'
}

Below is a snippet showcasing the simplicity of utilizing Depth:

// Initializing a Depth instance
val depth = Depth.getInstance()

// Loading an image recognition model
depth.loadModel(Model.IMAGE_CLASSIFICATION, "path/to/model.tflite")

// Recognizing an image
val results = depth.predictImage(BitmapFactory.decodeResource(resources, R.drawable.image))

// Printing the recognition results
for (result in results) {
    Log.d("Depth", result.label)
}

Upon execution, this snippet loads an image recognition model and recognizes the image specified in R.drawable.image.

Depth's versatility shines through with its support for custom model loading, as demonstrated below:

// Loading a custom model
depth.loadModel(Model.CUSTOM, "path/to/model.tflite")

// Using the custom model to recognize an image
val results = depth.predictImage(BitmapFactory.decodeResource(resources, R.drawable.image))

// Printing the recognition results
for (result in results) {
    Log.d("Depth", result.label)
}

Depth is more than just a library; it's a potent tool enabling a quick implementation of deep learning functionalities within Android applications.

Here are some additional snippets illustrating the ease of loading various models and updating them online:

// Loading different models
depth.loadModel(Model.IMAGE_CLASSIFICATION, "path/to/model.tflite")
depth.loadModel(Model.IMAGE_SEGMENTATION, "path/to/model.tflite")
depth.loadModel(Model.SPEECH_RECOGNITION, "path/to/model.tflite")
depth.loadModel(Model.NATURAL_LANGUAGE_PROCESSING, "path/to/model.tflite")

// Updating a model online
depth.updateModel(Model.IMAGE_CLASSIFICATION, "https://example.com/model.tflite")