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Android TensorFlow Lite Machine Learning Example

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Android TensorFlow Lite Machine Learning Example

TensorFlow Lite is the lightweight version of TensorFlow for mobile and embedded devices. It lets us run a Machine Learning model on the Android device itself, which makes it fast.

In this blog, we will see Android TensorFlow Lite Machine Learning example.

I am Amit Shekhar, Founder @ Outcome School, I have taught and mentored many developers, and their efforts landed them high-paying tech jobs, helped many tech companies in solving their unique problems, and created many open-source libraries being used by top companies. I am passionate about sharing knowledge through open-source, blogs, and videos.

I teach AI and Machine Learning, and Android at Outcome School.

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Using TensorFlow Lite Library For Object Detection

TensorFlow Lite is TensorFlow’s lightweight solution for mobile and embedded devices.

TensorFlow Lite is better as:

  • TensorFlow Lite enables on-device machine learning inference with low latency. Hence, it is fast.
  • TensorFlow Lite takes small binary size. Hence, good for mobile devices.
  • TensorFlow Lite also supports hardware acceleration with the Android Neural Networks API.

TensorFlow Lite uses many techniques for achieving low latency such as:

  • Optimizing the kernels for mobile apps.
  • Pre-fused activations.
  • Quantized kernels that allow smaller and faster (fixed-point math) models.

How to use TensorFlow Lite in an Android application?

The most important tricky part while using the TensorFlow Lite is to prepare the model(.tflite) which is different from the normal TensorFlow model.

A quick note for you

No matter which tech domain you work in, get familiar with these topics:

  • LLM
  • RAG
  • MCP
  • Agent
  • Fine-tuning
  • Quantization

We put it all together in one video:

AI Engineering Explained: LLM, RAG, MCP, Agent, Fine-Tuning, and Quantization

No need to stop reading - bookmark it and watch later when you get time. Future you will thank you.

Now, let's get back to the topic.

In order to run the model with the TensorFlow Lite, you will have to convert the model into the model(.tflite) which is accepted by the TensorFlow Lite. Follow the steps from here.

Now, you will have the model(.tflite) and the label file. You can start using these model and label files in your Android application to load the model and to predict the output using the TensorFlow Lite library.

I have created a complete running sample application using the TensorFlow Lite for object detection. Check out the project here.

Credit: The classifier example has been taken from Google TensorFlow example.

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Frequently Asked Questions

Can we use a normal TensorFlow model directly with TensorFlow Lite?

No. The TensorFlow Lite model is different from the normal TensorFlow model. We must first convert the model into the .tflite format that TensorFlow Lite accepts. Preparing this model is the most important tricky part of using TensorFlow Lite.

Why is TensorFlow Lite fast on mobile devices?

TensorFlow Lite runs machine learning inference on the device with low latency. It achieves this by optimizing the kernels for mobile apps, using pre-fused activations, and using quantized kernels that allow smaller and faster models. It also has a small binary size, which makes it good for mobile devices.

Does TensorFlow Lite support hardware acceleration on Android?

Yes. TensorFlow Lite supports hardware acceleration with the Android Neural Networks API. This is one of the reasons it is a good fit for on-device machine learning in Android applications.

That's it for now.

Thanks

Amit Shekhar
Founder @ Outcome School

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