5 TensorFlow Lite Models for Implementing Cutting-Edge Android/iOS Apps(Part 1)

Article mainly walks through Tensorflow lite library and its open sourced models.

Ever considered using machine learning in your mobile apps? Stay tuned for a discussion on free, popular models that suit your specific needs.

Currently, numerous open-source models and libraries are available for utilisation in applications, even if one lacks comprehensive knowledge of machine learning. Today, I’ll delve into one such library, TensorFlow Lite, and discuss some of its open-source models that can be incorporated into your applications.

Tensorflow Lite

TensorFlow Lite serves as a mobile library designed for deploying models on various platforms including mobile devices, microcontrollers, and other edge devices.

TensorFlow is an open-source machine learning framework developed by Google Brain for building and training machine learning models. It provides a comprehensive ecosystem of tools, libraries, and resources to facilitate the development, deployment, and scaling of machine learning applications across various domains and Tensorflow lite library is a subset of Tensorflow library with the additional support libraries to run and train these models on low end devices like mobile phone and all the other edge devices which are mentioned below.

If you want to picture it, it might looks something like below

I will not go into implementation in details, if you want more details around it you can follow the official webiste for it.

Next, let’s explore some of the popular models that come pre-packaged with the TensorFlow Lite library and examine the various applications where these features can be leveraged.

The models which I will be specially covering today will fall under following categories
- Vision
- Text
- Audio
- Others

Vision

Vision(Image and video) models play a crucial role in various applications, including image classification, object detection, image segmentation, video recognition, and more. TensorFlow Lite provides a range of pre-trained models specifically optimized for deployment on mobile and edge devices, making it easier to integrate these models into mobile applications.

Certainly! Below are the dependencies and links to the documentation for integrating TensorFlow Lite vision models into Android applications:

MobileNet:
Use Case: Image Classification
Description: MobileNet is a lightweight convolutional neural network architecture designed for mobile and embedded vision applications. It’s widely used for tasks such as image classification, where the goal is to assign a label or category to an input image.
Documentation: TensorFlow Lite Android Quickstart
Examples: A plant identification app, Pet identification app, A food recognition app, A shopping assistant app that lets users take pictures of products and finds similar items online.

YOLO (You Only Look Once):
Use Case: Object Detection
Description: YOLO is a real-time object detection system that can detect multiple objects in images or video streams with high accuracy and speed. It’s commonly used in applications such as surveillance systems, autonomous vehicles, and augmented reality.
Documentation: YOLOv4 Tiny on TensorFlow Lite
Dependency: No specific dependency, but you’ll need the TensorFlow Lite Android library.
Examples: Can be used in inventory management system, Autonomous Vehicles, Visual Search. (eg : Amazon Go, Tesla Autopilot, Google Lense)

SSD (Single Shot MultiBox Detector):
Use Case: Object Detection
Description: SSD is another popular object detection model that’s optimized for real-time applications. It can detect objects in images with high accuracy and efficiency, making it suitable for tasks like object tracking, image search, and industrial automation.
Documentation: Tensorflow Lite Object Detection Model
Examples: Similar to as that of YOLO, as both are object detection models(eg : Amazon Go, Tesla Autopilot, Google Lense).

DeepLab:
Use Case: Semantic Image Segmentation
Description: DeepLab is a state-of-the-art deep learning model for semantic image segmentation. It can segment images into different classes or categories, allowing for precise pixel-level understanding of the scene. It’s commonly used in applications like medical imaging, autonomous driving, and environmental monitoring.
Dependency: TensorFlow Lite Android library.
Documentation: [DeepLab Android Demo]
Example: Image Capture, Real-Time Image Analysis, Vegetation Mapping and Monitoring(eg : Blur applications, background remover application, Google Earth Engine)

PoseNet:
Use Case: Pose Estimation
Description: PoseNet is a pose estimation model that can estimate human poses in real-time from images or video streams. It’s commonly used in applications related to fitness tracking, gesture recognition, sports analysis, and augmented reality.
Dependency: TensorFlow Lite Android library.
Documentation: [PoseNet Android Demo]
Example: User Interaction, Real-Time Pose Estimation,Form Analysis and Feedback (eg : Nike Training Club (NTC), Yoga Studio: Mind & Body, FitOn)

For each model, the provided links will guide you through the process of integrating the model into your Android application, including setting up dependencies, loading the model, running inference, and displaying results. Additionally, the links include sample code and demos to help you get started quickly.

In this article, I have only discussed a select few of the most commonly used models available in the TensorFlow Lite library, focusing specifically on those in the Vision category. Stay tuned and follow me for future articles, where I will explore a wider range of machine learning models that can be utilized to create and deploy smart applications for the modern world especially from HuggingFace and Kaggle

The next part of the article is here

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