The app is optimized to run smoothly, ensuring that objectdetection is performed in real time without any significant lag, providing users with a fluid and responsive experience.
After creating a local version of the example code, you can import the project into AndroidStudio and run the app. For instructions, see the Setup Guide for Android. The following files contain the crucial code for the ObjectDetector example application:
In this codelab, you’ll build an Androidapp that can detectobjects in images. You’ll start with training a custom objectdetection model with TFLite Model Maker and then deploy it...
Discover how to leverage AndroidStudio to create a custom Android app for objectdetection. We'll provide a step-by-step guide, from project setup to integrating the TensorFlow Lite...
My goal was to develop an Androidapp capable of detectingobjects using the most suitable detection models for mobile devices. This meant the models should: Work in real time, offering...
The app is optimized to run smoothly, ensuring that objectdetection is performed in real time without any significant lag, providing users with a fluid and responsive experience.
In this tutorial, we will learn how to Create a real-time objectdetectionAndroidapp using TensorFlow Lite. We will use the camera2 API to directly Read frames from the camera and the TensorFlow Lite model for objectdetection.
To setup the objectdetectionapp, download the sample from GitHub and run it using AndroidStudio. The following sections of this tutorial explore the relevant sections of the code example, so you can apply them to your own Android apps.
