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TensorFlow Lite Object Detection Demo 2019 icône

TensorFlow Lite Object Detection Demo 2019


CodeDunK
1.0
  • BIENTÔT DISPONIBLE
    Update date
  • Everyone
  • 5.0 and up
    Android OS

À propos de TensorFlow Lite Object Detection Demo 2019

Une application de démonstration montrant les travaux de détection et de suivi d'objets TensorFlow Lite.

A sample app to show how TensorFlow Lite works real time on android phone.

Launch the app start viewing different objects in camera preview to see the bounding boxes and tracking in action.

TensorFlow Lite provides all the tools you need to convert and run TensorFlow models on mobile, embedded, and IoT devices.

TensorFlow Lite allows you to run TensorFlow models on a wide range of devices. A TensorFlow model is a data structure that contains the logic and knowledge of a machine learning network trained to solve a particular problem.

What is object detection?

Given an image or a video stream, an object detection model can identify which of a known set of objects might be present and provide information about their positions within the image.

An object detection model is trained to detect the presence and location of multiple classes of objects. For example, a model might be trained with images that contain various pieces of fruit, along with a label that specifies the class of fruit they represent (e.g. an apple, a banana, or a strawberry), and data specifying where each object appears in the image.

When we subsequently provide an image to the model, it will output a list of the objects it detects, the location of a bounding box that contains each object, and a score that indicates the confidence that detection was correct.

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Informations Application supplémentaires

Dernière version

1.0

Nécessite Android

5.0 and up

Classification du contenu

Everyone

Quoi de neuf dans la dernière version 1.0

Last updated on May 30, 2019

Minor bug fixes and improvements. Install or update to the newest version to check it out!

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TensorFlow Lite Object Detection Demo 2019 Captures d'écran

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