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TensorFlow Lite Object Detection Demo 2019 simgesi

TensorFlow Lite Object Detection Demo 2019


CodeDunK
1.0
  • PEK YAKINDA
    Update date
  • Everyone
  • 5.0 and up
    Android OS

TensorFlow Lite Object Detection Demo 2019 hakkında

TensorFlow Lite Nesne Algılama ve İzleme çalışmalarını göstermek için bir demo uygulaması.

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.

Daha Fazla Göster

Ek UYGULAMA Bilgileri

En Son Sürüm

1.0

Gereken Android sürümü

5.0 and up

İçerik derecelendirmesi

Everyone

En son sürümde yeni olan 1.0

Last updated on May 30, 2019

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

Daha Fazla Göster

TensorFlow Lite Object Detection Demo 2019 Ekran görüntüleri

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