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Objects Detection Machine Learning TensorFlow Demo
Objects Detection Machine Learning TensorFlow Demo APK
0.2 ․ 6 years agoFree ․ Amphan ⇣ Download APK (87.62 MB)
Objects Detection Machine Learning TensorFlow with Realtime Camera Demo
What's Objects Detection Machine Learning TensorFlow Demo APK?
Objects Detection Machine Learning TensorFlow Demo is a app for Android, It's developed by Amphan author.
First released on google play in 6 years ago and latest version released in 6 years ago.
This app has 55.8K download times on Google play and rated as 3.41 stars with 291 rated times.
This product is an app in Libraries & Demo category. More infomartion of Objects Detection Machine Learning TensorFlow Demo on google play
Objects Detection Machine Learning TensorFlow Demo.
Uses the Google TensorFlow Machine Learning Library Inception model to detect object with camera frames in real-time, displaying the label and overlay on the camera image.
Detect 1001 objects in this model
more info
http://androidcontrol.blogspot.com
What is TensorFlow?
TensorFlow is open source machine learning library from Google. Computation code is written in C++, but programmers can write their TensorFlow software in either C++ or Python and implemented for CPUs ,GPUs or both.
In November 2015, Google announced and open sourced TensorFlow, its latest and greatest machine learning library. This is a big deal for three reasons:
1.Machine Learning expertise: Google is a dominant force in machine learning. Its prominence in search owes a lot to the strides it achieved in machine learning.
2.Scalable : the announcement noted that TensorFlow was initially designed for internal use and that it’s already in production for some live product features.
3.Ability to run on Mobile.
This last reason is the operating reason for this post since we’ll be focusing on Android. If you examine the tensorflow repo on GitHub, you’ll find a little tensorflow/examples/android directory. I’ll try to shed some light on the Android TensorFlow example and some of the things going on under the hood.
original code
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android
My Website
http://softpowergroup.net/
My Blog
https://androidcontrol.blogspot.com
email : [email protected]
[email protected]
Tel .6681-6452400 ( Thailand )
Google+ https://plus.google.com/+SoftpowergroupNetThailand/
Facebook : https://www.facebook.com/softpowergroup/
Uses the Google TensorFlow Machine Learning Library Inception model to detect object with camera frames in real-time, displaying the label and overlay on the camera image.
Detect 1001 objects in this model
more info
http://androidcontrol.blogspot.com
What is TensorFlow?
TensorFlow is open source machine learning library from Google. Computation code is written in C++, but programmers can write their TensorFlow software in either C++ or Python and implemented for CPUs ,GPUs or both.
In November 2015, Google announced and open sourced TensorFlow, its latest and greatest machine learning library. This is a big deal for three reasons:
1.Machine Learning expertise: Google is a dominant force in machine learning. Its prominence in search owes a lot to the strides it achieved in machine learning.
2.Scalable : the announcement noted that TensorFlow was initially designed for internal use and that it’s already in production for some live product features.
3.Ability to run on Mobile.
This last reason is the operating reason for this post since we’ll be focusing on Android. If you examine the tensorflow repo on GitHub, you’ll find a little tensorflow/examples/android directory. I’ll try to shed some light on the Android TensorFlow example and some of the things going on under the hood.
original code
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android
My Website
http://softpowergroup.net/
My Blog
https://androidcontrol.blogspot.com
email : [email protected]
[email protected]
Tel .6681-6452400 ( Thailand )
Google+ https://plus.google.com/+SoftpowergroupNetThailand/
Facebook : https://www.facebook.com/softpowergroup/
