TensorFlowAndroidMNIST | Tensorflow MNIST demo on Android | Learning library

by miyosuda C++ Version: Current License: No License

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kandi X-RAY | TensorFlowAndroidMNIST Summary

TensorFlowAndroidMNIST is a C++ library typically used in Tutorial, Learning, Tensorflow applications. TensorFlowAndroidMNIST has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

This is a demo app for Android with Tensorflow to detect handwritten digits. This Android demo is based on Tensorflow tutorial. MNIST For ML Beginners Deep MNIST for Experts

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TensorFlowAndroidMNIST has a low active ecosystem.

It has 264 star(s) with 109 fork(s). There are 26 watchers for this library.

It had no major release in the last 6 months.

There are 12 open issues and 1 have been closed. On average issues are closed in 1 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of TensorFlowAndroidMNIST is current.

Quality

TensorFlowAndroidMNIST has no bugs reported.

Security

TensorFlowAndroidMNIST has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

TensorFlowAndroidMNIST does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

TensorFlowAndroidMNIST releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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TensorFlowAndroidMNIST Key Features

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TensorFlowAndroidMNIST Examples and Code Snippets

No Code Snippets are available at this moment for TensorFlowAndroidMNIST.

Community Discussions

Trending Discussions on TensorFlowAndroidMNIST

How to resize bitmap to low resolution without loosing image information?

QUESTION

How to resize bitmap to low resolution without loosing image information?

Asked 2017-Nov-15 at 01:34

I am creating a deep learning based android app. I have a Canvas where I am allowing the user to draw something. Then I will pass the bitmap of the Canvas to my model for classification. I am using Tensorflow MNIST project as the base for my project. My problem is, in the MNIST example user are allowed to draw on a 28x28 size Canvas. But I don't want to do that because drawing on that Canvas is pixelating the drawing. I am drawing on a full size Canvas but while sending the Bitmap of the canvas to the Tensorflow model I want to resize it to 28x28 for classification (else I am getting ArrayIndexOutOfBoundException).

How can I resize the bitmap to 28x28 without loosing information ? or any other possible solution for this ?

Here is the image of the MNIST canvas:

This is the image of my application canvas. I tried resizing it to 28x28 but I am loosing the image information:

...

ANSWER

Answered 2017-Nov-15 at 01:34

Let's say you have an input of 100 x 100 image and you want to resize it to 28 x 28.

100 x 100 pixels -> 10000 features

28 x 28 pixels -> 784 features

It is mathematically impossible not to lose input information while resizing.

However there are other ways to work around.

Get rid of white areas around drawing. It will already filter most of the unnecessary pixels. Then you can apply resizing.
Try a different interpolation method. During the resizing operation, we are using different kinds of interpolations (bilinear, bicubic etc.).
Try to make the input images bigger for your network (resize 28 x 28 MNIST to 56 x 56 for example) and train with bigger size. Then, you will be losing less information for inference in cost of speed as it will be slightly slower to classify larger image.

Source https://stackoverflow.com/questions/47274789

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install TensorFlowAndroidMNIST

Native .so files are already built in this project, but if you would like to build it by yourself, please install and setup NDK. First download, extract and place Android NDK. And then update your PATH environment variable. For example,. And build .so file in jni-build dir. and copy .so file into app/src/main/jniLibs/armeabi-v7a/ with. (Unlike original Android demo in Tensorflow, you don't need to install bazel to build this demo. Tensorflow library files (.a files) and header files are extracted from original Tensorflow Android demo r0.10.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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