CNNdroid | Open Source Library for GPU-Accelerated Execution | Machine Learning library

by ENCP Java Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | CNNdroid Summary

CNNdroid is a Java library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. CNNdroid has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However CNNdroid build file is not available. You can download it from GitHub.

CNNdroid is an open source library for execution of trained convolutional neural networks on Android devices. The main highlights of CNNdroid are as follows:. For more information about the library and installation guide, please refer to the [user guide] CNNdroid Complete Developers Guide and Installation Instruction.pdf).

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Support

CNNdroid has a low active ecosystem.

It has 544 star(s) with 184 fork(s). There are 64 watchers for this library.

It had no major release in the last 6 months.

There are 19 open issues and 17 have been closed. On average issues are closed in 15 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of CNNdroid is current.

Quality

CNNdroid has no bugs reported.

Security

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

License

CNNdroid is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

CNNdroid has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed CNNdroid and discovered the below as its top functions. This is intended to give you an instant insight into CNNdroid implemented functionality, and help decide if they suit your requirements.

Parse the network definition file
Derive a layer from the input string
Derives the string from the given string
Derives a number from a string
Computes the Nddroid
Converts a layer into a F1F image
Constructs a fully - qualified Renderscriptor with the given parameters
Fit the tuning algorithm
Computes the kernel
Produce a convolution layer
Builds a convolution layer from a FL4 feature layer
Builds a convolution layer using a FL4 feature layer
Pre - processes the net structure
Merges two arrays
Converts a list of long values to an array of longs
Merge sort
Compute the CNNdroid for the given input
Performs the actual sampling
Checks if the given string is corrupted
Run the input blob
Computes the knnroid
Performs the computation on the network
Computes the neural network for the given input
Performs the computing accuracy on the given input object
Returns the squared error
Computes the dense projection for a given input object

Get all kandi verified functions for this library.

CNNdroid Key Features

No Key Features are available at this moment for CNNdroid.

CNNdroid Examples and Code Snippets

No Code Snippets are available at this moment for CNNdroid.

Community Discussions

Trending Discussions on CNNdroid

Human pose estimation/matching on smartphone

QUESTION

Human pose estimation/matching on smartphone

Asked 2018-Mar-26 at 12:12

Im working on a project where a person must mimic a predefined pose. A picture is made from the person that mimics this predefined pose. Then, the human pose of the person is extracted from this image and compared with the predefined pose. Finally a scoring mechanism decides how well the two poses match or if they match at all.

I want to develop for smartphone, so ideally everything runs embedded on the smartphone itself. This means, the implementation is capable of running on CPU or smartphone GPU (example Moto G5 plus, Adreno 506 GPU on board -supports OpenGL-). Working embedded is not a must, i think it's also possible to outsource the estimation/matching algorithm to a central server containing a decent GPU. This particular choice, embedded or out-sourcing, is an issue that involves a lot of parameters (performance/computation power, server cost, accuracy, mobile battery usage, delay server communication, multi platform, scalability, mobile data usage -less important- , ...)

I know there are some frameworks out there for human pose estimation, like Openpose and deepercut. But as they all use deep learning, they require a descent GPU. Most of the new smartphones these days have a GPU on board, but are they capable of running these frameworks? To nuance for this case, the (multi-person) keypoint detection doesn't need to be realtime, as there is only 1 picture (no realtime video) and a delay time of 2 to 5 seconds is acceptable.

As I'm still in the research phase, I don't know what direction I should go. Is it even possible to port these frameworks to a smartphone platform? Like Openpose for example, which uses Caffe and OpenCV. Let's say I want to port Openpose to Android; I know there is a library CNNdroid that is capable of converting CNN models made with Caffe to CNNdroid format. Further OpenCV also shouldn't be a big problem as there is a Android version available. So, in theory it seems possible, but what in practice..

My question is: Is there someone who has experience with human pose detection/matching on smartphone? Is it even possible with the current GPU's available on smartphone. I know this is a broad question, but some directions/suggestions/experience could really help

UPDATE: I'm thinking about the option of porting Openpose (uses Caffe as ML framework) to TensorFlow. TensorFlow supports both Android & iOS

...

ANSWER

Answered 2017-Oct-14 at 15:55

You might be interested in looking at the techniques used by Krafka et al. for their Eye Tracking for Everyone project in which they compress a larger network for estimating gaze coordinates into a smaller network which can run on a smartphone. This is using a concept developed by Geoff Hinton which he called Dark Knowledge. Gaze detection is a special case of pose estimation, so in principle it would seem like these techniques would be helpful. However, I do not know whether they will be sufficiently effective for your purposes (I think that largely depends on your accuracy constraints).

Source https://stackoverflow.com/questions/46728358

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

Vulnerabilities

No vulnerabilities reported

Install CNNdroid

You can download it from GitHub.
You can use CNNdroid like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the CNNdroid component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

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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