Brainy Pi

Available to select audience (currently beta)

BrainyPi

AI on Rbian

Description

Makes it easy for Brainy Pi customers to build AI applications using different frameworks. AI on Rbian page shows working methods to install popular AI Frameworks with a test example. Additionally the page will link to individual framework pages where more ready to use examples are available.

INFO
This documentation is for Rbian OS version: 0.7.2-beta
To check the version of Rbian run the command in terminal
				
					os-version
Note: If the command fails or gives error then Rbian version is < 0.7.2-beta.

Installing Frameworks and using with example

This Document will give you a step wise instuctions for installing AI frameworks on BrainyPi and using it.

Tensorflow

Installing Tensorflow

  • Run this command on terminal
    pip3 install tensorflow
pip3 install tensorflow-io
  • Installs version: 2.10.0

TF sample program to classify 1000 imagenet classes

  • Clone the repository and navigate to folder
    git clone https://github.com/brainypi/BrainyPi-AI-Examples.git
cd BrainyPi-AI-Examples/Tensorflow
  • Now run Image classfication example. classifies Imagenet 1000 classes.
    python3 classify_image.py --image_file peacock.jpg
    • Input
      • Parameter1(Default: peacock.jpg): –image_file: Image file location
      • Parameter2(Default: 5): —-num_top_predictions: No of predictions you want to see
    • Output
      • Shows the Classified class label with probability on terminal

TFLite

Installing Tflite

  • Run this command on terminal
    pip3 install tflite-runtime
    • Install version: 2.11.0

TFlite sample program to classify 1000 imagenet classes

  • Clone the repository and navigate to folder
    git clone https://github.com/brainypi/BrainyPi-AI-Examples.git
cd BrainyPi-AI-Examples/TFLite
  • Now run Image classfication example. classifies Imagenet 1000 classes.
    python3 classify_image.py --image_file peacock.jpg
    • Input
      • Parameter1(Default: peacock.jpg): –image_file: Image file location
      • Parameter2(Default: 5): —-num_top_predictions: No of predictions you want to see
    • Output
      • Shows the Classified class label with probability on terminal

Pytorch

Installing Pytorch

  • Run this command on terminal
    pip3 install torch==1.13.1 torchvision==0.14.1
  • Installs version: torch – 1.13.1 & torchvision – 0.14.1

Pytorch Sample program to classify 1000 imagenet classes

  • Clone the repository and navigate to folder
    git clone https://github.com/brainypi/BrainyPi-AI-Examples.git
cd BrainyPi-AI-Examples/Pytorch
  • Now run Image classfication example. classifies Imagenet 1000 classes.
    python3 classify_image.py --image_file peacock.jpg
    • Input
      • Parameter1(Default: peacock.jpg): –image_file: Image file location
      • Parameter2(Default: 5): —-num_top_predictions: No of predictions you want to see
    • Output
      • Shows the Classified class label with probability on terminal

Opencv

Installing Opencv

  • Run this command on terminal
    pip3 install opencv-python
  • Installs version: 4.6.0.66

Opencv Sample program to blur the image

  • Clone the repository and navigate to folder
    git clone https://github.com/brainypi/BrainyPi-AI-Examples.git
cd BrainyPi-AI-Examples/Opencv
  • Now run Image Blur example.
    python3 blurImage.py --image_file peacock.jpg
    • Input
      • Parameter1(Default: peacock.jpg): –image_file: Image file location
    • Output
      • Stores output images with respective blue method names.

ONNX Runtime

Installing ONNX runtime

  • Run this command on terminal
    pip3 install tensorflow
pip3 install tensorflow-io
pip3 install onnxruntime
  • Installs version: onnxruntime – 1.13.1

ONNX Sample Program to Classify 1000 imagenet classes

  • Clone the repository and navigate to folder
    git clone https://github.com/brainypi/BrainyPi-AI-Examples.git
cd BrainyPi-AI-Examples/Onnx
  • Now run Image classfication example. classifies Imagenet 1000 classes.
    python3 classify_image.py --image_file peacock.jpg
    • Input
      • Parameter1(Default: peacock.jpg): –image_file: Image file location
      • Parameter2(Default: 5): —-num_top_predictions: No of predictions you want to see
    • Output
      • Shows the Classified class label with probability on terminal
  • Convert tensorflow, pytorch models to ONNX: Official Documentation

More Examples and Sample Projects on AI

NEED SUPPORT?
First, Ensure version of OS installed and the version this document is intended for match. If they match and yet problem persists. Please use this Forum link for community help.
If you are an enterprise customer please use the ticketing system login provided to you for priority support.

Previous

<< Raspberry Pi Compatibility

Next

Rbian OS Advances >>

Index