Deep learning using Tensorflow Lite on Raspberry Pi

About this Repository

This course is focused on Embedded Deep learning in Python . Raspberry PI 4 is utilized as a main hardware and we will be building practical projects with custom data .

• We will start with trigonometric functions approximation . In which we will generate random data and produce a model for Sin function approximation

• Next is a calculator that takes images as input and builds up an equation and produces a result .This Computer vision based project is going to be using convolution network architecture for Categorical classification

• Another amazing project is focused on convolution network but the data is custom voice recordings . We will involve a little bit of electronics to show the output by controlling our multiple LEDs using own voice .

• Unique learning point in this course is Post Quantization applied on Tensor flow models trained on Google Colab . Reducing size of models to 3 times and increasing inferencing speed up to 0.03 sec per input .

Note: This repo contains step by step approach to teach different things to students of our course. You may find some raw data / codes which are meant for learning purposes of students.

Installations

• Laptop/PC Installations

  • Rpi-Imager for installing RPI OS on SD CARD

    sudo apt install rpi-imager
    

  • Tensorflow

    pip install tensorflow
    

• Raspberry PI 4 installations

  • Tensorflow Lite Interpreter

    python3 -m pip install tflite-runtime
    

  • Install tightvnc server

    sudo apt-get install tightvncserver
    

• Common Installations

  • OPENCV

    pip3 install opencv-python
    sudo apt-get install libcblas-dev
    sudo apt-get install libhdf5-dev
    sudo apt-get install libhdf5-serial-dev
    sudo apt-get install libatlas-base-dev
    sudo apt-get install libjasper-dev
    sudo apt-get install libqtgui4
    sudo apt-get install libqt4-test
    sudo apt-get install libatlas-base-dev
    

  • Upgrade Numpy

    pip install -U numpy
    

  • Audio processing Dependencies

    pip install sounddevice
    sudo apt-get install libportaudio2
    pip install scipy
    

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Using Repository

• SSH into your RPI

  ssh pi@<IP_of_RPI>
  

• Access RPI through TeamViewer on PC

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