Deep learning using Tensorflow Lite on Raspberry Pi

About this Repository

Dive into Embedded Deep Learning using Python with Raspberry Pi 4. Here, we're building a unique Computer Vision project using custom data. Specifically:

• A calculator that processes images, forms equations, and provides results. It's crafted using a convolution network architecture, ideal for categorical classification.

• Learn about Post Quantization on TensorFlow models. The model is trained on Google Colab and is optimized — it's 3 times smaller with an inference speed of just 0.024 seconds per input.

Note: This repository is your guide, detailing each step of the project. (the data and scripts are in the development branch)

---

Features

• Real-Time Number Detection
• Visual Calculator Equation Solving
• • 

---

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
    

---

Using Repository

• Obtain the code using Git

  git clone --single-branch --branch Development https://github.com/jaafarsaf7/Embedded-Deep-Learning-on-RaspberryPi4.git
  

• SSH into your RPI

  ssh pi@<IP_of_RPI>
  

• Turn on the TightVNC Server to enable screen sharing

  tightvncserver :1
  

• Access RPI through VNC-Viwer on PC

---

Pre-Course Requirments

• PC : Ubuntu 22.04
• RPI4 : RPI Full OS
  • SD-CARD 16GB
  • RPI Camera V2
  • Power Bank with Type C cable
  • 3D printed Parts for Camera Holding
  • Fan on RPI for better thermals

---

Jaafar Safar