OpenMV Classification Training

This repository contains a Jupyter notebook that demonstrates how to perform post-training integer quantization on machine learning models. This technique is particularly useful for reducing model size and improving inference speed, especially on low-power devices like the OpenMV camera.

Features

• Post-training Integer Quantization: Optimize a model by converting 32-bit floating-point numbers to 8-bit fixed-point numbers.
• Low-Power Device Compatibility: The notebook is tailored for devices with limited computational resources, such as the OpenMV camera.
• Efficient Model Deployment: The techniques demonstrated ensure smaller model sizes and faster inference.

Getting Started

Setup

1. Clone the repository:

git clone https://github.com/thelocker98/openmv-classification-training.git cd openmv-classification-training

3. Run the notebook using Jupyter

jupyter lab

How to Use

• Open the Notebook: Launch the notebook and execute all code cells in sequence.
• Use Your Own Images: Follow the steps outlined in the notebook to use your own images for training or testing.
• Deploy the Model:
  • Copy the quantized model from the models folder and load it onto the OpenMV camera.
  • Transfer the boot.py and labels.txt files from the OpenMV code folder to the camera.
  • Unplug and reconnect the camera to automatically run the model.

References

• OpenMV Camera
• Google post on model quantization