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+# 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](https://openmv.io) 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:
+```bash
+git clone https://github.com/thelocker98/openmv-classification-training.git cd openmv-classification-training
+```
+3. Run the notebook using Jupyter
+```bash
+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](https://openmv.io)
+- [Google post on model quantization](https://ai.google.dev/edge/litert/models/post_training_integer_quant)