Welcome to the TensorFlow Lite Micro quickstart guide! This tutorial will walk you through the basics of deploying machine learning models on microcontrollers using TensorFlow Lite Micro.

🧰 Step-by-Step Guide

  1. Setup Development Environment

    • Install Arduino IDE or PlatformIO
    • Configure a supported microcontroller (e.g., STM32, ESP32)
    • Install TensorFlow Lite Micro library via GitHub

  2. Convert ML Model
    Use the TensorFlow Lite Model Converter to transform your TensorFlow model into a C array. 

    tflite_convert --input_file=model.pb --output_file=model_data.c --input_format=protobuf --output_format=c

  3. Integrate with Code

    • Include the generated C file in your project
    • Initialize the TensorFlow Lite interpreter with: 
      TfLiteContext context;
TfLiteModel* model = tflite::micro::GetModel(&context, model_data);
    • Run inference on your microcontroller

📚 Expand Your Knowledge

For deeper insights into TensorFlow Lite Micro, check out our Getting Started Guide to explore hardware compatibility and optimization techniques.

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