TensorFlow Lite Micro Model Conversion Tutorial 📌

Welcome to the TensorFlow Lite Micro model conversion guide! This tutorial walks you through converting TensorFlow models into the lightweight TensorFlow Lite Micro format, optimized for microcontroller deployment. ✅

Steps to Convert a Model 🛠️

1. Prepare Your TensorFlow Model

   • Ensure your model is saved in the .h5 or .pb format.
   • Use TensorFlow Lite Converter to convert it to a .tflite file.
   

2. Quantize for Efficiency

   • Quantize the model to reduce size and improve inference speed.
   • Use tf.lite.TFLiteConverter with the optimizations parameter.
   

3. Convert to TensorFlow Lite Micro

   • Use the TensorFlow Lite Micro converter to generate C code for microcontrollers.
   • Example:

     tflite-micro-converter --model=model.tflite --output=output.c
     

   

Additional Resources 📚

• TensorFlow Lite Micro Documentation
• Model Conversion Best Practices
• GitHub Repository for Examples

For visualizing the conversion workflow, check out the TensorFlow Lite Micro architecture diagram. 🌐