Conversion & Quantization
=========================

.. toctree::
    :hidden:

    convPytorchOnnx
    quantOnnx
    convOnnxTfLite
    tensorflow
    imx93
    neutron

.. note::
    See the :ref:`video-opt-pipeline` and other video tutorials on how-to use the GUI.

NXP’s edge computing solutions require machine learning models in quantized **TensorFlow Lite (TF Lite)** format
for optimal performance and deployment. However, most ML engineers develop and train models using popular
frameworks such as **PyTorch** or **TensorFlow**, which often produce formats not directly compatible with NXP hardware.

**eIQ AI Toolkit** bridges this gap by offering robust conversion and quantization tools
that transform trained models into deployment-ready formats.

Especially platforms like

* **i.MX93**
* **i.MX RT700**
* **MCX N series**

need models converted to a specialized TF Lite variants.
For platform-specific deployment instructions, see the :doc:`Deployment Guide <../deploy/index>`.

In the following guides, you’ll find all the steps needed to convert models into quantized TFLite format.
Specifically, you’ll learn how to:

* :doc:`Convert a TensorFlow model to quantized TFLite<./tensorflow>`
* :doc:`Convert a PyTorch model to ONNX<./convPytorchOnnx>`
* :doc:`Quantize an ONNX model<./quantOnnx>`
* :doc:`Convert a quantized ONNX model to quantized TFLite<./convOnnxTfLite>`
* :doc:`Convert quantized TFLite to the format supported by i.MX 93 NPU<./imx93>`
* :doc:`Convert quantized TFLite to the format supported by Neutron NPU<./neutron>`


These guides include practical examples demonstrating the **eIQ AI Toolkit**’s conversion API. For complete API documentation:

1. Set up **IQ AI Toolkit** using the :doc:`Installation Guide <../tools/aiToolkit/installRun>`
2. Launch the application
3. Visit http://localhost:8000/docs for interactive API documentation