Link Search Menu Expand Document

A Python Demo Framework for eIQ on i.MX Processors

PyeIQ is written on top of eIQ™ ML Software Development Environment and provides a set of Python classes allowing the user to run Machine Learning applications in a simplified and efficiently way without spending time on cross-compilations, deployments or reading extensive guides.

pip3 PyPI version GitHub issues Downloads Downloads Downloads Total Lines Repo Size Closed Issues Open Issues Gitter

  • Take as a disclaimer that PyeIQ should not be considered production-ready.
  • For further questions, please post a comment on eIQ™ community.
Free and Open Source: Framework under BSD-3-Clause fully extensible and customizable.
Ready-to-use: Cutting-edge ML samples demonstrating full power of the of framework.


Object Classification (~3ms) Object Detection (~15ms)
oc_1 od_1
oc_2 od_2

Official Releases

BSP Release PyeIQ Release PyeIQ Updates Board Date Status Notes
BSP tag   imx Apr, 2020 Build PoC
    tag imx May, 2020 Build  
BSP tag   imx Jun, 2020 Build Stable
    tag imx Jun, 2020 Build  
    tag imx Aug, 2020 Build  
BSP   tag imx Nov, 2020 Build  

blue yellow red

Major Changes


  • General major changes on project structure.
  • Split project into engine, modules, helpers, utils and apps.
  • Add base class to use on all demos avoiding repeated code.
  • Support for more demos and applications including Arm NN.
  • Support for building using Docker.
  • Support for download data from multiple servers.
  • Support for searching devices and build pipelines.
  • Support for appsink/appsrc for QM (not working on MPlus).
  • Support for camera and H.264 video.
  • Support for Full HD, HD and VGA resolutions.
  • Support video and image for all demos.
  • Add display info in the frame, such as: FPS, model and inference time.
  • Add manager tool to launch demos and applications.
  • Add document page for PyeIQ project.


  • Support demos based on TensorFlow Lite (2.1.0) and image classification.
  • Support inference running on GPU/NPU and CPU.
  • Support file and camera as input data.
  • Support SSD (Single Shot Detection).
  • Support downloads on the fly (models, labels, dataset, etc).
  • Support old eIQ demos from eiq_sample_apps CAF repository.
  • Support model training for host PC.
  • Support UI for switching inference between GPU/NPU/CPU on TensorFlow Lite.

Copyright 2020 NXP Semiconductors. Free use of this software is granted under the terms of the BSD 3-Clause License. See LICENSE for details.