eIQ Sample Apps - Overview

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   The eIQ Sample Apps repository hosts Machine Learning applications demos based on the eIQ^™ ML Software Development Environment. The following examples were tested and used for training purposes. To be understandable each application contains a read-me file allowing the user to get started with the eIQ demos.

   The eIQ samples apps target the latest eIQ release and are split in labs sections. Before starting with the examples, read the introduction part:

• eIQ Sample Apps - Introduction

1. Object Recognition using Arm NN
   

   This section contains samples for running inference and predicting different objects. It also includes an extension that can recognize any given camera input/object.

   • eIQ Sample Apps - Object Recognition using Arm NN
2. Handwritten Digit Recognition
   

   This section focuses on a comparison of inference time between different models for handwritten digits recognition.

   • eIQ Sample Apps - Handwritten Digit Recognition
3. Object Recognition using OpenCV DNN

   This section uses OpenCV DNN module for running inference and detecting objects from an image. It also includes an extension that can detect any given camera input/object.

   • eIQ Sample Apps - Object Recognition using OpenCV DNN
4. Face Recognition using TensorFlow Lite

   This section uses a model for running inference and recognizing faces.

   • eIQ Sample Apps - Face Recognition using TF Lite
5. TensorFlow Lite Quantization

   This tutorial demonstrates how to convert a TensorFlow model to TensorFlow Lite and then apply quantization.

   • eIQ Sample Apps - TFLite Quantization
6. TensorFlow Transfer Learning

   This lab takes a TensorFlow image classification model and re-trains it to categorize images of flowers. 

   • eIQ Transfer Learning Lab with i.MX 8

To deploy the demos from the eIQ Sample Apps repository to an i.MX8 board, please check: Deploying the eIQ Sample Apps to an i.MX8 board 

These labs sections will be updated frequently in order to keep all codes and tutorials up-to-date.

Check also: https://community.nxp.com/community/eiq/blog/2020/06/30/pyeiq-a-python-framework-for-eiq-on-imx-proc...