Run a computer vision model on a Himax microcontroller
Introduction
Overview
Set up the environment
Build the firmware
Flash firmware onto the microcontroller
Run additional models in the web toolkit
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  1 hr 30 min
Last updated:  13 Aug 2026
Skill level:
  Introductory
Reading time:
  1 hr 30 min
Last updated:
  13 Aug 2026
Authors:Chaodong Gong
Alex Su
Kieran Hejmadi, Arm
Arm IP:Cortex-M Ethos-U
Tags: ML Linux macOS Himax SDK Python Hugging Face
Authors:
  • Chaodong Gong
  • Alex Su
  • Kieran Hejmadi, Arm
Arm IP:
Cortex-M Ethos-U
Tags:
ML Linux macOS Himax SDK Python Hugging Face

Who is this for?

This is an introductory topic for developers who would like to learn about how to run a computer vision application on an embedded device from Himax.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Run a You-Only-Look-Once (YOLO) object detection model on a Himax WiseEye2 module.
  • Build the Himax Software Development Kit (SDK) and generate a firmware image file.
  • Update firmware on the Himax WiseEye2.
  • Connect to and use Grove Vision AI module.

Prerequisites

Before starting, you will need the following:

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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You’ll build and deploy a YOLO computer-vision example on a Himax WiseEye2 microcontroller with the Grove Vision AI Module V2. First, you’ll prepare the host, clone the example repository, and build firmware. Then, you’ll connect the camera, select an application in the makefile, flash the device, and verify its live feed on Cortex-M55 and Ethos-U55 hardware.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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How do I clone the correct Himax examples and make sure submodules are included?
Run git clone --recursive https://github.com/HimaxWiseEyePlus/Seeed_Grove_Vision_AI_Module_V2.git, then change into the new directory. The --recursive option pulls the required third-party subrepositories.
Where do I change the application type and which option should I pick for YOLO object detection?
Go to Seeed_Grove_Vision_AI_Module_V2/EPII_CM55M_APP_S and open the makefile. Set APP_TYPE to a value from the provided table. For object detection, use tflm_yolov8_od. Then, pass the corresponding model argument with the --model option when flashing.
How do I install the Xmodem dependency before flashing?
From the repository root, run pip install -r xmodem/requirements.txt. This installs an Xmodem file transfer utility used by the flashing scripts.
How should I connect the camera module to the Grove Vision AI V2?
Insert the flexible printed circuit (FPC) cable into the module’s connector and lift the dark grey latch, then secure the cable. Connect the board to the host with a USB-C cable.
What result should I expect after flashing, and how do I know the model is running?
You should be able to view a live camera feed with the computer vision application active. If you selected the YOLO option, expect object detection to appear in the feed.
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