Getting Started with HUSKYLENS 2: Setup & Guide

Compatible Controllers Usage Instructions

Last revision 2026/08/13

HUSKYLENS 2 offers controller‑specific vision examples and data outputs across Arduino, micro:bit, UNIHIKER K10,UNHIKER M10 and Raspberry Pi; it’s like a menu where you first pick your board and coding style, then choose functions such as face recognition, receiving basic IDs, confidence and bounding boxes on all MCUs, while higher memory boards additionally expose facial landmarks and 21‑point hand or 17‑point body keypoint coordinates for richer AI projects.

1.Compatible Controllers and Project Examples

HUSKYLENS 2 supports multiple controllers and programming environments. To help you quickly find the example you need, the programming examples are organized by controller and programming method.

Select your controller and programming method below, then click the corresponding function to open the example.

1.1 Arduino Code Programming Examples with Arduino IDE

The following examples are for Arduino programmed with Arduino IDE using C++.

Overview and Setup	Face Recognition	Object Recognition	Object Tracking	Color Recognition
Self-Learning Classifier	Instance Segmentation	Hand Recognition	Pose Recognition	License Plate Recognition (LPR)
Optical Character Recognition	Line Tracking	Face Emotion Recognition	Tag Recognition	QR Code Recognition
Barcode Recognition

1.2 Arduino Graphical Programming Examples with MindPlus

The following examples are for Arduino programmed with MindPlus in Upload Mode using graphical programming.

Overview and Setup	Face Recognition	Object Recognition	Object Tracking	Color Recognition
Self-Learning Classifier	Instance Segmentation	Hand Recognition	Pose Recognition	License Plate Recognition (LPR)
Optical Character Recognition	Line Tracking	Face Emotion Recognition	Tag Recognition	QR Code Recognition
Barcode Recognition

1.3 micro:bit Graphical Programming Examples with MakeCode

The following examples are for micro:bit programmed with MakeCode using graphical programming.

Overview and Setup	Face Recognition	Object Recognition	Object Tracking	Color Recognition
Object Classification	Self-Learning Classifier	Instance Segmentation	Hand Recognition	License Plate Recognition (LPR)
Pose Recognition	Optical Character Recognition	Line Tracking	Face Emotion Recognition	Tag Recognition
QR Code Recognition	Barcode Recognition

1.4 micro:bit Graphical Programming Examples with MindPlus

The following examples are for micro:bit programmed with MindPlus in Upload Mode using graphical programming.

Overview and Setup	Face Recognition	Object Recognition	Object Tracking	Color Recognition
Self-Learning Classifier	Instance Segmentation	Hand Recognition	Pose Recognition	License Plate Recognition (LPR)
Optical Character Recognition	Line Tracking	Face Emotion Recognition	Tag Recognition	QR Code Recognition
Barcode Recognition

1.5 UNIHIKER K10 Code Programming Examples with Arduino IDE

The following examples are for UNIHIKER K10 programmed with Arduino IDE using C++.

Overview and Setup	Face Recognition	Object Recognition	Object Tracking	Color Recognition
Self-Learning Classifier	Instance Segmentation	Hand Recognition	Pose Recognition	License Plate Recognition (LPR)
Optical Character Recognition	Line Tracking	Face Emotion Recognition	Tag Recognition	QR Code Recognition
Barcode Recognition

1.6 UNIHIKER K10 Graphical Programming Examples with MindPlus

The following examples are for UNIHIKER K10 programmed with MindPlus in Upload Mode using graphical programming.

Overview and Setup	Face Recognition	Object Recognition	Object Tracking	Color Recognition
Object Classification	Instance Segmentation	Hand Recognition	Pose Recognition	License Plate Recognition (LPR)
Optical Character Recognition	Line Tracking	Face Emotion Recognition	Tag Recognition	QR Code Recognition
Barcode Recognition	General Functions Example

1.7 UNIHIKER M10 Code Programming Examples with MindPlus Python

The following examples are for UNIHIKER M10 programmed with MindPlus in Python Mode.

Overview and Setup	Face Recognition	Object Recognition	Object Tracking	Color Recognition
Object Classification	Self-Learning Classifier	Instance Segmentation	Hand Recognition	Pose Recognition
License Plate Recognition (LPR)	Optical Character Recognition	Line Tracking	Face Emotion Recognition	Tag Recognition
QR Code Recognition	Barcode Recognition	Custom-Trained Model Example

1.8 UNIHIKER M10 Graphical Programming Examples with MindPlus

The following examples are for UNIHIKER M10 programmed with MindPlus in Python Mode using graphical programming.

Overview and Setup	Face Recognition	Object Recognition	Object Tracking	Color Recognition
Object Classification	Self-Learning Classifier	Instance Segmentation	Hand Recognition	Pose Recognition
License Plate Recognition (LPR)	Optical Character Recognition	Line Tracking	Face Emotion Recognition	Tag Recognition
QR Code Recognition	Barcode Recognition	Custom-Trained Model Example

1.9 Raspberry Pi Python Programming Examples

For Raspberry Pi Python programming examples, please refer to the HUSKYLENS 2 Raspberry Pi Python Examples.

2.Explanation of Differences in Data Acquisition Among Compatible Controllers

HUSKYLENS 2 is compatible with common main controllers in the market, including Arduino, micro:bit,Raspberry Pi,UNIHIKER K10, and UNIHIKER M10. Due to differences in the performance capabilities of each main controller, the visual recognition output data obtained from HUSKYLENS 2 may vary.

Basic data refers to the information accessible to all MCU-based main controllers, including the data returned by each visual recognition function: ID, confidence level, object name, recognition content, center X/Y coordinates of the bounding box, and bounding box width/height.

For main controllers such as the UNIHIKER K10 and UNIHIKER M10, which are equipped with high-memory MCUs, additional feature-specific visual recognition data can be accessed beyond the basic data. These include extended attributes such as facial landmark coordinates, hand keypoint positions, and other high-level feature representations unique to specific vision models.

The following provides details on these differences in data output for each HUSKYLENS 2-compatible main controller across all supported visual recognition functions.

Function \ Controller	Arduino	UNIHIKER K10	UNIHIKER M10	micro:bit	Raspberry Pi
Face Recognition	Basic Data	Basic Data Facial feature coordinates	Basic Data Facial feature coordinates	Basic Data Facial feature coordinates	Basic Data Facial feature coordinates
Object Recognition	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data
Object Classification	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data
Instance Segmentation	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data
Object Tracking	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data
Color Recognition	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data
Self-Learning Classifier	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data
Hand Recognition	Basic Data	Basic Data 21 hand keypoint coordinates	Basic Data 21 hand keypoint coordinates	Basic Data 21 hand keypoint coordinates	Basic Data 21 hand keypoint coordinates
Pose Recognition	Basic Data	Basic Data 17 body keypoint coordinates	Basic Data 17 body keypoint coordinates	Basic Data 17 body keypoint coordinates	Basic Data 17 body keypoint coordinates
License Plate Recognition	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data
Optical Character Recognition	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data
Line Tracking	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data
Face Emotion Recognition	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data
Tag Recognition	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data
QR Code Recognition	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data
Barcode Recognition	Basic Data	Basic Data	Basic Data	Basic Data	Basic Data

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