The M5Stack UnitV2 USB is the latest high efficiency AI recognition module from M5Stack, it adopts Sigmstar SSD202D (integrated dual-core Cortex-A7 1.2GHz processor) control core, 128MB-DDR3 memory and 512MB NAND Flash. It offers USB-A universal interface, which allows you to connect various UVC Cameras, Built-in Linux operating system, integrated with rich hardware and software resources and development tools brings you a simple and efficient AI development experience right out of the box!
Product Features
- Sigmstar SSD202D
- Dual-core Cortex-A7 1.2GHz processor
- 128MB DDR3
- 512MB NAND Flash
- USB-A universal interface, can be connected to various UVC cameras
- Wi-Fi 2.4GHz
- Development method:
- Equipped with 12 ways AI image functions: QR code, face detection, line tracking, movement, shape matching, image streaming, classification, color tracking, face recognition, target tracking, shape detection, custom object detection
- Support online preview, UIFlow (used as serial port json format)
- Linux system(OpenCV, SSH, JupyterNotebook)
Application
- AI recognition function development
- Industrial visual recognition classification
- Machine vision learning
Video
Learn
Quick Start
- Built-in recognition function use tutorial
- V-Training online AI model training service
- Jupyter Notebook Development Tutorial/Example
- SSH connection & WIFI configuration
- Firmware update tutorial
Examples
Arduino
Driver Installation
Download the corresponding SR9900 driver according to the operating system used.
Windows10
Extract the driver compressed package to the desktop path -> Enter the device manager and select the currently unrecognized device (named with SR9900) -> Right-click and select Custom Update -> Select the path where the compressed package is decompressed -> Click OK and wait for the update carry out.
MacOS
Extract the driver package -> double-click to open the SR9900_v1.x.pkg file -> follow the prompts and click Next to install. (The compressed package contains a detailed version of the driver installation tutorial pdf)
- After the installation is complete, if the network card cannot be enabled normally, you can open the terminal and use the command below to re-enable the network card.
sudo ifconfig en10 down
sudo ifconfig en10 up
Out Of The Box AI Recognition Function
- UnitV2 integrates not only the basic AI recognition developed by M5Stack, but also has built-in multiple recognition (such as face recognition, object tracking and other common functions), which can quickly help users build AI recognition applications.
- All features! Plug and play! UnitV2 has a built-in wired network card. When you connect to a PC through the TypeC interface, it will automatically establish a network connection with UnitV2.Flexibly Connectable, it can also be connected and debugged via Wi-Fi.
- UART serial port output, all identification content is automatically output in
JSONformat through the serial port for convenient use. - Built-in recognition function use tutorial
- Identify the source code of the service framework
- Firmware update tutorial
Development Efficiency Improvement
- UnitV2’s factory setting Linux image integrates a variety of basic peripherals and development tools (such as Jupyter Notebook etc.)
- Through SSH access, you can fully control the hardware resources of this camera
- Easily build a custom recognition model through M5Stack’s V-Training (AI model training service).
- V-Training online AI model training service
- Jupyter Notebook Development Tutorial/Example
- SSH connection & WIFI configuration
UNIT-V2 series comparison
| Spec | UNIT-V2 | UNIT-V2 M12 | UNIT-V2 USB |
|---|---|---|---|
| Lens equipment | Normal focal length (FOV 68°) | Normal focal length (FOV 85°) + wide-angle focal length (FOV: 150°) | Without lens, USB-A universal interface, can be connected to various UVC cameras |
| CMOS | GC2145 | GC2053 | / |
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