M5Stack UnitV2 is an ultra-compact Linux AI camera powered on Sigmastar SSD202D SoC with a dual-core Cortex-A7 processor @ 1.2 GHz, and 128MB on-chip DDR3 that was launched in April 2021 with a Full HD camera featuring a 68° field-of-view.
M5Stack has now introduced two new models, one called M5Stack UnitV2 USB without any camera at all, instead relying on an external USB UVC camera, and the other named M5Stack UnitV2 M12 equipped with an M12 socket and shipping with both a normal focal length camera with an 85° FoV and wide-angle focal length with a 150° FoV.
M5Stack UnitV2 USB
M5Stack Unitv2 USB specifications:
- SoC – SigmaStar SSD202D dual-core Cortex-A7 processor @ 1.2 GHz with 128MB on-chip DDR3
- Storage – 512MB on-chip NAND flash with around 100MB free space, MicroSD card socket
- Camera – N/A
- Audio – Built-in microphone
- Connectivity – 2.4GHz WIFi 4 up to 150 Mbps (RTL8188FTV), Ethernet via USB-C through SR9900 USB 2.0 to Ethernet chip
- USB
- USB 2.0 Type-A port to connect an external UVC camera
- USB 2.0 Type-C port
- Expansion – Grove connector
- Misc – Button, cooling fan
- Power Supply – 5V/500mA via USB-C port
- Dimensions – 48 x 24 x 24 mm
- Weight – TBD
M5Stack UnitV2 USB ships with a 16GB microSD card, a USB-C cable, and a back brick (LEGO-compatible?). Just like the original model, it is designed for AI recognition, industrial visual recognition classification, and machine vision learning. As a Linux system, it supports OpenCV orJupyter Notebook, and can also be programmed with the company’s UIFlow visual programming IDE.
The system supports several functions out of the box with QR code, face detection, line tracking, movement, shape matching, image streaming, classification, color tracking, face recognition, target tracking, shape detection, and it’s also possible to customize objects detection to your own requirements. More details to get started can be found on the documentation website.
M5Stack UnitV2 M12
M5Stack Unitv2 M12 specifications:
- SoC – SigmaStar SSD202D dual-core Cortex-A7 processor @ 1.2 GHz with 128MB on-chip DDR3
- Storage – 512MB on-chip NAND flash with around 100MB free space, MicroSD card socket
- Camera
- GC2145 1080P Colored Sensor
- M12 socket supporting regular focal length (FOV: 85°) or wide-angle fisheye lens (FOV: 150°)
- Audio – Built-in microphone
- Connectivity – 2.4GHz WIFi 4 up to 150 Mbps (RTL8188FTV), Ethernet via USB-C through SR9900 USB 2.0 to Ethernet chip
- USB – USB Type-C port
- Expansion – Grove connector
- Misc – Button, cooling fan
- Power Supply – 5V/500mA via USB-C port
- Dimensions – 48 x 33 x 24 mm
- Weight – TBD
M5Stack UnitV2 USB ships with a 16GB microSD card, a USB-C cable, a back brick, and a stand. The applications and method of development are exactly the same as for the USB version, but there’s stil a separate Wiki.
M5Stack Unitv2 price
Since it does not come with any camera, the M5Stack Unitv2 USB model is the cheapest of the lot at $65, or $10 less than the original model, while M5Stack Unitv2 M12 goes for $95.
Jean-Luc started CNX Software in 2010 as a part-time endeavor, before quitting his job as a software engineering manager, and starting to write daily news, and reviews full time later in 2011.
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I wonder why they used SSD202D when R329 has better CPU and an AI accelerator that claims to be n times faster than A7 for AI tasks?
I guess price and using NPUs being such a mess right now. Sigmastar have chips with NPU etc. I think they have or will have a dual A53 + NPU chip.
You need to use their funky toolchain to convert normal ML models into something that can run on those NPUs and I guess it’s the same for Allwinner etc.
For people that have these there is an advantage that these will at some point work with mainline Linux.