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114 lines
3.3 KiB
Markdown
114 lines
3.3 KiB
Markdown
# UnitV2
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## Description
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UnitV2 is a high-efficiency AI recognition module launched by M5Stack, using Sigmstar SSD202D (integrated dual-core Cortex-A7 1.2Ghz processor) control core, integrated 256MB-DDR3 memory, 512MB NAND Flash, and 1080P camera. With embedded Linux operating system and rich software and hardware resources and development tools integrated, UnitV2 is committed to bringing users a simple and efficient AI development experience out of the box.
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## Out Of The Box AI Recognition Function
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- UnitV2 integrates not only the basic AI recognition service developed by M5Stack, but also has built-in multiple recognition functions (such as face recognition, object tracking and other common functions), which can quickly help users build AI recognition applications.
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- 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. With highly free connectable style, it can also be connected and debugged via WiFi.
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- UART serial port output, all identification content is automatically output in `JSON` format through the serial port, which is convenient to call.
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## Development Efficiency Improvement
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- UnitV2's factory Linux image integrates a variety of basic peripherals and development tools (such as Jupter Notebook etc.)
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- Through SSH access, you can fully control the hardware resources of this camera
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- Easily build a custom recognition model through M5Stack's V-Training (AI model training service).
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## Software Support
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- The M5Stack team will update more identification function services through the release of firmware in the future, and users can directly update the software through TFCard.
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## Product Features
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- Sigmstar SSD202D
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- Dual Cortex-A7 1.2Ghz Processor
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- 256MB DDR3
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- 512MB NAND Flash
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- GC2145 1080P Colored Sensor
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- Microphone
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- WiFi 2.4GHz
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## Include
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- 1x UnitV2
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## Applications
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- AI recognition function development
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- Industrial visual identification sorting
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- Machine vision learning
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## Specification
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<table>
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<thead>
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<tr>
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<th>Resources</th>
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<th>Parameter</th>
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</tr>
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</thead>
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<tr>
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<td>Sigmstar SSD202D</td>
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<td>Dual Cortex-A7 1.2Ghz Processor</td>
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</tr>
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<tr>
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<td>Flash</td>
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<td>512MB NAND</td>
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</tr>
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<tr>
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<td>RAM</td>
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<td>256MB-DDR3</td>
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</tr>
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<tr>
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<td>Camera</td>
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<td>GC2145 1080P Colored Sensor </td>
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</tr>
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<tr>
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<td>Lens</td>
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<td>FOV 68° , DOF= 60cm- ∞</td>
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</tr>
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<tr>
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<td>Power Input</td>
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<td>5V @ 500mA</td>
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</tr>
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<tr>
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<td>Peripherals</td>
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<td>TypeC x1, UART x1, TFCard x1, Button x1, Microphone x1, Fan x1</td>
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</tr>
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<tr>
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<td>Indicator light</td>
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<td>Red, While</td>
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</tr>
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<tr>
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<td>WiFi</td>
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<td>150Mbps 2.4GHz 802.11 b/g/n</td>
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</tr>
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<tr>
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<td>Operating Temperature</td>
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<td>32°F to 104°F ( 0°C to 40°C )</td>
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</tr>
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<tr>
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<td>Net weight</td>
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<td>18g</td>
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<tr>
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<td>Gross weight</td>
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<td>62g</td>
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</tr>
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<tr>
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<td>Product Size</td>
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<td>48*18.5*24mm</td>
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</tr>
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<tr>
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<td>Package Size</td>
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<td>157*38*38mm</td>
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</tr>
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<tr>
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<td>Case Material</td>
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<td>Plastic ( PC )</td>
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</tr>
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</table>
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