reComputer J1010 -Edge AI Device with Jetson Nano module, M.2 Key E Slot, Type-C connectors, Aluminium case, pre-installed JetPack System
J1010 is a hand-size edge AI box built with a Jetson Nano production module, rich set of IOs, aluminum case, passive heatsink, and pre-installed JetPack System, ready for your next AI application development and deployment.
- 10+: $209.00
PRODUCT DETAILS
Description
reComputer J1010 is a compact edge computer built with NVIDIA Jetson Nano 4GB production module, comes with 128 NVIDIA CUDA® cores deliver 0.5 TFLOPs (FP16) to run AI frameworks and applications like image classification, object detection, and speech processing. The production modules offers 16GB eMMC, a longer warranty, and 5-10 year operating life in a production environment(Jetson FAQ). We also have reComputer J20 series built with a Jetson Xavier NX module, delivering 21 TOPS AI performance for more complex AI workloads.
Besides the Jetson module, reComputer J1010 also includes J101 carrier board with onboard microSD card slot, 1*USB 3.0, 2*USB2.0, HDMI, M.2 Key E for WiFI, LTE and Bluetooth, RTC, Raspberry Pi GPIO 40-pin, and so on, as well as a heatsink, and aluminum case. The device has been pre-installed Jetpack 4.6.1, just plug in a USB C 5V/3A power supply, keyboard, mouse, and ethernet cable, you are ready to start your embedded AI journey! If you need more USB 3.0 and onboard M.2 key M for attaching SSD, you can choose reComputer J1020.
Note: We received customer inquiries they need more storage than 16GB eMMC offered. For orders after July 30th, 2022, we have included the microSD card slot on the carrier board of reComputer J1010. Please check the guide on boot image to microSD card and adjust I/O speed.
Features
- Hand-size edge AI full system delivering modern AI power of 0.5 TFLOPs (FP16) and rich interfaces for embedded development.
- Ready for development and deployment: pre-installed NVIDIA JetPack supports the entire Jetson software stack and industry-leading AI developer tools for building robust AI applications such as logistics, retail, service, agriculture, smart city, healthcare, and life sciences, etc
- Power efficient: powered by Type C 5V/3A, consuming as little as 5 watts.
- Expandable with the onboard interfaces and reComputer case, able to mount on the wall with mounting holes on the back.
If you are a beginner in Deep Learning, NVIDIA prepared this deep learning tutorial of Hello AI World and Two Days to a Demo. You can even earn certificates to demonstrate your understanding of Jetson and AI when you complete free, open-source courses. For most applications, you also need to connect with cameras, and you might need plug-and-play Grove sensors to use with Raspberry Pi HAT to extend more ideas.
Please also check out Seeed wiki guide including getting started guide also different projects such as use helmet detection and deploy a custom YOLOv5 model at Jetson Nano(fewer datasets, faster inference at 27FPS on reComputer J10 and 60FPS on J2021 of Jetson Xavier NX).
Application
- Object detection use YOLOv5 and roboflow such as roadsign detection, wild fire smoke detection. You can also follow this guide to train and deploy a custom model!
- Hard hat detection uses Edge Impulse for workplace safety
- Pedestrian detection by Edge Impulse
- nanosaur: the smallest Jetson Nano dinosaur robot, open-source, fully 3D printable, based on ROS2 & Isaac ROS.
- DeepCamera NVR (network video recorder)
- Pose estimation uses alwaysAI
- Retail store items detection
- Wildfire detection
- Animal detection
- Visual anomaly detection using NVIDIA Deepstream IoT
Find in the Jetson Community to inspire your next project!
If you work for an AI enterprise of ISV or system integrator, welcome to check out our free Edge AI partner program. We are looking forward to leveraging local and global resources to accelerate next-gen AI products together with you.
Compare NVIDIA Jetson Nano Dev Kit B01 with reComputer J10 series
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Product |
reComputer J1010 |
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Module |
Jetson Nano 4GB (production version) |
Nano (not production version) |
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Storage |
16 GB eMMC |
MicroSD (Card not included) |
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SD Card Slot |
Included (On the carrier board) |
- |
Included (On the module) |
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Video Encoder |
4K30 | 2x1080p60 | 4x1080p30 | 4x720p60 | 9x720p30 (H.265 & H.264) |
4Kp30 | 4x 1080p30 | 9x 720p30 (H.264/H.265) |
|
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Video Decoder |
4K60 | 2x 4K30 | 4x 1080p60 | 8x 1080p30 | 9x 720p60 (H.265 & H.264) |
4Kp60 | 2x 4Kp30 | 8x 1080p30 | 18x 720p30 (H.264/H.265) |
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Gigabit Ethernet |
1*RJ45 Gigabit Ethernet Connector (10/100/1000) |
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USB |
1 * USB 3.0 Type A; 2 * USB 2.0 Type A; 1 * USB Type C for device mode; 1 * USB Type C for 5V power input |
4 * USB 3.0 Type-A ； 1 * Micro-USB port for device mode; |
4 * USB 3.0 Type-A; 1 * Micro-USB port for 5V power input or for device mode |
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CSI Camera Connect |
2*CSI Camera (15 pos, 1mm pitch, MIPI CSI-2 ) |
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Display |
1*HDMI Type A |
1*HDMI Type A; 1*DP |
1*HDMI Type A; 1*DP |
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FAN |
1* FAN Connector (5V PWM) |
1* FAN (5V PWM) |
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M.2 KEY E |
1*M.2 Key E |
1*M.2 Key E (Disabled) |
1*M.2 Key E |
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M.2 KEY M |
- |
1*M.2 Key M |
- |
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RTC |
RTC socket(reserved) |
- |
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Multifunctional port |
1* 40-Pin header |
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Power Supply |
USB-Type C 5V⎓3A |
DC Jack 12V/2A |
DC Jack 5V⎓4A； Micro-USB 5V⎓2A |
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Mechanical |
130 mm x 120 mm x 50 mm (with case) |
130mm x120mm x 50mm (with case) |
100 mm x 80 mm x 29 mm |
Note
If use some GPIO libraries that causes floating voltage 1.2V~2V , GPIO issues on the carrier board could happen as the normal voltage should be 3V.This is a common situation on the carrier boards, for both the reComputer series and official Nvidia Jetson developer kit.
Thus, aftersales / warrenty does not come into effect regarding this isssue.
For further information, refer to NVIDIA official document.
Compare NVIDIA Jetson Nano and Jetson Xavier NX
With Jetson Nano, developers can use highly accurate pre-trained models from TAO Toolkit and deploy with DeepStream. Jetson Nano can achieve 11 FPS for PeopleNet- ResNet34 of People Detection, 19 FPS for DashCamNet-ResNet18 of Vehicle Detection, and 101 FPS for FaceDetect-IR-ResNet18 of Face Detection.
Jetson Xavier NX can achieve 172 FPS for PeopleNet- ResNet34 of People Detection, 274 FPS for DashCamNet-ResNet18 of Vehicle Detection, and 1126 FPS for FaceDetect-IR-ResNet18 of Face Detection. Benchmark details can be found on NVIDIA®’s DeepStream SDK website.
Hardware overview
Reference carrier board
Nearly the same functional design as Jetson Nano Developer Kit. We have upgraded the carrier board with micro SD card slot.
The old version
The latest version
Desktop, Wall Mount, Expandable or fit in anywhere
The back screw holes allow you to hang the product as you need. We also provide other editions case like blue, silver, and silver metal, whose stackable structure allows you to stack more middle layers to create rooms very easily.
Part List
| Acrylic Cover | x1 |
|---|---|
| Aluminum Frame | x1 |
| Jetson Nano module | x1 |
| Heatsink | x1 |
| J101 Carrier board | x1 |
We will not include a power supply, please add this power adapter.
We will not include a RTC (CR1220)
FAQ
1. What type of RTC is recommended for RTC socket?
CR1220 and ML1220. The non-rechargeable design of the RTC circuit of the carrier board allows both rechargeable and non-rechargeable RTC batteries to be used.
ECCN/HTS
| HSCODE | 8471419000 |
| USHSCODE | 8543708800 |
| UPC | |
| EUHSCODE | 8471707000 |
| COO | CHINA |
| CCATS | 1 |
| CE | 1 |
| EU DoC | 1 |
| FCC | 1 |
| KC | 1 |
| REACH | 1 |
| RoHS | 1 |
| UK DoC | 1 |
| UKCA | 1 |
LEARN AND DOCUMENTS
SHARED BY USERS
REVIEWS
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from order viewgood experience for me in buying this item. Excellent quality of product. Thank you.
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from product detail summaryA powerful tool for machine learning applications, with fantastic performance for the price.
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from order viewyes.........
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from order viewHa llegado rapido y sin problemas, era lo esperado
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from order viewProduct was on time and seems to work correctly. I am basically a complete beginning and was not aware of the complexity of usage for the device but I am learning and this is a great tool for me to learn more advanced topics. So far so good! Just know that if your moving from Raspberry Pi and only did basic/intermediate stuff you may have a big learning curve from this as I did.