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NVIDIA Jetson Nano Developer Kit-B01

SKU
102110417
Rating:
96% of 100

Join the Revolution and Bring the Power of AI to Millions of Devices. 

The NVIDIA® Jetson Nano™ Developer Kit delivers the compute performance to run modern AI workloads at an unprecedented size, power, and cost. Developers, learners, and makers can now run AI frameworks and models for applications like image classification, object detection, segmentation, and speech processing.

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PRODUCT DETAILS

Description

The NVIDIA® Jetson Nano™ Developer Kit delivers the compute performance to run modern AI workloads at an unprecedented size, power, and cost. Developers, learners, and makers can now run AI frameworks and models for applications like image classification, object detection, segmentation, and speech processing.

The developer kit can be powered by micro-USB and comes with extensive I/Os, ranging from GPIO to CSI. This makes it simple for developers to connect a diverse set of new sensors to enable a variety of AI applications. It’s incredibly power-efficient, consuming as little as 5 watts.

Jetson Nano is also supported by NVIDIA JetPack, which includes a board support package (BSP), Linux OS, NVIDIA CUDA®, cuDNN, and TensorRT™ software libraries for deep learning, computer vision, GPU computing, multimedia processing, and much more. The software is even available using an easy-to-flash SD card image, making it fast and easy to get started.

The same JetPack SDK is used across the entire NVIDIA Jetson™ family of products and is fully compatible with NVIDIA’s world-leading AI platform for training and deploying AI software. This proven software stack reduces complexity and overall effort for developers. 

Note

Please pay attention and follow the steps below to insert/remove an SD card.

Inserting an SD card: Slightly push the SD card into the card slot until you hear a click sound. If you hear that sound, that means the SD card has been successfully inserted.

Removing an SD card: Slight push the SD card inside the card slot until you hear a click sound. Then release it and the SD card will automatically pop out.

Key Features

 Jetson Nano Module

  • 128-core NVIDIA Maxwell™ GPU
  • Quad-core ARM® A57 CPU
  • 4 GB 64-bit LPDDR4
  • 10/100/1000BASE-T Ethernet 

 Power Options

  • Micro-USB 5V 2A
  • DC power adapter 5V 4A

 I/O

  • USB 3.0 Type A
  • USB 2.0 Micro-B
  • HDMI/DisplayPort
  • M.2 Key E
  • Gigabit Ethernet
  • GPIOs, I2C, I2S, SPI, UART
  • MIPI-CSI camera connector
  • Fan connector
  • PoE connector

 Kit Contents

  • NVIDIA Jetson Nano module and carrier board
  • Quick Start Guide and Support Guide  

Changes with the B01 Kit

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Diagonal Field of View (FOV) 77° 77° 130° 160° 160° 200° 83°
IR LED Modules None 2 None None 2 None None
Aperture 2.0 2.0 1.8 2.35 2.35 2.0 /
Focal Length 2.96mm 2.96mm 1.88mm 3.15mm 3.15mm 0.87mm 2.6mm
Lens Construction 4P 4P 4E+IR 6G+IR 6G+IR 1G4P+IR /
Distortion <1% <1% <7.6% <14.3% <14.3% <18.6% <1%
EFL 2.93mm 2.93mm 1.85mm 3.15mm 3.15mm 0.9mm /
BFL (Optical) 1.16mm 1.16mm 1.95mm 3.15mm 3.15mm 1.41mm /

Reinforce Your Projects with Grove Pi HAT

If you want to use Grove sensors with Jetson Nano, grab the grove.py Python library and get your sensors up in running in minutes! Currently, there are more than 20 Grove modules supported on Jetson Nano and we will keep adding more. You can connect Grove modules using Base HAT for Raspberry Pi or Raspberry Pi Zero with Jetson Nano.

 

Note

We provide a wide selection of AI-related products including Machine Learning, Computer Vision, Edge Computing, Speech Recognition & NLP and Neural Networks Acceleration. Check here for more products you may need.

We are also calling for feedback and inputs from the developers. Any suggestions on the product features are welcomed at Seeed Forum!

Specifications

  • GPU: 128-core Maxwell
  • CPU:Quad-core ARM A57 @ 1.43 GHz
  • Memory: 4 GB 64-bit LPDDR4 25.6 GB/s
  • Storage: microSD (not included)
  • Video Encoder: 4K @ 30 | 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265)
  • Video Decoder: 4K @ 60 | 2x 4K @ 30 | 8x 1080p @ 30 | 18x 720p @ 30|(H.264/H.265)
  • Camera: 2x MIPI CSI-2 DPHY lanes
  • Connectivity: Gigabit Ethernet, M.2 Key E
  • Display: HDMI 2.0 and eDP 1.4
  • USB: 4x USB 3.0, USB 2.0 Micro-B
  • Others: GPIO, I2C, I2S, SPI, UART
  • Mechanical: 100 mm x 80 mm x 29 mm

Version Change Information for B01 from A02

  • The B01 revision carrier is compatible with the production specification Jetson Nano module. The A02 revision carrier is not.
  • Removed Button Header [J40] Position
  • Removed Serial Port Header [J44] Position
  • Adjusted the Position of Power Select Jumper [J48]
  • Adjusted the Position of Camera Connector [J13]
  • Added Camera Connector [J49] location
  • Factory JetPack Upgrade from 4.2 SDK to 4.3 SDK

Note

If you are looking for open source SBC for commercial and industrial needs. Seeed provides customization service based on BeagleBone series boards. Seeed Studio BeagleBone® Green(BBG) and Seeed Studio BeagleBone® Green Wireless (BBGW) provide more stable industrial deployment scenarios.

ECCN/HTS

HSCODE 8543709990
USHSCODE 8517620090
UPC

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REVIEWS

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  1. Rating
    100%
    Great product
    Regarding the hardness to use an USB camera, the board is excellent and the support for tensorflowRT is amazing,
    By
  2. Rating
    80%
    Very nice unit
    Early days still, but the Nano is going great. DHL delivery took too long, but worth the wait.
    By
  3. Rating
    80%
    Not too bad, nice package
    I'm pretty happy so far. Setting up python enviro nment a little finicky - suggest you use virtualenv.
    Don't try 16GB, you will run out of space quickly. Get a 160MB/s microSD card at least 32GB. I created a 4GB swapfile which improved dev env build speed
    By
  4. Rating
    80%
    Not too bad, nice package
    I'm pretty happy so far. Setting up python enviro nment a little finicky - suggest you use virtualenv.
    Don't try 16GB, you will run out of space quickly. Get a 160MB/s microSD card at least 32GB. I created a 4GB swapfile which improved dev env build speed
    By
  5. Rating
    100%
    Great hardware
    Nice quick quad core arm64 SBC with a decent amount of computing power available from the nVidia GPU.
    It is definitely a specialised board. While it can be used for watching videos, or running OpenGL or GLES games it's really not for that.

    It takes a while to load what it needs to utilise CUDA as a compute device, so the overhead on short tests is large. Once it is loaded it's quite fast, so it's good in real world application.

    One thing to be wary of is the OS image is set to full speed 10 watt mode, but the hardware is configured to use MicroUSB for power ie 5W mode. Either set the OS to lower power mode or use a jumper (not included) to set the board to use a higher wattage power supply via the barrel connector.
    If either of these is not done, the board will crash under load because of insufficient available curre
    By

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