关于Jetson Nano Developer Kit
Jetson nano搭载四核Cortex-A57 MPCore 处理器,采用128 核 Maxwell™ GPU。支持JetPack SDK. 支持主流的AI框架和算法,例如TensorFlow, PyTorch, Caffe/Caffe2, Keras, MXNet等。
支持人脸识别,物体识别追踪,对象检测和定位等应用。
板载资源
- Micro SD 卡卡槽: 可接入TF卡(16G以上),烧写系统镜像
- 40PIN GPIO扩展接口(兼容树莓派40PIN接口)
- Micro USB接口:用于5V电源输入或者USB数据传输
- 千兆以太网口: 10/100/1000Base-T 自适应以太网端口
- USB3.0接口:4个USB3.0接口
- HDMI高清接口:用于外接HDMI屏幕
- DisplayPort接口:用于外接DP屏幕
- DC电源接口:用于外接5V电源(外径5.5, 内径2.1)
- MIPS CSI 摄像头接口:兼容树莓派摄像头接口
性能
下面这一份表格是NVIDIA官方给出的性能对比表格,以供参考
DNR表示无法运行。
Model
|
Application
|
Framework
|
NVIDIA Jetson Nano
|
Raspberry Pi 3
|
Raspberry Pi 3 + Intel Neural Compute Stick 2
|
Google Edge TPU Dev Board
|
ResNet-50
(224×224)
|
Classification
|
TensorFlow
|
36 FPS
|
1.4 FPS
|
16 FPS
|
DNR
|
MobileNet-v2
(300×300)
|
Classification
|
TensorFlow
|
64 FPS
|
2.5 FPS
|
30 FPS
|
130 FPS
|
SSD ResNet-18 (960×544)
|
Object Detection
|
TensorFlow
|
5 FPS
|
DNR
|
DNR
|
DNR
|
SSD ResNet-18 (480×272)
|
Object Detection
|
TensorFlow
|
16 FPS
|
DNR
|
DNR
|
DNR
|
SSD ResNet-18 (300×300)
|
Object Detection
|
TensorFlow
|
18 FPS
|
DNR
|
DNR
|
DNR
|
SSD Mobilenet-V2 (960×544)
|
Object
Detection
|
TensorFlow
|
8 FPS
|
DNR
|
1.8 FPS
|
DNR
|
SSD Mobilenet-V2 (480×272)
|
Object Detection
|
TensorFlow
|
27 FPS
|
DNR
|
7 FPS
|
DNR
|
SSD Mobilenet-V2
(300×300)
|
Object Detection
|
TensorFlow
|
39 FPS
|
1 FPS
|
11 FPS
|
48 FPS
|
Inception V4
(299×299)
|
Classification
|
PyTorch
|
11 FPS
|
DNR
|
DNR
|
9 FPS
|
Tiny YOLO V3
(416×416)
|
Object Detection
|
Darknet
|
25 FPS
|
0.5 FPS
|
DNR
|
DNR
|
OpenPose
(256×256)
|
Pose Estimation
|
Caffe
|
14 FPS
|
DNR
|
5 FPS
|
DNR
|
VGG-19 (224×224)
|
Classification
|
MXNet
|
10 FPS
|
0.5 FPS
|
5 FPS
|
DNR
|
Super Resolution (481×321)
|
Image Processing
|
PyTorch
|
15 FPS
|
DNR
|
0.6 FPS
|
DNR
|
Unet
(1x512x512)
|
Segmentation
|
Caffe
|
18 FPS
|
DNR
|
5 FPS
|
DNR
|