Jetson-inference optimized for devices without cache-coherent mapped memory

Post-process time using default jetson-inference by Dusty-nv on a TX2

Optimized code

Devices

The following devices should see a significant performance increase by using the optimized code

• Jetson TX2
• Jetson TX1
• Jetson TK1
• Jetson Nano
• Any pre-pascal Nvidia GPU

Associated issue

dusty-nv#1369

Deploying Deep Learning

Welcome to our instructional guide for inference and realtime DNN vision library for NVIDIA Jetson Nano/TX1/TX2/Xavier NX/AGX Xavier.

This repo uses NVIDIA TensorRT for efficiently deploying neural networks onto the embedded Jetson platform, improving performance and power efficiency using graph optimizations, kernel fusion, and FP16/INT8 precision.

Vision primitives, such as imageNet for image recognition, detectNet for object detection, segNet for semantic segmentation, and poseNet for pose estimation inherit from the shared tensorNet object. Examples are provided for streaming from live camera feed and processing images. See the API Reference section for detailed reference documentation of the C++ and Python libraries.

Follow the Hello AI World tutorial for running inference and transfer learning onboard your Jetson, including collecting your own datasets and training your own models. It covers image classification, object detection, semantic segmentation, pose estimation, and mono depth.

Table of Contents

• Hello AI World
• Video Walkthroughs
• API Reference
• Code Examples
• Pre-Trained Models
• System Requirements
• Change Log

>   JetPack 4.6 is now supported, along with updated containers.
>   Try the new Pose Estimation and Mono Depth tutorials!
>   See the Change Log for the latest updates and new features.

Hello AI World

Hello AI World can be run completely onboard your Jetson, including inferencing with TensorRT and transfer learning with PyTorch. The inference portion of Hello AI World - which includes coding your own image classification and object detection applications for Python or C++, and live camera demos - can be run on your Jetson in roughly two hours or less, while transfer learning is best left to leave running overnight.

System Setup

• Setting up Jetson with JetPack
• Running the Docker Container
• Building the Project from Source

Inference

• Classifying Images with ImageNet
  • Using the ImageNet Program on Jetson
  • Coding Your Own Image Recognition Program (Python)
  • Coding Your Own Image Recognition Program (C++)
  • Running the Live Camera Recognition Demo
• Locating Objects with DetectNet
  • Detecting Objects from Images
  • Running the Live Camera Detection Demo
  • Coding Your Own Object Detection Program
• Semantic Segmentation with SegNet
  • Segmenting Images from the Command Line
  • Running the Live Camera Segmentation Demo
• Pose Estimation with PoseNet
• Monocular Depth with DepthNet

Training

• Transfer Learning with PyTorch
• Classification/Recognition (ResNet-18)
  • Re-training on the Cat/Dog Dataset
  • Re-training on the PlantCLEF Dataset
  • Collecting your own Classification Datasets
• Object Detection (SSD-Mobilenet)
  • Re-training SSD-Mobilenet
  • Collecting your own Detection Datasets

Appendix

• Camera Streaming and Multimedia
• Image Manipulation with CUDA
• Deep Learning Nodes for ROS/ROS2

Video Walkthroughs

Below are screencasts of Hello AI World that were recorded for the Jetson AI Certification course:

Description	Video
Hello AI World Setup Download and run the Hello AI World container on Jetson Nano, test your camera feed, and see how to stream it over the network via RTP.
Image Classification Inference Code your own Python program for image classification using Jetson Nano and deep learning, then experiment with realtime classification on a live camera stream.
Training Image Classification Models Learn how to train image classification models with PyTorch onboard Jetson Nano, and collect your own classification datasets to create custom models.
Object Detection Inference Code your own Python program for object detection using Jetson Nano and deep learning, then experiment with realtime detection on a live camera stream.
Training Object Detection Models Learn how to train object detection models with PyTorch onboard Jetson Nano, and collect your own detection datasets to create custom models.
Semantic Segmentation Experiment with fully-convolutional semantic segmentation networks on Jetson Nano, and run realtime segmentation on a live camera stream.

API Reference

Below are links to reference documentation for the C++ and Python libraries from the repo:

jetson-inference

	C++	Python
Image Recognition	imageNet	imageNet
Object Detection	detectNet	detectNet
Segmentation	segNet	segNet
Pose Estimation	poseNet	poseNet
Monocular Depth	depthNet	depthNet

jetson-utils

• C++
• Python

These libraries are able to be used in external projects by linking to libjetson-inference and libjetson-utils.

Code Examples

Introductory code walkthroughs of using the library are covered during these steps of the Hello AI World tutorial:

• Coding Your Own Image Recognition Program (Python)
• Coding Your Own Image Recognition Program (C++)

Additional C++ and Python samples for running the networks on static images and live camera streams can be found here:

	C++	Python
Image Recognition	imagenet.cpp	imagenet.py
Object Detection	detectnet.cpp	detectnet.py
Segmentation	segnet.cpp	segnet.py
Pose Estimation	posenet.cpp	posenet.py
Monocular Depth	depthnet.cpp	depthnet.py

note: for working with numpy arrays, see Converting to Numpy Arrays and Converting from Numpy Arrays

These examples will automatically be compiled while Building the Project from Source, and are able to run the pre-trained models listed below in addition to custom models provided by the user. Launch each example with --help for usage info.

Pre-Trained Models

The project comes with a number of pre-trained models that are available through the Model Downloader tool:

Image Recognition

Network	CLI argument	NetworkType enum
AlexNet	alexnet	ALEXNET
GoogleNet	googlenet	GOOGLENET
GoogleNet-12	googlenet-12	GOOGLENET_12
ResNet-18	resnet-18	RESNET_18
ResNet-50	resnet-50	RESNET_50
ResNet-101	resnet-101	RESNET_101
ResNet-152	resnet-152	RESNET_152
VGG-16	vgg-16	VGG-16
VGG-19	vgg-19	VGG-19
Inception-v4	inception-v4	INCEPTION_V4

Object Detection

Network	CLI argument	NetworkType enum	Object classes
SSD-Mobilenet-v1	ssd-mobilenet-v1	SSD_MOBILENET_V1	91 (COCO classes)
SSD-Mobilenet-v2	ssd-mobilenet-v2	SSD_MOBILENET_V2	91 (COCO classes)
SSD-Inception-v2	ssd-inception-v2	SSD_INCEPTION_V2	91 (COCO classes)
DetectNet-COCO-Dog	coco-dog	COCO_DOG	dogs
DetectNet-COCO-Bottle	coco-bottle	COCO_BOTTLE	bottles
DetectNet-COCO-Chair	coco-chair	COCO_CHAIR	chairs
DetectNet-COCO-Airplane	coco-airplane	COCO_AIRPLANE	airplanes
ped-100	pednet	PEDNET	pedestrians
multiped-500	multiped	PEDNET_MULTI	pedestrians, luggage
facenet-120	facenet	FACENET	faces

Semantic Segmentation

Dataset	Resolution	CLI Argument	Accuracy	Jetson Nano	Jetson Xavier
Cityscapes	512x256	fcn-resnet18-cityscapes-512x256	83.3%	48 FPS	480 FPS
Cityscapes	1024x512	fcn-resnet18-cityscapes-1024x512	87.3%	12 FPS	175 FPS
Cityscapes	2048x1024	fcn-resnet18-cityscapes-2048x1024	89.6%	3 FPS	47 FPS
DeepScene	576x320	fcn-resnet18-deepscene-576x320	96.4%	26 FPS	360 FPS
DeepScene	864x480	fcn-resnet18-deepscene-864x480	96.9%	14 FPS	190 FPS
Multi-Human	512x320	fcn-resnet18-mhp-512x320	86.5%	34 FPS	370 FPS
Multi-Human	640x360	fcn-resnet18-mhp-512x320	87.1%	23 FPS	325 FPS
Pascal VOC	320x320	fcn-resnet18-voc-320x320	85.9%	45 FPS	508 FPS
Pascal VOC	512x320	fcn-resnet18-voc-512x320	88.5%	34 FPS	375 FPS
SUN RGB-D	512x400	fcn-resnet18-sun-512x400	64.3%	28 FPS	340 FPS
SUN RGB-D	640x512	fcn-resnet18-sun-640x512	65.1%	17 FPS	224 FPS

• If the resolution is omitted from the CLI argument, the lowest resolution model is loaded
• Accuracy indicates the pixel classification accuracy across the model's validation dataset
• Performance is measured for GPU FP16 mode with JetPack 4.2.1, nvpmodel 0 (MAX-N)

Legacy Segmentation Models

Network	CLI Argument	NetworkType enum	Classes
Cityscapes (2048x2048)	fcn-alexnet-cityscapes-hd	FCN_ALEXNET_CITYSCAPES_HD	21
Cityscapes (1024x1024)	fcn-alexnet-cityscapes-sd	FCN_ALEXNET_CITYSCAPES_SD	21
Pascal VOC (500x356)	fcn-alexnet-pascal-voc	FCN_ALEXNET_PASCAL_VOC	21
Synthia (CVPR16)	fcn-alexnet-synthia-cvpr	FCN_ALEXNET_SYNTHIA_CVPR	14
Synthia (Summer-HD)	fcn-alexnet-synthia-summer-hd	FCN_ALEXNET_SYNTHIA_SUMMER_HD	14
Synthia (Summer-SD)	fcn-alexnet-synthia-summer-sd	FCN_ALEXNET_SYNTHIA_SUMMER_SD	14
Aerial-FPV (1280x720)	fcn-alexnet-aerial-fpv-720p	FCN_ALEXNET_AERIAL_FPV_720p	2

Pose Estimation

Model	CLI argument	NetworkType enum	Keypoints
Pose-ResNet18-Body	resnet18-body	RESNET18_BODY	18
Pose-ResNet18-Hand	resnet18-hand	RESNET18_HAND	21
Pose-DenseNet121-Body	densenet121-body	DENSENET121_BODY	18

Recommended System Requirements

• Jetson Nano Developer Kit with JetPack 4.2 or newer (Ubuntu 18.04 aarch64).
• Jetson Nano 2GB Developer Kit with JetPack 4.4.1 or newer (Ubuntu 18.04 aarch64).
• Jetson Xavier NX Developer Kit with JetPack 4.4 or newer (Ubuntu 18.04 aarch64).
• Jetson AGX Xavier Developer Kit with JetPack 4.0 or newer (Ubuntu 18.04 aarch64).
• Jetson TX2 Developer Kit with JetPack 3.0 or newer (Ubuntu 16.04 aarch64).
• Jetson TX1 Developer Kit with JetPack 2.3 or newer (Ubuntu 16.04 aarch64).

The Transfer Learning with PyTorch section of the tutorial speaks from the perspective of running PyTorch onboard Jetson for training DNNs, however the same PyTorch code can be used on a PC, server, or cloud instance with an NVIDIA discrete GPU for faster training.

Extra Resources

In this area, links and resources for deep learning are listed:

• ros_deep_learning - TensorRT inference ROS nodes
• NVIDIA AI IoT - NVIDIA Jetson GitHub repositories
• Jetson eLinux Wiki - Jetson eLinux Wiki

Two Days to a Demo (DIGITS)

note: the DIGITS/Caffe tutorial from below is deprecated. It's recommended to follow the Transfer Learning with PyTorch tutorial from Hello AI World.

Expand this section to see original DIGITS tutorial (deprecated)

The DIGITS tutorial includes training DNN's in the cloud or PC, and inference on the Jetson with TensorRT, and can take roughly two days or more depending on system setup, downloading the datasets, and the training speed of your GPU.

• DIGITS Workflow
• DIGITS System Setup
• Setting up Jetson with JetPack
• Building the Project from Source
• Classifying Images with ImageNet
  • Using the Console Program on Jetson
  • Coding Your Own Image Recognition Program
  • Running the Live Camera Recognition Demo
  • Re-Training the Network with DIGITS
  • Downloading Image Recognition Dataset
  • Customizing the Object Classes
  • Importing Classification Dataset into DIGITS
  • Creating Image Classification Model with DIGITS
  • Testing Classification Model in DIGITS
  • Downloading Model Snapshot to Jetson
  • Loading Custom Models on Jetson
• Locating Objects with DetectNet
  • Detection Data Formatting in DIGITS
  • Downloading the Detection Dataset
  • Importing the Detection Dataset into DIGITS
  • Creating DetectNet Model with DIGITS
  • Testing DetectNet Model Inference in DIGITS
  • Downloading the Detection Model to Jetson
  • DetectNet Patches for TensorRT
  • Detecting Objects from the Command Line
  • Multi-class Object Detection Models
  • Running the Live Camera Detection Demo on Jetson
• Semantic Segmentation with SegNet
  • Downloading Aerial Drone Dataset
  • Importing the Aerial Dataset into DIGITS
  • Generating Pretrained FCN-Alexnet
  • Training FCN-Alexnet with DIGITS
  • Testing Inference Model in DIGITS
  • FCN-Alexnet Patches for TensorRT
  • Running Segmentation Models on Jetson

^{© 2016-2019 NVIDIA | Table of Contents}