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README.md

Deploying Deep Learning

Welcome to our instructional guide for inference and realtime DNN vision library for NVIDIA Jetson Nano/TX1/TX2/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 localization, and segNet for semantic segmentation, 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.

There are multiple tracks of the tutorial that you can choose to follow, including Hello AI World for running inference and transfer learning onboard your Jetson, or the full Two Days to a Demo tutorial for training on a PC or server with DIGITS.

It's recommended to walk through the Hello AI World module first to familiarize yourself with machine learning and inference with TensorRT, before proceeding to training in the cloud with DIGITS.

Table of Contents

>   Jetson Xavier NX and JetPack 4.4 Developer Preview is now supported in the repo.
>   See our latest technical blog on the NVIDIA Jetson Xavier NX Developer Kit.

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 application for C++ or Python, object detection, 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.

Two Days to a Demo (DIGITS)

The full tutorial includes training 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.

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

jetson-utils

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:

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

Images Camera
C++ (examples)
   Image Recognition imagenet-console imagenet-camera
   Object Detection detectnet-console detectnet-camera
   Segmentation segnet-console segnet-camera
Python (python/examples)
   Image Recognition imagenet-console.py imagenet-camera.py
   Object Detection detectnet-console.py detectnet-camera.py
   Segmentation segnet-console.py segnet-camera.py

note: for working with numpy arrays, see cuda-from-numpy.py and cuda-to-numpy.py

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

Recommended System Requirements

Training GPU: Maxwell, Pascal, Volta, Turing, or Ampere-based GPU (ideally with at least 6GB video memory)
                        optionally, AWS P2/P3 instance or Microsoft Azure N-series
                        Ubuntu 16.04/18.04 x86_64

Deployment:   Jetson Nano Developer Kit with JetPack 4.2 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).

Note that TensorRT samples from the repo are intended for deployment onboard Jetson, however when cuDNN and TensorRT have been installed on the host side, the TensorRT samples in the repo can be compiled for PC.

Extra Resources

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

Legacy Links

Since the documentation has been re-organized, below are links mapping the previous content to the new locations.        (click on the arrow above to hide this section)

DIGITS Workflow

See DIGITS Workflow

System Setup

See DIGITS Setup

Running JetPack on the Host

See JetPack Setup

Installing Ubuntu on the Host

See DIGITS Setup

Setting up host training PC with NGC container

See DIGITS Setup

Installing the NVIDIA driver

See DIGITS Setup

Installing Docker

See DIGITS Setup

NGC Sign-up

See DIGITS Setup

Setting up data and job directories

See DIGITS Setup

Starting DIGITS container

See DIGITS Setup

Natively setting up DIGITS on the Host

See DIGITS Native Setup

Installing NVIDIA Driver on the Host

See DIGITS Native Setup

Installing cuDNN on the Host

See DIGITS Native Setup

Installing NVcaffe on the Host

See DIGITS Native Setup

Installing DIGITS on the Host

See DIGITS Native Setup

Starting the DIGITS Server

See DIGITS Native Setup

Building from Source on Jetson

See Building the Repo from Source

Cloning the Repo

See Building the Repo from Source

Configuring with CMake

See Building the Repo from Source

Compiling the Project

See Building the Repo from Source

Digging Into the Code

See Building the Repo from Source

Classifying Images with ImageNet

See Classifying Images with ImageNet

Using the Console Program on Jetson

See Classifying Images with ImageNet

Running the Live Camera Recognition Demo

See Running the Live Camera Recognition Demo

Re-training the Network with DIGITS

See Re-Training the Recognition Network

Downloading Image Recognition Dataset

See Re-Training the Recognition Network

Customizing the Object Classes

See Re-Training the Recognition Network

Importing Classification Dataset into DIGITS

See Re-Training the Recognition Network

Creating Image Classification Model with DIGITS

See Re-Training the Recognition Network

Testing Classification Model in DIGITS

See Re-Training the Recognition Network

Downloading Model Snapshot to Jetson

See Downloading Model Snapshots to Jetson

Loading Custom Models on Jetson

See Loading Custom Models on Jetson

Locating Object Coordinates using DetectNet

See Locating Object Coordinates using DetectNet

Detection Data Formatting in DIGITS

See Locating Object Coordinates using DetectNet

Downloading the Detection Dataset

See Locating Object Coordinates using DetectNet

Importing the Detection Dataset into DIGITS

See Locating Object Coordinates using DetectNet

Creating DetectNet Model with DIGITS

See Locating Object Coordinates using DetectNet

Selecting DetectNet Batch Size

See Locating Object Coordinates using DetectNet

Specifying the DetectNet Prototxt

See Locating Object Coordinates using DetectNet

Training the Model with Pretrained Googlenet

See Locating Object Coordinates using DetectNet

Testing DetectNet Model Inference in DIGITS

See Locating Object Coordinates using DetectNet

Downloading the Model Snapshot to Jetson

See Downloading the Detection Model to Jetson

DetectNet Patches for TensorRT

See Downloading the Detection Model to Jetson

Processing Images from the Command Line on Jetson

See Detecting Objects from the Command Line

Launching With a Pretrained Model

See Detecting Objects from the Command Line

Pretrained DetectNet Models Available

See Detecting Objects from the Command Line

Running Other MS-COCO Models on Jetson

See Detecting Objects from the Command Line

Running Pedestrian Models on Jetson

See Detecting Objects from the Command Line

Multi-class Object Detection Models

See Detecting Objects from the Command Line

Running the Live Camera Detection Demo on Jetson

See Running the Live Camera Detection Demo

Image Segmentation with SegNet

See Semantic Segmentation with SegNet

Downloading Aerial Drone Dataset

See Semantic Segmentation with SegNet

Importing the Aerial Dataset into DIGITS

See Semantic Segmentation with SegNet

Generating Pretrained FCN-Alexnet

See Generating Pretrained FCN-Alexnet

Training FCN-Alexnet with DIGITS

See Training FCN-Alexnet with DIGITS

Testing Inference Model in DIGITS

See Training FCN-Alexnet with DIGITS

FCN-Alexnet Patches for TensorRT

See FCN-Alexnet Patches for TensorRT

Running Segmentation Models on Jetson

See Running Segmentation Models on Jetson

© 2016-2019 NVIDIA | Table of Contents

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