# jetson-inference **Repository Path**: duping812/jetson-inference ## Basic Information - **Project Name**: jetson-inference - **Description**: Nvidia公司出品的Hello AI world教程 - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: https://developer.nvidia.com/embedded/twodaystoademo - **GVP Project**: No ## Statistics - **Stars**: 5 - **Forks**: 2 - **Created**: 2021-10-09 - **Last Updated**: 2024-08-16 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # jetson-inference国内快速安装教程 nvidia公司出品的[Hello AI world教程](https://developer.nvidia.com/embedded/twodaystoademo#hello_ai_world)写的很好，配合jetson nano自带的GPU加速，非常适合低成本的AI入门学习（图像分类，物体识别，语义分割、姿态检测等）。 相应代码 [https://github.com/dusty-nv/jetson-inference](https://github.com/dusty-nv/jetson-inference) 但是github国内访问很不稳定，还有些大文件放在box上，安装起来很折腾。我这里整理了一个不用vpn从国内网站安装的教程，整个安装过程只需1小时左右。 ## 1. jetson nano系统 首先需要安装好jetson nano的ubuntu系统，以及最新的jetpack 4.6。安装方法如下： - jetson nano 2G: [https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-2gb-devkit](https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-2gb-devkit) - jetson nano 4G: [https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit](https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit) ## 2. 下载inference代码 github代码已拷贝到国内gitee，最新提交2021/10/04。 [https://gitee.com/duping812/jetson-inference](https://gitee.com/duping812/jetson-inference) ```bash $ git clone --recursive https://gitee.com/duping812/jetson-inference ``` 注意--recursive选项会同时安6个submodule, jetson_utils, camera-capture, pytorch-classification, pytorch-detection, pytorch-segmentation, trt_pose。其中pytorch-detection还有个子模块pytorch-ssd，我都已在.gitmodules文件里把地址统一改为gitee的源了。 ## 3. 用docker方式安装 docker容器的好处，所有相关程序、环境都已配置好，直接运行即可，比从源码安装方便很多。 ```bash $ cd jetson-inference $ docker/run.sh ``` ![在这里插入图片描述](https://img-blog.csdnimg.cn/b3f84e86b7384afabeb6f7ee30fd6fc9.png?x-oss-process=image/watermark,type_ZHJvaWRzYW5zZmFsbGJhY2s,shadow_50,text_Q1NETiBAZHVwaW5nODEy,size_14,color_FFFFFF,t_70,g_se,x_16#pic_center) 启动界面，默认会勾选一些常用的模型，这些放在box国内连不上。模型全部不选，直接安装docker镜像，好在镜像文件所在网站国内能直连。安装往后看到root@xxx提示符就说明装好了。这时先退出docker. ``` # exit ``` ## 4. 模型下载 前面用到的模型，我传到了网盘。 - 百度网盘 [https://pan.baidu.com/s/1O8L70uIyn7Cuj-APsA5pAw](https://pan.baidu.com/s/1O8L70uIyn7Cuj-APsA5pAw) 提取码：82ex - 清华云盘 [https://cloud.tsinghua.edu.cn/d/7bd45188869e4434b28b/](https://cloud.tsinghua.edu.cn/d/7bd45188869e4434b28b/) 后面用到哪个，下载下来，解压缩后，拷到~/jetson-inference/data/networks ## 5. 测试是否安装成功 再次启动docker ```bash $ docker/run.sh ``` 进入可执行程序目录 ``` # cd build/aarch64/bin ``` 查看摄像头（需要在docker启动前插上摄像头） ``` # ./video-viewer /dev/video0 ``` 图像分类，默认使用GoogleNet模型。注意首次运行会花几分钟优化模型，优化后的模型缓存到sd卡上，下次运行就会很快了。 ``` # ./imagenet images/jellyfish.jpg images/test/jellyfish.jpg ``` ![输入图片说明](jellyfish.png) 物体识别，默认使用SSD-Mobilenet-v2模型 ``` # ./detectnet images/peds_0.jpg images/test/peds_0.jpg ``` ![输入图片说明](ped0.png) # Deploying Deep Learning Welcome to our instructional guide for inference and realtime [DNN vision](#api-reference) library for NVIDIA **[Jetson Nano/TX1/TX2/Xavier NX/AGX Xavier](http://www.nvidia.com/object/embedded-systems.html)**. This repo uses NVIDIA **[TensorRT](https://developer.nvidia.com/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`](docs/imagenet-console-2.md) for image recognition, [`detectNet`](docs/detectnet-console-2.md) for object detection, [`segNet`](docs/segnet-console-2.md) for semantic segmentation, and [`poseNet`](docs/posenet.md) for pose estimation inherit from the shared [`tensorNet`](c/tensorNet.h) object. Examples are provided for streaming from live camera feed and processing images. See the **[API Reference](#api-reference)** section for detailed reference documentation of the C++ and Python libraries. Follow the [Hello AI World](#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](#hello-ai-world) * [Video Walkthroughs](#video-walkthroughs) * [API Reference](#api-reference) * [Code Examples](#code-examples) * [Pre-Trained Models](#pre-trained-models) * [System Requirements](#recommended-system-requirements) * [Change Log](CHANGELOG.md) > >   JetPack 4.6 is now supported, along with [updated containers](docs/aux-docker.md).
> >   Try the new [Pose Estimation](docs/posenet.md) and [Mono Depth](docs/depthnet.md) tutorials!
> >   See the [Change Log](CHANGELOG.md) 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](docs/jetpack-setup-2.md) * [Running the Docker Container](docs/aux-docker.md) * [Building the Project from Source](docs/building-repo-2.md) #### Inference * [Classifying Images with ImageNet](docs/imagenet-console-2.md) * [Using the ImageNet Program on Jetson](docs/imagenet-console-2.md) * [Coding Your Own Image Recognition Program (Python)](docs/imagenet-example-python-2.md) * [Coding Your Own Image Recognition Program (C++)](docs/imagenet-example-2.md) * [Running the Live Camera Recognition Demo](docs/imagenet-camera-2.md) * [Locating Objects with DetectNet](docs/detectnet-console-2.md) * [Detecting Objects from Images](docs/detectnet-console-2.md#detecting-objects-from-the-command-line) * [Running the Live Camera Detection Demo](docs/detectnet-camera-2.md) * [Coding Your Own Object Detection Program](docs/detectnet-example-2.md) * [Semantic Segmentation with SegNet](docs/segnet-console-2.md) * [Segmenting Images from the Command Line](docs/segnet-console-2.md#segmenting-images-from-the-command-line) * [Running the Live Camera Segmentation Demo](docs/segnet-camera-2.md) * [Pose Estimation with PoseNet](docs/posenet.md) * [Monocular Depth with DepthNet](docs/depthnet.md) #### Training * [Transfer Learning with PyTorch](docs/pytorch-transfer-learning.md) * Classification/Recognition (ResNet-18) * [Re-training on the Cat/Dog Dataset](docs/pytorch-cat-dog.md) * [Re-training on the PlantCLEF Dataset](docs/pytorch-plants.md) * [Collecting your own Classification Datasets](docs/pytorch-collect.md) * Object Detection (SSD-Mobilenet) * [Re-training SSD-Mobilenet](docs/pytorch-ssd.md) * [Collecting your own Detection Datasets](docs/pytorch-collect-detection.md) #### Appendix * [Camera Streaming and Multimedia](docs/aux-streaming.md) * [Image Manipulation with CUDA](docs/aux-image.md) * [Deep Learning Nodes for ROS/ROS2](https://github.com/dusty-nv/ros_deep_learning) ## Video Walkthroughs Below are screencasts of Hello AI World that were recorded for the [Jetson AI Certification](https://developer.nvidia.com/embedded/learn/jetson-ai-certification-programs) 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++](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/index.html) and [Python](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/python/jetson.html) libraries from the repo: #### jetson-inference | | [C++](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/group__deepVision.html) | [Python](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/python/jetson.inference.html) | |-------------------|--------------|--------------| | Image Recognition | [`imageNet`](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/classimageNet.html) | [`imageNet`](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/python/jetson.inference.html#imageNet) | | Object Detection | [`detectNet`](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/classdetectNet.html) | [`detectNet`](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/python/jetson.inference.html#detectNet) | Segmentation | [`segNet`](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/classsegNet.html) | [`segNet`](https://rawgit.com/dusty-nv/jetson-inference/pytorch/docs/html/python/jetson.inference.html#segNet) | | Pose Estimation | [`poseNet`](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/classposeNet.html) | [`poseNet`](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/python/jetson.inference.html#poseNet) | | Monocular Depth | [`depthNet`](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/classdepthNet.html) | [`depthNet`](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/python/jetson.inference.html#depthNet) | #### jetson-utils * [C++](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/group__util.html) * [Python](https://rawgit.com/dusty-nv/jetson-inference/dev/docs/html/python/jetson.utils.html) 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)](docs/imagenet-example-python-2.md) * [Coding Your Own Image Recognition Program (C++)](docs/imagenet-example-2.md) 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`](examples/imagenet/imagenet.cpp) | [`imagenet.py`](python/examples/imagenet.py) | |    Object Detection | [`detectnet.cpp`](examples/detectnet/detectnet.cpp) | [`detectnet.py`](python/examples/detectnet.py) | |    Segmentation | [`segnet.cpp`](examples/segnet/segnet.cpp) | [`segnet.py`](python/examples/segnet.py) | |    Pose Estimation | [`posenet.cpp`](examples/posenet/posenet.cpp) | [`posenet.py`](python/examples/posenet.py) | |    Monocular Depth | [`depthnet.cpp`](examples/depthnet/segnet.cpp) | [`depthnet.py`](python/examples/depthnet.py) | > **note**: for working with numpy arrays, see [Converting to Numpy Arrays](docs/aux-image.md#converting-to-numpy-arrays) and [Converting from Numpy Arrays](docs/aux-image.md#converting-from-numpy-arrays) These examples will automatically be compiled while [Building the Project from Source](docs/building-repo-2.md), 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**](docs/building-repo-2.md#downloading-models) 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](data/networks/ssd_coco_labels.txt)) | | SSD-Mobilenet-v2 | `ssd-mobilenet-v2` | `SSD_MOBILENET_V2` | 91 ([COCO classes](data/networks/ssd_coco_labels.txt)) | | SSD-Inception-v2 | `ssd-inception-v2` | `SSD_INCEPTION_V2` | 91 ([COCO classes](data/networks/ssd_coco_labels.txt)) | | 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](https://www.cityscapes-dataset.com/) | 512x256 | `fcn-resnet18-cityscapes-512x256` | 83.3% | 48 FPS | 480 FPS | | [Cityscapes](https://www.cityscapes-dataset.com/) | 1024x512 | `fcn-resnet18-cityscapes-1024x512` | 87.3% | 12 FPS | 175 FPS | | [Cityscapes](https://www.cityscapes-dataset.com/) | 2048x1024 | `fcn-resnet18-cityscapes-2048x1024` | 89.6% | 3 FPS | 47 FPS | | [DeepScene](http://deepscene.cs.uni-freiburg.de/) | 576x320 | `fcn-resnet18-deepscene-576x320` | 96.4% | 26 FPS | 360 FPS | | [DeepScene](http://deepscene.cs.uni-freiburg.de/) | 864x480 | `fcn-resnet18-deepscene-864x480` | 96.9% | 14 FPS | 190 FPS | | [Multi-Human](https://lv-mhp.github.io/) | 512x320 | `fcn-resnet18-mhp-512x320` | 86.5% | 34 FPS | 370 FPS | | [Multi-Human](https://lv-mhp.github.io/) | 640x360 | `fcn-resnet18-mhp-512x320` | 87.1% | 23 FPS | 325 FPS | | [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) | 320x320 | `fcn-resnet18-voc-320x320` | 85.9% | 45 FPS | 508 FPS | | [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) | 512x320 | `fcn-resnet18-voc-512x320` | 88.5% | 34 FPS | 375 FPS | | [SUN RGB-D](http://rgbd.cs.princeton.edu/) | 512x400 | `fcn-resnet18-sun-512x400` | 64.3% | 28 FPS | 340 FPS | | [SUN RGB-D](http://rgbd.cs.princeton.edu/) | 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](#training) 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](http://www.github.com/dusty-nv/ros_deep_learning) - TensorRT inference ROS nodes * [NVIDIA AI IoT](https://github.com/NVIDIA-AI-IOT) - NVIDIA Jetson GitHub repositories * [Jetson eLinux Wiki](https://www.eLinux.org/Jetson) - 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](#training) 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](docs/digits-workflow.md) * [DIGITS System Setup](docs/digits-setup.md) * [Setting up Jetson with JetPack](docs/jetpack-setup.md) * [Building the Project from Source](docs/building-repo.md) * [Classifying Images with ImageNet](docs/imagenet-console.md) * [Using the Console Program on Jetson](docs/imagenet-console.md#using-the-console-program-on-jetson) * [Coding Your Own Image Recognition Program](docs/imagenet-example.md) * [Running the Live Camera Recognition Demo](docs/imagenet-camera.md) * [Re-Training the Network with DIGITS](docs/imagenet-training.md) * [Downloading Image Recognition Dataset](docs/imagenet-training.md#downloading-image-recognition-dataset) * [Customizing the Object Classes](docs/imagenet-training.md#customizing-the-object-classes) * [Importing Classification Dataset into DIGITS](docs/imagenet-training.md#importing-classification-dataset-into-digits) * [Creating Image Classification Model with DIGITS](docs/imagenet-training.md#creating-image-classification-model-with-digits) * [Testing Classification Model in DIGITS](docs/imagenet-training.md#testing-classification-model-in-digits) * [Downloading Model Snapshot to Jetson](docs/imagenet-snapshot.md) * [Loading Custom Models on Jetson](docs/imagenet-custom.md) * [Locating Objects with DetectNet](docs/detectnet-training.md) * [Detection Data Formatting in DIGITS](docs/detectnet-training.md#detection-data-formatting-in-digits) * [Downloading the Detection Dataset](docs/detectnet-training.md#downloading-the-detection-dataset) * [Importing the Detection Dataset into DIGITS](docs/detectnet-training.md#importing-the-detection-dataset-into-digits) * [Creating DetectNet Model with DIGITS](docs/detectnet-training.md#creating-detectnet-model-with-digits) * [Testing DetectNet Model Inference in DIGITS](docs/detectnet-training.md#testing-detectnet-model-inference-in-digits) * [Downloading the Detection Model to Jetson](docs/detectnet-snapshot.md) * [DetectNet Patches for TensorRT](docs/detectnet-snapshot.md#detectnet-patches-for-tensorrt) * [Detecting Objects from the Command Line](docs/detectnet-console.md) * [Multi-class Object Detection Models](docs/detectnet-console.md#multi-class-object-detection-models) * [Running the Live Camera Detection Demo on Jetson](docs/detectnet-camera.md) * [Semantic Segmentation with SegNet](docs/segnet-dataset.md) * [Downloading Aerial Drone Dataset](docs/segnet-dataset.md#downloading-aerial-drone-dataset) * [Importing the Aerial Dataset into DIGITS](docs/segnet-dataset.md#importing-the-aerial-dataset-into-digits) * [Generating Pretrained FCN-Alexnet](docs/segnet-pretrained.md) * [Training FCN-Alexnet with DIGITS](docs/segnet-training.md) * [Testing Inference Model in DIGITS](docs/segnet-training.md#testing-inference-model-in-digits) * [FCN-Alexnet Patches for TensorRT](docs/segnet-patches.md) * [Running Segmentation Models on Jetson](docs/segnet-console.md)
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