# jetson-inference **Repository Path**: songkemail/jetson-inference ## Basic Information - **Project Name**: jetson-inference - **Description**: Guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson. - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2019-12-10 - **Last Updated**: 2024-05-05 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

# Deploying Deep Learning Welcome to our instructional guide for inference and realtime [DNN vision](#api-reference) library for NVIDIA **[Jetson Nano/TX1/TX2/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`](c/imageNet.h) for image recognition, [`detectNet`](c/detectNet.h) for object localization, and [`segNet`](c/segNet.h) for semantic segmentation, 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.

There are multiple tracks of the tutorial that you can choose to follow, including [Hello AI World](#hello-ai-world) for running inference and transfer learning onboard your Jetson, or the full [Two Days to a Demo](#two-days-to-a-demo-digits) 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 * [Hello AI World](#hello-ai-world) * [Two Days to a Demo](#two-days-to-a-demo-digits) * [API Reference](#api-reference) * [Code Examples](#code-examples) * [Pre-Trained Models](#pre-trained-models) * [System Requirements](#recommended-system-requirements) * [Extra Resources](#extra-resources) > > Jetson Nano Developer Kit and JetPack 4.2.2 is now supported in the repo.
> > See our latest technical blog including benchmarks, [`Jetson Nano Brings AI Computing to Everyone`](https://devblogs.nvidia.com/jetson-nano-ai-computing/).
> > Hello AI World now supports Python and onboard training with PyTorch! ## 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. * [Setting up Jetson with JetPack](docs/jetpack-setup-2.md) * [Building the Project from Source](docs/building-repo-2.md) * [Classifying Images with ImageNet](docs/imagenet-console-2.md) * [Using the Console Program on Jetson](docs/imagenet-console-2.md#using-the-console-program-on-jetson) * [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 the Command Line](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) * [Transfer Learning with PyTorch](docs/pytorch-transfer-learning.md) * [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 Datasets](docs/pytorch-collect.md) ## 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. * [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) ## API Reference Below are links to reference documentation for the [C++](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/index.html) and [Python](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/python/jetson.html) libraries from the repo: #### jetson-inference | | [C++](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/group__deepVision.html) | [Python](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/python/jetson.inference.html) | |-------------------|--------------|--------------| | Image Recognition | [`imageNet`](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/classimageNet.html) | [`imageNet`](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/python/jetson.inference.html#imageNet) | | Object Detection | [`detectNet`](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/classdetectNet.html) | [`detectNet`](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/python/jetson.inference.html#detectNet) | Segmentation | [`segNet`](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/classsegNet.html) | [`segNet`](https://rawgit.com/dusty-nv/jetson-inference/pytorch/docs/html/python/jetson.inference.html#segNet) | #### jetson-utils * [C++](https://rawgit.com/dusty-nv/jetson-inference/python/docs/html/group__util.html) * [Python](https://rawgit.com/dusty-nv/jetson-inference/python/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: | | Images | Camera | |-------------------|---------------------|---------------------| | **C++ ([`examples`](examples/))** | | | Image Recognition | [`imagenet-console`](examples/imagenet-console/imagenet-console.cpp) | [`imagenet-camera`](examples/imagenet-camera/imagenet-camera.cpp) | | Object Detection | [`detectnet-console`](examples/detectnet-console/detectnet-console.cpp) | [`detectnet-camera`](examples/detectnet-camera/detectnet-camera.cpp) | Segmentation | [`segnet-console`](examples/segnet-console/segnet-console.cpp) | [`segnet-camera`](examples/segnet-camera/segnet-camera.cpp) | | **Python ([`python/examples`](python/examples/))** | | | | Image Recognition | [`imagenet-console.py`](python/examples/imagenet-console.py) | [`imagenet-camera.py`](python/examples/imagenet-camera.py) | | Object Detection | [`detectnet-console.py`](python/examples/detectnet-console.py) | [`detectnet-camera.py`](python/examples/detectnet-camera.py) | | Segmentation | [`segnet-console.py`](python/examples/segnet-console.py) | [`segnet-camera.py`](python/examples/segnet-camera.py) | > **note**: for working with numpy arrays, see [`cuda-from-numpy.py`](https://github.com/dusty-nv/jetson-utils/blob/master/python/examples/cuda-from-numpy.py) and [`cuda-to-numpy.py`](https://github.com/dusty-nv/jetson-utils/blob/master/python/examples/cuda-to-numpy.py) 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 |

## Recommended System Requirements **Training GPU:** Maxwell, Pascal, Volta, or Turing-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 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: * [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 ## 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](docs/digits-workflow.md) ### System Setup See [DIGITS Setup](docs/digits-setup.md) #### Running JetPack on the Host See [JetPack Setup](docs/jetpack-setup.md) #### Installing Ubuntu on the Host See [DIGITS Setup](docs/digits-setup.md#installing-ubuntu-on-the-host) #### Setting up host training PC with NGC container See [DIGITS Setup](docs/digits-setup.md#setting-up-host-training-pc-with-ngc-container) #### Installing the NVIDIA driver See [DIGITS Setup](docs/digits-setup.md#installing-the-nvidia-driver) #### Installing Docker See [DIGITS Setup](docs/digits-setup.md#installing-docker) #### NGC Sign-up See [DIGITS Setup](docs/digits-setup.md#ngc-sign-up) #### Setting up data and job directories See [DIGITS Setup](docs/digits-setup.md#setting-up-data-and-job-directories) #### Starting DIGITS container See [DIGITS Setup](docs/digits-setup.md#starting-digits-container) #### Natively setting up DIGITS on the Host See [DIGITS Native Setup](docs/digits-native.md) #### Installing NVIDIA Driver on the Host See [DIGITS Native Setup](docs/digits-native.md#installing-nvidia-driver-on-the-host) #### Installing cuDNN on the Host See [DIGITS Native Setup](docs/digits-native.md#installing-cudnn-on-the-host) #### Installing NVcaffe on the Host See [DIGITS Native Setup](docs/digits-native.md#installing-nvcaffe-on-the-host) #### Installing DIGITS on the Host See [DIGITS Native Setup](docs/digits-native.md#installing-digits-on-the-host) #### Starting the DIGITS Server See [DIGITS Native Setup](docs/digits-native.md#starting-the-digits-server) ### Building from Source on Jetson See [Building the Repo from Source](docs/building-repo.md) #### Cloning the Repo See [Building the Repo from Source](docs/building-repo.md#cloning-the-repo) #### Configuring with CMake See [Building the Repo from Source](docs/building-repo.md#configuring-with-cmake) #### Compiling the Project See [Building the Repo from Source](docs/building-repo.md#compiling-the-project) #### Digging Into the Code See [Building the Repo from Source](docs/building-repo.md#digging-into-the-code) ### Classifying Images with ImageNet See [Classifying Images with ImageNet](docs/imagenet-console.md) #### Using the Console Program on Jetson See [Classifying Images with ImageNet](docs/imagenet-console.md#using-the-console-program-on-jetson) ### Running the Live Camera Recognition Demo See [Running the Live Camera Recognition Demo](docs/imagenet-camera.md) ### Re-training the Network with DIGITS See [Re-Training the Recognition Network](docs/imagenet-training.md) #### Downloading Image Recognition Dataset See [Re-Training the Recognition Network](docs/imagenet-training.md#downloading-image-recognition-dataset) #### Customizing the Object Classes See [Re-Training the Recognition Network](docs/imagenet-training.md#customizing-the-object-classes) #### Importing Classification Dataset into DIGITS See [Re-Training the Recognition Network](docs/imagenet-training.md#importing-classification-dataset-into-digits) #### Creating Image Classification Model with DIGITS See [Re-Training the Recognition Network](docs/imagenet-training.md#creating-image-classification-model-with-digits) #### Testing Classification Model in DIGITS See [Re-Training the Recognition Network](docs/imagenet-training.md#testing-classification-model-in-digits) #### Downloading Model Snapshot to Jetson See [Downloading Model Snapshots to Jetson](docs/imagenet-snapshot.md) ### Loading Custom Models on Jetson See [Loading Custom Models on Jetson](docs/imagenet-custom.md) ### Locating Object Coordinates using DetectNet See [Locating Object Coordinates using DetectNet](docs/detectnet-training.md) #### Detection Data Formatting in DIGITS See [Locating Object Coordinates using DetectNet](docs/detectnet-training.md#detection-data-formatting-in-digits) #### Downloading the Detection Dataset See [Locating Object Coordinates using DetectNet](docs/detectnet-training.md#downloading-the-detection-dataset) #### Importing the Detection Dataset into DIGITS See [Locating Object Coordinates using DetectNet](docs/detectnet-training.md#importing-the-detection-dataset-into-digits) #### Creating DetectNet Model with DIGITS See [Locating Object Coordinates using DetectNet](docs/detectnet-training.md#creating-detectnet-model-with-digits) #### Selecting DetectNet Batch Size See [Locating Object Coordinates using DetectNet](docs/detectnet-training.md#selecting-detectnet-batch-size) #### Specifying the DetectNet Prototxt See [Locating Object Coordinates using DetectNet](docs/detectnet-training.md#specifying-the-detectnet-prototxt) #### Training the Model with Pretrained Googlenet See [Locating Object Coordinates using DetectNet](docs/detectnet-training.md#training-the-model-with-pretrained-googlenet) #### Testing DetectNet Model Inference in DIGITS See [Locating Object Coordinates using DetectNet](docs/detectnet-training.md#testing-detectnet-model-inference-in-digits) #### Downloading the Model Snapshot to Jetson See [Downloading the Detection Model to Jetson](docs/detectnet-snapshot.md) #### DetectNet Patches for TensorRT See [Downloading the Detection Model to Jetson](docs/detectnet-snapshot.md#detectnet-patches-for-tensorrt) ### Processing Images from the Command Line on Jetson See [Detecting Objects from the Command Line](docs/detectnet-console.md) #### Launching With a Pretrained Model See [Detecting Objects from the Command Line](docs/detectnet-console.md#launching-with-a-pretrained-model) #### Pretrained DetectNet Models Available See [Detecting Objects from the Command Line](docs/detectnet-console.md#pretrained-detectnet-models-available) #### Running Other MS-COCO Models on Jetson See [Detecting Objects from the Command Line](docs/detectnet-console.md#running-other-ms-coco-models-on-jetson) #### Running Pedestrian Models on Jetson See [Detecting Objects from the Command Line](docs/detectnet-console.md#running-pedestrian-models-on-jetson) #### Multi-class Object Detection Models See [Detecting Objects from the Command Line](docs/detectnet-console.md#multi-class-object-detection-models) ### Running the Live Camera Detection Demo on Jetson See [Running the Live Camera Detection Demo](docs/detectnet-camera.md) ### Image Segmentation with SegNet See [Semantic Segmentation with SegNet](docs/segnet-dataset.md) #### Downloading Aerial Drone Dataset See [Semantic Segmentation with SegNet](docs/segnet-dataset.md#downloading-aerial-drone-dataset) #### Importing the Aerial Dataset into DIGITS See [Semantic Segmentation with SegNet](docs/segnet-dataset.md#importing-the-aerial-dataset-into-digits) #### Generating Pretrained FCN-Alexnet See [Generating Pretrained FCN-Alexnet](docs/segnet-pretrained.md) ### Training FCN-Alexnet with DIGITS See [Training FCN-Alexnet with DIGITS](docs/segnet-training.md) #### Testing Inference Model in DIGITS See [Training FCN-Alexnet with DIGITS](docs/segnet-training.md#testing-inference-model-in-digits) #### FCN-Alexnet Patches for TensorRT See [FCN-Alexnet Patches for TensorRT](docs/segnet-patches.md) ### Running Segmentation Models on Jetson See [Running Segmentation Models on Jetson](docs/segnet-console.md)