jetson-inference完整版的项目代码 避免被墙导致编译错误

<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%"> # 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. <img src="https://github.com/dusty-nv/jetson-inference/raw/dev/docs/images/deep-vision-primitives.jpg"> 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) > &gt; &nbsp; JetPack 4.6 is now supported, along with [updated containers](docs/aux-docker.md). <br/> > &gt; &nbsp; Try the new [Pose Estimation](docs/posenet.md) and [Mono Depth](docs/depthnet.md) tutorials! <br/> > &gt; &nbsp; See the [Change Log](CHANGELOG.md) for the latest updates and new features. <br/> ## 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 | |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <a href="https://www.youtube.com/watch?v=QXIwdsyK7Rw&list=PL5B692fm6--uQRRDTPsJDp4o0xbzkoyf8&index=9" target="_blank">**Hello AI World Setup**</a><br/>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. | <a href="https://www.youtube.com/watch?v=QXIwdsyK7Rw&list=PL5B692fm6--uQRRDTPsJDp4o0xbzkoyf8&index=9" target="_blank"><img src=https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/thumbnail_setup.jpg width="750"></a> | | <a href="https://www.youtube.com/watch?v=QatH8iF0Efk&list=PL5B692fm6--uQRRDTPsJDp4o0xbzkoyf8&index=10" target="_blank">**Image Classification Inference**</a><br/>Code your own Python program for image classification using Jetson Nano and deep learning, then experiment with realtime classification on a live camera stream. | <a href="https://www.youtube.com/watch?v=QatH8iF0Efk&list=PL5B692fm6--uQRRDTPsJDp4o0xbzkoyf8&index=10" target="_blank"><img src=https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/thumbnail_imagenet.jpg width="750"></a> | | <a href="https://www.youtube.com/watch?v=sN6aT9TpltU&list=PL5B692fm6--uQRRDTPsJDp4o0xbzkoyf8&index=11" target="_blank">**Training Image Classification Models**</a><br/>Learn how to train image classification models with PyTorch onboard Jetson Nano, and collect your own classification datasets to create custom models. | <a href="https://www.youtube.com/watch?v=sN6aT9TpltU&list=PL5B692fm6--uQRRDTPsJDp4o0xbzkoyf8&index=11" target="_blank"><img src=https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/thumbnail_imagenet_training.jpg width="750"></a> | | <a href="https://www.youtube.com/watch?v=obt60r8ZeB0&list=PL5B692fm6--uQRRDTPsJDp4o0xbzkoyf8&index=12" target="_blank">**Object Detection Inference**</a><br/>Code your own Python program for object detection using Jetson Nano and deep learning, then experiment with realtime detection on a live camera stream. | <a href="https://www.youtube.com/watch?v=ob