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Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
| Date | Stars |
|---|---|
| 2026-07-24 | 8939 |
| 2026-07-25 | 8939 |
| 2026-07-28 | 8941 |
| 2026-07-30 | 8941 |
| 2026-07-31 | 8944 |
| 2026-08-06 | 8953 |
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<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 vision [DNN library](#api-reference) for **[NVIDIA Jetson](https://developer.nvidia.com/embedded-computing)** devices. This project uses **[TensorRT](https://developer.nvidia.com/tensorrt)** to run optimized networks on GPUs from C++ or Python, and PyTorch for training models. Supported DNN vision primitives include [`imageNet`](docs/imagenet-console-2.md) for image classification, [`detectNet`](docs/detectnet-console-2.md) for object detection, [`segNet`](docs/segnet-console-2.md) for semantic segmentation, [`poseNet`](docs/posenet.md) for pose estimation, and [`actionNet`](docs/actionnet.md) for action recognition. Examples are provided for streaming from live camera feeds, making webapps with WebRTC, and support for ROS/ROS2. <img src="https://github.com/dusty-nv/jetson-inference/raw/master/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, training your own models with PyTorch, and deploying them with TensorRT. ### Table of Contents * [Hello AI World](#hello-ai-world) * [Jetson AI Lab](#jetson-ai-lab) * [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 6 is now supported on Orin devices ([developer.nvidia.com/jetpack](https://developer.nvidia.com/embedded/jetpack)) <br/> > > Check out the Generative AI and LLM tutorials on [Jetson AI Lab](https://www.jetson-ai-lab.com/)! <br/> > > 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 live inferencing with TensorRT and transfer learning with PyTorch. For installation instructions, see [System Setup](#system-setup). It's then recommended to start with the [Inference](#inference) section to familiarize yourself with the concepts, before diving into [Training](#training) your own models. #### 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 * [Image Classification](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) * [Multi-Label Classification for Image Tagging](docs/imagenet-tagging.md) * [Object Detection](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) * [Using TAO Detection Models](docs/detectnet-tao.md) * [Object Tracking on Video](docs/detectnet-tracking.md) * [Semantic Segmentation](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](docs/posenet.md) * [Action Recognition](docs/actionnet.md) * [Background Removal](docs/backgroundnet.md) * [Monocular Depth](docs/depthnet.md) #### Training * [Transfer Learning with PyTorch](docs/pytorch-transfer-learning.md) * Classification/Recognition (ResNet-18) * [Re-training on the
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b20351c83acecdc6, topic:computer-vision, topic:object-detection, readme:object detection
matched fp:b20351c83acecdc6, topic:inference, topic:tensorrt