Jetson Generative AI for Edge AI and Computer Vision

Jetson Generative AI is a game-changer for Edge AI and Computer Vision. It's a software platform that enables real-time AI processing on edge devices, such as cameras and sensors.

This means that AI models can be deployed directly on the edge device, reducing latency and increasing efficiency. No more relying on cloud computing or long-distance data transmission.

Jetson Generative AI is specifically designed for applications like surveillance, robotics, and autonomous vehicles, where real-time processing is crucial. It supports a wide range of AI frameworks, including TensorFlow and PyTorch.

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NVIDIA Jetson AGX Orin is a powerful embedded AI computer that can run generative AI models, thanks to its 12 Cortex-A78AE cores, powerful GPU, and up to 64GB RAM.

The NVIDIA Jetson Generative AI Lab provides tools and tutorials for deploying open-source LLMs, diffusion models, and vision language models on Jetson devices.

NVIDIA has made significant upgrades to the Isaac platform, particularly its perception and simulation features, making it a popular choice for creating robust robotics solutions.

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Isaac ROS software offers GPU-accelerated functions like depth perception and 3D scene reconstruction.

NVIDIA has also introduced AI reference workflows to reduce development time and costs, with plans to unveil NVIDIA JetPack 6 later this year.

JetPack 6 will allow AI developers to access the latest computing features without fully upgrading Jetson Linux, and will work with various Linux distributions.

Here are some of the major categories of foundation models in the generative AI ecosystem, as focused on by NVIDIA:

The NVIDIA Jetson Generative AI Lab serves as a hub for discovering the latest generative AI models and applications and learning how to run them on Jetson devices.

NVIDIA's jetson-containers project builds containers for Jetson devices, enabling developers to quickly test the latest AI models, libraries, and applications on Jetson without hassle.

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The NVIDIA Jetson platform is revolutionizing the field of computer vision with its powerful GPU and AI accelerators. This technology enables real-time object detection and classification, making it ideal for applications like retail security and traffic analysis.

Generative AI models like the Transformer PeopleNet model can detect people in videos with high accuracy, while the Detection Anything model uses vision transformers to perceive and classify objects in real-time. These models are freely available for public use and can be downloaded to try out.

The Multi-Modal AI Visual Agent model facilitates text-to-image and image-to-image contextual searches, opening up new possibilities for applications like image recognition and object detection. With the Jetson platform, developers can create and deploy these models with minimal coding.

The NVIDIA Jetson Generative AI Lab provides a playground for developers to try out these models and explore their potential. The lab offers tools and tutorials for deploying open-source LLMs, diffusion models, and vision language models on the Jetson platform.

Here are some of the exciting generative AI applications that run on the NVIDIA Jetson device available in the Jetson Generative AI lab:

The NVIDIA Jetson Orin is a powerful embedded AI computer that makes it suitable for generative AI and can replace legacy CNNs. It enables a language interface, allowing users to interact with the AI model using a chatbot.

NVIDIA JetPack 6 SDK is expected to be released next month with support for various Linux distributions, including Ubuntu, Wind River Linux, and Redhawk Linux.

This upgrade will be easier and less time-consuming, as AI developers won't need to perform a full Jetson Linux upgrade. The JetPack 6 SDK will be based on Ubuntu 22.04, according to the Jetson Software roadmap.

The new JetPack 6 SDK will also introduce new system services, such as IoT Edge Stack, Diagnostics, and Fleet management, and enhanced security features like secure boot and memory encryption.

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The NVIDIA Jetpack 6 SDK is expected to be released next month with support for Canonical's optimized and certified Ubuntu OS, as well as other operating systems like Wind River Linux and Redhawk Linux.

NVIDIA has stated that AI developers will not be required to perform a full Jetson Linux upgrade to switch to the JetPack 6 SDK, making the upgrade easier and less time-consuming.

JetPack 6 will allow AI developers to access the latest computing features without fully upgrading Jetson Linux, providing a more streamlined development process.

The JetPack 6 SDK will be based on Ubuntu 22.04, which is a relatively recent release, but it's still a year old, which might be a concern for developers who prefer to work with newer code bases and programming tools.

NVIDIA is providing packages for multiple Linux distributions, including Ubuntu, Wind River Linux, and Redhawk Linux, to cater to the diverse needs of its developer community.

The new system services included in JetPack 6 will be IoT Edge Stack, Diagnostics, and Fleet management, which will enhance the overall performance and security of the Jetson platform.

Additional details about the release will be made available at the time of the release, but it's expected to include enhanced security features such as secure boot, memory encryption, and more.

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NanoOWL is a project that optimizes the Open World Localization with Vision Transformers (OWL-ViT) model with NVIDIA TensorRT to achieve real-time performance on NVIDIA Jetson Orin Platforms.

This optimization enables OWL-ViT to run at ~95FPS encoding speed on Jetson AGX Orin, allowing it to keep up with common camera frame rates.

NanoOWL also includes a new tree detection pipeline that combines the accelerated OWL-ViT model with CLIP for zero-shot detection and classification at any level.

To unlock applications at the edge, NanoOWL is a valuable tool that can be used to rapidly develop new applications without needing to train a new model.

The project's performance means that you can run OWL-ViT well above the common camera frame rates, making it suitable for real-time applications.

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