NVIDIA TAO is a framework designed to simplify and accelerate the development and deployment of AI models. It enables you to use pretrained models, fine-tune them with your own data, and optimize the models for specific use cases without needing deep AI expertise. TAO integrates seamlessly with the NVIDIA hardware and software ecosystem, providing tools for efficient AI model training…
]]>Phi-3-Medium accelerates research with logic-rich features in both short (4K) and long (128K) context.
]]>NVIDIA JetPack SDK powers NVIDIA Jetson modules, offering a comprehensive solution for building end-to-end accelerated AI applications. JetPack 6 expands the Jetson platform’s flexibility and scalability with microservices and a host of new features. It’s the most downloaded version of JetPack in 2024. With the JetPack 6.0 production release now generally available…
]]>NVIDIA Metropolis Microservices for Jetson has been renamed to Jetson Platform Services, and is now part of NVIDIA JetPack SDK 6.0. Building vision AI applications for the edge often comes with notoriously long and costly development cycles. At the same time, quickly developing edge AI applications that are cloud-native, flexible, and secure has never been more important. Now…
]]>Optical Character Detection (OCD) and Optical Character Recognition (OCR) are computer vision techniques used to extract text from images. Use cases vary across industries and include extracting data from scanned documents or forms with handwritten texts, automatically recognizing license plates, sorting boxes or objects in a fulfillment center based on serial numbers…
]]>NVIDIA Triton Inference Server streamlines and standardizes AI inference by enabling teams to deploy, run, and scale trained ML or DL models from any framework on any GPU- or CPU-based infrastructure. It helps developers deliver high-performance inference across cloud, on-premises, edge, and embedded devices. The nvOCDR library is integrated into Triton for inference.
]]>NVIDIA TAO Toolkit provides a low-code AI framework to accelerate vision AI model development suitable for all skill levels, from novice beginners to expert data scientists. With the TAO Toolkit, developers can use the power and efficiency of transfer learning to achieve state-of-the-art accuracy and production-class throughput in record time with adaptation and optimization.
]]>Vision Transformers (ViTs) are taking computer vision by storm, offering incredible accuracy, robust solutions for challenging real-world scenarios, and improved generalizability. The algorithms are playing a pivotal role in boosting computer vision applications and NVIDIA is making it easy to integrate ViTs into your applications using NVIDIA TAO Toolkit and NVIDIA L4 GPUs.
]]>There has been tremendous growth in AI over the years. With that, comes a larger demand for AI models and applications. Creating production-quality AI requires expertise in AI and data science and can still be intimidating for many developers. To develop accurate AI, you must choose what model architecture to use, what data to collect, and finally how to tune the model to meet the desired…
]]>A fundamental shift is currently taking place in how AI applications are built and deployed. AI applications are becoming more sophisticated and applied to broader use cases. This requires end-to-end AI lifecycle management—from data preparation, to model development and training, to deployment and management of AI apps. This approach can lower upfront costs, improve scalability…
]]>The first post in this series covered how to train a 2D pose estimation model using an open-source COCO dataset with the BodyPoseNet app in NVIDIA TAO Toolkit. In this post, you learn how to optimize the pose estimation model in TAO Toolkit. It walks you through the steps of model pruning and INT8 quantization to optimize the model for inference. This section covers few topics of…
]]>Human pose estimation is a popular computer vision task of estimating key points on a person’s body such as eyes, arms, and legs. This can help classify a person’s actions, such as standing, sitting, walking, lying down, jumping, and so on. Understanding the context of what a person might be doing in a scene has broad application across a wide range of industries. In a retail setting…
]]>Businesses are constantly overhauling their existing infrastructure and processes to be more efficient, safe, and usable for employees, customers, and the community. With the ongoing pandemic, it’s even more important to have advanced analytics apps and services in place to mitigate risk. For public safety and health, authorities are recommending the use of face masks and coverings to…
]]>Whether it’s a warehouse looking to balance product distribution and optimize traffic, a factory assembly line inspection, or hospital management, making sure that employees and caregivers use personal protection equipment (PPE) while attending to patients, advanced intelligent video analytics (IVA) turn out to be useful. At the foundational layer, there are billions of cameras and IoT…
]]>Get notified about production releases: Whether it’s a warehouse looking to balance product distribution and optimize traffic, a factory assembly line inspection, or hospital management making sure that employees and caregivers use personal protection equipment (PPE) while attending to patients, advanced intelligent video analytics (IVA) turn out to be useful. At the foundational layer…
]]>This post is the second in a series (Part 1) that addresses the challenges of training an accurate deep learning model using a large public dataset and deploying the model on the edge for real-time inference using NVIDIA DeepStream. In the previous post, you learned how to train a RetinaNet network with a ResNet34 backbone for object detection. This included pulling a container…
]]>Some of the biggest challenges in deploying an AI-based application are the accuracy of the model and being able to extract insights in real time. There’s a trade-off between accuracy and inference throughput. Making the model more accurate makes the model larger which reduces the inference throughput. This post series addresses both challenges. In part 1, you train an accurate…
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