This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. The introduction of the NVIDIA Jetson Orin Nano Super Developer Kit sparked a new age of generative AI for small edge devices. The new Super Mode delivered an unprecedented generative AI performance boost of up to 1.7x […]
This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. When improving an object detection model, many engineers focus solely on tweaking the model architecture and hyperparameters. However, the root cause of mediocre performance often lies in the data itself. In this collaborative post between Roboflow and
Improving Vision Model Performance Using Roboflow and Tenyks Read More +
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. In the first article of our mini-series on Federated Learning (FL), Privacy-First AI: Exploring Federated Learning, we introduced the basic concepts behind the decentralized training approach, and we also presented potential applications in certain domains. Undoubtedly, FL
Federated Learning: Risks and Challenges Read More +
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. The smart home industry is on the brink of a significant leap forward, fueled by generative AI and edge capabilities The smart home is evolving to include advanced capabilities, such as digital assistants that interact like friends
This blog post was originally published at e-con Systems’ website. It is reprinted here with the permission of e-con Systems. Single and multiple camera setups play a vital role in today’s vision systems, enhancing applications such as Automated sports broadcasting, Industrial robots, Traffic monitoring and more. Explore the key differences, advantages, and real-world applications of
Single- vs. Multi-camera Systems: The Ultimate Guide Read More +
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Generative AI has evolved from text-based models to multimodal models, with a recent expansion into video, opening up new potential uses across various industries. Video models can create new experiences for users or simulate scenarios for training
This blog post was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. During this series, we will use Tenyks to build a data-centric pipeline to debug and fix a model trained with the NVIDIA TAO Toolkit. Part 1. We demystify the NVIDIA ecosystem and define a data-centric pipeline based
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. As the pace of artificial intelligence innovation accelerates, we’re seeing AI and computer vision go from science fiction tropes to enabling highly efficient and compelling applications. This integration is particularly potent at the edge, where devices locally
Computer Vision and AI at the Edge with a Thermal Camera Provider and a Toy Manufacturer Read More +
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. From more on-device AI features on your phone to the future of cars, 2025 is shaping up to be a big year Over the last two years, generative AI (GenAI) has shaken up, well, everything. Heading into
How AI On the Edge Fuels the 7 Biggest Consumer Tech Trends of 2025 Read More +
This blog post was originally published at e-con Systems’ website. It is reprinted here with the permission of e-con Systems. Developing cutting-edge vision systems requires more than just advanced hardware—it demands the ability to customize and optimize image processing to meet specific application needs. That’s why e-con Systems offers a suite of high-performance Image Signal
Customize e-con Systems’ FPGA IP Cores to Meet Unique Vision Needs Read More +
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