This article was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. We performed a side-by-side comparison of three models from leading providers in Generative AI for Vision. This is what we found: Despite the subjectivity involved in Human Evaluation, this is the best approach to evaluate state-of-the-art GenAI Vision […]
DALL-E vs Gemini vs Stability: GenAI Evaluations Read More +
This article was originally published at Digica’s website. It is reprinted here with the permission of Digica. Remember the anxiety felt back in the 1990s after the publications of the first quantum algorithms by Deutsh and Jozsa (1992), Shor (1994), and Grover (1996). Most of us expected quantum computers to be of practical use within
Spikes are the Next Digits Read More +
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Large-scale, use–case-specific synthetic data has become increasingly important in real-world computer vision and AI workflows. That’s because digital twins are a powerful way to create physics-based virtual replicas of factories, retail spaces, and other assets, enabling precise simulations
Enhance Multi-camera Tracking Accuracy by Fine-tuning AI Models with Synthetic Data Read More +
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Intelligent Transportation Systems (ITS) applications are becoming increasingly valuable and prevalent in modern urban environments. The benefits of using ITS applications include: Increasing traffic efficiency: By analyzing real-time traffic data, ITS can optimize traffic flow, reducing congestion and
Generate Traffic Insights Using YOLOv8 and NVIDIA JetPack 6.0 Read More +
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. NVIDIA DeepStream is a powerful SDK that unlocks GPU-accelerated building blocks to build end-to-end vision AI pipelines. With more than 40+ plugins available off-the-shelf, you can deploy fully optimized pipelines with cutting-edge AI Inference, object tracking, and seamless
NVIDIA DeepStream 7.0 Milestone Release for Next-gen Vision AI Development Read More +
See “Quantization of Convolutional Neural Networks: Model Quantization” for the previous article in this series. In the previous articles in this series, we discussed quantization schemes and the effect of different choices on model accuracy. The ultimate choice of quantization scheme depends on the available tools. TFlite and Pytorch are the most popular tools used
Quantization of Convolutional Neural Networks: Quantization Analysis Read More +
See “From Theory to Practice: Quantizing Convolutional Neural Networks for Practical Deployment” for the previous article in this series. Significant progress in Convolutional Neural Networks (CNNs) has focused on enhancing model complexity while managing computational demands. Key advancements include efficient architectures like MobileNet1, SqueezeNet2, ShuffleNet3, and DenseNet4, which prioritize compute and memory efficiency. Further innovations
Quantization of Convolutional Neural Networks: Model Quantization Read More +
In this dynamic technology landscape, the fusion of artificial intelligence and edge computing is revolutionizing real-time data processing. Embedded vision and edge AI take center stage, offering unparalleled potential for precision and efficiency at the edge. However, the challenge lies in executing vision tasks on resource-limited edge devices. Model compression techniques, notably quantization, emerge as
This article was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. Learn about the Foundation Models — for object classification, object detection, and segmentation — that are redefining Computer Vision. Foundation models have come to computer vision! Initially limited to language tasks, foundation models can now serve as the backbone of computer
The Foundation Models Reshaping Computer Vision Read More +
This article was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. In Part 2 of our NVIDIA TAO Toolkit series, we describe & address the common challenges of model deployment, in particular edge deployment. We explore practical solutions to these challenges, especially on the issues surrounding model comparison. Here
Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.
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