Click belowCard to follow “Machine Vision and AI Deep Learning“ Visual/Image processing insights delivered promptly! This project is named SPEED-SAM-C++-TENSORRT, a high-performance implementation of the Segment Anything Model (SAM), utilizing NVIDIA’s TensorRT for efficient inference and optimizing GPU utilization with CUDA. Below is a detailed introduction to how this implementation works, how to compile it, … Read more
Click the blue text above to follow us WeChat Official Account:OpenCV Academy Follow us for more knowledge on computer vision and deep learning Introduction to the Padim Model The Padim model primarily generates feature vectors from a series of normal samples using a CNN network, calculates multiple variance Gaussian matrices of the feature vectors, and … Read more
This project has been uploaded to GitHub https://github.com/Bayesianovich/yolov8-fire-smoke-detection, feel free to star ⭐ ⭐ Project Overview This project is a high-performance fire and smoke detection system based on YOLOv8 and TensorRT, implemented in C++, with the following features: Real-time Detection Performance: Achieves high-performance inference using TensorRT GPU acceleration Intelligent Dual Condition Trigger: Alarm is triggered … Read more
People often ask me various “small” questions in the group: What is the memory size of Jetson TX2? What are the dimensions of the Jetson TX2 development board? Can you provide a hole position diagram? How many cameras can Jetson TX2 support at most? Can Jetson TX2 support this brand of camera? …. What I … Read more
This article is reprinted from: Note: The code may not be fully visible on mobile, you can swipe left and right to view the code! 1How to Use YOLOv4 First, you need to set up the darknet environment by downloading the darknet GitHub repository: $ git clone https://github.com/AlexeyAB/darknet.git $ cd darknet Next, you need to … Read more
Follow our public account to discover the beauty of CV technology This article is adapted from the Frontier of Aerial Robotics, authored by Liang Jiachen, an engineer at Westlake University. With the continuous improvement of computing power and the growth of data, deep learning algorithms have made significant advancements. These algorithms are increasingly applied across … Read more
We are very excited to share some amazing advancements in bringing the latest generative AI and large language models (LLMs) to edge computing. Since the introduction of Transformers and NVIDIA’s launch of the Ampere GPU in 2020, we have witnessed a rapid increase in the scale and intelligence of models, approaching human levels. This has … Read more
This article is from: Source: https://medium.com/@stephen.gardner.ag/integration-of-nvidia-jetson-systems-in-industrial-networks-abb8a17f1745 When industrial networks utilize NVIDIA®’s Jetson™ Xavier system, it opens up a world of machine learning applications. Xavier is based on the successful Tegra system, which is the most powerful system in the NVIDIA® Jetson™ series. The performance of Xavier’s 8-core ARM processor is comparable to many systems, and … Read more
This Tuesday evening, NVIDIA conducted an internal training for developers participating in the NVIDIA Jetson development competition. We have organized the highlights of the training session, focusing on several key points (especially the comparison charts of specifications, which everyone should save): NVIDIA Jetson Module Product Line: Developers who are new to NVIDIA Jetson products can … Read more
Recently, NVIDIA launched the Jetson Generative AI Lab, enabling developers to explore the limitless possibilities of generative AI in the real world using NVIDIA Jetson edge devices. Unlike other embedded platforms, Jetson can run large language models (LLMs), visual transformers, and stable diffusion locally, including the Llama-2-70B model running interactively on Jetson AGX Orin. Figure … Read more