InHand Networks’ Post

(23th December 2024) Add a New #OPALR0T #Target #OP4510 to a fresh installation #RTLab This short video shows how to add an OPAL-RT Target (in this example, an OP-4510) to a fresh (new) OPAL-RT RT-Lab installation. I hope it's useful for you! #fglongatt #fglongattLife #opalrt #DigitalRealTimeSimulation #realtimesimulation #Matlab #Simulink #realtime #digital #modelling #simulation #YouTube #4kVideo #Video: https://lnkd.in/egs_KiNR

This week I came across an opportunity to make the Llama model run faster using the beauty of AVX SIMD programming. Sometimes rethinking simple operations like matrix multiplications can bring about a lot of improvement. I have written down a detailed journal of how I went about modifying the matmul function to achieve that. #HighPerformanceComputing #HPC #AVX #SIMDProgramming #LLAMA2 #Optimization #LLMModel #CProgramming #DeepLearning #MachineLearning #ParallelComputing #Vectorization #PerformanceOptimization #ComputationalScience #ScientificComputing

Jina CLIP v1 released: a new state-of-the-art multimodal embedding model that outperforms #OpenAI CLIP in text-image retrieval! Contributed ONNX weights so it's now compatible with Transformers.js v3 and runs with WebGPU acceleration! Try out the demo! https://lnkd.in/gqsqdp8W #JinaClip

Explore how to implement real-time object detection with YOLOv9 and OpenCV. Our guide covers running inference, webcam integration, and tracking your experiments. Read here: https://lnkd.in/eaurtiJq

Dive into Real-Time Object Detection with YOLOv9 and Webcam! Check out our latest tutorial: "Real-Time Object Detection with YOLOv9 and Webcam: Step-by-step Tutorial"! Watch Now: https://lnkd.in/dpFMsWhA... In this tutorial, we'll walk you through setting up YOLOv9 for real-time object detection using your webcam. Perfect for anyone looking to explore the latest in computer vision technology. What's Covered: Introduction to YOLOv9 for Object Detection Setting Up Your Development Environment Integrating YOLOv9 with Your Webcam Real-Time Object Detection Implementation Testing and Fine-Tuning the Model Tips for Optimizing Performance Ready to see YOLOv9 in action? Click the link above to start watching the tutorial now! Don't forget to like, subscribe, and share with your fellow tech enthusiasts for more cutting-edge tutorials. Share this post with your network and let's explore the future of computer vision together! #YOLOv9 #ObjectDetection #ComputerVisionTutorial #RealTimeDetection #pyresearch

Hey everyone! 👋 Have you heard about the latest buzz in the world of computer vision? 🤓 Yolo v8 object detection is taking the industry by storm and making object detection faster and more accurate than ever before. 🚀 Whether you're a developer, researcher or just a curious mind, this is definitely worth checking out. #YOLOv8 #ObjectDetection #ComputerVision #AIMERSOCIETY #AIMERS

Mystified about how to debug a GPU hang or crash? Jeremy Gebben of LunarG, will talk about the new Vulkan Crash Diagnostic Layer at Vulkanised 2025, Feb. 11-13, Cambridge, U.K. This awesome new tool can save your developers hours and hours of debugging time. Register here: https://lnkd.in/ggtJamyh #Vulkanised2025 #Vulkan.

A few days ago, someone asked me about my thoughts on Ultralytics and YOLOv10. We've made lots of improvements with Darknet/YOLO since May 2023 when HANK (hank.ai) first published their fork of the Darknet/YOLO codebase. I figured now might be a good time to put a video together to compare the results. TLDR: Darknet/YOLO is a great open-source solution when looking for an object detection framework. Darknet running YOLOv3 and YOLOv4 continues to beat newer versions of YOLO. https://lnkd.in/gbdYxNn9 #darknet #yolo #neuralnetworks #computervision

YOLOv10 just released! 🔥🔥We have a new member and version in the YOLO family. For the nano version of YOLOv10 we are talking 1ms per image (that's 1000 FPS) 🤯 Have not seen such a big jump in performance from any other version release before. Better mAP on the COCO benchmark dataset and close to 2x lower latency compared to the other models. It's based on the Ultralytics framework so we can use it easily in just a few lines of code for both training and inference. We are definitely going to work on some cool videos! Key highlights 🔑 ✅ NMS-free training: Improved performance and reduced latency. ✅ Uses spatial-channel decoupled downsampling for ops efficiency ✅ Adds new compact inverted block (CIB) ✅ Holistic design: Optimized components for efficiency and capability. ✅ YOLOv10: New generation for real-time object detection.

It's amazing how far the YOLO family has come with each new version. The difference in training models on GPU architectures is clear. Even Apple's architecture can't match a GPU architecture, even using the ‘mps’ device configuration. Adding a single GPU (device=0) already improves the training metrics each epoch the data passes through the model, even if the same learning rate settings, optimization algorithm and dataset are used. On a smaller scale, this proves the superiority of a GPU architecture and may even explain the success of companies such as NVIDIA, which is focused on developing new GPUs.