Anyscale’s Post

Advancing Real-Time Object Detection with YOLOv10 Over the past years, YOLO models have dominated real-time object detection, balancing computational cost and detection performance. Despite improvements in architectural designs and optimization strategies, reliance on non-maximum suppression (NMS) hampers end-to-end deployment and increases inference latency. Key highlights : - A holistic design optimizing multiple components to reduce computational overhead. - Significant performance improvements, with YOLOv10-S being 1.8x faster than RT-DETR-R18 with comparable accuracy. - Utilization of 2.8x fewer parameters, making it highly efficient. Check the comment for the links #yolov10 #objectdetection #airesearch #research

With AutoFeedback you can 10x your Subject Matter Expert review of LLM-based applications with as few as twenty human-labeled examples. See our Latent Space Readout Technology-enabled AutoFeedback in action, auto-grading news summaries and outperforming LLM-as-a-judge 👇 #LLM #LLMOps #DataScience

We have put some things out regarding how you can utilize AI better through Uniscale. However today I want to deep dive into how we make a direct impact into driving software through commercial viability and how we allow the right roles to flourish when doing their part of the specifications. The key takeaways here are problem validation before system design. https://lnkd.in/d8HzZXgU

Unlocking the Power of YOLOv10: Step-by-Step Guide with Real-World Examples In the rapidly evolving field of computer vision, YOLO (You Only Look Once) models have consistently stood out for their remarkable balance between computational cost and detection performance. YOLOv10, the latest iteration, addresses key inefficiencies and introduces a slew of innovations, making it a game-changer for real-time object detection. This guide will walk you through the significant improvements in YOLOv10, provide a comparison with older YOLO versions and other models, and offer step-by-step instructions to implement object detection and region counting projects using YOLOv10.

Nodely handles only a tiny portion of #Algorand traffic, yet almost 100'000 devices daily transact using our globally distributed infra. With Algorand's 1.4s average TX commit time, the API needs to be hosted close to end-users to ensure the "happy eyeballs" effect. See our (public) infra in realtime @ https://status.nodely.io

pub.towardsai.net: The primary focus of YOLOv10 is speed, specifically faster inference, emphasizing its significance in the technology. For further details, please refer to the source.

As the VLMs are gaining momentum in terms of adoption , here is a deep dive video on the different components of VLMs: https://lnkd.in/ggcqe3mi Video covers the below topics: - Joint embedding based decoder architecture - Working principles of CLIP with code example - Cross attention based VLM archietecture - Llama 3.2 model understanding with code example for Image Q&A - Training process of VLMs Prerequisite: ViT(Vision transformer from scratch): https://lnkd.in/gvghnRyR #VLM #LLM #GenAI

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐘𝐎𝐋𝐎𝐯𝟖 𝐢𝐧 𝐎𝐛𝐣𝐞𝐜𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 📸 "Did you know that YOLOv8 is one of the most efficient models for real-time object detection? Here's why: ✅ Faster inference with optimized architectures ✅ Higher accuracy with improved bounding box prediction ✅ Versatile for multiple use cases – from security to smart cities! 🏙️ Recently, I’ve been working with YOLOv8 for helmet detection and traffic signal violation projects. The results have been impressive! What are your thoughts on YOLOv8? Let's discuss! #ComputerVision #YOLOv8 #DeepLearning #AIResearch"

🔍 Could SIM-V have helped prevent #GhostWrite? Our SIM-V RISC-V reference model is engineered for precision, accurately simulating every instruction. By comparing your processor’s execution state with SIM-V, potential flaws—like those in the GhostWrite sequence—are detected instantly. As shown in the screenshot, SIM-V handles the GhostWrite instruction sequence flawlessly. 🚀 Experience it yourself in our free, public demo: https://mwa.re/simv-demo What is GhostWrite: https://lnkd.in/gTSFkikV

Very interesting concept of "Large Vision Models" from Armand Ruiz of IBM. I'm wondering whether this concept can incorporate videos as well or if we're looking at models that specialize in interpreting data from static images. #ArtificialIntelligence #LargeVisionModels #AIdata