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Check out this computer vision application developed using #YOLOv9. Which brings a major breakthrough in real-time object detection, featuring innovative techniques like Programmable Gradient Information (PGi) and the Generalized Efficient Layer Aggregation Network (GELAN). This model sets new standards in efficiency, accuracy, and adaptability, achieving impressive results on the #COCO dataset. Developed by: Shah Faisal #AI #MachineLearning #ComputerVision NVIDIA Datalytics Labs OpenCV

💥 It is now possible to fine tune Mochi-1 in diffusers! This reimplementation stays true to the original, and uses LoRA for enhanced performance. The script is designed to run efficiently, even without intermediate validation, requiring under 40GB of GPU VRAM. Super interesting! Can't wait to see the wave of SOTA video model finetuning, 2025 will be a great year 🔗 Repo: https://lnkd.in/eVvzsp8J ⤵ Helpful? Follow me and join ⚡️ AI Pulse (https://lnkd.in/eWudwDsd) for daily, curated, bite-sized updates on AI—focused on what truly matters to keep you ahead of the curve 🔥

If you depend on a 3rd party software platform to make your product work better, do you really have a good product to begin with? At Spotter Global our philosophy is a little different. Our Radar Hardware and Software solutions are developed, designed and built 100% in house, with the highest detection rate and the lowest false alarm rate in the industry. Bar none! Utilizing "state beyond the art" Machine Learning and Artificial Intelligence (AI) and the most advanced filtering system in the industry for the automatic classification of targets, the NetworkedIO Virtual Machine integrates multiple Spotter radars and PTZ cameras using a geo-referenced user interface that works right from your browser. Nothing else is needed, it's that simple. It's a concept often copied and imitated and as Picasso so eloquently said "good artist copy, great artist steal".....but remember nothing beats an original master piece😉

Successfully Completed My First Object Detection Project Using YOLO v8 Model! I am thrilled to share that I have successfully completed my first project in object detection using the YOLO v8 model on Google Colab! Under the excellent guidance of my mentor Sai Satish Sir from AIMER Society - Artificial Intelligence Medical and Engineering Researchers SocietyI developed a model that detects cars in an uploaded traffic video. This project has been a significant milestone in my journey in AI and computer vision. It has provided me with valuable hands-on experience in using state-of-the-art object detection techniques. Key Highlights: ->Utilized YOLO v8 model for object detection ->Implemented the project on Google Colab ->Successfully detected cars in traffic video footage #ObjectDetection #YOLOv8 #AI #ComputerVision #MachineLearning #DeepLearning #GoogleColab #TrafficVideo #AimerSociety #LearningJourney #MIRACLEEDUGROUP

Congratulations on the release of Dynamic Quantization 🫡. Your solution to selectively preserve 16-bit precision for critical parameters while maintaining 4-bit compression elsewhere is brilliant. It elegantly addresses one of the biggest challenges in model quantization - maintaining performance while reducing model size. The insight to consider both activation and weight quantization errors, particularly for handling outliers, shows deep understanding of the practical challenges in model compression. The result seems very promising too without sacrificing much VRAM! #MachineLearning #AI #ModelCompression #Innovation #LLMs

Great piece of information regarding model deployment and optimization. For those of you aware of a framework that takes care of the entire ML pipeline.

Just came across this incredible update from Unsloth AI on dynamic 4-bit quantization, and I highly recommend exploring their approach! Quantizing models to 4 bits is an efficient way to save memory and computational resources, but as they’ve highlighted, it can sometimes break models or degrade performance. With their dynamic 4-bit quantization, Unsloth AI chooses some parameters to stay in 16-bit, achieving a remarkable balance between efficiency and accuracy.

AI Limitations and Challenges Even the most advanced AI models face challenges when presented with seemingly simple tasks. The "bouncing ball in a rotating square" test highlights the complexities of simulating realistic physics, collision detection, and dynamic adaptation. simulating a bouncing ball within a rotating square. This creative test provides valuable insights into the strengths and weaknesses of different AI architectures, such as Grok 3, o1 pro, and DeepSeek R1, and can inform future research and development. @EndritRestelica #AI #ArtificialIntelligence #MachineLearning #DeepLearning #AIResearch #AIdevelopment #Grok3 #o1pro #DeepSeekR1 #AIchallenge #Benchmarking #AIcomparison #AIComparison #TechTalk @OpenAI #TechInsights #AICommunity @TechCrunch @MITTechnologyReview

Deploy Ultralytics YOLO11 on Raspberry Pi! 🌟 Supercharge your AI projects by running YOLO11 on Raspberry Pi with Coral Edge TPU. Achieve lightning-fast inference for tasks like object detection, tracking, and segmentation while maintaining energy efficiency. Learn more ➡️ https://ow.ly/jx9e50UrRzi

"Anecdotal evidence suggests smaller models to only use 6 to 8 bit quantization, and larger models like 8B and larger to use 4 bit quantization."