Optimize Ultralytics YOLOv8 models for efficiency: A guide to exporting to NCNN format 🔥 Deploying computer vision models on devices with limited resources, such as mobile or embedded systems, presents unique challenges. By optimizing your Ultralytics YOLOv8 models for lightweight deployment through conversion to NCNN format, you can significantly improve their performance on a wide range of devices. This guide offers step-by-step instructions to seamlessly convert your models to NCNN format, ensuring they operate efficiently on mobile and embedded platforms. Learn more ➡️ https://lnkd.in/ecPeumqa #computervision #ncnn #objectdetection #yolov8
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Quality Assurance Analyst @Ultralytics | Empowering 87K Learners on YouTube | Proficiency in Python Selenium WebDriver | Appium | Automation | PyTest | Docker |Jenkins CI/CD | LambdaTest
Great guide on optimizing Ultralytics YOLOv8 models for NCNN format – essential reading for anyone looking to deploy on mobile or embedded systems. #YOLOv8 #ComputerVision #ModelOptimization
Optimize Ultralytics YOLOv8 models for efficiency: A guide to exporting to NCNN format 🔥 Deploying computer vision models on devices with limited resources, such as mobile or embedded systems, presents unique challenges. By optimizing your Ultralytics YOLOv8 models for lightweight deployment through conversion to NCNN format, you can significantly improve their performance on a wide range of devices. This guide offers step-by-step instructions to seamlessly convert your models to NCNN format, ensuring they operate efficiently on mobile and embedded platforms. Learn more ➡️ https://lnkd.in/ecPeumqa #computervision #ncnn #objectdetection #yolov8
NCNN
docs.ultralytics.com
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Computer vision engineer @ultralytics | solving real-world problems using computer vision | Influencer | Open source contributor | Technical writer & community leader VisionAI | Computer vision engineer | LLMs | EdgeAI
Export Ultralytics YOLOv8 to NCNN 🔥🔥🔥 Deploying computer vision models on low-power devices like mobile or embedded systems requires optimization for optimal performance. The export to NCNN format in Ultralytics YOLOv8 facilitates this, making models suitable for various lightweight applications on mobile and embedded devices. What's Included 😻 • Key Features of NCNN Models • Deployment Options with NCNN • Export to NCNN: Converting Your YOLOv8 Model Explore More ➡ https://lnkd.in/dzVnazB2 Kudos to Abirami Vina for articulating this effectively :) #computervision #objectdetection #yolov8 #researchanddevelopment #ncnn
NCNN
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Our new Ultralytics Tencent NCNN export Docs page by Abirami Vina. In some environments like our GitHub ARM64 macOS M1 runners NCNN appears to be the fastest format we offer. #ultralytics #yolo #ai
Computer vision engineer @ultralytics | solving real-world problems using computer vision | Influencer | Open source contributor | Technical writer & community leader VisionAI | Computer vision engineer | LLMs | EdgeAI
Export Ultralytics YOLOv8 to NCNN 🔥🔥🔥 Deploying computer vision models on low-power devices like mobile or embedded systems requires optimization for optimal performance. The export to NCNN format in Ultralytics YOLOv8 facilitates this, making models suitable for various lightweight applications on mobile and embedded devices. What's Included 😻 • Key Features of NCNN Models • Deployment Options with NCNN • Export to NCNN: Converting Your YOLOv8 Model Explore More ➡ https://lnkd.in/dzVnazB2 Kudos to Abirami Vina for articulating this effectively :) #computervision #objectdetection #yolov8 #researchanddevelopment #ncnn
NCNN
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With a RISC-V core, two NPU coprocessors, and a DSP, this tiny chip draws under 260μW while performing on-device inference. Full article ➡ https://lnkd.in/dhyReZ-9 #artificialintelligence #EdgeAI #FullStack #machinelearning
AONDevices Promises Ultra-Low-Power Always-On Edge AI with Its AON1120 RISC-V Chip - AONDevices
https://aondevices.com
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III, CMU SV : : Author: Leadership Lessons with The Beatles : : Cofounder, Retail Solutions (Now part of Circana) : : Mentor : : Author, "Roots and Wings": : DTM : : Non-Profit Board Experience
"AlterEgo is a non-invasive, wearable, peripheral neural interface that allows humans to converse in natural language with machines, artificial intelligence assistants, services, and other people without any voice—without opening their mouth, and without externally observable movements—simply by articulating words internally. ... A primary focus of this project is to help support communication for people with speech disorders including conditions like ALS (amyotrophic lateral sclerosis) and MS (multiple sclerosis). Beyond that, the system has the potential to seamlessly integrate humans and computers—such that computing, the Internet, and AI would weave into our daily life as a "second self" and augment our cognition and abilities." https://lnkd.in/gK-jJHnz
AlterEgo: Interfacing with devices through silent speech
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AI in the palm of your hand.
Snapdragon Computex 2024 Keynote: The PC Reborn
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Check out the new blog post from Quadric about how the Chimera GPNPU can help future-proof your AI/ML SoC for the ML networks that will inevitably come down the road.
New ML networks far outperform old ones. What can you do with old hardware? Nothing. Your hardware needs to be programmable to use the new networks. https://lnkd.in/g8KeaZnB
New ML Networks Far Outperform Old Standbys
https://quadric.io
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Introducing YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems
Introducing YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems
medium.com
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Now you can speed up your large language model (LLM) fine-tuning process using "Unsloth." Unsloth is a breakthrough library designed to work seamlessly with HuggingFace, enhancing the efficiency of LLM fine-tuning on NVIDIA GPUs. You can leverage Unsloth for fine-tuning different architectures, including "Llama" and "Mistral," using the TRL trainers (SFTTrainer, DPOTrainer, PPOTrainer). I tried to fine-tune a Mistral 7B LLM (4bit) on the IMDB dataset for text generation, all within Google Colab using Unsloth. Here's the link to my hugging face model - https://lnkd.in/gJ6Bxb9X Unsloth github - https://lnkd.in/gSxk5s96
Saumohan/unsloth_mistral_imdb_model · Hugging Face
huggingface.co
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If you work at the intersection of #AI and #infrastructure, you need to read this blog by VAST Data's very own Dr. Subramanian Kartik, Ph. D. As with any other workload, understanding true application (or in this case, #LLM model size) requirements will help ensure optimal performance while reducing spend. "The bottom line of this example is that the size of a checkpoint doesn’t depend at all on the size of the data or the number of GPUs, only on the size of the model."
When building out an AI system, you need to rely on math and science to drive design. As an example, checkpoint operations can take advantage of multiple modes of parallelism to minimize bandwidth requirements. Colleen Tartow, Ph.D. and Subramanian Kartik, Ph. D breakdown how parallelism affects recoverability and operations in the most complex models today. #ArtificialIntelligence #GPU #DeepLearning
A Checkpoint on Checkpoints in Large Language Models
vastdata.com
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