Ultralytics YOLO11 is Here! ?? ?? We proudly unveiled the YOLO11 models last Friday at our annual hybrid event, YOLO Vision 2024. Today, we’re thrilled to share that the YOLO11 models are now available in the Ultralytics Python package! Jing Qiu and Glenn Jocher have done an amazing job on the research and implementation of Ultralytics YOLO11. This launch is a testament to our team’s dedication and hard work over the past few months. Key highlights: ? Improved architecture for precise detection and complex tasks. ? Faster processing with balanced accuracy. ? Higher precision using 22% fewer parameters. ? Easily deployable on edge, cloud, and GPU systems. ? Handles detection, segmentation, classification, pose, and OBB. ?? Run Inference ```yolo predict model="yolo11n.pt"``` Learn more ?? https://ow.ly/mKOC50Tyyok
关于我们
Ultralytics is on a mission to empower people and companies to unleash the positive potential of AI. We make model development accessible, efficient to train, and easy to deploy. It’s been a remarkable journey, but we’re just getting started. Bring your models to life with our vision AI tools: ?? Ultralytics HUB - Create and train sophisticated models in seconds with no code for web and mobile ?? Ultralytics YOLO - Explore our state-of-the-art AI architecture to train and deploy your highly accurate AI models like a pro
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https://ultralytics.com
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How can AI help the healthcare industry? ?? Our latest blog by Abirami Vina dives into how computer vision can help reshape fields of the healthcare industry like mental health care. From mood detection using facial expressions to behavioral analysis via pose estimation, AI is enabling early detection and intervention like never before. Learn more ?? https://ow.ly/VmmE50UgNkz
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Solar panel counting using Ultralytics YOLO11 ??????
Computer Vision, Growth @ ultralytics | visionusecases.com | 120K+ Reads & 250K+ Views on Medium | Open Source Contributor | YOLO11 ?? | Vision Language Models
Within a few lines of code, I have done solar panel counting using Ultralytics YOLO11 ?? It's pretty simple, all I have done is fine-tune the model on the custom dataset, created by my team. This highlights a scenario where accuracy can be significantly affected by light radiations. Ideally, a good use case is the inspection process of solar panels ???? ???????? "" yolo solutions count model="path/to/model/file.mp4" "" Read more ?? https://lnkd.in/d-4Zk2Q5
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New tutorial | Model evaluation and fine-tuning with Ultralytics! ?? Join us in this comprehensive walkthrough to optimize your model's performance. This session dives into key metrics like confidence scores, IOU, mAP, and proven techniques for fine-tuning. What's covered: ? Key insights and best practices for model evaluation. ? An in-depth look at accuracy metrics and their significance. ? Understanding mAP: What it is and how to improve it. Watch now ?? https://lnkd.in/dU2PB9fE
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AI in the mining industry: From ore to optimization! ?? In our latest blog, Abirami Vina explores how AI is reshaping mining operations—from automated ore grading and predictive equipment maintenance to enhancing worker safety and sustainability initiatives. Learn more ?? https://ow.ly/xlJ650UfXOq
AI in the Mining Industry: From Ore to Optimization by Abirami Vina
ultralytics.com
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Ultralytics转发了
Computer Vision, Growth @ ultralytics | visionusecases.com | 120K+ Reads & 250K+ Views on Medium | Open Source Contributor | YOLO11 ?? | Vision Language Models
We made it very simple to count the objects | Ultralytics YOLO11 ???? I'm sure you've come across my various computer vision projects where I have used object counting methods across industries like manufacturing, retail, and transportation. Here's a simple code snippet I frequently use for object counting. It doesn't rely on third-party libraries, just the Ultralytics package installed on your system. Give it a try ??????
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?? Exciting News: Ultralytics v8.3.38 is Here! Ultralytics’ new release delivers powerful upgrades to video interaction and segmentation features, optimized performance, and enhanced platform compatibility. ?? Key Highlights: - SAM2VideoPredictor: A game-changing feature for video segmentation and object tracking with interactive prompts, non-overlapping masks, and improved memory efficiency. - Extended Device Support: Broader compatibility for NVIDIA Jetson devices, making AI deployment on edge devices easier. - Simplified Configurations: Streamlined setup with the removal of deprecated parameters like `label_smoothing`. ?? Ready to experience these improvements? Check the latest release notes and give it a try today! ?? Release Notes https://lnkd.in/dtvxwyYX ?? Documentation https://lnkd.in/g632W5nF ?? We value your feedback! Share your experience and let us know how we can keep improving. ??
Release v8.3.38 - `ultralytics 8.3.38` SAM 2 video inference (#14851) · ultralytics/ultralytics
github.com
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Check out another captivating session from YOLO Vision 2024 (YV24)! ?? In his talk, Luca Antiga, CTO of Lightning AI, dives deep into AI's transformative potential with Lightning AI. From developing applications to training models with PyTorch Lightning, this session offers invaluable takeaways for developers and AI enthusiasts. Key Highlights: ? What is Lightning AI: The platform's vision and capabilities. ? Building Applications: How to use Ultralytics YOLO with Lightning AI Watch now ?? https://lnkd.in/dAktXHGU
Going YOLO on Lightning Studios - Lightning AI ?? | Ultralytics YOLO VISION 2024 | YV24 Talk #06
https://www.youtube.com/
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Awesome dog-pose dataset ?? Explore more ?? https://lnkd.in/dcRgMyCz
Computer Vision, Growth @ ultralytics | visionusecases.com | 120K+ Reads & 250K+ Views on Medium | Open Source Contributor | YOLO11 ?? | Vision Language Models
Ultralytics Dogs ?????? Last month, both ?? and ?? visited our office, and Paula Derrenger showed these in our call. Both of them enjoy stirring up fun and I also have the pictures when they are sleeping ??? Since then, I thought it would be a great idea to feature them in our documentation, which attracts millions of visitors each month. I fine-tuned Ultralytics YOLO11 on the Stanford dogs dataset. The results look impressive, though some points were missed, likely due to training for just 20 epochs. Which is your favorite, drop your thoughts in the comments!!!
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Ultralytics转发了
I emulated #Drone Technologies using Ultralytics YOLO11 and Google Earth ??. Using a trained YOLO11 model, I can count objects + train a model effortlessly. Combining this insane technology with Google Earth Studios created this sick overhead image of a drone flying. Blog post on how to build this is coming soon ??? Feel free to hit me up for any new drone ideas!