Thanks Nicolai for this fantastic overview of Ultralytics YOLO11! 🚀 Really appreciate the detailed walkthrough, benchmarks, and live demos you provided. It's exciting to see the model's potential in action and how it pushes the boundaries of object detection. Keep up the great work! 💥
That's awesome to hear! I'm glad you're excited. If you need any help getting started with YOLO11, feel free to check out our documentation docs.ultralytics.com/models/yolo11/. Happy coding! 🚀
Congratulations on the YOLO11 release! With its impressive benchmarks, I'm curious about how it handles object tracking compared to previous models-specifically in dynamic environments. Does the integration slow down real-time performance, or has it improved handling frame drops? Looking forward to diving into these practical aspects!
Thank you! YOLO11 offers enhanced object tracking capabilities, maintaining real-time performance even in dynamic environments. The integration is optimized to handle frame drops more efficiently, ensuring smoother tracking. For more details, check out our documentation docs.ultralytics.com/models/yolo11/. 🚀
That's awesome! Glad you enjoyed it. Have fun teaching on Monday! 😊 If you need any resources, check out our documentation docs.ultralytics.com/models/yolo11/.
Awesome! YOLO11 is packed with exciting features. If you need any guidance, feel free to check out our documentation: docs.ultralytics.com/models/yolo11/ 🚀
YOLO11 offers impressive segmentation capabilities, but whether it's the state-of-the-art (SOTA) depends on your specific needs and benchmarks. For more details, check out the YOLO11 documentation docs.ultralytics.com/models/sam/. 😊
I wanted to ask that does all yolo models above yolov8 use same YOLOv8 format. I have my data ready in yolov8 version but I also want to test yolov10 and yolov11 so same format can be used for these updated models
Yes, YOLOv10 and YOLO11 use the same format as YOLOv8. You can seamlessly test your data with these models. For more details, check out the YOLOv10 documentation: YOLOv10 Documentation docs.ultralytics.com/models/yolov10/ 🚀
@@Ultralytics Hy there. I hope I will get the response. I wanted to ask about imgsz parameter in YOLO does (1440, 1920) mean that width is 1440 and height is 1920 or vice versa I am so confused about this. I have data ready in 1440 (width) and 1920(height) and i want to train on the exact same size so i get good results on a real time video please help because I searched a lot on web and didn't find CLEAR answer
Hey there! 😊 In YOLO, the `imgsz` parameter is typically specified as (width, height). So for your data, you would set it to (1440, 1920). This applies to both training and prediction. For more info, check out the YOLO documentation: docs.ultralytics.com/guides/preprocessing_annotated_data/
Glad you enjoyed it! 😊 If you have any questions about YOLO11, feel free to ask. You can also check out the documentation for more details: docs.ultralytics.com/models/yolo11/
Thanks for sharing your thoughts! YOLO11 focuses on refining efficiency and accuracy, building on the strengths of YOLOv10. Each version aims to address specific improvements, and feedback like yours is valuable for future updates. For more details, check out the YOLO11 documentation docs.ultralytics.com/models/yolo11/. 😊
YOLO11 seems like a powerhouse, but will it handle o ritmo frenético of real-time tracking in crowded environments? 🎶 What’s the secret sauce behind its improved benchmark performance that an orchestra could adopt for perfect harmony?
Absolutely, YOLO11 is designed for high-speed, real-time tracking even in crowded environments. Its secret sauce lies in advanced algorithms and optimized architecture that enhance accuracy and efficiency. For more details, check out the YOLO11 documentation docs.ultralytics.com/models/yolo11/. 🎶
🔗 YOLO11 Docs: docs.ultralytics.com/models/yolo11/
🔗 YOLO11 Blog Post: www.ultralytics.com/blog/all-you-need-to-know-about-ultralytics-yolo11-and-its-applications
Thanks Nicolai for this fantastic overview of Ultralytics YOLO11! 🚀 Really appreciate the detailed walkthrough, benchmarks, and live demos you provided. It's exciting to see the model's potential in action and how it pushes the boundaries of object detection. Keep up the great work! 💥
Can't wait to apply this to my own projects. Thanks for sharing! 🥰
That's awesome to hear! I'm glad you're excited. If you need any help getting started with YOLO11, feel free to check out our documentation docs.ultralytics.com/models/yolo11/. Happy coding! 🚀
Congratulations on the YOLO11 release! With its impressive benchmarks, I'm curious about how it handles object tracking compared to previous models-specifically in dynamic environments. Does the integration slow down real-time performance, or has it improved handling frame drops? Looking forward to diving into these practical aspects!
Thank you! YOLO11 offers enhanced object tracking capabilities, maintaining real-time performance even in dynamic environments. The integration is optimized to handle frame drops more efficiently, ensuring smoother tracking. For more details, check out our documentation docs.ultralytics.com/models/yolo11/. 🚀
WOOHOO! Nice, thank you for sharing!
You're welcome! Glad you enjoyed it! If you have any questions about YOLO11 or need more info, feel free to ask. 😊🚀
loved it, gonna teach on Monday!
That's awesome! Glad you enjoyed it. Have fun teaching on Monday! 😊 If you need any resources, check out our documentation docs.ultralytics.com/models/yolo11/.
Looking foward to trying this out
Awesome! YOLO11 is packed with exciting features. If you need any guidance, feel free to check out our documentation: docs.ultralytics.com/models/yolo11/ 🚀
Is this the SOTA for segmentation?
YOLO11 offers impressive segmentation capabilities, but whether it's the state-of-the-art (SOTA) depends on your specific needs and benchmarks. For more details, check out the YOLO11 documentation docs.ultralytics.com/models/sam/. 😊
I wanted to ask that does all yolo models above yolov8 use same YOLOv8 format. I have my data ready in yolov8 version but I also want to test yolov10 and yolov11 so same format can be used for these updated models
Yes, YOLOv10 and YOLO11 use the same format as YOLOv8. You can seamlessly test your data with these models. For more details, check out the YOLOv10 documentation: YOLOv10 Documentation docs.ultralytics.com/models/yolov10/ 🚀
@@Ultralytics Hy there. I hope I will get the response.
I wanted to ask about imgsz parameter in YOLO does (1440, 1920) mean that width is 1440 and height is 1920 or vice versa I am so confused about this. I have data ready in
1440 (width) and 1920(height) and i want to train on the exact same size so i get good results on a real time video please help because I searched a lot on web and didn't find CLEAR answer
@@Ultralytics and plz plz tell me that predict also uses exactly the same order (width, height) or vice versa so I don't again get confused
Hey there! 😊 In YOLO, the `imgsz` parameter is typically specified as (width, height). So for your data, you would set it to (1440, 1920). This applies to both training and prediction. For more info, check out the YOLO documentation: docs.ultralytics.com/guides/preprocessing_annotated_data/
Fantastic ❤
Glad you enjoyed it! 😊 If you have any questions about YOLO11, feel free to ask. You can also check out the documentation for more details: docs.ultralytics.com/models/yolo11/
This is like a joke. your model is just a little better than v10. But other versions have reasonable improvement compared to last version.
Thanks for sharing your thoughts! YOLO11 focuses on refining efficiency and accuracy, building on the strengths of YOLOv10. Each version aims to address specific improvements, and feedback like yours is valuable for future updates. For more details, check out the YOLO11 documentation docs.ultralytics.com/models/yolo11/. 😊
YOLO11 seems like a powerhouse, but will it handle o ritmo frenético of real-time tracking in crowded environments? 🎶 What’s the secret sauce behind its improved benchmark performance that an orchestra could adopt for perfect harmony?
Absolutely, YOLO11 is designed for high-speed, real-time tracking even in crowded environments. Its secret sauce lies in advanced algorithms and optimized architecture that enhance accuracy and efficiency. For more details, check out the YOLO11 documentation docs.ultralytics.com/models/yolo11/. 🎶