How to Use Ultralytics YOLO11 for Object Detection and Tracking| How to Benchmark

How to Use Ultralytics YOLO11 for Object Detection and Tracking| How to Benchmark | YOLO11 RELEASED🚀

Рет қаралды 21,619

Күн бұрын

Пікірлер: 53

@Ultralytics 2 ай бұрын

🔗 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

@Ultralytics 2 ай бұрын

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! 💥

@YogendraSingh-jh1lz 2 ай бұрын

Can't wait to apply this to my own projects. Thanks for sharing! 🥰

@Ultralytics 2 ай бұрын

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! 🚀

@キラキラ輝く-c9e 28 күн бұрын

Marvelous!New YOLO series model emerges!I just hope that it's not too late for me to learn i!😂🤣

@Ultralytics 28 күн бұрын

It's never too late to start learning, especially with YOLO11! Its user-friendly features make it perfect for beginners and pros alike. Dive in through the Ultralytics Python package pypi.org/project/ultralytics/ or the intuitive Ultralytics HUB www.ultralytics.com/hub to explore and experiment with ease. Happy learning! 🚀

@TejasNarkhede-u6e 6 күн бұрын

Thank you for important info!!

@Ultralytics 6 күн бұрын

You're very welcome! Glad you found it helpful 😊 Feel free to explore more about Ultralytics' mission, values, and workflows in our Handbook handbook.ultralytics.com/ as we continue to build it out! 🚀

@josetomasl.4611 2 ай бұрын

loved it, gonna teach on Monday!

@Ultralytics 2 ай бұрын

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/.

@TeamDman 2 ай бұрын

WOOHOO! Nice, thank you for sharing!

@Ultralytics 2 ай бұрын

You're welcome! Glad you enjoyed it! If you have any questions about YOLO11 or need more info, feel free to ask. 😊🚀

@salmankmohammed Ай бұрын

Thanks, Nicolai. I am working on a project and want to train the Yolov11 from scratch. However, I don't know which parameter to recognize to choose the best number of epochs. Could you guide me well, please!

@Ultralytics Ай бұрын

Hey! Choosing the right number of epochs depends on monitoring your model's performance. Watch for when the validation loss stops decreasing or starts increasing, which might indicate overfitting. You can also use early stopping to automate this process. For more tips, check out our guide here: docs.ultralytics.com/guides/model-training-tips/ 😊

@lekha777 22 күн бұрын

Hi! What's editor you used in this video?

@Ultralytics 22 күн бұрын

Hi! The editor used in our videos is Visual Studio Code. It's lightweight, versatile, and perfect for working with Ultralytics YOLO models. You can explore our guide on optimizing your experience with VSCode here: docs.ultralytics.com/integrations/vscode/ 😊

@haseebkhawaja1050 2 ай бұрын

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

@Ultralytics 2 ай бұрын

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/ 🚀

@haseebkhawaja1050 2 ай бұрын

@@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

@haseebkhawaja1050 2 ай бұрын

@@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

@Ultralytics 2 ай бұрын

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/

@loulamicha 11 күн бұрын

If i have a special dataset of cars on motion at night ..is yolo11 annotate automaticly sir ...??...also you speak very fast ...

@Ultralytics 11 күн бұрын

Yes, YOLO11 can help automate annotation using its tools, but you'll need to preprocess your dataset for best results. Check out our guide here: docs.ultralytics.com/reference/data/annotator/ 😊 And sorry if I was too fast-I'll keep it slower next time! Let me know if you need more clarification. 🚗✨

@rasheedhb3558 Ай бұрын

Is there any way to fix the ID assigned? In this case, we want to keep the ID:3 fixed for that car so that when it reappears on the frame, it shows as ID:3 instead of ID:7. This will allow us to count the cars or people that appeared in the video.

@Ultralytics Ай бұрын

To maintain consistent IDs for objects like cars across frames, you can use a robust object tracking algorithm. YOLO11 supports multi-object tracking that can help with this. Ensure your environment is updated to the latest versions of `ultralytics` and related packages. For more details on tracking with YOLO, check out our documentation: docs.ultralytics.com/modes/track/ 🚗

@rasheedhb3558 Ай бұрын

@Ultralytics Thankyou so much. And for re identification of people?

@Ultralytics Ай бұрын

For re-identifying people, you can use advanced tracking solutions that incorporate re-identification techniques. These can maintain consistent IDs even after objects leave and re-enter the frame. Check out our tracking guide for more insights: docs.ultralytics.com/modes/track/ 🕵️‍♂️

@ProfessorK-nx9gl 2 ай бұрын

Is this the SOTA for segmentation?

@Ultralytics 2 ай бұрын

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/. 😊

@koftigandi3136 Ай бұрын

Can it also be converted into tfjs already?

@Ultralytics Ай бұрын

Yes, you can export YOLO11 models to TensorFlow.js (TF.js) format. Use the following command to convert your model: ```python from ultralytics import YOLO model = YOLO("yolo11n.pt") model.export(format="tfjs") ``` For more details, check out the Ultralytics documentation on TF.js integration docs.ultralytics.com/integrations/tfjs/. 😊

@iamdihan 2 ай бұрын

Looking foward to trying this out

@Ultralytics 2 ай бұрын

Awesome! YOLO11 is packed with exciting features. If you need any guidance, feel free to check out our documentation: docs.ultralytics.com/models/yolo11/ 🚀

@neuromaiden Ай бұрын

Fantastic tool! I am new to Ultralytics and would like to ask if I can track position and time that an animal spends in specific zone, please. Thank you!!!

@Ultralytics Ай бұрын

Absolutely! You can use Ultralytics YOLO models for tracking animals and analyzing their behavior in specific zones. Check out our guides docs.ultralytics.com/guides/ for detailed instructions on setting up tracking and analyzing movement patterns. If you have more questions, feel free to ask! 😊

@neuromaiden Ай бұрын

@@Ultralytics Thank you so much!

@Ultralytics Ай бұрын

You're welcome! If you have any more questions or need further assistance, feel free to reach out. Happy tracking! 😊

@dianisirimewan351 2 ай бұрын

Fantastic ❤

@Ultralytics 2 ай бұрын

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/

@Moneyyfloyd214 Ай бұрын

Can i use this for gaming?

@Ultralytics Ай бұрын

Absolutely! AI is transforming gaming by creating more immersive and dynamic experiences. With AI, games can have more realistic NPCs, smarter gameplay, and personalized experiences. Check out our blog on AI in gaming for more insights: www.ultralytics.com/blog/ai-in-video-games-shaping-the-future-of-gaming 🎮

@hasibulislam3669 Ай бұрын

How I can use my GPU for processing?

@Ultralytics Ай бұрын

To use your GPU for processing with Ultralytics, ensure you have the necessary drivers and software installed. If you're using Docker, install the NVIDIA Docker runtime to enable GPU support. Here's a quick command to run a Docker container with GPU access: ```bash sudo docker run -it --ipc=host --gpus all ultralytics/ultralytics:latest ``` Make sure your system has the NVIDIA drivers installed. For more details, check out our Docker Quickstart Guide docs.ultralytics.com/guides/docker-quickstart/. If you're using Kaggle, you can also access GPUs for free. Learn more about using Kaggle here docs.ultralytics.com/integrations/kaggle/. 🚀

@mohammadaghili8422 2 ай бұрын

This is like a joke. your model is just a little better than v10. But other versions have reasonable improvement compared to last version.

@Ultralytics 2 ай бұрын

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/. 😊

@Tungdang-tv7sd 15 күн бұрын

how to ultralytics tracking subtitles

@Ultralytics 14 күн бұрын

Ultralytics YOLO11 doesn't natively support subtitle generation for tracking, but you can use the tracking data (like object IDs or positions) to overlay subtitles or labels on your video frames. For example, you can use `model.track` to assign object IDs and dynamically add subtitles or labels for each tracked object. Check out our detailed guide on tracking with YOLO11 docs.ultralytics.com/guides/instance-segmentation-and-tracking/ for more insights.