models/yolo11/ #16541
Replies: 48 comments 109 replies
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👋 Hello, thank you for your interest in Ultralytics and the exciting new YOLO11 model 🚀! For new users or those curious about YOLO11, we recommend checking out the Docs, which are packed with information on the latest advancements and usage examples. If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us diagnose the issue effectively. If you have a custom training ❓ Question, please include detailed information such as dataset examples and training logs, and ensure you're following our Tips for Best Training Results. We're excited to have you in the Ultralytics community! If you need real-time support or want to engage in discussions, join us on Discord 🎧. For long-form topics, you might prefer Discourse, or dive into our Subreddit. UpgradeEnsure you have the latest pip install -U ultralytics EnvironmentsYOLOv8 and, presumably, YOLO11 can be run in these environments, all configured with necessary dependencies:
StatusThis badge indicates the current status of Ultralytics CI tests, ensuring all YOLO models, including upcoming releases, run smoothly across platforms. This response was generated automatically 🔄. An Ultralytics engineer will also assist soon. Thank you for your patience and support! |
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hi |
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First of all its great to have another powerful model and congratulations. I have fine tuned and trained yolo11x.pt for my custom objects but I'm facing some issues. My images data resolution is around 6000 x 2000 but while trained the model I set the imgsz=(1280, 640). It work well when I adjust the video stream by zoom in-out and detect all my 60 objects well (objects are small) but when there is some zoom or squeezed then it completely dont detect any object or few only. Please let me know if I'm missing something or I have to do something else? |
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Error AMP: checks failed ❌. Anomalies were detected with AMP on your system that may lead to NaN losses or zero-mAP results, so AMP will be disabled during training. "Overriding model.yaml nc=80 with nc=16 |
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What is the license for yolov11? |
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Hi, |
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Hi, Thanks for your great job!. |
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Hi! Cool to see a new version released! I will soon be doing research (as part of my thesis) on the use of Computer Vision models for physical rehabilitation purposes, and Pose Estimation with accurate keypoint detection will thus be a key factor in the success of my research. Does YOLO11 have integrated keypoint detection? I am mostly referring to the fact that my predecessor working on a similar project had trouble with YOLOv8, so I am now doing some pre-research on suitable open source models. |
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Hey! , Can the model be accelerated using the tensorRT runtime? (Converting the model to .engine etc) |
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Hi, thanks for your contribution on YOLO. In YOLOv11, I encountered a question. I loaded the YOLOv11n model using the following code: |
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thanks for your reply。I had solved this error.why this error happened is that i use ultralytics-main files which downloaded from yolov8。then i downloaded yolo11 model main file on github again,and the model run successfully
发自我的iPhone
…------------------ Original ------------------
From: Glenn Jocher ***@***.***>
Date: Fri,Oct 4,2024 8:36 PM
To: ultralytics/ultralytics ***@***.***>
Cc: xiuyuan mao ***@***.***>, Comment ***@***.***>
Subject: Re: [ultralytics/ultralytics] models/yolo11/ (Discussion #16541)
It seems like the error might be related to a missing or incorrect module. Please ensure your Ultralytics package is up to date. If the issue persists, try reinstalling the package. For further assistance, you can check the Ultralytics documentation.
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Reply to this email directly, view it on GitHub, or unsubscribe.
You are receiving this because you commented.
发自我的iPhone
------------------ Original ------------------
From: Glenn Jocher ***@***.***>
Date: Fri,Oct 4,2024 8:36 PM
To: ultralytics/ultralytics ***@***.***>
Cc: xiuyuan mao ***@***.***>, Comment ***@***.***>
Subject: Re: [ultralytics/ultralytics] models/yolo11/ (Discussion #16541)
It seems like the error might be related to a missing or incorrect module. Please ensure your Ultralytics package is up to date. If the issue persists, try reinstalling the package. For further assistance, you can check the Ultralytics documentation.
—
Reply to this email directly, view it on GitHub, or unsubscribe.
You are receiving this because you commented.Message ID: ***@***.***>
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First of all, what an amazing job
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How to solve it in the latest yolov11 environment TypeError: load_inference_source() got an unexpected keyword argument 'img_size' |
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Hey, I am trying to implement a bird detection system. I would like to feed in images and detect birds. - - Is it possible to set YOLO to only look for birds and ignore everything else?
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Hi, |
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Hi! |
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did you published a paper for this model (YOLOv11) ? |
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hi! how can i know which layer belong to backbone? i want to freeze those layers during training. |
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I am converting the yolo11n.pt model to .tflite model. After running output Unknown (float32) Coordinates of detected objects, [1,34,8400] []/[] But my app need the outputs like: location BoundingBox (float32) [] []/[] How can i make the designed outputs and can you explain the meaning of [1,34,8400]? |
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Hello, or how i can change initial weights in yoloV11. |
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Hi, I would like to use YOLO11 as a feature extractor for similarity measurements. How can I extract the image features from the model? Thanks |
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Is there a way to integrate extended Kalman filters with YOLO11? |
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What are segmentation model loss function and the weight? |
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How do I access "box_loss" on yolo11? |
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Hello, does YOLO11 support transfer learning? Freezing the backbone? Can't get it to work |
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I am trying to create educational content based on a project I'm doing, and have made a diagram of yolov11 and am hoping to get feedback on if it is accurate. I don't see a way to upload it here, but am happy to send it to someone if that's an option. The diagram I have is very similar to this post: https://medium.com/@nikhil-rao-20/yolov11-explained-next-level-object-detection-with-enhanced-speed-and-accuracy-2dbe2d376f71 |
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models/yolo11/
Discover YOLO11, the latest advancement in state-of-the-art object detection, offering unmatched accuracy and efficiency for diverse computer vision tasks.
https://docs.ultralytics.com/models/yolo11/
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