DOCS . ULTRALYTICS . COM {}

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We are analyzing https://docs.ultralytics.com/tasks/obb/.

Title:
Oriented Bounding Boxes Object Detection - Ultralytics YOLO Docs
Description:
Discover how to detect objects with rotation for higher precision using YOLO11 OBB models. Learn, train, validate, and export OBB models effortlessly.
Website Age:
11 years and 4 months (reg. 2014-02-13).

Matching Content Categories {📚}

Photography
Style & Fashion
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Content Management System {📝}

What CMS is docs.ultralytics.com built with?

Custom-built

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Traffic Estimate {📈}

What is the average monthly size of docs.ultralytics.com audience?

🌟 Strong Traffic: 100k - 200k visitors per month

Based on our best estimate, this website will receive around 100,019 visitors per month in the current month.
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How Does Docs.ultralytics.com Make Money? {💸}

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Keywords {🔍}

model, yolo, obb, dataset, imgsz, bounding, device, ultralytics, export, boxes, format, batch, load, models, yolonobb, train, oriented, yoloobb, python, cli, predict, half, object, detection, nms, int, val, onnx, validate, import, yoloyolonobbpt, custom, pretrained, results, arguments, applications, training, dotav, dota, data, datasets, accuracy, angle, objects, image, full, page, mapb, details, fraction,

Topics {✒️}

train ultralytics yolo-obb yolo predict model=yolo11n-obb ultralytics yolo-obb track storage tanks top previous pose training yolo11-obb models trained yolo11n-obb model latest ultralytics release yolo11-obb export formats yaml device=0 split=test introducing yolo11-obb models oriented bounding boxes numerous practical applications traditional bounding boxes standard object detection oriented object detector models download automatically axis-aligned rectangles regular bounding boxes rotated bounding boxes yolo processes losses dotav1 val images yolo11-obb model obb dataset formats full export details single-model multiscale inspecting solar panels full validation details yolo11n-obb model speed t4 tensorrt10 object detection device tf savedmodel device tf graphdef device tf lite submit merged results speed cpu onnx include unnecessary background settings remembered metrics applications benefit val section yolo-obb detect table faq model = yolo exported models category predict //ultralytics predict page

Questions {❓}

How can I export a YOLO11-OBB model to ONNX format?
How do I train a YOLO11n-obb model using a custom dataset?
How do I validate the accuracy of a YOLO11n-obb model?
What are Oriented Bounding Boxes (OBB) and how do they differ from regular bounding boxes?
What datasets can I use for training YOLO11-OBB models?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:OBB
      image:
         https://github.com/ultralytics/docs/releases/download/0/ships-detection-using-obb.avif
      datePublished:2024-01-05 03:00:26 +0100
      dateModified:2025-05-30 06:13:06 +0600
      author:
            type:Organization
            name:Ultralytics
            url:https://ultralytics.com/
      abstract:Discover how to detect objects with rotation for higher precision using YOLO11 OBB models. Learn, train, validate, and export OBB models effortlessly.
      mainEntity:
            type:Question
            name:What are Oriented Bounding Boxes (OBB) and how do they differ from regular bounding boxes?
            acceptedAnswer:
               type:Answer
               text:Oriented Bounding Boxes (OBB) include an additional angle to enhance object localization accuracy in images. Unlike regular bounding boxes, which are axis-aligned rectangles, OBBs can rotate to fit the orientation of the object better. This is particularly useful for applications requiring precise object placement, such as aerial or satellite imagery (Dataset Guide).
            type:Question
            name:How do I train a YOLO11n-obb model using a custom dataset?
            acceptedAnswer:
               type:Answer
               text:To train a YOLO11n-obb model with a custom dataset, follow the example below using Python or CLI: For more training arguments, check the Configuration section.
            type:Question
            name:What datasets can I use for training YOLO11-OBB models?
            acceptedAnswer:
               type:Answer
               text:YOLO11-OBB models are pretrained on datasets like DOTAv1 but you can use any dataset formatted for OBB. Detailed information on OBB dataset formats can be found in the Dataset Guide.
            type:Question
            name:How can I export a YOLO11-OBB model to ONNX format?
            acceptedAnswer:
               type:Answer
               text:Exporting a YOLO11-OBB model to ONNX format is straightforward using either Python or CLI: For more export formats and details, refer to the Export page.
            type:Question
            name:How do I validate the accuracy of a YOLO11n-obb model?
            acceptedAnswer:
               type:Answer
               text:To validate a YOLO11n-obb model, you can use Python or CLI commands as shown below: See full validation details in the Val section.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:What are Oriented Bounding Boxes (OBB) and how do they differ from regular bounding boxes?
      acceptedAnswer:
         type:Answer
         text:Oriented Bounding Boxes (OBB) include an additional angle to enhance object localization accuracy in images. Unlike regular bounding boxes, which are axis-aligned rectangles, OBBs can rotate to fit the orientation of the object better. This is particularly useful for applications requiring precise object placement, such as aerial or satellite imagery (Dataset Guide).
      name:How do I train a YOLO11n-obb model using a custom dataset?
      acceptedAnswer:
         type:Answer
         text:To train a YOLO11n-obb model with a custom dataset, follow the example below using Python or CLI: For more training arguments, check the Configuration section.
      name:What datasets can I use for training YOLO11-OBB models?
      acceptedAnswer:
         type:Answer
         text:YOLO11-OBB models are pretrained on datasets like DOTAv1 but you can use any dataset formatted for OBB. Detailed information on OBB dataset formats can be found in the Dataset Guide.
      name:How can I export a YOLO11-OBB model to ONNX format?
      acceptedAnswer:
         type:Answer
         text:Exporting a YOLO11-OBB model to ONNX format is straightforward using either Python or CLI: For more export formats and details, refer to the Export page.
      name:How do I validate the accuracy of a YOLO11n-obb model?
      acceptedAnswer:
         type:Answer
         text:To validate a YOLO11n-obb model, you can use Python or CLI commands as shown below: See full validation details in the Val section.
Answer:
      text:Oriented Bounding Boxes (OBB) include an additional angle to enhance object localization accuracy in images. Unlike regular bounding boxes, which are axis-aligned rectangles, OBBs can rotate to fit the orientation of the object better. This is particularly useful for applications requiring precise object placement, such as aerial or satellite imagery (Dataset Guide).
      text:To train a YOLO11n-obb model with a custom dataset, follow the example below using Python or CLI: For more training arguments, check the Configuration section.
      text:YOLO11-OBB models are pretrained on datasets like DOTAv1 but you can use any dataset formatted for OBB. Detailed information on OBB dataset formats can be found in the Dataset Guide.
      text:Exporting a YOLO11-OBB model to ONNX format is straightforward using either Python or CLI: For more export formats and details, refer to the Export page.
      text:To validate a YOLO11n-obb model, you can use Python or CLI commands as shown below: See full validation details in the Val section.

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