DOCS . ULTRALYTICS . COM {}

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

Title:
Object Detection - Ultralytics YOLO Docs
Description:
Learn about object detection with YOLO11. Explore pretrained models, training, validation, prediction, and export details for efficient object recognition.
Website Age:
11 years and 4 months (reg. 2014-02-13).

Matching Content Categories {📚}

Photography
Crafts
Style & Fashion

Content Management System {📝}

What CMS is docs.ultralytics.com built with?

Custom-built

No common CMS systems were detected on Docs.ultralytics.com, and no known web development framework was identified.

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? {💸}

We can't figure out the monetization strategy.

The purpose of some websites isn't monetary gain; they're meant to inform, educate, or foster collaboration. Everyone has unique reasons for building websites. This could be an example. Docs.ultralytics.com might be earning cash quietly, but we haven't detected the monetization method.

Keywords {🔍}

model, yolo, models, ultralytics, dataset, imgsz, device, load, export, pretrained, batch, train, format, object, val, detection, detect, predict, coco, python, cli, half, map, nms, int, custom, validate, formats, onnx, import, trained, yolon, page, yoloyolonpt, accuracy, arguments, data, tasks, pose, image, speed, tensorrt, full, list, results, yolopathtobestpt, metrics, coreml, fraction, segment,

Topics {✒️}

popular datasets classification tasks single-model single-scale ultralytics yolo library yolo predict model=yolo11n latest ultralytics release ultralytics yolo11 offers classify models trained yolo model pre-trained model models download automatically default yolo11 models ultralytics yolo11 utilize models pretrained pose models coco val images trained yolo11 model real-time inference speed t4 tensorrt10 full export details settings remembered metrics custom dataset model device tf savedmodel device tf graphdef device tf lite coco val2017 dataset detect trained yolo11n model custom dataset involves detailed configuration options speed cpu onnx table export models model = yolo yolo format ultralytics //ultralytics onnx format model pose estimation yolo11 export formats val page pretrained models category predict predict page exported models models section exporting models top-left export export segment

Questions {❓}

How can I validate the accuracy of my trained YOLO model?
How do I train a YOLO11 model on my custom dataset?
What formats can I export a YOLO11 model to?
What pretrained models are available in YOLO11?
Why should I use Ultralytics YOLO11 for object detection?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:Detect
      image:
         https://github.com/ultralytics/docs/releases/download/0/object-detection-examples.avif
      datePublished:2023-11-12 02:49:37 +0100
      dateModified:2025-03-20 20:24:06 +0100
      author:
            type:Organization
            name:Ultralytics
            url:https://ultralytics.com/
      abstract:Learn about object detection with YOLO11. Explore pretrained models, training, validation, prediction, and export details for efficient object recognition.
      mainEntity:
            type:Question
            name:How do I train a YOLO11 model on my custom dataset?
            acceptedAnswer:
               type:Answer
               text:Training a YOLO11 model on a custom dataset involves a few steps: For detailed configuration options, visit the Configuration page.
            type:Question
            name:What pretrained models are available in YOLO11?
            acceptedAnswer:
               type:Answer
               text:Ultralytics YOLO11 offers various pretrained models for object detection, segmentation, and pose estimation. These models are pretrained on the COCO dataset or ImageNet for classification tasks. Here are some of the available models: For a detailed list and performance metrics, refer to the Models section.
            type:Question
            name:How can I validate the accuracy of my trained YOLO model?
            acceptedAnswer:
               type:Answer
               text:To validate the accuracy of your trained YOLO11 model, you can use the .val() method in Python or the yolo detect val command in CLI. This will provide metrics like mAP50-95, mAP50, and more. For more validation details, visit the Val page.
            type:Question
            name:What formats can I export a YOLO11 model to?
            acceptedAnswer:
               type:Answer
               text:Ultralytics YOLO11 allows exporting models to various formats such as ONNX, TensorRT, CoreML, and more to ensure compatibility across different platforms and devices. Check the full list of supported formats and instructions on the Export page.
            type:Question
            name:Why should I use Ultralytics YOLO11 for object detection?
            acceptedAnswer:
               type:Answer
               text:Ultralytics YOLO11 is designed to offer state-of-the-art performance for object detection, segmentation, and pose estimation. Here are some key advantages: Explore our Blog for use cases and success stories showcasing YOLO11 in action.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:How do I train a YOLO11 model on my custom dataset?
      acceptedAnswer:
         type:Answer
         text:Training a YOLO11 model on a custom dataset involves a few steps: For detailed configuration options, visit the Configuration page.
      name:What pretrained models are available in YOLO11?
      acceptedAnswer:
         type:Answer
         text:Ultralytics YOLO11 offers various pretrained models for object detection, segmentation, and pose estimation. These models are pretrained on the COCO dataset or ImageNet for classification tasks. Here are some of the available models: For a detailed list and performance metrics, refer to the Models section.
      name:How can I validate the accuracy of my trained YOLO model?
      acceptedAnswer:
         type:Answer
         text:To validate the accuracy of your trained YOLO11 model, you can use the .val() method in Python or the yolo detect val command in CLI. This will provide metrics like mAP50-95, mAP50, and more. For more validation details, visit the Val page.
      name:What formats can I export a YOLO11 model to?
      acceptedAnswer:
         type:Answer
         text:Ultralytics YOLO11 allows exporting models to various formats such as ONNX, TensorRT, CoreML, and more to ensure compatibility across different platforms and devices. Check the full list of supported formats and instructions on the Export page.
      name:Why should I use Ultralytics YOLO11 for object detection?
      acceptedAnswer:
         type:Answer
         text:Ultralytics YOLO11 is designed to offer state-of-the-art performance for object detection, segmentation, and pose estimation. Here are some key advantages: Explore our Blog for use cases and success stories showcasing YOLO11 in action.
Answer:
      text:Training a YOLO11 model on a custom dataset involves a few steps: For detailed configuration options, visit the Configuration page.
      text:Ultralytics YOLO11 offers various pretrained models for object detection, segmentation, and pose estimation. These models are pretrained on the COCO dataset or ImageNet for classification tasks. Here are some of the available models: For a detailed list and performance metrics, refer to the Models section.
      text:To validate the accuracy of your trained YOLO11 model, you can use the .val() method in Python or the yolo detect val command in CLI. This will provide metrics like mAP50-95, mAP50, and more. For more validation details, visit the Val page.
      text:Ultralytics YOLO11 allows exporting models to various formats such as ONNX, TensorRT, CoreML, and more to ensure compatibility across different platforms and devices. Check the full list of supported formats and instructions on the Export page.
      text:Ultralytics YOLO11 is designed to offer state-of-the-art performance for object detection, segmentation, and pose estimation. Here are some key advantages: Explore our Blog for use cases and success stories showcasing YOLO11 in action.

Social Networks {👍}(4)

External Links {🔗}(38)

Analytics and Tracking {📊}

Google Analytics
Google Analytics 4
Google Tag Manager

Libraries {📚}

Clipboard.js

CDN Services {📦}

Cloudflare
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Weglot

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