DOCS . ULTRALYTICS . COM {}

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

Title:
Instance Segmentation - Ultralytics YOLO Docs
Description:
Master instance segmentation using YOLO11. Learn how to detect, segment and outline objects in images with detailed guides and examples.
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're unsure how the site profits.

Not all websites focus on profit; some are designed to educate, connect people, or share useful tools. People create websites for numerous reasons. And this could be one such example. Docs.ultralytics.com has a revenue plan, but it's either invisible or we haven't found it.

Keywords {🔍}

model, yolo, segmentation, dataset, imgsz, format, load, device, export, batch, ultralytics, pretrained, instance, models, python, train, cli, segment, predict, half, object, onnx, nms, int, val, image, import, yoloyolonsegpt, custom, validate, coco, arguments, objects, yolonseg, page, results, data, tasks, detection, masks, formats, metricsboxmap, mapb, metricssegmap, mapm, fraction, detect, classify, shape, speed,

Topics {✒️}

convert datasets instance segmentation tasks yolo11 segment models yolo predict model=yolo11n-seg single-model single-scale latest ultralytics release pose models trained yolo11n-seg model tasks classify models models download automatically top previous detect ultralytics yolo11 yolo segmentation model yolo segmentation format coco val images yolo11-seg export formats yolo supports training yolo11 segmentation model instance segmentation model speed t4 tensorrt10 art model recognized assessing model performance speed cpu onnx real-time performance ensuring robust performance ultralytics //ultralytics exported models full export details coco val2017 dataset segment device tf savedmodel device tf graphdef device tf lite yolo11n-seg model coco8-seg dataset model = yolo settings remembered metrics onnx format model table drawing bounding boxes yolo format category predict predict page high accuracy model model pretrained model instance segmentation export export

Questions {❓}

How can I export a YOLO segmentation model to ONNX format?
How do I load and validate a pretrained YOLO segmentation model?
How do I train a YOLO11 segmentation model on a custom dataset?
What is the difference between object detection and instance segmentation in YOLO11?
Why use YOLO11 for instance segmentation?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:Segment
      image:
         https://github.com/ultralytics/docs/releases/download/0/instance-segmentation-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:Master instance segmentation using YOLO11. Learn how to detect, segment and outline objects in images with detailed guides and examples.
      mainEntity:
            type:Question
            name:How do I train a YOLO11 segmentation model on a custom dataset?
            acceptedAnswer:
               type:Answer
               text:To train a YOLO11 segmentation model on a custom dataset, you first need to prepare your dataset in the YOLO segmentation format. You can use tools like JSON2YOLO to convert datasets from other formats. Once your dataset is ready, you can train the model using Python or CLI commands: Check the Configuration page for more available arguments.
            type:Question
            name:What is the difference between object detection and instance segmentation in YOLO11?
            acceptedAnswer:
               type:Answer
               text:Object detection identifies and localizes objects within an image by drawing bounding boxes around them, whereas instance segmentation not only identifies the bounding boxes but also delineates the exact shape of each object. YOLO11 instance segmentation models provide masks or contours that outline each detected object, which is particularly useful for tasks where knowing the precise shape of objects is important, such as medical imaging or autonomous driving.
            type:Question
            name:Why use YOLO11 for instance segmentation?
            acceptedAnswer:
               type:Answer
               text:Ultralytics YOLO11 is a state-of-the-art model recognized for its high accuracy and real-time performance, making it ideal for instance segmentation tasks. YOLO11 Segment models come pretrained on the COCO dataset, ensuring robust performance across a variety of objects. Additionally, YOLO supports training, validation, prediction, and export functionalities with seamless integration, making it highly versatile for both research and industry applications.
            type:Question
            name:How do I load and validate a pretrained YOLO segmentation model?
            acceptedAnswer:
               type:Answer
               text:Loading and validating a pretrained YOLO segmentation model is straightforward. Here's how you can do it using both Python and CLI: These steps will provide you with validation metrics like Mean Average Precision (mAP), crucial for assessing model performance.
            type:Question
            name:How can I export a YOLO segmentation model to ONNX format?
            acceptedAnswer:
               type:Answer
               text:Exporting a YOLO segmentation model to ONNX format is simple and can be done using Python or CLI commands: For more details on exporting to various formats, refer to the Export page.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:How do I train a YOLO11 segmentation model on a custom dataset?
      acceptedAnswer:
         type:Answer
         text:To train a YOLO11 segmentation model on a custom dataset, you first need to prepare your dataset in the YOLO segmentation format. You can use tools like JSON2YOLO to convert datasets from other formats. Once your dataset is ready, you can train the model using Python or CLI commands: Check the Configuration page for more available arguments.
      name:What is the difference between object detection and instance segmentation in YOLO11?
      acceptedAnswer:
         type:Answer
         text:Object detection identifies and localizes objects within an image by drawing bounding boxes around them, whereas instance segmentation not only identifies the bounding boxes but also delineates the exact shape of each object. YOLO11 instance segmentation models provide masks or contours that outline each detected object, which is particularly useful for tasks where knowing the precise shape of objects is important, such as medical imaging or autonomous driving.
      name:Why use YOLO11 for instance segmentation?
      acceptedAnswer:
         type:Answer
         text:Ultralytics YOLO11 is a state-of-the-art model recognized for its high accuracy and real-time performance, making it ideal for instance segmentation tasks. YOLO11 Segment models come pretrained on the COCO dataset, ensuring robust performance across a variety of objects. Additionally, YOLO supports training, validation, prediction, and export functionalities with seamless integration, making it highly versatile for both research and industry applications.
      name:How do I load and validate a pretrained YOLO segmentation model?
      acceptedAnswer:
         type:Answer
         text:Loading and validating a pretrained YOLO segmentation model is straightforward. Here's how you can do it using both Python and CLI: These steps will provide you with validation metrics like Mean Average Precision (mAP), crucial for assessing model performance.
      name:How can I export a YOLO segmentation model to ONNX format?
      acceptedAnswer:
         type:Answer
         text:Exporting a YOLO segmentation model to ONNX format is simple and can be done using Python or CLI commands: For more details on exporting to various formats, refer to the Export page.
Answer:
      text:To train a YOLO11 segmentation model on a custom dataset, you first need to prepare your dataset in the YOLO segmentation format. You can use tools like JSON2YOLO to convert datasets from other formats. Once your dataset is ready, you can train the model using Python or CLI commands: Check the Configuration page for more available arguments.
      text:Object detection identifies and localizes objects within an image by drawing bounding boxes around them, whereas instance segmentation not only identifies the bounding boxes but also delineates the exact shape of each object. YOLO11 instance segmentation models provide masks or contours that outline each detected object, which is particularly useful for tasks where knowing the precise shape of objects is important, such as medical imaging or autonomous driving.
      text:Ultralytics YOLO11 is a state-of-the-art model recognized for its high accuracy and real-time performance, making it ideal for instance segmentation tasks. YOLO11 Segment models come pretrained on the COCO dataset, ensuring robust performance across a variety of objects. Additionally, YOLO supports training, validation, prediction, and export functionalities with seamless integration, making it highly versatile for both research and industry applications.
      text:Loading and validating a pretrained YOLO segmentation model is straightforward. Here's how you can do it using both Python and CLI: These steps will provide you with validation metrics like Mean Average Precision (mAP), crucial for assessing model performance.
      text:Exporting a YOLO segmentation model to ONNX format is simple and can be done using Python or CLI commands: For more details on exporting to various formats, refer to the Export page.

Social Networks {👍}(4)

External Links {🔗}(37)

Analytics and Tracking {📊}

Google Analytics
Google Analytics 4
Google Tag Manager

Libraries {📚}

Clipboard.js

CDN Services {📦}

Cloudflare
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Weglot

2.78s.