DOCS . ULTRALYTICS . COM {}

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We are analyzing https://docs.ultralytics.com/datasets/segment/coco/.

Title:
COCO-Seg Dataset - Ultralytics YOLO Docs
Description:
Explore the COCO-Seg dataset, an extension of COCO, with detailed segmentation annotations. Learn how to train YOLO models with COCO-Seg.
Website Age:
11 years and 4 months (reg. 2014-02-13).

Matching Content Categories {📚}

Photography
Careers
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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? {💸}

We're unsure if the website is profiting.

Earning money isn't the goal of every website; some are designed to offer support or promote social causes. People have different reasons for creating websites. This might be one such reason. Docs.ultralytics.com could have a money-making trick up its sleeve, but it's undetectable for now.

Keywords {🔍}

dataset, cocoseg, images, coco, model, segmentation, models, train, training, instance, object, yolo, ultralytics, original, pretrained, annotations, metrics, evaluation, key, performance, detailed, tasks, datasets, objects, features, speed, yolonseg, subset, download, image, subsets, resource, size, categories, masks, average, val, results, information, dir, path, import, epochs, load, detection, yaml, usage, extension, common, context,

Topics {✒️}

/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco coco8-seg dataset ultralytics/cfg/datasets/coco /ultralytics/assets/releases/download/v0 object detection applications coco-seg coco-seg dataset structured coco-seg dataset includes instance segmentation tasks coco-seg includes ultralytics models page yolo train data=coco coco-seg dataset //docs annotations coco-seg coco-seg introduces author={tsung-yi lin instance segmentation masks coco-seg lightweight yolo11n-seg yolo models coco dataset website detailed segmentation annotations object instance segmentation yolo11n-seg model original coco dataset powerful yolo11x-seg original coco paper coco evaluation server title={microsoft coco dataset structure combines multiple images pretrained models sample images instance segmentation benchmarking trained models yolo11n-seg 2 yolo11x-seg coco dataset key features 4 yolo11s-seg 5 yolo11m-seg 3 yolo11l-seg original coco pathlib import path enabling effective comparison //ultralytics ultralytics computer vision community standardized evaluation metrics

Questions {❓}

How can I train a YOLO11 model using the COCO-Seg dataset?
How is the COCO-Seg dataset structured and what subsets does it contain?
What are the key features of the COCO-Seg dataset?
What is the COCO-Seg dataset and how does it differ from the original COCO dataset?
What pretrained models are available for COCO-Seg, and what are their performance metrics?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:COCO
      image:
         https://github.com/ultralytics/docs/releases/download/0/mosaiced-training-batch-3.avif
      datePublished:2023-11-12 02:49:37 +0100
      dateModified:2025-03-17 21:52:48 +0100
      author:
            type:Organization
            name:Ultralytics
            url:https://ultralytics.com/
      abstract:Explore the COCO-Seg dataset, an extension of COCO, with detailed segmentation annotations. Learn how to train YOLO models with COCO-Seg.
      mainEntity:
            type:Question
            name:What is the COCO-Seg dataset and how does it differ from the original COCO dataset?
            acceptedAnswer:
               type:Answer
               text:The COCO-Seg dataset is an extension of the original COCO (Common Objects in Context) dataset, specifically designed for instance segmentation tasks. While it uses the same images as the COCO dataset, COCO-Seg includes more detailed segmentation annotations, making it a powerful resource for researchers and developers focusing on object instance segmentation.
            type:Question
            name:How can I train a YOLO11 model using the COCO-Seg dataset?
            acceptedAnswer:
               type:Answer
               text:To train a YOLO11n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a detailed list of available arguments, refer to the model Training page.
            type:Question
            name:What are the key features of the COCO-Seg dataset?
            acceptedAnswer:
               type:Answer
               text:The COCO-Seg dataset includes several key features:
            type:Question
            name:What pretrained models are available for COCO-Seg, and what are their performance metrics?
            acceptedAnswer:
               type:Answer
               text:The COCO-Seg dataset supports multiple pretrained YOLO11 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics: These models range from the lightweight YOLO11n-seg to the more powerful YOLO11x-seg, offering different trade-offs between speed and accuracy to suit various application requirements. For more information on model selection, visit the Ultralytics models page.
            type:Question
            name:How is the COCO-Seg dataset structured and what subsets does it contain?
            acceptedAnswer:
               type:Answer
               text:The COCO-Seg dataset is partitioned into three subsets for specific training and evaluation needs: For smaller experimentation needs, you might also consider using the COCO8-seg dataset, which is a compact version containing just 8 images from the COCO train 2017 set.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:What is the COCO-Seg dataset and how does it differ from the original COCO dataset?
      acceptedAnswer:
         type:Answer
         text:The COCO-Seg dataset is an extension of the original COCO (Common Objects in Context) dataset, specifically designed for instance segmentation tasks. While it uses the same images as the COCO dataset, COCO-Seg includes more detailed segmentation annotations, making it a powerful resource for researchers and developers focusing on object instance segmentation.
      name:How can I train a YOLO11 model using the COCO-Seg dataset?
      acceptedAnswer:
         type:Answer
         text:To train a YOLO11n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a detailed list of available arguments, refer to the model Training page.
      name:What are the key features of the COCO-Seg dataset?
      acceptedAnswer:
         type:Answer
         text:The COCO-Seg dataset includes several key features:
      name:What pretrained models are available for COCO-Seg, and what are their performance metrics?
      acceptedAnswer:
         type:Answer
         text:The COCO-Seg dataset supports multiple pretrained YOLO11 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics: These models range from the lightweight YOLO11n-seg to the more powerful YOLO11x-seg, offering different trade-offs between speed and accuracy to suit various application requirements. For more information on model selection, visit the Ultralytics models page.
      name:How is the COCO-Seg dataset structured and what subsets does it contain?
      acceptedAnswer:
         type:Answer
         text:The COCO-Seg dataset is partitioned into three subsets for specific training and evaluation needs: For smaller experimentation needs, you might also consider using the COCO8-seg dataset, which is a compact version containing just 8 images from the COCO train 2017 set.
Answer:
      text:The COCO-Seg dataset is an extension of the original COCO (Common Objects in Context) dataset, specifically designed for instance segmentation tasks. While it uses the same images as the COCO dataset, COCO-Seg includes more detailed segmentation annotations, making it a powerful resource for researchers and developers focusing on object instance segmentation.
      text:To train a YOLO11n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a detailed list of available arguments, refer to the model Training page.
      text:The COCO-Seg dataset includes several key features:
      text:The COCO-Seg dataset supports multiple pretrained YOLO11 segmentation models with varying performance metrics. Here's a summary of the available models and their key metrics: These models range from the lightweight YOLO11n-seg to the more powerful YOLO11x-seg, offering different trade-offs between speed and accuracy to suit various application requirements. For more information on model selection, visit the Ultralytics models page.
      text:The COCO-Seg dataset is partitioned into three subsets for specific training and evaluation needs: For smaller experimentation needs, you might also consider using the COCO8-seg dataset, which is a compact version containing just 8 images from the COCO train 2017 set.

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