DOCS . ULTRALYTICS . COM {}

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

Title:
COCO Dataset - Ultralytics YOLO Docs
Description:
Explore the COCO dataset for object detection and segmentation. Learn about its structure, usage, pretrained models, and key features.
Website Age:
11 years and 4 months (reg. 2014-02-13).

Matching Content Categories {📚}

Photography
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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 see no obvious way the site makes money.

While profit motivates many websites, others exist to inspire, entertain, or provide valuable resources. Websites have a variety of goals. And this might be one of them. Docs.ultralytics.com might be plotting its profit, but the way they're doing it isn't detectable yet.

Keywords {🔍}

coco, dataset, images, object, model, training, models, train, yolo, detection, segmentation, ultralytics, categories, evaluation, pretrained, annotations, datasets, objects, computer, vision, common, captioning, image, download, tasks, key, features, yaml, trained, benchmarking, standardized, metrics, average, map, performance, subset, val, results, file, dir, path, import, load, usage, context, variety, essential, researchers, size, speed,

Topics {✒️}

/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco pose estimation tasks top previous argoverse ultralytics/cfg/datasets/coco /ultralytics/assets/releases/download/v0 large-scale object detection yolo train data=coco author={tsung-yi lin pretrained yolo11 models coco dataset structured coco dataset website coco dataset includes coco evaluation server ultralytics yolo training object detection title={microsoft coco //docs segmentation tasks benchmarking trained models dataset structure object detection key features sample images keypoint detection coco dataset large-scale dataset standardized evaluation metrics pathlib import path segmentation masks faster r-cnn instance segmentation mask r-cnn computer vision community trained models coco consortium coco due enhance model generalization speed cpu onnx speed t4 tensorrt10 pretrained model combines multiple images ground truth annotations yolo model comparing model performance computer vision researchers mosaiced dataset images applications org/zips/test2017 //ultralytics ultralytics

Questions {❓}

How can I train a YOLO model using the COCO dataset?
How is the COCO dataset structured and how do I use it?
What are the key features of the COCO dataset?
What is the COCO dataset and why is it important for computer vision?
Where can I find pretrained YOLO11 models trained on the COCO dataset?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:COCO
      image:
         https://github.com/ultralytics/docs/releases/download/0/mosaiced-coco-dataset-sample.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 dataset for object detection and segmentation. Learn about its structure, usage, pretrained models, and key features.
      mainEntity:
            type:Question
            name:What is the COCO dataset and why is it important for computer vision?
            acceptedAnswer:
               type:Answer
               text:The COCO dataset (Common Objects in Context) is a large-scale dataset used for object detection, segmentation, and captioning. It contains 330K images with detailed annotations for 80 object categories, making it essential for benchmarking and training computer vision models. Researchers use COCO due to its diverse categories and standardized evaluation metrics like mean Average Precision (mAP).
            type:Question
            name:How can I train a YOLO model using the COCO dataset?
            acceptedAnswer:
               type:Answer
               text:To train a YOLO11 model using the COCO dataset, you can use the following code snippets: Refer to the Training page for more details on available arguments.
            type:Question
            name:What are the key features of the COCO dataset?
            acceptedAnswer:
               type:Answer
               text:The COCO dataset includes:
            type:Question
            name:Where can I find pretrained YOLO11 models trained on the COCO dataset?
            acceptedAnswer:
               type:Answer
               text:Pretrained YOLO11 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include: These models vary in size, mAP, and inference speed, providing options for different performance and resource requirements.
            type:Question
            name:How is the COCO dataset structured and how do I use it?
            acceptedAnswer:
               type:Answer
               text:The COCO dataset is split into three subsets: The dataset's YAML configuration file is available at coco.yaml, which defines paths, classes, and dataset details.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:What is the COCO dataset and why is it important for computer vision?
      acceptedAnswer:
         type:Answer
         text:The COCO dataset (Common Objects in Context) is a large-scale dataset used for object detection, segmentation, and captioning. It contains 330K images with detailed annotations for 80 object categories, making it essential for benchmarking and training computer vision models. Researchers use COCO due to its diverse categories and standardized evaluation metrics like mean Average Precision (mAP).
      name:How can I train a YOLO model using the COCO dataset?
      acceptedAnswer:
         type:Answer
         text:To train a YOLO11 model using the COCO dataset, you can use the following code snippets: Refer to the Training page for more details on available arguments.
      name:What are the key features of the COCO dataset?
      acceptedAnswer:
         type:Answer
         text:The COCO dataset includes:
      name:Where can I find pretrained YOLO11 models trained on the COCO dataset?
      acceptedAnswer:
         type:Answer
         text:Pretrained YOLO11 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include: These models vary in size, mAP, and inference speed, providing options for different performance and resource requirements.
      name:How is the COCO dataset structured and how do I use it?
      acceptedAnswer:
         type:Answer
         text:The COCO dataset is split into three subsets: The dataset's YAML configuration file is available at coco.yaml, which defines paths, classes, and dataset details.
Answer:
      text:The COCO dataset (Common Objects in Context) is a large-scale dataset used for object detection, segmentation, and captioning. It contains 330K images with detailed annotations for 80 object categories, making it essential for benchmarking and training computer vision models. Researchers use COCO due to its diverse categories and standardized evaluation metrics like mean Average Precision (mAP).
      text:To train a YOLO11 model using the COCO dataset, you can use the following code snippets: Refer to the Training page for more details on available arguments.
      text:The COCO dataset includes:
      text:Pretrained YOLO11 models on the COCO dataset can be downloaded from the links provided in the documentation. Examples include: These models vary in size, mAP, and inference speed, providing options for different performance and resource requirements.
      text:The COCO dataset is split into three subsets: The dataset's YAML configuration file is available at coco.yaml, which defines paths, classes, and dataset details.

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