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

Title:
COCO-Pose Dataset - Ultralytics YOLO Docs
Description:
Explore the COCO-Pose dataset for advanced pose estimation. Learn about datasets, pretrained models, metrics, and applications for training with YOLO.
Website Age:
11 years and 4 months (reg. 2014-02-13).

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Keywords {🔍}

dataset, cocopose, images, coco, model, training, pose, estimation, models, train, keypoints, yolo, ultralytics, metrics, tasks, annotations, evaluation, pretrained, performance, datasets, key, yolonpose, objects, features, applications, subset, download, image, yaml, split, mappose, human, figures, detailed, standardized, annotated, val, results, file, documentation, dir, path, import, epochs, load, usage, provided, evaluating, common, context,

Topics {✒️}

/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose ultralytics/cfg/datasets/coco-pose keypoint detection /ultralytics/assets/releases/download/v0 yolo train data=coco-pose coco-pose pretrained models coco-pose dataset extends coco-pose dataset span coco-pose dataset author={tsung-yi lin pose estimation tasks detailed pose estimation coco-pose {url}coco2017labels-pose coco dataset website original coco dataset coco evaluation server title={microsoft coco ultralytics yolo //docs leveraging pretrained models dataset structure yolo11n-pose model sample images object keypoint similarity key metrics include key features coco dataset include 17 keypoints pose estimation predicted keypoints pathlib import path speed cpu onnx speed t4 tensorrt10 computer vision researchers computer vision community 56599 coco images standardized evaluation metrics yolo11n-pose benchmarking trained models evaluating model performance coco consortium 6 yolo11s-pose 2 yolo11m-pose 7 yolo11l-pose 7 yolo11x-pose yolo11s-pose human-computer interaction comparing model performance 200k images labeled

Questions {❓}

How can I train a YOLO11 model on the COCO-Pose dataset?
How is the dataset structured and split for the COCO-Pose dataset?
What are the different metrics provided by the COCO-Pose dataset for evaluating model performance?
What are the key features and applications of the COCO-Pose dataset?
What is the COCO-Pose dataset and how is it used with Ultralytics YOLO for pose estimation?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:COCO
      image:
         https://github.com/ultralytics/docs/releases/download/0/pose-sample-image.avif
      datePublished:2023-11-12 02:49:37 +0100
      dateModified:2024-12-26 02:33:15 +0800
      author:
            type:Organization
            name:Ultralytics
            url:https://ultralytics.com/
      abstract:Explore the COCO-Pose dataset for advanced pose estimation. Learn about datasets, pretrained models, metrics, and applications for training with YOLO.
      mainEntity:
            type:Question
            name:What is the COCO-Pose dataset and how is it used with Ultralytics YOLO for pose estimation?
            acceptedAnswer:
               type:Answer
               text:The COCO-Pose dataset is a specialized version of the COCO (Common Objects in Context) dataset designed for pose estimation tasks. It builds upon the COCO Keypoints 2017 images and annotations, allowing for the training of models like Ultralytics YOLO for detailed pose estimation. For instance, you can use the COCO-Pose dataset to train a YOLO11n-pose model by loading a pretrained model and training it with a YAML configuration. For training examples, refer to the Training documentation.
            type:Question
            name:How can I train a YOLO11 model on the COCO-Pose dataset?
            acceptedAnswer:
               type:Answer
               text:Training a YOLO11 model on the COCO-Pose dataset can be accomplished using either Python or CLI commands. For example, to train a YOLO11n-pose model for 100 epochs with an image size of 640, you can follow the steps below: For more details on the training process and available arguments, check the training page.
            type:Question
            name:What are the different metrics provided by the COCO-Pose dataset for evaluating model performance?
            acceptedAnswer:
               type:Answer
               text:The COCO-Pose dataset provides several standardized evaluation metrics for pose estimation tasks, similar to the original COCO dataset. Key metrics include the Object Keypoint Similarity (OKS), which evaluates the accuracy of predicted keypoints against ground truth annotations. These metrics allow for thorough performance comparisons between different models. For instance, the COCO-Pose pretrained models such as YOLO11n-pose, YOLO11s-pose, and others have specific performance metrics listed in the documentation, like mAPpose50-95 and mAPpose50.
            type:Question
            name:How is the dataset structured and split for the COCO-Pose dataset?
            acceptedAnswer:
               type:Answer
               text:The COCO-Pose dataset is split into three subsets: These subsets help organize the training, validation, and testing phases effectively. For configuration details, explore the coco-pose.yaml file available on GitHub.
            type:Question
            name:What are the key features and applications of the COCO-Pose dataset?
            acceptedAnswer:
               type:Answer
               text:The COCO-Pose dataset extends the COCO Keypoints 2017 annotations to include 17 keypoints for human figures, enabling detailed pose estimation. Standardized evaluation metrics (e.g., OKS) facilitate comparisons across different models. Applications of the COCO-Pose dataset span various domains, such as sports analytics, healthcare, and human-computer interaction, wherever detailed pose estimation of human figures is required. For practical use, leveraging pretrained models like those provided in the documentation (e.g., YOLO11n-pose) can significantly streamline the process (Key Features). If you use the COCO-Pose dataset in your research or development work, please cite the paper with the following BibTeX entry.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:What is the COCO-Pose dataset and how is it used with Ultralytics YOLO for pose estimation?
      acceptedAnswer:
         type:Answer
         text:The COCO-Pose dataset is a specialized version of the COCO (Common Objects in Context) dataset designed for pose estimation tasks. It builds upon the COCO Keypoints 2017 images and annotations, allowing for the training of models like Ultralytics YOLO for detailed pose estimation. For instance, you can use the COCO-Pose dataset to train a YOLO11n-pose model by loading a pretrained model and training it with a YAML configuration. For training examples, refer to the Training documentation.
      name:How can I train a YOLO11 model on the COCO-Pose dataset?
      acceptedAnswer:
         type:Answer
         text:Training a YOLO11 model on the COCO-Pose dataset can be accomplished using either Python or CLI commands. For example, to train a YOLO11n-pose model for 100 epochs with an image size of 640, you can follow the steps below: For more details on the training process and available arguments, check the training page.
      name:What are the different metrics provided by the COCO-Pose dataset for evaluating model performance?
      acceptedAnswer:
         type:Answer
         text:The COCO-Pose dataset provides several standardized evaluation metrics for pose estimation tasks, similar to the original COCO dataset. Key metrics include the Object Keypoint Similarity (OKS), which evaluates the accuracy of predicted keypoints against ground truth annotations. These metrics allow for thorough performance comparisons between different models. For instance, the COCO-Pose pretrained models such as YOLO11n-pose, YOLO11s-pose, and others have specific performance metrics listed in the documentation, like mAPpose50-95 and mAPpose50.
      name:How is the dataset structured and split for the COCO-Pose dataset?
      acceptedAnswer:
         type:Answer
         text:The COCO-Pose dataset is split into three subsets: These subsets help organize the training, validation, and testing phases effectively. For configuration details, explore the coco-pose.yaml file available on GitHub.
      name:What are the key features and applications of the COCO-Pose dataset?
      acceptedAnswer:
         type:Answer
         text:The COCO-Pose dataset extends the COCO Keypoints 2017 annotations to include 17 keypoints for human figures, enabling detailed pose estimation. Standardized evaluation metrics (e.g., OKS) facilitate comparisons across different models. Applications of the COCO-Pose dataset span various domains, such as sports analytics, healthcare, and human-computer interaction, wherever detailed pose estimation of human figures is required. For practical use, leveraging pretrained models like those provided in the documentation (e.g., YOLO11n-pose) can significantly streamline the process (Key Features). If you use the COCO-Pose dataset in your research or development work, please cite the paper with the following BibTeX entry.
Answer:
      text:The COCO-Pose dataset is a specialized version of the COCO (Common Objects in Context) dataset designed for pose estimation tasks. It builds upon the COCO Keypoints 2017 images and annotations, allowing for the training of models like Ultralytics YOLO for detailed pose estimation. For instance, you can use the COCO-Pose dataset to train a YOLO11n-pose model by loading a pretrained model and training it with a YAML configuration. For training examples, refer to the Training documentation.
      text:Training a YOLO11 model on the COCO-Pose dataset can be accomplished using either Python or CLI commands. For example, to train a YOLO11n-pose model for 100 epochs with an image size of 640, you can follow the steps below: For more details on the training process and available arguments, check the training page.
      text:The COCO-Pose dataset provides several standardized evaluation metrics for pose estimation tasks, similar to the original COCO dataset. Key metrics include the Object Keypoint Similarity (OKS), which evaluates the accuracy of predicted keypoints against ground truth annotations. These metrics allow for thorough performance comparisons between different models. For instance, the COCO-Pose pretrained models such as YOLO11n-pose, YOLO11s-pose, and others have specific performance metrics listed in the documentation, like mAPpose50-95 and mAPpose50.
      text:The COCO-Pose dataset is split into three subsets: These subsets help organize the training, validation, and testing phases effectively. For configuration details, explore the coco-pose.yaml file available on GitHub.
      text:The COCO-Pose dataset extends the COCO Keypoints 2017 annotations to include 17 keypoints for human figures, enabling detailed pose estimation. Standardized evaluation metrics (e.g., OKS) facilitate comparisons across different models. Applications of the COCO-Pose dataset span various domains, such as sports analytics, healthcare, and human-computer interaction, wherever detailed pose estimation of human figures is required. For practical use, leveraging pretrained models like those provided in the documentation (e.g., YOLO11n-pose) can significantly streamline the process (Key Features). If you use the COCO-Pose dataset in your research or development work, please cite the paper with the following BibTeX entry.

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