COCO8-Pose Dataset

Introduction

Ultralytics COCO8-Pose is a small but versatile pose estimation dataset composed of the first 8 images of the COCO train 2017 set (4 for training, 4 for validation), using a 17-keypoint schema for the single "person" class. This dataset is ideal for testing and debugging pose estimation models, or for experimenting with new keypoint-detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training on the full COCO-Pose dataset.

Dataset Structure

Total images: 8 (4 train / 4 val).
Classes: 1 (person) with 17 keypoint types per annotation.
Download size: ~1 MB.
Recommended directory layout: datasets/coco8-pose/images/{train,val} and datasets/coco8-pose/labels/{train,val} with YOLO-format keypoints stored as .txt files.

Explore COCO8-Pose on Ultralytics Platform to browse every image with its keypoint skeletons, view the keypoint and class distributions in the Charts tab, and clone it to train your own model in the cloud.

Dataset YAML

A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8-Pose dataset, the coco8-pose.yaml file is maintained at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml.

!!! example "ultralytics/cfg/datasets/coco8-pose.yaml"

```yaml
--8<-- "ultralytics/cfg/datasets/coco8-pose.yaml"
```

Usage

To train a YOLO26n-pose model on the COCO8-Pose dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Training page.

!!! example "Train Example"

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo26n-pose.pt")  # load a pretrained model (recommended for training)

    # Train the model
    results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
    ```

=== "CLI"

    ```bash
    # Start training from a pretrained *.pt model
    yolo pose train data=coco8-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
    ```

Sample Images and Annotations

Here are some examples of images from the COCO8-Pose dataset, along with their corresponding annotations:

Mosaiced Image: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.

Citations and Acknowledgments

If you use the COCO dataset in your research or development work, please cite the following paper:

!!! quote ""

=== "BibTeX"

    ```bibtex
    @misc{lin2015microsoft,
          title={Microsoft COCO: Common Objects in Context},
          author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
          year={2015},
          eprint={1405.0312},
          archivePrefix={arXiv},
          primaryClass={cs.CV}
    }
    ```

We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the COCO dataset website.

FAQ

What is the COCO8-Pose dataset, and how is it used with Ultralytics YOLO26?

The COCO8-Pose dataset is a small, versatile pose estimation dataset that includes the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It's designed for testing and debugging pose estimation models and experimenting with new keypoint-detection approaches. This dataset is ideal for quick experiments with Ultralytics YOLO26. For more details on dataset configuration, check out the dataset YAML file.

How do I train a YOLO26 model using the COCO8-Pose dataset in Ultralytics?

Load yolo26n-pose.pt and call model.train(data="coco8-pose.yaml", epochs=100, imgsz=640) — see the Train Example above for the full Python and CLI snippets, and the model Training page for a comprehensive list of arguments.

What are the benefits of using the COCO8-Pose dataset?

With 8 total images (4 train / 4 val), 1 class, a 17-keypoint schema, and a ~1 MB download, COCO8-Pose is small enough to manage in seconds yet diverse enough to sanity-check a pose training pipeline for errors before scaling up to the full COCO-Pose dataset. For more about its features and usage, see the Dataset Introduction section.

How does mosaicing benefit the YOLO26 training process using the COCO8-Pose dataset?

Mosaicing combines multiple images into one, increasing the variety of objects and scenes within each training batch, which helps the model generalize across object sizes, aspect ratios, and contexts. See the Sample Images and Annotations section for an example.

Where can I find the COCO8-Pose dataset YAML file and how do I use it?

The COCO8-Pose dataset YAML file can be found at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml. This file defines the dataset configuration, including paths, classes, and other relevant information. Use this file with the YOLO26 training scripts as mentioned in the Train Example section.

For more on keypoint models, see the Pose Estimation task docs.

COCO8-Pose Estimation Dataset