Link to this sectionCOCO8-Pose Dataset#

Link to this sectionIntroduction#

Ultralytics COCO8-Pose is a small but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new 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 larger datasets.

Link to this sectionDataset Structure#

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

This dataset is intended for use with Ultralytics Platform and YOLO26.

Link to this sectionDataset 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.

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# COCO8-pose dataset (first 8 images from COCO train2017) by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/pose/coco8-pose
# Example usage: yolo train data=coco8-pose.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── coco8-pose ← downloads here (1 MB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: coco8-pose # dataset root dir
train: images/train # train images (relative to 'path') 4 images
val: images/val # val images (relative to 'path') 4 images
test: # test images (optional)

# Keypoints
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]

# Classes
names:
  0: person

# Keypoint names per class
kpt_names:
  0:
    - nose
    - left_eye
    - right_eye
    - left_ear
    - right_ear
    - left_shoulder
    - right_shoulder
    - left_elbow
    - right_elbow
    - left_wrist
    - right_wrist
    - left_hip
    - right_hip
    - left_knee
    - right_knee
    - left_ankle
    - right_ankle

# Download script/URL (optional)
download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-pose.zip

Link to this sectionUsage#

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.

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)

Link to this sectionSample 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.

The example showcases the variety and complexity of the images in the COCO8-Pose dataset and the benefits of using mosaicing during the training process.

Link to this sectionCitations and Acknowledgments#

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

@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.

Link to this sectionFAQ#

Link to this sectionWhat is the COCO8-Pose dataset, and how is it used with Ultralytics YOLO26?#

The COCO8-Pose dataset is a small, versatile pose detection 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 object detection models and experimenting with new detection approaches. This dataset is ideal for quick experiments with Ultralytics YOLO26. For more details on dataset configuration, check out the dataset YAML file.

Link to this sectionHow do I train a YOLO26 model using the COCO8-Pose dataset in Ultralytics?#

To train a YOLO26n-pose model on the COCO8-Pose dataset for 100 epochs with an image size of 640, follow these examples:

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n-pose.pt")

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

For a comprehensive list of training arguments, refer to the model Training page.

Link to this sectionWhat are the benefits of using the COCO8-Pose dataset?#

The COCO8-Pose dataset offers several benefits:

• Compact Size: With only 8 images, it is easy to manage and perfect for quick experiments.
• Diverse Data: Despite its small size, it includes a variety of scenes, useful for thorough pipeline testing.
• Error Debugging: Ideal for identifying training errors and performing sanity checks before scaling up to larger datasets.

For more about its features and usage, see the Dataset Introduction section.

Link to this sectionHow does mosaicing benefit the YOLO26 training process using the COCO8-Pose dataset?#

Mosaicing, demonstrated in the sample images of the COCO8-Pose dataset, combines multiple images into one, increasing the variety of objects and scenes within each training batch. This technique helps improve the model's ability to generalize across various object sizes, aspect ratios, and contexts, ultimately enhancing model performance. See the Sample Images and Annotations section for example images.

Link to this sectionWhere 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 FAQs and detailed documentation, visit the Ultralytics Documentation.