COCO 2017 Pose

COCO 2017 Pose

A public keypoint dataset of 69,730 images with 2,025,673 keypoints on the person skeleton (17 keypoints).

Pictograph Research · a custom license · Public · Updated 2026-11-05

Overview

This dataset holds 69,730 images labelled with keypoints on the person skeleton (17 keypoints), one instance per object. The most common auto-detected tags are playing, person, eating, child, walking, skiing. It is ready to import into a workspace, export in a dozen formats, or train a model on.

At a glance

Skeleton

Each object is one instance: its keypoints share an instance_id and are named for their joint. A keypoint that is labelled but hidden carries occluded: true. The joints are in this order in every export (a COCO keypoint array follows it).

What's in these images

Tags auto-detected across the dataset, most common first.

Use this dataset

from pictograph import Client, Dataset

client = Client()  # reads PICTOGRAPH_API_KEY
dataset: Dataset = client.datasets.get("COCO 2017 Pose")
print(dataset.image_count, dataset.annotation_count)

• Import it into your own workspace with one click from its page.
• Pull it with the Pictograph SDK (shown above) or the CLI.
• Export to COCO, YOLO, Darwin V7, Pascal VOC, and more.
• Train a model directly from an export.

License

Non-commercial use only. Annotations: CC BY 4.0, the COCO Consortium. Images: the COCO Consortium does not own their copyright; each image comes from Flickr under its own license, recorded per image in COCO's annotation files, and most of them (68.9% of train2017) are Creative Commons NonCommercial. Use of the images must abide by the Flickr Terms of Use and each image's license. Cite Lin et al., Microsoft COCO: Common Objects in Context (ECCV 2014).

Source

The images and annotations come from COCO 2017, created by COCO Consortium (Lin et al., ECCV 2014) and released under CC-BY-4.0. Please credit the original authors when you use this data.

Citation

@inproceedings{lin2014coco,
  title = {Microsoft {COCO}: Common Objects in Context},
  author = {Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C. Lawrence},
  booktitle = {Computer Vision - ECCV 2014},
  pages = {740--755},
  publisher = {Springer},
  year = {2014}
}