# COCO 2017 Pose

COCO 2017 Pose: 69,730 images with keypoints for 18 classes, including person, nose, left eye and right eye. Based on COCO 2017. Import it into a Pictograph workspace to export COCO or YOLO-Pose, or train a model on it. Published on Pictograph by Pictograph Research.

## Overview

- Type: public computer vision dataset
- Images: 69,730
- Classes: 18
- Annotation types: keypoint
- Tasks: Keypoint detection (https://pictograph.io/explore/task/keypoint-detection)
- Owner: Pictograph Research
- License: Custom License
- Source: COCO 2017 (https://cocodataset.org), created by COCO Consortium (Lin et al., ECCV 2014). Credit the original authors when you use this data.
- Stars: 2
- Page: https://pictograph.io/datasets/pictograph-research/coco-2017-pose
- Hugging Face: https://huggingface.co/datasets/pictograph/coco-2017-pose
- Export formats: Pictograph JSON, Darwin V7, COCO, YOLO, YOLO-OBB, YOLO-Pose, DOTA, Pascal VOC, CVAT, Datumaro, LabelMe, CSV

## License terms

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).

## Classes

- person
- 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

## Models trained on this dataset

- PG-COCO-Pose: https://pictograph.io/models/pictograph-research/pg-coco-pose

## About

# COCO 2017 Pose

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

**[Pictograph Research](/org/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

| Metric | Value |
| :--- | ---: |
| Images | 69,730 |
| Keypoints | 2,025,673 |
| Skeleton | person, 17 keypoints |
| Annotation types | keypoint |
| Folders | 3 |
| Storage | Standard |
| Visibility | Public |

## 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).

| # | Keypoint | Connects to |
| ---: | :--- | :--- |
| 0 | nose | left_eye, right_eye |
| 1 | left_eye | right_eye, nose, left_ear |
| 2 | right_eye | left_eye, nose, right_ear |
| 3 | left_ear | left_eye, left_shoulder |
| 4 | right_ear | right_eye, right_shoulder |
| 5 | left_shoulder | left_hip, right_shoulder, left_elbow, left_ear |
| 6 | right_shoulder | right_hip, left_shoulder, right_elbow, right_ear |
| 7 | left_elbow | left_shoulder, left_wrist |
| 8 | right_elbow | right_shoulder, right_wrist |
| 9 | left_wrist | left_elbow |
| 10 | right_wrist | right_elbow |
| 11 | left_hip | left_knee, right_hip, left_shoulder |
| 12 | right_hip | right_knee, left_hip, right_shoulder |
| 13 | left_knee | left_ankle, left_hip |
| 14 | right_knee | right_ankle, right_hip |
| 15 | left_ankle | left_knee |
| 16 | right_ankle | right_knee |

## What's in these images

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

| Tag | Category | Images |
| :--- | :--- | ---: |
| playing | Attributes | 18,766 |
| person | Objects | 14,448 |
| eating | Attributes | 8,352 |
| child | Objects | 6,196 |
| walking | Attributes | 5,466 |
| skiing | Attributes | 5,183 |
| ski | Objects | 5,167 |
| jumping | Attributes | 4,918 |
| crowd | Objects | 4,857 |
| skateboard | Objects | 4,176 |
| surfing | Attributes | 4,077 |
| sitting | Attributes | 3,905 |
| eye-level view | Attributes | 3,806 |
| working | Attributes | 3,603 |
| racket | Objects | 3,562 |

## Use this dataset

```python
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](https://pictograph.io/docs/api-reference/datasets) (shown above) or the CLI.
- [Export](https://pictograph.io/docs/export-conversion) to COCO, YOLO, Darwin V7, Pascal VOC, and more.
- [Train a model](https://pictograph.io/docs/guides/train) 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](https://cocodataset.org), created by COCO Consortium (Lin et al., ECCV 2014) and released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). Please credit the original authors when you use this data.

## Citation

```bibtex
@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}
}
```


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