README.md

---
license: cc-by-4.0
size_categories:
- n<1K
task_categories:
- object-detection
language:
- en
pretty_name: COCO Keypoints
---

# Dataset Card for "COCO Keypoints"

## Quick Start
### Usage
```python
>>> from datasets.load import load_dataset

>>> dataset = load_dataset('whyen-wang/coco_keypoints')
>>> example = dataset['train'][0]
>>> print(example)
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x360>,
 'bboxes': [
   [339.8800048828125, 22.15999984741211,
    153.8800048828125, 300.7300109863281],
   [471.6400146484375, 172.82000732421875,
    35.91999816894531, 48.099998474121094]],
 'keypoints': [[
    [368, 61, 1], [369, 52, 2], [0, 0, 0], [382, 48, 2], [0, 0, 0],
    [368, 84, 2], [435, 81, 2], [362, 125, 2], [446, 125, 2], [360, 153, 2],
    [0, 0, 0], [397, 167, 1], [439, 166, 1], [369, 193, 2], [461, 234, 2],
    [361, 246, 2], [474, 287, 2]
   ], [[...]]
 ]}
```

### Visualization
```python
>>> import cv2
>>> import numpy as np
>>> from PIL import Image

>>> def visualize(example):
        image = np.array(example['image'])
        bboxes = np.array(example['bboxes']).round().astype(int)
        bboxes[:, 2:] += bboxes[:, :2]
        keypoints = example['keypoints']
        n = len(bboxes)
        for i in range(n):
            color = (255, 0, 0)
            cv2.rectangle(image, bboxes[i, :2], bboxes[i, 2:], color, 2)
            ks = keypoints[i]
            for k in ks:
                if k[-1] == 2:
                    cv2.circle(
                        image, k[:2], 5, (0, 255, 0), 1
                    )
        return image

>>> Image.fromarray(visualize(example))
```

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://cocodataset.org/
- **Repository:** None
- **Paper:** [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312)
- **Leaderboard:** [Papers with Code](https://paperswithcode.com/dataset/coco)
- **Point of Contact:** None

### Dataset Summary

COCO is a large-scale object detection, segmentation, and captioning dataset.

### Supported Tasks and Leaderboards

[Object Detection](https://huggingface.co/tasks/object-detection)

### Languages

en

## Dataset Structure

### Data Instances

An example looks as follows.

```
{
    "image": PIL.Image(mode="RGB"),
    "bboxes": [
        [339.8800048828125, 22.15999984741211,
            153.8800048828125, 300.7300109863281],
        [471.6400146484375, 172.82000732421875,
            35.91999816894531, 48.099998474121094]],
    "keypoints": [[
        [368, 61, 1], [369, 52, 2], [0, 0, 0], [382, 48, 2], [0, 0, 0],
        [368, 84, 2], [435, 81, 2], [362, 125, 2], [446, 125, 2], [360, 153, 2],
        [0, 0, 0], [397, 167, 1], [439, 166, 1], [369, 193, 2], [461, 234, 2],
        [361, 246, 2], [474, 287, 2]
      ], [[...]]
    ]
}
```

### Data Fields

[More Information Needed]

### Data Splits

|    name |  train | validation |
| ------- | -----: | ---------: |
| default | 64,115 |      2,693 |

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

Creative Commons Attribution 4.0 License

### Citation Information

```
@article{cocodataset,
  author    = {Tsung{-}Yi Lin and Michael Maire and Serge J. Belongie and Lubomir D. Bourdev and Ross B. Girshick and James Hays and Pietro Perona and Deva Ramanan and Piotr Doll{'{a} }r and C. Lawrence Zitnick},
  title     = {Microsoft {COCO:} Common Objects in Context},
  journal   = {CoRR},
  volume    = {abs/1405.0312},
  year      = {2014},
  url       = {http://arxiv.org/abs/1405.0312},
  archivePrefix = {arXiv},
  eprint    = {1405.0312},
  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

### Contributions

Thanks to [@github-whyen-wang](https://github.com/whyen-wang) for adding this dataset.