File size: 5,852 Bytes
ccc4a63 2f23893 ccc4a63 2f23893 fae9fa7 2f23893 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 |
---
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.
|