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Dataset Card for "COCO Stuff"
Quick Start
Usage
>>> from datasets.load import load_dataset
>>> dataset = load_dataset('whyen-wang/coco_stuff')
>>> example = dataset['train'][500]
>>> print(example)
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x426>,
'bboxes': [
[192.4199981689453, 220.17999267578125,
129.22999572753906, 148.3800048828125],
[76.94000244140625, 146.6300048828125,
104.55000305175781, 109.33000183105469],
[302.8800048828125, 115.2699966430664,
99.11000061035156, 119.2699966430664],
[0.0, 0.800000011920929,
592.5700073242188, 420.25]],
'categories': [46, 46, 46, 55],
'inst.rles': {
'size': [[426, 640], [426, 640], [426, 640], [426, 640]],
'counts': [
'gU`2b0d;...', 'RXP16m<=...', ']Xn34S=4...', 'n:U2o8W2...'
]}}
Visualization
>>> import cv2
>>> import numpy as np
>>> from PIL import Image
>>> def transforms(examples):
sem_rles = examples.pop('sem.rles')
annotation = []
for i in sem_rles:
sem_rles = [
{'size': size, 'counts': counts}
for size, counts in zip(i['size'], i['counts'])
]
annotation.append(maskUtils.decode(sem_rles))
examples['annotation'] = annotation
return examples
>>> def visualize(example, colors):
image = np.array(example['image'])
categories = example['categories']
masks = example['annotation']
n = len(categories)
for i in range(n):
c = categories[i]
color = colors[c]
image[masks[..., i] == 1] = image[masks[..., i] == 1] // 2 + color // 2
return image
>>> dataset.set_transform(transforms)
>>> names = dataset['train'].features['categories'].feature.names
>>> colors = np.ones((92, 3), np.uint8) * 255
>>> colors[:, 0] = np.linspace(0, 255, 92)
>>> colors = cv2.cvtColor(colors[None], cv2.COLOR_HSV2RGB)[0]
>>> example = dataset['train'][500]
>>> Image.fromarray(visualize(example, colors))
Dataset Summary
COCO is a large-scale object detection, segmentation, and captioning dataset.
Supported Tasks and Leaderboards
Languages
en
Dataset Structure
Data Instances
An example looks as follows.
{
"image": PIL.Image(mode="RGB"),
"categories": [29, 73, 91],
"sem.rles": {
"size": [[426, 640], [426, 640], [426, 640]],
"counts": [
"S=7T=O1O0000000000...",
"c1Y3P:10O1O010O100...",
"n:U2o8W2N1O1O2M2N2..."
]
}
}
Data Fields
[More Information Needed]
Data Splits
| name | train | validation |
|---|---|---|
| default | 118,287 | 5,000 |
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 for adding this dataset.
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