README.md

metadata

license: cc-by-4.0
size_categories:
  - n<1K
task_categories:
  - image-to-text
language:
  - en
pretty_name: COCO Detection

Dataset Card for "COCO Detection"

Quick Start

Usage

>>> from datasets.load import load_dataset

>>> dataset = load_dataset('whyen-wang/coco_detection')
>>> 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):
        inst_rles = examples.pop('inst.rles')
        annotation = []
        for i in inst_rles:
            inst_rles = [
                {'size': size, 'counts': counts}
                for size, counts in zip(i['size'], i['counts'])
            ]
            annotation.append(maskUtils.decode(inst_rles))
        examples['annotation'] = annotation
        return examples

>>> def visualize(example, names, colors):
        image = np.array(example['image'])
        bboxes = np.array(example['bboxes']).round().astype(int)
        bboxes[:, 2:] += bboxes[:, :2]
        categories = example['categories']
        masks = example['annotation']
        n = len(bboxes)
        for i in range(n):
            c = categories[i]
            color, name = colors[c], names[c]
            cv2.rectangle(image, bboxes[i, :2], bboxes[i, 2:], color.tolist(), 2)
            cv2.putText(
                image, name, bboxes[i, :2], cv2.FONT_HERSHEY_SIMPLEX,
                1, color.tolist(), 2, cv2.LINE_AA, False
            )
            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((80, 3), np.uint8) * 255
>>> colors[:, 0] = np.linspace(0, 255, 80)
>>> colors = cv2.cvtColor(colors[None], cv2.COLOR_HSV2RGB)[0]

>>> example = dataset['train'][500]
>>> Image.fromarray(example)

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://cocodataset.org/
• Repository: None
• Paper: Microsoft COCO: Common Objects in Context
• Leaderboard: Papers with Code
• Point of Contact: None

Dataset Summary

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

Supported Tasks and Leaderboards

Object Detection Image Segmentation

Languages

en

Dataset Structure

Data Instances

An example looks as follows.

{
    "image": PIL.Image(mode="RGB"),
    "captions": [
        "Closeup of bins of food that include broccoli and bread.",
        "A meal is presented in brightly colored plastic trays.",
        "there are containers filled with different kinds of foods",
        "Colorful dishes holding meat, vegetables, fruit, and bread.",
        "A bunch of trays that have different food."
    ]
}

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.