COCO-style geographically unbiased image dataset for computer vision applications

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COCO-style geographically unbiased image dataset for computer vision applications
Bayet, Theophile, 2023, "COCO-style geographically unbiased image dataset for computer vision applications", https://doi.org/10.23708/N2UY4C, DataSuds, V1
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Description
There are already a lot of datasets linked to computer vision tasks (Imagenet, MS COCO, Pascal VOC, OpenImages, and numerous others), but they all suffer from important bias. One bias of significance for us is the data origin: most datasets are composed of data coming from developed countries.

Facing this situation, and the need of data with local context in developing countries, we try here to adapt common data generation process to inclusive data, meaning data drawn from locations and cultural context that are unseen or poorly represented.

We chose to replicate MS COCO's data generation process, as it is well documented and easy to implement. Data was collected from January to April 2022 through Flickr platform.

This dataset contains the results of our data collection process, as follows :
  • 23 text files containing comma separated URLs for each of the 23 geographic zones identified in the UN M49 norm. These text files are named according to the names of the geographic zones they cover.
  • Annotations for 400 images per geographic zones. Those annotations are COCO-style, and inform on the presence or absence of 91 categories of objects or concepts on the images. They are shared in a JSON format.
  • Licenses for the 400 annotations per geographic zones, based on the original licenses of the data and specified per image. Those licenses are shared under CSV format.
  • A document explaining the objectives and methodology underlying the data collection, also describing the different components of the dataset.
(2022-11-02)
Subject Computer and Information Science
Keyword Images, Computer Vision, Developing countries, Bias, Classification
Related Publication Theophile Bayet, Christophe Denis, Alassane Bah, Jean-Daniel Zucker. Distribution Shift nested in Web Scraping : Adapting MS COCO for Inclusive Data. ICML Workshop on Principles of Distribution Shift 2022, Jul 2022, Baltimore, United States. ⟨hal-03777066⟩ doi: hal-03777066
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COCO_World_URLs_Annotations.zip
COCO_World_URLs/ZIP Archive - 301.2 KB
Published Jan 13, 2023
10 Downloads
MD5: 7d00b44bfd43f82760b0d1500f661c5e
Annotations for 400 images per geographic zones, JSON format (COCO-style): presence or absence of 91 categories of objects or concepts on the images.
Data
COCO_World_URLs_Data.zip
COCO_World_URLs/ZIP Archive - 168.1 MB
Published Jan 13, 2023
9 Downloads
MD5: b3467eebbefb50907f880809a97fb8df
23 text files: comma separated URLs for each of the 23 geographic zones. Files are named according to geographic zones (UN M49 standard)
Data
COCO_World_URLs_Data_Management_Plan.pdf
COCO_World_URLs_Data_Management_Plan.pdf
COCO_World_URLs/Adobe PDF - 55.9 KB
Published Jan 13, 2023
11 Downloads
MD5: 808d3cb240f0b589e1ae55e03294151c
Data Management Plan of the project, in French (6 pages)
Documentation
COCO_World_URLs_Description.pdf
COCO_World_URLs_Description.pdf
COCO_World_URLs/Adobe PDF - 81.4 KB
Published Jan 13, 2023
9 Downloads
MD5: 0e8500cc3f9e2060ccc0bd6ca8c99986
Detailed description of the dataset, in English
Documentation
COCO_World_URLs_Licenses.zip
COCO_World_URLs/ZIP Archive - 213.9 KB
Published Jan 13, 2023
7 Downloads
MD5: 4288be6f0763234a9becf9bd6f10d2f4
Licenses for the 400 annotations per geographic zones (based on the original licenses specified per image), CSV format.
Data
COCO_World_URLs_Readme.txt
COCO_World_URLs/Plain Text - 1.7 KB
Published Jan 13, 2023
7 Downloads
MD5: adddf89525947a701c5725394ff712fb
Dataset citation and summary
Documentation
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