Datasets:
Tasks:
Sentence Similarity
Modalities:
Text
Formats:
json
Sub-tasks:
semantic-similarity-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
metadata
license: mit
language:
- en
paperswithcode_id: embedding-data/coco_captions
pretty_name: coco_captions
Dataset Card for "coco_captions"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://cocodataset.org/#home
- Repository: https://github.com/cocodataset/cocodataset.github.io
- Paper: More Information Needed
- Point of Contact: [email protected]
- Size of downloaded dataset files:
- Size of the generated dataset:
- Total amount of disk used: 6.32 MB
Dataset Summary
COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features:
- Object segmentation
- Recognition in context
- Superpixel stuff segmentation
- 330K images (>200K labeled)
- 1.5 million object instances
- 80 object categories
- 91 stuff categories
- 5 captions per image
- 250,000 people with keypoints
Disclaimer: The team releasing COCO did not upload the dataset to the Hub and did not write a dataset card. These steps were done by the Hugging Face team.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
Data Splits
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
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
The annotations in this dataset along with this website belong to the COCO Consortium and are licensed under a Creative Commons Attribution 4.0 License
Citation Information
Contributions
Thanks to:
- Tsung-Yi Lin - Google Brain
- Genevieve Patterson - MSR, Trash TV
- Matteo R. - Ronchi Caltech
- Yin Cui - Google
- Michael Maire - TTI-Chicago
- Serge Belongie - Cornell Tech
- Lubomir Bourdev - WaveOne, Inc.
- Ross Girshick - FAIR
- James Hays - Georgia Tech
- Pietro Perona - Caltech
- Deva Ramanan - CMU
- Larry Zitnick - FAIR
- Piotr Dollár - FAIR
for adding this dataset.