license: cc-by-4.0
task_categories:
- image-to-text
- object-detection
- visual-question-answering
language:
- en
size_categories:
- 100K<n<1M
tags:
- visual-genome
- scene-graph
- region-descriptions
- visual-relationships
- attributes
- object-detection
- image-captioning
- vqa
pretty_name: Visual Genome Raw Data
Visual Genome Raw Data
This repository provides a convenient copy of the raw Visual Genome dataset files for easier access and downloading.
This is not an official Visual Genome release.
The original dataset was created and released by the Visual Genome authors.
Please refer to the official Visual Genome website and paper for authoritative information.
Dataset Summary
Visual Genome is a large-scale vision-language dataset with dense image annotations, including:
- Images
- Region descriptions
- Object annotations
- Attribute annotations
- Relationship annotations
- Scene graphs
- Visual question-answering annotations
- WordNet synset mappings
The dataset contains approximately:
- 108K images
- 5.4M region descriptions
- 1.7M visual question-answer pairs
- 3.8M object instances
- 2.8M attributes
- 2.3M relationships
Intended Use
This dataset can be used for research on:
- Image-text representation learning
- Image captioning
- Dense captioning
- Object detection
- Attribute recognition
- Visual relationship detection
- Scene graph generation
- Visual question answering
- Vision-language pretraining
Source
Original Visual Genome website:
https://visualgenome.org/
Dataset page:
https://homes.cs.washington.edu/~ranjay/visualgenome/
License
Visual Genome is licensed under the Creative Commons Attribution 4.0 International License:
CC BY 4.0
https://creativecommons.org/licenses/by/4.0/
Users should comply with the original license terms, including proper attribution to the Visual Genome authors.
Citation
If you use this dataset, please cite the original Visual Genome paper:
@article{krishna2017visualgenome,
title={Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations},
author={Krishna, Ranjay and Zhu, Yuke and Groth, Oliver and Johnson, Justin and Hata, Kenji and Kravitz, Joshua and Chen, Stephanie and Kalantidis, Yannis and Li, Li-Jia and Shamma, David A and Bernstein, Michael and Fei-Fei, Li},
journal={International Journal of Computer Vision},
year={2017}
}
Disclaimer
This repository is not affiliated with, endorsed by, or maintained by the original Visual Genome authors.
The data is provided as-is for convenience. Users are responsible for verifying the dataset version, file integrity, license terms, and suitability for their own use cases.