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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:
```text
https://visualgenome.org/
```
Dataset page:
```text
https://homes.cs.washington.edu/~ranjay/visualgenome/
```
## License
Visual Genome is licensed under the Creative Commons Attribution 4.0 International License:
```text
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:
```bibtex
@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. |