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Datasets:
SakanaAI
/
JA-VG-VQA-500

Tasks:
Visual Question Answering
Modalities:
Image
Text
Formats:
parquet
Languages:
Japanese
Size:
1K - 10K
ArXiv:
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1
JA-VG-VQA-500
File size: 3,170 Bytes 
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---
language:
- ja
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- visual-question-answering
dataset_info:
  features:
  - name: image_id
    dtype: int64
  - name: url
    dtype: string
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: coco_id
    dtype: float64
  - name: flickr_id
    dtype: float64
  - name: qas
    list:
    - name: a_objects
      sequence: 'null'
    - name: answer
      dtype: string
    - name: q_objects
      sequence: 'null'
    - name: qa_id
      dtype: int64
    - name: question
      dtype: string
  - name: image
    dtype: image
  splits:
  - name: test
    num_bytes: 73348776.0
    num_examples: 500
  download_size: 355977607
  dataset_size: 357916367.0
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# JA-VG-VQA-500


## Dataset Description 

**JA-VG-VQA-500** is a 500-sample subset of [Japanese Visual Genome VQA dataset](https://github.com/yahoojapan/ja-vg-vqa).
This dataset was used in the evaluation of [EvoVLM-JP-v1-7B](https://huggingface.co/SakanaAI/EvoVLM-JP-v1-7B). 
Please refer to our [report](https://arxiv.org/abs/2403.13187) and [blog](https://sakana.ai/evolutionary-model-merge/) for more details. 
We are grateful to the developers for making the dataset available under [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/legalcode). 

- [Visual Genome](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html)
- [Japanese Visual Genome VQA dataset](https://github.com/yahoojapan/ja-vg-vqa)

## Usage

Use the code below to get started with the dataset.

```python
from datasets import load_dataset

dataset = load_dataset("SakanaAI/JA-VG-VQA-500", split="test")
```

See [our GitHub repository](https://github.com/SakanaAI/evolutionary-model-merge) to evaluate Japanese VLMs. 

## Acknowledgement

We would like to thank the developers of the source datasets for their contributions and for making their work available.

## Citation

```bibtex
@article{Krishna2016VisualGC,
  title   = {Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations},
  author. = {Ranjay Krishna and Yuke Zhu and Oliver Groth and Justin Johnson and Kenji Hata and Joshua Kravitz and Stephanie Chen and Yannis Kalantidis and Li-Jia Li and David A. Shamma and Michael S. Bernstein and Li Fei-Fei},
  journal = {International Journal of Computer Vision},
  year.   = {2017},
  volume. = {123},
  pages.  = {32-73},
  URL     = {https://doi.org/10.1007/s11263-016-0981-7},
  doi     = {10.1007/s11263-016-0981-7}
}
```

```bibtex
@InProceedings{C18-1163,
  author    = "Shimizu, Nobuyuki and Rong, Na and Miyazaki, Takashi",
  title     = "Visual Question Answering Dataset for Bilingual Image Understanding: A Study of Cross-Lingual Transfer Using Attention Maps",
  booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
  year      = "2018",
  publisher = "Association for Computational Linguistics",
  pages     = "1918--1928",
  location  = "Santa Fe, New Mexico, USA",
  url       = "http://aclweb.org/anthology/C18-1163"
}
```