---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license: gpl-3.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- multiple-choice
- question-answering
- visual-question-answering
task_ids:
- multiple-choice-qa
- visual-question-answering
- multi-class-classification
tags:
- multi-modal-qa
- figure-qa
- vqa
- scientific-figure
- geometry-diagram
- chart
- chemistry
dataset_info:
  features:
  - name: image_path
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: prompt_reasoning
    dtype: string
  - name: prompt_no_reasoning
    dtype: string
  - name: image_category
    dtype: string
  - name: task_category
    dtype: string
  - name: question_type
    dtype: string
  - name: response_options
    sequence: string
  - name: source
    dtype: string
  - name: id
    dtype: string
  - name: decoded_image
    dtype: image
  splits:
  - name: geometry__triangle
    num_bytes: 242889.0
    num_examples: 50
  - name: geometry__quadrilateral
    num_bytes: 210787.0
    num_examples: 50
  - name: geometry__length
    num_bytes: 271748.0
    num_examples: 50
  - name: geometry__angle
    num_bytes: 255692.0
    num_examples: 50
  download_size: 607689
  dataset_size: 981116.0
configs:
- config_name: default
  data_files:
  - split: geometry__triangle
    path: data/geometry__triangle-*
  - split: geometry__quadrilateral
    path: data/geometry__quadrilateral-*
  - split: geometry__length
    path: data/geometry__length-*
  - split: geometry__angle
    path: data/geometry__angle-*
---
# VisOnlyQA

This repository contains the code and data for the paper "VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information".

VisOnlyQA is designed to evaluate the visual perception capability of large vision language models (LVLMs) on geometric information of scientific figures. The evaluation set includes 1,200 mlutiple choice questions in 12 visual perception tasks on 4 categories of scientific figures. We also provide a training dataset consisting of 70k instances.

* Datasets:
  * Eval-Real: [https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Real](https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Real)
  * Eval-Synthetic: [https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Synthetic](https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Synthetic)
  * Train: [https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Train](https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Train)
* Code: [https://github.com/psunlpgroup/VisOnlyQA](https://github.com/psunlpgroup/VisOnlyQA)

<p align="center">
<img src="readme_figures/accuracy_radar_chart.png" width="500">
</p>

```bibtex
@misc{kamoi2024visonlyqa,
    title={VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information}, 
    author={Ryo Kamoi and Yusen Zhang and Sarkar Snigdha Sarathi Das and Ranran Haoran Zhang and Rui Zhang},
    year={2024},
}
```

## Dataset

The dataset is provided in Hugging Face Dataset.

* Eval-Real: [https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Real](https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Real)
  * 500 instances for questions on figures in existing datasets (e.g., MathVista, MMMU, and CharXiv)
* Eval-Synthetic: [https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Synthetic](https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Synthetic)
  * 700 instances for questions on synthetic figures
* Train: [https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Train](https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Train)
  * 70,000 instances for training (synthetic figures)

[dataset](https://github.com/psunlpgroup/VisOnlyQA/tree/main/dataset) folder of the GitHub repository includes identical datasets, except for the training data.

### Examples

<p align="center">
<img src="readme_figures/examples.png" width="800">
</p>

### Usage

```python
from datasets import load_dataset

real_eval = load_dataset("ryokamoi/VisOnlyQA_Eval_Real")
real_synthetic = load_dataset("ryokamoi/VisOnlyQA_Eval_Synthetic")

# Splits
print(real_eval.keys())
# dict_keys(['geometry__triangle', 'geometry__quadrilateral', 'geometry__length', 'geometry__angle', 'geometry__area', 'geometry__diameter_radius', 'chemistry__shape_single', 'chemistry__shape_multi', 'charts__extraction', 'charts__intersection'])

print(real_synthetic.keys())
# dict_keys(['syntheticgeometry__triangle', 'syntheticgeometry__quadrilateral', 'syntheticgeometry__length', 'syntheticgeometry__angle', 'syntheticgeometry__area', '3d__size', '3d__angle'])

# Prompt
print(real_eval['geometry__triangle'][0]['prompt_no_reasoning'])
# There is no triangle ADP in the figure. True or False?

# A triangle is a polygon with three edges and three vertices, which are explicitly connected in the figure.

# Your response should only include the final answer (True, False). Do not include any reasoning or explanation in your response.

# Image
print(real_eval['geometry__triangle'][0]['decoded_image'])
# <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=103x165 at 0x7FB4F83236A0>

# Answer
print(real_eval['geometry__triangle'][0]['answer'])
# False
```

### Data Format

Each instance of VisOnlyQA dataset has the following attributes:

#### Features
* `decoded_image`: [PIL.Image] Input image
* `question`: [string] Question (without instruction)
* `prompt_reasoning`: [string] Prompt with intstruction to use chain-of-thought
* `prompt_no_reasoning`: [string] Prompt with intstruction **not** to use chain-of-thought
* `answer`: [string] Correct answer (e.g., `True`, `a`)

#### Metadata
* `image_path`: [string] Path to the image file
* `image_category`: [string] Category of the image (e.g., `geometry`, `chemistry`)
* `question_type`: [string] `single_answer` or `multiple answers`
* `task_category`: [string] Category of the task (e.g., `triangle`)
* `response_options`: [List[string]] Multiple choice options (e.g., `['True', 'False']`, `['a', 'b', 'c', 'd', 'e']`)
* `source`: [string] Source dataset
* `id`: [string] Unique ID

### Statistics

<p align="center">
<img src="readme_figures/stats.png" width="800">
</p>

## License

Please refer to [LICENSE.md](./LICENSE.md).

## Contact

If you have any questions, feel free to open an issue or reach out directly to [Ryo Kamoi](https://ryokamoi.github.io/) (ryokamoi@psu.edu).