metadata

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
      num_examples: 50
    - name: geometry__quadrilateral
      num_bytes: 210787
      num_examples: 50
    - name: geometry__length
      num_bytes: 271748
      num_examples: 50
    - name: geometry__angle
      num_bytes: 255692
      num_examples: 50
    - name: geometry__area
      num_bytes: 255062
      num_examples: 50
    - name: geometry__diameter_radius
      num_bytes: 269208
      num_examples: 50
    - name: chemistry__shape_single
      num_bytes: 1198593
      num_examples: 50
    - name: chemistry__shape_multi
      num_bytes: 1855862
      num_examples: 50
    - name: charts__extraction
      num_bytes: 3735234
      num_examples: 50
    - name: charts__intersection
      num_bytes: 2896121
      num_examples: 50
  download_size: 8276769
  dataset_size: 11191196
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-*
      - split: geometry__area
        path: data/geometry__area-*
      - split: geometry__diameter_radius
        path: data/geometry__diameter_radius-*
      - split: chemistry__shape_single
        path: data/chemistry__shape_single-*
      - split: chemistry__shape_multi
        path: data/chemistry__shape_multi-*
      - split: charts__extraction
        path: data/charts__extraction-*
      - split: charts__intersection
        path: data/charts__intersection-*

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:
- VisOnlyQA is available at VLMEvalKit 🔥🔥🔥
  - VisOnlyQA in VLMEvalKit is different from the original one. Refer to this section for details.
- Hugging Face
  - Eval-Real: https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Real
  - Eval-Synthetic: https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Eval_Synthetic
  - Train: https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Train
Code: https://github.com/psunlpgroup/VisOnlyQA

@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},
    journal={arXiv preprint arXiv:2412.00947}
}

Dataset

VisOnlyQA is provided in two formats: VLMEvalKit and Hugging Face Dataset. You can use either of them to evaluate your models and report the results in your papers. However, when you report the results, please explicitly mention which version of the dataset you used because the two versions are different.

Examples

VLMEvalKit

VLMEvalKit provides one-command evaluation. However, VLMEvalKit is not designed to reproduce the results in the paper. We welcome using it to report the results on VisOnlyQA in your papers, but please explicitly mention that you used VLMEvalKit.

The major differences are:

VisOnlyQA on VLMEvalKit does not include the chemistry__shape_multi split
VLMEvalKit uses different prompts and postprocessing.

Refer to this document for the installation and setup of VLMEvalKit. After setting up the environment, you can evaluate any supported models on VisOnlyQA with the following command (this example is for InternVL2-26B).

python run.py --data VisOnlyQA-VLMEvalKit --model InternVL2-26B

Hugging Face Dataset

The original VisOnlyQA dataset is provided in Hugging Face Dataset. If you want to reproduce the results in our paper, please use this version and code in the GitHub repository.

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
- 700 instances for questions on synthetic figures
Train: https://huggingface.co/datasets/ryokamoi/VisOnlyQA_Train
- 70,000 instances for training (synthetic figures)

dataset folder of the GitHub repository includes identical datasets, except for the training data.

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

License

Please refer to LICENSE.md.

Contact

If you have any questions, feel free to open an issue or reach out directly to Ryo Kamoi (ryokamoi@psu.edu).