---
license: apache-2.0
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - name: id
    dtype: int32
  - name: image
    dtype: image
  - name: sensor_type
    dtype: string
  - name: question_type
    dtype: string
  - name: question
    dtype: string
  - name: question_query
    dtype: string
  - name: answer
    dtype: string
  splits:
  - name: train
    num_bytes: 1455392605.0
    num_examples: 6248
  download_size: 903353168
  dataset_size: 1455392605.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# SPARK (multi-vision Sensor Perception And Reasoning benchmarK)

<!-- Provide a quick summary of the dataset. -->

SPARK can reduce the fundamental multi-vision sensor information gap between images and multi-vision sensors. We generated 6,248 vision-language test samples automatically to investigate multi-vision sensory perception and multi-vision sensory reasoning on physical sensor knowledge proficiency across different formats, covering different types of sensor-related questions.

## Dataset Details


## Uses

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### Direct Use

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### Source Data


#### Data Collection and Processing

These instructions are built from five public datasets: [MS-COCO](https://arxiv.org/abs/1405.0312), [M3FD](https://arxiv.org/abs/2203.16220v1), [Dog&People](https://public.roboflow.com/object-detection/thermal-dogs-and-people), [RGB-D scene dataset](https://arxiv.org/abs/2110.11590), and [UNIFESP X-ray Body Part Classifier Competition dataset](https://www.kaggle.com/competitions/unifesp-x-ray-body-part-classifier). 


## Citation 

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

[More Information Needed]


## Contact

[SangYun Chung](https://sites.google.com/view/sang-yun-chung/profile): jelarum@kaist.ac.kr