README.md

---
license: apache-2.0
task_categories:
- visual-question-answering
language:
- en
pretty_name: BenchLMM
size_categories:
- n<1K
---

# Dataset Card for BenchLMM

BenchLMM is a benchmarking dataset focusing on the cross-style visual capability of large multimodal models. It evaluates these models' performance in various visual contexts.

## Dataset Details

### Dataset Description

- **Curated by:** Rizhao Cai, Zirui Song, Dayan Guan, Zhenhao Chen, Xing Luo, Chenyu Yi, and Alex Kot.
- **Funded by :** Supported in part by the Rapid-Rich Object Search (ROSE) Lab of Nanyang Technological University and the NTU-PKU Joint Research Institute.
- **Shared by :** AIFEG.
- **Language(s) (NLP):** English.
- **License:** Apache-2.0.

### Dataset Sources 

- **Repository:** [GitHub - AIFEG/BenchLMM](https://github.com/AIFEG/BenchLMM)
- **Paper :** Cai, R., Song, Z., Guan, D., et al. (2023). BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models. arXiv:2312.02896.

## Uses

### Direct Use

The dataset can be used to benchmark large multimodal models, especially focusing on their capability to interpret and respond to different visual styles.

## Dataset Structure

- **Directory Structure:**
  - `baseline/`: Baseline code for LLaVA and InstructBLIP.
  - `evaluate/`: Python code for model evaluation.
  - `evaluate_results/`: Evaluation results of baseline models.
  - `jsonl/`: JSONL files with questions, image locations, and answers.

## Dataset Creation

### Curation Rationale

Developed to assess large multimodal models' performance in diverse visual contexts, helping to understand their capabilities and limitations.

### Source Data

#### Data Collection and Processing

The dataset consists of various visual questions and corresponding answers, structured to evaluate multimodal model performance.

## Bias, Risks, and Limitations

Users should consider the specific visual contexts and question types included in the dataset when interpreting model performance.

## Citation 

**BibTeX:**
@misc{cai2023benchlmm,
title={BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models},
author={Rizhao Cai and Zirui Song and Dayan Guan and Zhenhao Chen and Xing Luo and Chenyu Yi and Alex Kot},
year={2023},
eprint={2312.02896},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

**APA:**
Cai, R., Song, Z., Guan, D., Chen, Z., Luo, X., Yi, C., & Kot, A. (2023). BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models. arXiv preprint arXiv:2312.02896.

## Acknowledgements

This research is supported in part by the Rapid-Rich Object Search (ROSE) Lab of Nanyang Technological University and the NTU-PKU Joint Research Institute.