Datasets:

RunsenXu
/

MMSI-Bench

---
language:
- en
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- question-answering
- visual-question-answering
- multiple-choice
pretty_name: MMSI-Bench
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: images
    sequence: image
  - name: question_type
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: thought
    dtype: string
  - name: mean_normed_duration_seconds
    dtype: float64
  - name: difficulty
    dtype:
      class_label:
        names:
          '0': easy
          '1': medium
          '2': hard
  splits:
    - name: test
      num_examples: 1000

configs:
  - config_name: default
    data_files:
      - split: test
        path: MMSI_Bench.parquet
---

# MMSI-Bench
This repo contains evaluation code for the paper "MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence" 

[**🌐 Homepage**](https://runsenxu.com/projects/MMSI_Bench/) | [**🤗 Dataset**](https://huggingface.co/datasets/RunsenXu/MMSI-Bench) | [**📑 Paper**](https://arxiv.org/pdf/2505.23764) | [**💻 Code**](https://github.com/OpenRobotLab/MMSI-Bench) | [**📖 arXiv**](https://arxiv.org/abs/2505.23764)



## 🔔News
  **🔥[2025-10-23]: We added the normalized human response time for each MMSI-Bench sample and its difficulty level to our dataset on Hugging Face.**

  **🔥[2025-06-18]: MMSI-Bench has been supported in the [LMMs-Eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) repository.**

  **✨[2025-06-11]: MMSI-Bench was used for evaluation in the experiments of [VILASR](https://arxiv.org/abs/2506.09965).**

  **🔥[2025-06-9]: MMSI-Bench has been supported in the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) repository.**

  **🔥[2025-05-30]: We released the ArXiv paper.**

## Load Dataset
```
from datasets import load_dataset

mmsi_bench = load_dataset("RunsenXu/MMSI-Bench")
print(mmsi_bench)
```

## After downloading the parquet file, read each record, decode images from binary, and save them as JPG files.
```
import pandas as pd
import os

df = pd.read_parquet('MMSI_Bench.parquet')

output_dir = './images'
os.makedirs(output_dir, exist_ok=True)

for idx, row in df.iterrows():
    id_val = row['id']
    images = row['images']  
    question_type = row['question_type']
    question = row['question']
    answer = row['answer']
    thought = row['thought']

    image_paths = []
    if images is not None:
        for n, img_data in enumerate(images):
            image_path = f"{output_dir}/{id_val}_{n}.jpg"
            with open(image_path, "wb") as f:
                f.write(img_data)
            image_paths.append(image_path)
    else:
        image_paths = []

    print(f"id: {id_val}")
    print(f"images: {image_paths}")
    print(f"question_type: {question_type}")
    print(f"question: {question}")
    print(f"answer: {answer}")
    print(f"thought: {thought}")
    print("-" * 50)
```

## Evaluation
Please refer to the [evaluation guidelines](https://github.com/open-compass/VLMEvalKit/blob/main/docs/en/Quickstart.md) of [VLMEvalKit](https://github.com/open-compass/VLMEvalKit)
 
<!-- <img src="assets/radar_v1.png" width="400" /> -->

## 🏆 MMSI-Bench Leaderboard

| Model                        | Avg. (%) | Type         |
|------------------------------|:--------:|:-------------|
| 🥇 **Human Level**           | 97.2     | Baseline     |
| 🥈 o3                        | 41.0     | Proprietary  |
| 🥉 GPT-4.5                   | 40.3     | Proprietary  |
| Gemini-2.5-Pro--Thinking     | 37.0     | Proprietary  |
| Gemini-2.5-Pro               | 36.9     | Proprietary  |
| Doubao-1.5-pro               | 33.0     | Proprietary  |
| GPT-4.1                      | 30.9     | Proprietary  |
| Qwen2.5-VL-72B               | 30.7     | Open-source  |
| NVILA-15B                    | 30.5     | Open-source  |
| GPT-4o                       | 30.3     | Proprietary  |
| Claude-3.7-Sonnet--Thinking  | 30.2     | Proprietary  |
| Seed1.5-VL                   | 29.7     | Proprietary  |
| InternVL2.5-2B               | 29.0     | Open-source  |
| InternVL2.5-8B               | 28.7     | Open-source  |
| DeepSeek-VL2-Small           | 28.6     | Open-source  |
| InternVL3-78B                | 28.5     | Open-source  |
| InternVL2.5-78B              | 28.5     | Open-source  |
| LLaVA-OneVision-72B          | 28.4     | Open-source  |
| NVILA-8B                     | 28.1     | Open-source  |
| InternVL2.5-26B              | 28.0     | Open-source  |
| DeepSeek-VL2                 | 27.1     | Open-source  |
| InternVL3-1B                 | 27.0     | Open-source  |
| InternVL3-9B                 | 26.7     | Open-source  |
| Qwen2.5-VL-3B                | 26.5     | Open-source  |
| InternVL2.5-1B               | 26.1     | Open-source  |
| InternVL2.5-4B               | 26.3     | Open-source  |
| Qwen2.5-VL-7B                | 25.9     | Open-source  |
| InternVL3-8B                 | 25.7     | Open-source  |
| Llama-3.2-11B-Vision         | 25.4     | Open-source  |
| InternVL3-2B                 | 25.3     | Open-source  |
| 🃏 **Random Guessing**        | 25.0     | Baseline     |
| LLaVA-OneVision-7B           | 24.5     | Open-source  |
| DeepSeek-VL2-Tiny            | 24.0     | Open-source  |
| Blind GPT-4o                 | 22.7     | Baseline     |

## Acknowledgment
MMSI-Bench makes use of data from existing image datasets: [ScanNet](http://www.scan-net.org/), [nuScenes](https://www.nuscenes.org/), [Matterport3D](https://niessner.github.io/Matterport/), [Ego4D](https://ego4d-data.org/), [AgiBot-World](https://agibot-world.cn/), [DTU](https://roboimagedata.compute.dtu.dk/?page_id=36), [DAVIS-2017](https://davischallenge.org/) ,and [Waymo](https://waymo.com/open/). We thank these teams for their open-source contributions.

## Contact
- Sihan Yang: sihany077@gmail.com
- Runsen Xu:  runsxu@gmail.com

## Citation
```bibtex
@article{yang2025mmsi,
  title={MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence},
  author={Yang, Sihan and Xu, Runsen and Xie, Yiman and Yang, Sizhe and Li, Mo and Lin, Jingli and Zhu, Chenming and Chen, Xiaochen and Duan, Haodong and Yue, Xiangyu and Lin, Dahua and Wang, Tai and Pang, Jiangmiao},
  journal={arXiv preprint arXiv:2505.23764},
  year={2025}
}
```