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--- |
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language: |
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- en |
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license: cc-by-4.0 |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- question-answering |
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- visual-question-answering |
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- multiple-choice |
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pretty_name: MMSI-Bench |
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dataset_info: |
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features: |
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- name: id |
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dtype: int64 |
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- name: images |
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sequence: image |
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- name: question_type |
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dtype: string |
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- name: question |
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dtype: string |
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- name: answer |
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dtype: string |
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- name: thought |
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dtype: string |
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splits: |
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- name: test |
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num_examples: 1000 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: MMSI_Bench.parquet |
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--- |
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# MMSI-Bench |
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This repo contains evaluation code for the paper "[MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence]" |
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[**🌐 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) |
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## 🔔News |
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<!-- **🔥[2025-05-31]: MMSI-Bench has been supported in the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) repository.** --> |
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**🔥[2025-05-30]: We released the ArXiv paper.** |
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## Load Dataset |
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``` |
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from datasets import load_dataset |
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mmsi_bench = load_dataset("RunsenXu/MMSI-Bench") |
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print(dataset) |
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``` |
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## Evaluation |
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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) |
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<!-- <img src="assets/radar_v1.png" width="400" /> --> |
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## 🏆 MMSI-Bench Leaderboard |
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| Model | Avg. (%) | Type | |
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|------------------------------|:--------:|:-------------| |
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| 🥇 **Human Level** | 97.2 | Baseline | |
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| 🥈 o3 | 41.0 | Proprietary | |
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| 🥉 GPT-4.5 | 40.3 | Proprietary | |
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| Gemini-2.5-Pro--Thinking | 37.0 | Proprietary | |
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| Gemini-2.5-Pro | 36.9 | Proprietary | |
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| Doubao-1.5-pro | 33.0 | Proprietary | |
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| GPT-4.1 | 30.9 | Proprietary | |
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| Qwen2.5-VL-72B | 30.7 | Open-source | |
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| NVILA-15B | 30.5 | Open-source | |
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| GPT-4o | 30.3 | Proprietary | |
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| Claude-3.7-Sonnet--Thinking | 30.2 | Proprietary | |
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| Seed1.5-VL | 29.7 | Proprietary | |
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| InternVL2.5-2B | 29.0 | Open-source | |
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| InternVL2.5-8B | 28.7 | Open-source | |
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| DeepSeek-VL2-Small | 28.6 | Open-source | |
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| InternVL3-78B | 28.5 | Open-source | |
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| InternVL2.5-78B | 28.5 | Open-source | |
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| LLaVA-OneVision-72B | 28.4 | Open-source | |
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| NVILA-8B | 28.1 | Open-source | |
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| InternVL2.5-26B | 28.0 | Open-source | |
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| DeepSeek-VL2 | 27.1 | Open-source | |
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| InternVL3-1B | 27.0 | Open-source | |
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| InternVL3-9B | 26.7 | Open-source | |
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| Qwen2.5-VL-3B | 26.5 | Open-source | |
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| InternVL2.5-1B | 26.1 | Open-source | |
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| InternVL2.5-4B | 26.3 | Open-source | |
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| Qwen2.5-VL-7B | 25.9 | Open-source | |
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| InternVL3-8B | 25.7 | Open-source | |
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| Llama-3.2-11B-Vision | 25.4 | Open-source | |
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| InternVL3-2B | 25.3 | Open-source | |
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| 🃏 **Random Guessing** | 25.0 | Baseline | |
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| LLaVA-OneVision-7B | 24.5 | Open-source | |
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| DeepSeek-VL2-Tiny | 24.0 | Open-source | |
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| Blind GPT-4o | 22.7 | Baseline | |
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## Acknowledgment |
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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. |
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## Contact |
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- Sihan Yang: sihany077@gmail.com |
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- Runsen Xu: runsxu@gmail.com |
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## Citation |
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```bibtex |
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@article{yang2025mmsi, |
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title={MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence}, |
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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}, |
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journal={arXiv preprint arXiv:2505.23764}, |
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year={2025} |
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} |
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``` |
