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MMSI_Bench.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c762def08ff9875455672f1ace2c44a9705b963d2e8f806b186a250399dc9017
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README.md

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+# 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**] | [**💻 Code**](https://github.com/OpenRobotLab/MMSI_Bench/tree/main) | [**📖 arXiv**] | 
+
+
+
+## 🔔News
+  **🔥[2025-05-31]: 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
+
+vsi_bench = load_dataset("RunsenXu/MMSI-Bench")
+print(dataset)
+```
+
+
+## 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  |
+| Qwen2.5-VL-72B               | 30.7     | Open-source  |
+| NVILA-15B                    | 30.5     | Open-source  |
+| GPT-4.1                      | 30.9     | Proprietary  |
+| GPT-4o                       | 30.3     | Proprietary  |
+| Claude-3.7-Sonnet--Thinking  | 30.2     | Proprietary  |
+| Seed1.5-VL                   | 29.7     | Proprietary  |
+| DeepSeek-VL2-Small           | 28.6     | Open-source  |
+| InternVL2.5-8B               | 28.7     | Open-source  |
+| InternVL3-78B                | 28.5     | Open-source  |
+| InternVL2.5-78B              | 28.5     | Open-source  |
+| LLaVA-OneVision-72B          | 28.4     | Open-source  |
+| InternVL2.5-2B               | 29.0     | Open-source  |
+| InternVL2.5-26B              | 28.0     | Open-source  |
+| NVILA-8B                     | 28.1     | 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  |
+| InternVL3-8B                 | 25.7     | Open-source  |
+| Qwen2.5-VL-7B                | 25.9     | Open-source  |
+| InternVL3-2B                 | 25.3     | Open-source  |
+| Llama-3.2-11B-Vision         | 25.4     | 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: [email protected]
+- Runsen Xu:  [email protected]
+
+## Citation
+
+**BibTeX:**
+```bibtex
+
+```