Enhance dataset card with detailed description, tags, and usage instructions (#3)

- Enhance dataset card with detailed description, tags, and usage instructions (53064a7b02be8a3b7597188d441a716b4d30e9c1)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -2,6 +2,116 @@
 license: mit
 task_categories:
 - image-text-to-text
+tags:
+- multimodal
+- benchmark
+- spatial-reasoning
+- indoor-scenes
 ---
 
-This repository contains the dataset for the paper [SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial Intelligence](https://huggingface.co/papers/2506.07966).
+This repository contains the dataset for the paper [SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial Intelligence](https://huggingface.co/papers/2506.07966).
+
+<div align="center">
+<h1><img src="https://raw.githubusercontent.com/Cuzyoung/SpaCE-10/main/assets/space-10-logo.png" width="8%"> SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial Intelligence</h1>
+</div>
+
+**GitHub Repository:** [https://github.com/Cuzyoung/SpaCE-10](https://github.com/Cuzyoung/SpaCE-10)
+
+---
+# 🧠 What is SpaCE-10?
+
+**SpaCE-10** is a **compositional spatial intelligence benchmark** for evaluating **Multimodal Large Language Models (MLLMs)** in indoor environments. Our contribution as follows:
+
+- 🧬 We define an **Atomic Capability Pool**, proposing 10 **atomic spatial capabilities.**
+- 🔗 Based on the composition of different atomic capabilities, we design **8 compositional QA types**.
+- 📈 SpaCE-10 benchmark contains 5,000+ QA pairs.
+- 🏠 All QA pairs come from 811 indoor scenes (ScanNet++, ScanNet, 3RScan, ARKitScene)
+- 🌍 SpaCE-10 spans both 2D and 3D MLLM evaluations and can be seamlessly adapted to MLLMs that accept 3D scan input.
+
+<div align="center">
+<br><br>
+<img src="https://raw.githubusercontent.com/Cuzyoung/SpaCE-10/main/assets/space-10-teaser.png" width="100%">
+<br><br>
+</div>
+
+---
+# 🔥🔥🔥 News
+- [2025/07/12] Adjust some QAs of Space-10 and update RemyxAI models' performance to leader board. 
+- [2025/06/11] Scans for 3D MLLMs and our manually collected 3D snapshots will be coming soon.
+- [2025/06/10] Evaluation code is released at followings.
+- [2025/06/09] We have released the benchmark for 2D MLLMs at [Hugging Face](https://huggingface.co/datasets/Cusyoung/SpaCE-10).
+- [2025/06/09] The paper of SpaCE-10 is released at [Arxiv](https://arxiv.org/abs/2506.07966v1)!
+---
+
+# Performance Leader Board - Single-Choice
+🎉 LLaVA-OneVision-72B achieves the Rank 1 in all tested models.
+
+🎉 GPT-4o achieves the best score in tested Close-Source models.
+
+A large gap still exists between human and models in compositional spatial intelligence.
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/Cuzyoung/SpaCE-10/main/assets/Perfomance_Leader_Board.png" width="100%">
+<br>
+</div>
+
+# Single-Choice vs. Double-Choice
+<div align="center">
+<img src="https://raw.githubusercontent.com/Cuzyoung/SpaCE-10/main/assets/single-double.png" width="100%">
+<br>
+</div>
+
+# Capability Score Ranking - Single-Choice
+<div align="center">
+<img src="https://raw.githubusercontent.com/Cuzyoung/SpaCE-10/main/assets/Capability_Score_Matrix.png" width="100%">
+<br>
+</div>
+
+# Environment
+The evaluation of SpaCE-10 is based on lmms-eval. Thus, we follow the environment settings of lmms-eval.
+```bash
+git clone https://github.com/Cuzyoung/SpaCE-10.git
+cd SpaCE-10
+uv venv dev --python=3.10
+source dev/bin/activate
+uv pip install -e .
+```
+
+# Evaluation
+Take InternVL2.5-8B as an example:
+```bash
+cd lmms-eval/run_bash
+bash internvl2.5-8b.sh
+```
+Notably, each time we test a new model, the corresponding environment of this model needs to be installed.
+
+---
+# Sample Usage
+
+You can load the dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Cusyoung/SpaCE-10")
+
+# To explore the dataset splits:
+print(dataset)
+
+# Example of accessing a split (assuming a 'train' split exists):
+# train_split = dataset["train"]
+# print(train_split[0])
+```
+
+---
+# Citation
+If you use this dataset, please cite the original paper:
+
+```bibtex
+@article{gong2025space10,
+  title={SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial Intelligence},
+  author={Ziyang Gong, Wenhao Li, Oliver Ma, Songyuan Li, Jiayi Ji, Xue Yang, Gen Luo, Junchi Yan, Rongrong Ji},
+  journal={arXiv preprint arXiv:2506.07966},
+  year={2025}
+}
+```