---
title: TimeTravel
canonical_url: "https://www.modelscope.cn/datasets/MBZUAI/TimeTravel"
md_url: "https://www.modelscope.cn/datasets/MBZUAI/TimeTravel.md"
repository: MBZUAI/TimeTravel
last_updated: 2025-03-17
license: "Apache License 2.0"
storage_size: "516 MB"
downloads: 2043
stars: 0
---

# TimeTravel

> TimeTravel - MBZUAI 在 ModelScope 开源的数据集。TimeTravel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts Sara Ghaboura   Ketan More   Retish Thawkar   Wafa Alghallabi   Omkar Thawakar   Fahad Shahbaz Khan…

MBZUAI/TimeTravel 是 ModelScope 魔搭社区上的数据集，存储大小 516 MB，采用 Apache License 2.0 许可。

- **Repository**: MBZUAI/TimeTravel
- **License**: Apache License 2.0
- **Storage size**: 516 MB
- **Downloads**: 2043
- **Stars**: 0
- **Last updated**: 2025-03-17

Source: https://www.modelscope.cn/datasets/MBZUAI/TimeTravel

---

<div align="center"  style="margin-top:10px;">
 <img src='asset/logo.png' align="left" width="7%" />
 </div>
 
 <div style="margin-top:50px;">
      <h1 style="font-size: 30px; margin: 0;"> TimeTravel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts</h1>
 </div>
   
 <div  align="center" style="margin-top:10px;"> 
    
  [Sara Ghaboura](https://huggingface.co/SLMLAH) <sup> * </sup> &nbsp;
  [Ketan More](https://github.com/ketanmore2002) <sup> * </sup> &nbsp;
  [Retish Thawkar](https://huggingface.co/SLMLAH) &nbsp;
  [Wafa Alghallabi](https://huggingface.co/SLMLAH) &nbsp;
  [Omkar Thawakar](https://omkarthawakar.github.io)  &nbsp;
  <br>
  [Fahad Shahbaz Khan](https://scholar.google.com/citations?hl=en&user=zvaeYnUAAAAJ) &nbsp;
  [Hisham Cholakkal](https://scholar.google.com/citations?hl=en&user=bZ3YBRcAAAAJ) &nbsp;
  [Salman Khan](https://scholar.google.com/citations?hl=en&user=M59O9lkAAAAJ) &nbsp;
  [Rao M. Anwer](https://scholar.google.com/citations?hl=en&user=_KlvMVoAAAAJ)<br>
  <em> <sup> *Equal Contribution  </sup> </em>
  <br>
  </div>
   <div  align="center" style="margin-top:10px;"> 
[![arXiv](https://img.shields.io/badge/arXiv-2502.14865-F6D769)](https://arxiv.org/abs/2502.14865)
[![Our Page](https://img.shields.io/badge/Visit-Our%20Page-E7DAB7?style=flat)](https://mbzuai-oryx.github.io/TimeTravel/)

     

## 🏛 TimeTravel Taxonomy and Diversity
<p align="left">
TimeTravel Taxonomy maps artifacts from 10 civilizations, 266 cultures, and 10k+ verified samples for AI-driven historical analysis.
</p>
<p align="center">
   <img src="asset/Intro.png" width="750px" height="400px" alt="tax"  style="margin-right: 2px";/>
</p>
</div>
<br>

## 🌟 Key Features
TimeTravel is the first large-scale, open-source benchmark designed to evaluate Large Multimodal Models (LMMs) on historical and cultural artifacts. It covers:

- **266** Cultural Groups across **10** Historical Regions
- **10,000+** Expert-Verified Artifact Samples
- **Multimodal Image-Text Dataset** for AI-driven historical research
- A **publicly available dataset** and evaluation framework to advance AI applications in **history and archaeology**.

<br>

## 🔄 TimeTravel Creation Pipeline
The TimeTravel dataset follows a structured pipeline to ensure the accuracy, completeness, and contextual richness of historical artifacts.<br>

<p align="center">
   <img src="asset/pipe_last.png" width="750px" height="150px" alt="pipeline"  style="margin-right: 2px";/>
</p> 

Our approach consists of four key phases:

- **Data Selection:** Curated 10,250 artifacts from museum collections, spanning 266 cultural groups, with expert validation to ensure historical accuracy and diversity.<br>
- **Data Cleaning:** Addressed missing or incomplete metadata (titles, dates, iconography) by cross-referencing museum archives and academic sources, ensuring data consistency.<br>
- **Generation & Verification:** Used GPT-4o to generate context-aware descriptions, which were refined and validated by historians and archaeologists for authenticity.<br>
- **Data Aggregation:** Standardized and structured dataset into image-text pairs, making it a valuable resource for AI-driven historical analysis and cultural heritage research.<br>

<br>

## 🏆 TimeTravel Evaluation
The table below showcases the performance comparison of various closed and open-source models on our proposed TimeTravel benchmark.

<div align="center";>
<h5>
<table>
    <thead>
        <tr style="background-color: #EBD9B3; color: white;">
            <th>Model</th>
            <th>BLEU</th>
            <th>METEOR</th>
            <th>ROUGE-L</th>
            <th>SPICE</th>
            <th>BERTScore</th>
            <th>LLM-Judge</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>GPT-4o-0806</td>
            <td><b>0.1758🏅</b></td>
            <td>0.2439</td>
            <td><b>0.1230🏅</b></td>
            <td><b>0.1035🏅</b></td>
            <td><b>0.8349🏅</b></td>
            <td><b>0.3013🏅</b></td>
        </tr>
        <tr>
            <td>Gemini-2.0-Flash</td>
            <td>0.1072</td>
            <td>0.2456</td>
            <td>0.0884</td>
            <td>0.0919</td>
            <td>0.8127</td>
            <td>0.2630</td>
        </tr>
        <tr>
            <td>Gemini-1.5-Pro</td>
            <td>0.1067</td>
            <td>0.2406</td>
            <td>0.0848</td>
            <td>0.0901</td>
            <td>0.8172</td>
            <td>0.2276</td>
        </tr>
        <tr>
            <td>GPT-4o-mini-0718</td>
            <td>0.1369</td>
            <td><b>0.2658🏅</b></td>
            <td>0.1027</td>
            <td>0.1001</td>
            <td>0.8283</td>
            <td>0.2492</td>
        </tr>
        <tr>
            <td>Llama-3.2-Vision-Inst</td>
            <td>0.1161</td>
            <td>0.2072</td>
            <td>0.1027</td>
            <td>0.0648</td>
            <td>0.8111</td>
            <td>0.1255</td>
        </tr>
        <tr>
            <td>Qwen-2.5-VL</td>
            <td>0.1155</td>
            <td>0.2648</td>
            <td>0.0887</td>
            <td>0.1002</td>
            <td>0.8198</td>
            <td>0.1792</td>
        </tr>
        <tr>
            <td>Llava-Next</td>
            <td>0.1118</td>
            <td>0.2340</td>
            <td>0.0961</td>
            <td>0.0799</td>
            <td>0.8246</td>
            <td>0.1161</td>
        </tr>
    </tbody>
</table>
</h5>
<p>


<div align="center";>
<h5>
<table>
    <thead>
        <tr style="background-color: #EBD9B3; color: white;">
            <th>Model</th>
            <th>India</th>
            <th>Roman Emp.</th>
            <th>China</th>
            <th>British Isles</th>
            <th>Iran</th>
            <th>Iraq</th>
            <th>Japan</th>
            <th>Cent. America</th>
            <th>Greece</th>
            <th>Egypt</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>GPT-4o-0806</td>
            <td><b>0.2491🏅</b></td>
            <td><b>0.4463🏅</b></td>
            <td><b>0.2491🏅</b></td>
            <td><b>0.1899🏅</b></td>
            <td><b>0.3522🏅</b></td>
            <td><b>0.3545🏅</b></td>
            <td><b>0.2228🏅</b></td>
            <td><b>0.3144🏅</b></td>
            <td><b>0.2757🏅</b></td>
            <td><b>0.3649🏅</b></td>
        </tr>
        <tr>
            <td>Gemini-2.0-Flash</td>
            <td>0.1859</td>
            <td>0.3358</td>
            <td>0.2059</td>
            <td>0.1556</td>
            <td>0.3376</td>
            <td>0.3071</td>
            <td>0.2000</td>
            <td>0.2677</td>
            <td>0.2582</td>
            <td>0.3602</td>
        </tr>
        <tr>
            <td>Gemini-1.5-Pro</td>
            <td>0.1118</td>
            <td>0.2632</td>
            <td>0.2139</td>
            <td>0.1545</td>
            <td>0.3320</td>
            <td>0.2587</td>
            <td>0.1871</td>
            <td>0.2708</td>
            <td>0.2088</td>
            <td>0.2908</td>
        </tr>
        <tr>
            <td>GPT-4o-mini-0718</td>
            <td>0.2311</td>
            <td>0.3612</td>
            <td>0.2207</td>
            <td>0.1866</td>
            <td>0.2991</td>
            <td>0.2632</td>
            <td>0.2087</td>
            <td>0.3195</td>
            <td>0.2101</td>
            <td>0.2501</td>
        </tr>
        <tr>
            <td>Llama-3.2-Vision-Inst</td>
            <td>0.0744</td>
            <td>0.1450</td>
            <td>0.1227</td>
            <td>0.0777</td>
            <td>0.2000</td>
            <td>0.1155</td>
            <td>0.1075</td>
            <td>0.1553</td>
            <td>0.1351</td>
            <td>0.1201</td>
        </tr>
        <tr>
            <td>Qwen-2.5-VL</td>
            <td>0.0888</td>
            <td>0.1578</td>
            <td>0.1192</td>
            <td>0.1713</td>
            <td>0.2515</td>
            <td>0.1576</td>
            <td>0.1771</td>
            <td>0.1442</td>
            <td>0.1442</td>
            <td>0.2660</td>
        </tr>
        <tr>
            <td>Llava-Next</td>
            <td>0.0788</td>
            <td>0.0961</td>
            <td>0.1455</td>
            <td>0.1091</td>
            <td>0.1464</td>
            <td>0.1194</td>
            <td>0.1353</td>
            <td>0.1917</td>
            <td>0.1111</td>
            <td>0.0709</td>
      </tr>
    </tbody>
</table>
</h5>
<p>


<div align="left"></div>

<br>

## 🖼 TimeTravel Examples
<p align="left">
The figure illustrates the cultural and material diversity of the TimeTravel dataset.
</p>
<p align="center">
   <img src="asset/fig0.png" width="1000px" height="250px" alt="tax"  style="margin-right: 2px";/>
</p>

<div align="left";>
<br>
<div class="tree-container">
    <h2>📂 TimeTravle Dataset Schema</h2>
    <div class="tree">
        <ul>
            <li><span class="leaf">📷 Image</span> (image)</li>
            <li><span class="leaf">🔹 id</span> (string)</li>
            <li><span class="leaf">📅 Production date</span> (string)</li>
            <li><span class="leaf">📍 Find spot</span> (string)</li>
            <li><span class="leaf">🔸 Materials</span> (string)</li>
            <li><span class="leaf">🛠 Technique</span> (string)</li>
            <li><span class="leaf">📝 Inscription</span> (string)</li>
            <li><span class="leaf">🎭 Subjects</span> (string)</li>
            <li><span class="leaf">📛 Assoc name</span> (string)</li>
            <li><span class="leaf">🏛 Culture</span> (string)</li>
            <li><span class="leaf">📂 Section</span> (string)</li>
            <li><span class="leaf">🌍 Place</span> (string)</li>
            <li><span class="leaf">📝 description</span> (string)</li>
        </ul>
    </div>
</div>
</div>

<br>

## 📚 Citation
<p align="left">
If you use TimeTravle dataset in your research, please consider citing:
</p>

<div align="left">
  
```bibtex
@misc{ghaboura2025timetravelcomprehensivebenchmark,
      title={Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts}, 
      author={Sara Ghaboura and Ketan More and Ritesh Thawkar and Wafa Alghallabi and Omkar Thawakar and Fahad Shahbaz Khan and Hisham Cholakkal and Salman Khan and Rao Muhammad Anwer},
      year={2025},
      eprint={2502.14865},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.14865}, 
}
```

</div>
</div>
---