Add initial dataset

README.md

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-license: apache-2.0
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+<div align="center">
+
+<p align="center">
+  <img src="assets/icon.png" width="220" alt="VL-RouterBench logo" />
+</p>
+
+### VL-RouterBench: A Benchmark for Vision–Language Model Routing
+
+[![arXiv](https://img.shields.io/badge/arXiv-2512.23562-red.svg)](https://arxiv.org/abs/2512.23562)
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-black.svg)](https://github.com/K1nght/VL-RouterBench)
+
+</div>
+
+## Overview
+
+We provides a clean, reproducible implementation of **VL-RouterBench**, a benchmark and toolkit for **routing across a pool of Vision–Language Models (VLMs)** under both **performance** and **performance–cost** objectives.
+
+<p align="center">
+  <img src="assets/pipeline.png" width="900" alt="VL-RouterBench pipeline" />
+</p>
+
+## 📦 Data Preparation
+
+VL-RouterBench converts [**VLMEvalKit**](https://github.com/open-compass/VLMEvalKit) outputs into a unified routing benchmark.
+
+To make data setup easier, we provide a pre-packaged archive **`vlm_router_data.tar.gz`** that contains everything needed to run the pipeline. You can download it from any of the following channels and extract it under the repo root:
+
+- **Google Drive**: [vlm_router_data.tar.gz](https://drive.google.com/file/d/1Va18MW8nJqvatxDXQDQq0t9NAqr93hMg/view?usp=sharing)
+- **Baidu Netdisk**: [vlm_router_data.tar.gz](https://pan.baidu.com/s/1D_P8YwY_E5kDA5dUB-ovng) (code: xb1s)
+- **Hugging Face**: [vlm_router_data.tar.gz](https://huggingface.co/datasets/KinghtH/VL-RouterBench)
+
+After downloading, extract it as:
+
+```bash
+tar -xzf vlm_router_data.tar.gz
+```
+
+By default, the pipeline expects the following directories (relative to repo root):
+
+```bash
+vlm_router_data/
+  VLMEvalKit_evaluation/   # required (for is_correct / evaluation)
+  VLMEvalKit_inference/    # required for accurate output-token counting (Step 2)
+  TSV_images/              # optional (for TSV-packed image datasets)
+```
+
+Notes:
+- **`VLMEvalKit_evaluation/`** is used by Step 1 & 4 (contains correctness signals).
+- **`VLMEvalKit_inference/`** is used by Step 2 (extract real model outputs to count output tokens).
+- **`TSV_images/`** is used by routers for training and inference to make routing decisions.
+
+## 📝 Citation
+
+If you find this benchmark useful, please cite:
+
+```bibtex
+@misc{huang2025vlrouterbenchbenchmarkvisionlanguagemodel,
+      title={VL-RouterBench: A Benchmark for Vision-Language Model Routing}, 
+      author={Zhehao Huang and Baijiong Lin and Jingyuan Zhang and Jingying Wang and Yuhang Liu and Ning Lu and Tao Li and Xiaolin Huang},
+      year={2025},
+      eprint={2512.23562},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2512.23562}, 
+}
+```
+
+---

vlm_router_data.tar.gz

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