Improve model card: Add project page link and evaluation section, update citation

README.md

@@ -18,6 +18,7 @@ tags:
 This repository contains the InfiGUI-G1-3B model from the paper **[InfiGUI-G1: Advancing GUI Grounding with Adaptive Exploration Policy Optimization](https://arxiv.org/abs/2508.05731)**.
 
 [![GitHub Repo](https://img.shields.io/badge/GitHub-Repo-181717?style=flat&logo=github&logoColor=white)](https://github.com/InfiXAI/InfiGUI-G1)
+[![Project Page](https://img.shields.io/badge/Project%20Page-Website-blue?style=flat)](https://osatlas.github.io/)
 
 ## Paper Abstract
 
@@ -217,7 +218,92 @@ On the widely-used ScreenSpot-V2 benchmark, which provides comprehensive coverag
   <img src="https://raw.githubusercontent.com/InfiXAI/InfiGUI-G1/main/assets/results_screenspot-v2.png" width="90%" alt="ScreenSpot-V2 Results">
 </div>
 
-## Citation Information
+## ⚙️ Evaluation
+
+This section provides instructions for reproducing the evaluation results reported in our paper.
+
+### 1. Getting Started
+
+Clone the repository and navigate to the project directory:
+
+```bash
+git clone https://github.com/InfiXAI/InfiGUI-G1.git
+cd InfiGUI-G1
+```
+
+### 2. Environment Setup
+
+The evaluation pipeline is built upon the [vLLM](https://github.com/vllm-project/vllm) library for efficient inference. For detailed installation guidance, please refer to the official vLLM repository. The specific versions used to obtain the results reported in our paper are as follows:
+
+-   **Python**: `3.10.12`
+-   **PyTorch**: `2.6.0`
+-   **Transformers**: `4.50.1`
+-   **vLLM**: `0.8.2`
+-   **CUDA**: `12.6`
+
+The reported results were obtained on a server equipped with 4 x NVIDIA H800 GPUs.
+
+### 3. Model Download
+
+Download the InfiGUI-G1 models from the Hugging Face Hub into the `./models` directory.
+
+```bash
+# Create a directory for models
+mkdir -p ./models
+
+# Download InfiGUI-G1-3B
+huggingface-cli download --resume-download InfiX-ai/InfiGUI-G1-3B --local-dir ./models/InfiGUI-G1-3B
+
+# Download InfiGUI-G1-7B
+huggingface-cli download --resume-download InfiX-ai/InfiGUI-G1-7B --local-dir ./models/InfiGUI-G1-7B
+```
+
+### 4. Dataset Download and Preparation
+
+Download the required evaluation benchmarks into the `./data` directory.
+
+```bash
+# Create a directory for datasets
+mkdir -p ./data
+
+# Download benchmarks
+huggingface-cli download --repo-type dataset --resume-download likaixin/ScreenSpot-Pro --local-dir ./data/ScreenSpot-Pro
+huggingface-cli download --repo-type dataset --resume-download ServiceNow/ui-vision --local-dir ./data/ui-vision
+huggingface-cli download --repo-type dataset --resume-download OS-Copilot/ScreenSpot-v2 --local-dir ./data/ScreenSpot-v2
+huggingface-cli download --repo-type dataset --resume-download OpenGVLab/MMBench-GUI --local-dir ./data/MMBench-GUI
+huggingface-cli download --repo-type dataset --resume-download vaundys/I2E-Bench --local-dir ./data/I2E-Bench
+```
+
+After downloading, some datasets require unzipping compressed image files.
+
+```bash
+# Unzip images for ScreenSpot-v2
+unzip ./data/ScreenSpot-v2/screenspotv2_image.zip -d ./data/ScreenSpot-v2/
+
+# Unzip images for MMBench-GUI
+unzip ./data/MMBench-GUI/MMBench-GUI-OfflineImages.zip -d ./data/MMBench-GUI/
+```
+
+### 5. Running the Evaluation
+
+To run the evaluation, use the `eval/eval.py` script. You must specify the path to the model, the benchmark name, and the tensor parallel size.
+
+Here is an example command to evaluate the `InfiGUI-G1-3B` model on the `screenspot-pro` benchmark using 4 GPUs:
+
+```bash
+python eval/eval.py \
+    ./models/InfiGUI-G1-3B \
+    --benchmark screenspot-pro \
+    --tensor-parallel 4
+```
+
+-   **`model_path`**: The first positional argument specifies the path to the downloaded model directory (e.g., `./models/InfiGUI-G1-3B`).
+-   **`--benchmark`**: Specifies the benchmark to evaluate. Available options include `screenspot-pro`, `screenspot-v2`, `ui-vision`, `mmbench-gui`, and `i2e-bench`.
+-   **`--tensor-parallel`**: Sets the tensor parallelism size, which should typically match the number of available GPUs.
+
+Evaluation results, including detailed logs and performance metrics, will be saved to the `./output/{model_name}/{benchmark}/` directory.
+
+## 📚 Citation Information
 
 If you find this work useful, we would be grateful if you consider citing the following papers:
 
@@ -245,8 +331,12 @@ If you find this work useful, we would be grateful if you consider citing the fo
 ```bibtex
 @article{liu2025infiguiagent,
   title={InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection},
-  author={Liu, Yuhang and Li, Pengxiang and Wei, Zishu and Xie, Congkai and Hu, Xueyu and Xu, Xinchen and Zhang, Shengyu and Han, Xiaotian and Yang, Hongxia and Wu, Fei},
+  author={Liu, Yuhang and Li, Pengxiang and Wei, Zishu and Xie, Congkai and Hu, Xueyu and Zhang, Shengyu and Han, Xiaotian and Yang, Hongxia and Wu, Fei},
   journal={arXiv preprint arXiv:2501.04575},
   year={2025}
 }
-```
+```
+
+## 🙏 Acknowledgements
+
+We would like to express our gratitude for the following open-source projects: [VERL](https://github.com/volcengine/verl), [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL) and [vLLM](https://github.com/vllm-project/vllm).