Improve model card: Add pipeline tag, links, description, and usage

This PR significantly enhances the model card for EditScore by:
- Adding the `pipeline_tag: image-text-to-text` to better categorize the model's function as an image editing reward model.
- Including a link to the official Hugging Face paper: [EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modeling](https://huggingface.co/papers/2509.23909).
- Providing links to the project page ([https://vectorspacelab.github.io/EditScore](https://vectorspacelab.github.io/EditScore)) and the GitHub repository ([https://github.com/VectorSpaceLab/EditScore](https://github.com/VectorSpaceLab/EditScore)).
- Adding a comprehensive model description based on the paper's abstract, highlights, and the project's introduction.
- Incorporating a "Quick Start" section with environment setup and a practical Python code snippet for inference, directly from the GitHub README.
- Including relevant images from the GitHub README for better visual representation.

These additions will make the model more discoverable and easier to use for the community.

README.md

@@ -1,3 +1,134 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: image-text-to-text
+---
+
+<p align="center">
+  <img src="https://github.com/VectorSpaceLab/EditScore/raw/main/assets/logo.png" width="65%">
+</p>
+
+This repository contains **EditScore**, a series of state-of-the-art open-source reward models (7B–72B) designed to evaluate and enhance instruction-guided image editing.
+
+The model was presented in the paper [EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modeling](https://huggingface.co/papers/2509.23909).
+
+- 📚 [Paper](https://huggingface.co/papers/2509.23909)
+- 🌐 [Project Page](https://vectorspacelab.github.io/EditScore)
+- 💻 [Code Repository](https://github.com/VectorSpaceLab/EditScore)
+
+## ✨ Highlights
+- **State-of-the-Art Performance**: Effectively matches the performance of leading proprietary VLMs. With a self-ensembling strategy, **our largest model surpasses even GPT-5** on our comprehensive benchmark, **EditReward-Bench**.
+- **A Reliable Evaluation Standard**: We introduce **EditReward-Bench**, the first public benchmark specifically designed for evaluating reward models in image editing, featuring 13 subtasks, 11 state-of-the-art editing models (*including proprietary models*) and expert human annotations.
+- **Simple and Easy-to-Use**: Get an accurate quality score for your image edits with just a few lines of code.
+- **Versatile Applications**: Ready to use as a best-in-class reranker to improve editing outputs, or as a high-fidelity reward signal for **stable and effective Reinforcement Learning (RL) fine-tuning**.
+
+## 📖 Introduction
+While Reinforcement Learning (RL) holds immense potential for this domain, its progress has been severely hindered by the absence of a high-fidelity, efficient reward signal.
+
+To overcome this barrier, we provide a systematic, two-part solution:
+
+- **A Rigorous Evaluation Standard**: We first introduce **EditReward-Bench**, a new public benchmark for the direct and reliable evaluation of reward models. It features 13 diverse subtasks and expert human annotations, establishing a gold standard for measuring reward signal quality.
+
+- **A Powerful & Versatile Tool**: Guided by our benchmark, we developed the **EditScore** model series. Through meticulous data curation and an effective self-ensembling strategy, EditScore sets a new state of the art for open-source reward models, even surpassing the accuracy of leading proprietary VLMs.
+
+<p align="center">
+  <img src="https://github.com/VectorSpaceLab/EditScore/raw/main/assets/table_reward_model_results.png" width="95%">
+  <br>
+  <em>Benchmark results on EditReward-Bench.</em>
+</p>
+
+We demonstrate the practical utility of EditScore through two key applications:
+
+- **As a State-of-the-Art Reranker**: Use EditScore to perform Best-of-*N* selection and instantly improve the output quality of diverse editing models.
+- **As a High-Fidelity Reward for RL**: Use EditScore as a robust reward signal to fine-tune models via RL, enabling stable training and unlocking significant performance gains where general-purpose VLMs fail.
+
+This repository releases both the **EditScore** models and the **EditReward-Bench** dataset to facilitate future research in reward modeling, policy optimization, and AI-driven model improvement.
+
+<p align="center">
+  <img src="https://github.com/VectorSpaceLab/EditScore/raw/main/assets/figure_edit_results.png" width="95%">
+  <br>
+  <em>EditScore as a superior reward signal for image editing.</em>
+</p>
+
+## 🚀 Quick Start
+
+### 🛠️ Environment Setup
+
+#### ✅ Recommended Setup
+
+```bash
+# 1. Clone the repo
+git clone git@github.com:VectorSpaceLab/EditScore.git
+cd EditScore
+
+# 2. (Optional) Create a clean Python environment
+conda create -n editscore python=3.12
+conda activate editscore
+
+# 3. Install dependencies
+# 3.1 Install PyTorch (choose correct CUDA version)
+pip install torch==2.7.1 torchvision --extra-index-url https://download.pytorch.org/whl/cu126
+
+# 3.2 Install other required packages
+pip install -r requirements.txt
+
+# EditScore runs even without vllm, though we recommend install it for best performance.
+pip install vllm
+```
+
+#### 🌏 For users in Mainland China
+
+```bash
+# Install PyTorch from a domestic mirror
+pip install torch==2.7.1 torchvision --index-url https://mirror.sjtu.edu.cn/pytorch-wheels/cu126
+
+# Install other dependencies from Tsinghua mirror
+pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+# EditScore runs even without vllm, though we recommend install it for best performance.
+pip install vllm -i https://pypi.tuna.tsinghua.edu.cn/simple
+```
+
+---
+
+### 🧪 Usage Example
+Using EditScore is straightforward. The model will be automatically downloaded from the Hugging Face Hub on its first run.
+```python
+from PIL import Image
+from editscore import EditScore
+
+# Load the EditScore model. It will be downloaded automatically.
+# Replace with the specific model version you want to use.
+model_path = "Qwen/Qwen2.5-VL-7B-Instruct"
+lora_path = "EditScore/EditScore-7B"
+
+scorer = EditScore(
+    backbone="qwen25vl", # set to "qwen25vl_vllm" for faster inference
+    model_name_or_path=model_path,
+    enable_lora=True,
+    lora_path=lora_path,
+    score_range=25,
+    num_pass=1, # Increase for better performance via self-ensembling
+)
+
+input_image = Image.open("example_images/input.png")
+output_image = Image.open("example_images/output.png")
+instruction = "Adjust the background to a glass wall."
+
+result = scorer.evaluate([input_image, output_image], instruction)
+print(f"Edit Score: {result['final_score']}")
+# Expected output: A dictionary containing the final score and other details.
+```
+
+---
+
+## ❤️ Citing Us
+If you find this repository or our work useful, please consider giving a star ⭐ and citation 🦖, which would be greatly appreciated:
+
+```bibtex
+@article{luo2025editscore,
+  title={EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modeling},
+  author={Xin Luo and Jiahao Wang and Chenyuan Wu and Shitao Xiao and Xiyan Jiang and Defu Lian and Jiajun Zhang and Dong Liu and Zheng Liu},
+  journal={arXiv preprint arXiv:2509.23909},
+  year={2025}
+}
+```