Add metadata.jsonl and dataset card README with image viewer columns

README.md

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+---
+pretty_name: TextEdit-Bench
+license: mit
+task_categories:
+- image-to-image
+tags:
+- computer-vision
+- image-editing
+- benchmark
+
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: metadata.jsonl
+
+dataset_info:
+  features:
+  - name: original_image
+    dtype: image
+  - name: gt_image
+    dtype: image
+  - name: id
+    dtype: int64
+  - name: category
+    dtype: string
+  - name: source_text
+    dtype: string
+  - name: target_text
+    dtype: string
+  - name: prompt
+    dtype: string
+  - name: gt_caption
+    dtype: string
+---
+
+<div align="center">
+
+# TextEdit: A High-Quality, Multi-Scenario Text Editing Benchmark for Generation Models
+
+
+<p align="center">
+  <a href='https://arxiv.org/abs/2508.18265'>
+    <img src='https://img.shields.io/badge/Paper-2508.18265-brown?style=flat&logo=arXiv' alt='arXiv PDF'>
+  </a>
+  <a href='https://huggingface.co/collections/OpenGVLab/TextEdit'>
+    <img src='https://img.shields.io/badge/Huggingface-Data-blue?style=flat&logo=huggingface' alt='data img/data'>
+  </a>
+  
+[Danni Yang](https://scholar.google.com/citations?user=qDsgBJAAAAAJ&hl=zh-CN&oi=sra),
+[Sitao Chen](https://github.com/fudan-chen),
+[Changyao Tian](https://scholar.google.com/citations?user=kQ3AisQAAAAJ&hl=zh-CN&oi=ao)
+
+If you find our work helpful, please give us a ⭐ or cite our paper. See the InternVL-U technical report appendix for more details.
+
+</div>
+
+## 🎉 News
+- **[2026/02/25]** TextEdit benchmark released. 
+- **[2026/02/25]** Evaluation code and initial baselines released.
+- **[2026/02/25]** Leaderboard updated with latest models.
+
+
+
+## 📖 Introduction
+ <img src="assets/intro.png" width="100%">
+Text editing is a fundamental yet challenging capability for modern image generation and editing models.  An increasing number of powerful multimodal generation models, such as Qwen-Image and Nano-Banana-Pro, are emerging with strong text rendering and editing capabilities.
+For text editing task, unlike general image editing, text manipulation requires:
+
+- Precise spatial alignment
+- Font and style consistency
+- Background preservation
+- Layout-constrained reasoning
+
+We introduce **TextEdit**, a **high-quality**,  **multi-scenario benchmark** designed to evaluate **fine-grained text editing capabilities** in image generation models.
+
+TextEdit covers a diverse set of real-world and virtual scenarios, spanning **18 subcategories** with a total of **2,148 high-quality source images** and **manually annotated edited ground-truth images**.
+
+To comprehensively assess model performance, we combine **classic OCR, image-fidelity metrics and modern multimodal LLM-based evaluation** across _target accuracy_, _text preservation_, _scene integrity_, _local realism_ and _visual coherence_.  This dual-track protocol enables comprehensive assessment.
+
+Our goal is to provide a **standardized, realistic, and scalable** benchmark for text editing research.
+
+---
+
+## 🏆 LeadBoard
+<details>
+  <summary><strong>📊 Full Benchmark Results</strong></summary>
+<div style="max-width:1050px; margin:auto;">
+
+<table>
+<thead>
+  <tr>
+    <th rowspan="2" align="left">Models</th>
+    <th rowspan="2" align="center"># Params</th>
+    <th colspan="7" align="center">Real</th>
+    <th colspan="7" align="center">Virtual</th>
+  </tr>
+  <tr>
+    <th>OA</th>
+    <th>OP</th>
+    <th>OR</th>
+    <th>F1</th>
+    <th>NED</th>
+    <th>CLIP</th>
+    <th>AES</th>
+    <th>OA</th>
+    <th>OP</th>
+    <th>OR</th>
+    <th>F1</th>
+    <th>NED</th>
+    <th>CLIP</th>
+    <th>AES</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td colspan="16"><strong><em>Generation Models</em></strong></td>
+  </tr>
+  <tr>
+    <td>Qwen-Image-Edit</td>
+    <td align="center">20B</td>
+    <td>0.75</td><td>0.68</td><td>0.66</td><td>0.67</td><td>0.71</td><td>0.75</td><td>5.72</td>
+    <td>0.78</td><td>0.75</td><td>0.73</td><td>0.74</td><td>0.75</td><td>0.81</td><td>5.21</td>
+  </tr>
+  <tr>
+    <td>GPT-Image-1.5</td>
+    <td align="center">-</td>
+    <td>0.74</td><td>0.69</td><td>0.67</td><td>0.68</td><td>0.68</td><td>0.75</td><td>5.78</td>
+    <td>0.73</td><td>0.72</td><td>0.71</td><td>0.71</td><td>0.70</td><td>0.80</td><td>5.28</td>
+  </tr>
+  <tr>
+    <td>Nano Banana Pro</td>
+    <td align="center">-</td>
+    <td>0.77</td><td>0.72</td><td>0.70</td><td>0.71</td><td>0.72</td><td>0.75</td><td>5.79</td>
+    <td>0.80</td><td>0.78</td><td>0.77</td><td>0.78</td><td>0.78</td><td>0.81</td><td>5.28</td>
+  </tr>
+
+  <tr>
+    <td colspan="16"><strong><em>Unified Models</em></strong></td>
+  </tr>
+  <tr>
+    <td>Lumina-DiMOO</td>
+    <td align="center">8B</td>
+    <td>0.22</td><td>0.23</td><td>0.19</td><td>0.20</td><td>0.19</td><td>0.69</td><td>5.53</td>
+    <td>0.22</td><td>0.25</td><td>0.21</td><td>0.22</td><td>0.20</td><td>0.72</td><td>4.76</td>
+  </tr>
+  <tr>
+    <td>Ovis-U1</td>
+    <td align="center">2.4B+1.2B</td>
+    <td>0.40</td><td>0.37</td><td>0.34</td><td>0.35</td><td>0.35</td><td>0.72</td><td>5.32</td>
+    <td>0.37</td><td>0.40</td><td>0.38</td><td>0.39</td><td>0.33</td><td>0.75</td><td>4.66</td>
+  </tr>
+  <tr>
+    <td>BAGEL</td>
+    <td align="center">7B+7B</td>
+    <td>0.60</td><td>0.59</td><td>0.53</td><td>0.55</td><td>0.55</td><td>0.74</td><td>5.71</td>
+    <td>0.57</td><td>0.60</td><td>0.56</td><td>0.57</td><td>0.54</td><td>0.78</td><td>5.19</td>
+  </tr>
+  <tr>
+    <td><strong>InternVL-U (Ours)</strong></td>
+    <td align="center">2B+1.7B</td>
+    <td>0.77</td><td>0.73</td><td>0.70</td><td>0.71</td><td>0.72</td><td>0.75</td><td>5.70</td>
+    <td>0.79</td><td>0.77</td><td>0.75</td><td>0.75</td><td>0.77</td><td>0.80</td><td>5.12</td>
+  </tr>
+</tbody>
+</table>
+
+</div>
+
+<div style="max-width:1050px; margin:auto;">
+
+<table>
+<thead>
+  <tr>
+    <th rowspan="2" align="left">Models</th>
+    <th rowspan="2" align="center"># Params</th>
+    <th colspan="6" align="center">Real</th>
+    <th colspan="6" align="center">Virtual</th>
+  </tr>
+  <tr>
+    <th>TA</th>
+    <th>TP</th>
+    <th>SI</th>
+    <th>LR</th>
+    <th>VC</th>
+    <th>Avg</th>
+    <th>TA</th>
+    <th>TP</th>
+    <th>SI</th>
+    <th>LR</th>
+    <th>VC</th>
+    <th>Avg</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td colspan="14"><strong><em>Generation Models</em></strong></td>
+  </tr>
+  <tr>
+    <td>Qwen-Image-Edit</td>
+    <td align="center">20B</td>
+    <td>0.92</td><td>0.82</td><td>0.75</td><td>0.57</td><td>0.80</td><td>0.77</td>
+    <td>0.57</td><td>0.79</td><td>0.92</td><td>0.80</td><td>0.77</td><td>0.77</td>
+  </tr>
+  <tr>
+    <td>GPT-Image-1.5</td>
+    <td align="center">-</td>
+    <td>0.96</td><td>0.94</td><td>0.86</td><td>0.80</td><td>0.93</td><td>0.90</td>
+    <td>0.82</td><td>0.93</td><td>0.96</td><td>0.91</td><td>0.87</td><td>0.90</td>
+  </tr>
+  <tr>
+    <td>Nano Banana Pro</td>
+    <td align="center">-</td>
+    <td>0.96</td><td>0.95</td><td>0.85</td><td>0.88</td><td>0.93</td><td>0.91</td>
+    <td>0.87</td><td>0.92</td><td>0.96</td><td>0.94</td><td>0.89</td><td>0.92</td>
+  </tr>
+  <tr>
+    <td colspan="14"><strong><em>Unified Models</em></strong></td>
+  </tr>
+  <tr>
+    <td>Lumina-DiMOO</td>
+    <td align="center">8B</td>
+    <td>0.17</td><td>0.06</td><td>0.04</td><td>0.02</td><td>0.05</td><td>0.09</td>
+    <td>0.02</td><td>0.06</td><td>0.16</td><td>0.05</td><td>0.03</td><td>0.08</td>
+  </tr>
+  <tr>
+    <td>Ovis-U1</td>
+    <td align="center">2.4B+1.2B</td>
+    <td>0.31</td><td>0.12</td><td>0.12</td><td>0.07</td><td>0.18</td><td>0.18</td>
+    <td>0.06</td><td>0.16</td><td>0.31</td><td>0.14</td><td>0.13</td><td>0.19</td>
+  </tr>
+  <tr>
+    <td>BAGEL</td>
+    <td align="center">7B+7B</td>
+    <td>0.68</td><td>0.60</td><td>0.38</td><td>0.35</td><td>0.56</td><td>0.53</td>
+    <td>0.38</td><td>0.51</td><td>0.68</td><td>0.62</td><td>0.42</td><td>0.54</td>
+  </tr>
+  <tr>
+    <td><strong>InternVL-U (Ours)</strong></td>
+    <td align="center">2B+1.7B</td>
+    <td>0.94</td><td>0.90</td><td>0.71</td><td>0.80</td><td>0.80</td><td>0.88</td>
+    <td>0.87</td><td>0.86</td><td>0.91</td><td>0.82</td><td>0.62</td><td>0.83</td>
+  </tr>
+</tbody>
+</table>
+
+</div>
+</details>
+
+<details>
+  <summary><strong>📊 Mini-set Benchmark Results(500 samples)</strong></summary>
+<div style="max-width:1050px; margin:auto;">
+<table>
+<thead>
+  <tr>
+    <th rowspan="2" align="left">Models</th>
+    <th rowspan="2" align="center"># Params</th>
+    <th colspan="7" align="center">Real</th>
+    <th colspan="7" align="center">Virtual</th>
+  </tr>
+  <tr>
+    <th>OA</th>
+    <th>OP</th>
+    <th>OR</th>
+    <th>F1</th>
+    <th>NED</th>
+    <th>CLIP</th>
+    <th>AES</th>
+    <th>OA</th>
+    <th>OP</th>
+    <th>OR</th>
+    <th>F1</th>
+    <th>NED</th>
+    <th>CLIP</th>
+    <th>AES</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td colspan="16"><strong><em>Generation Models</em></strong></td>
+  </tr>
+  <tr>
+    <td>Qwen-Image-Edit</td>
+    <td align="center">20B</td>
+    <td>0.76</td><td>0.69</td><td>0.67</td><td>0.67</td><td>0.70</td><td>0.75</td><td>5.81</td>
+    <td>0.74</td><td>0.71</td><td>0.70</td><td>0.70</td><td>0.70</td><td>0.80</td><td>5.27</td>
+  </tr>
+  <tr>
+    <td>GPT-Image-1.5</td>
+    <td align="center">-</td>
+    <td>0.72</td><td>0.68</td><td>0.66</td><td>0.67</td><td>0.67</td><td>0.75</td><td>5.85</td>
+    <td>0.68</td><td>0.69</td><td>0.68</td><td>0.68</td><td>0.65</td><td>0.80</td><td>5.32</td>
+  </tr>
+  <tr>
+    <td>Nano Banana Pro</td>
+    <td align="center">-</td>
+    <td>0.76</td><td>0.71</td><td>0.69</td><td>0.70</td><td>0.70</td><td>0.75</td><td>5.86</td>
+    <td>0.77</td><td>0.76</td><td>0.75</td><td>0.75</td><td>0.76</td><td>0.81</td><td>5.32</td>
+  </tr>
+  <tr>
+    <td colspan="16"><strong><em>Unified Models</em></strong></td>
+  </tr>
+  <tr>
+    <td>Lumina-DiMOO</td>
+    <td align="center">8B</td>
+    <td>0.20</td><td>0.22</td><td>0.18</td><td>0.19</td><td>0.19</td><td>0.70</td><td>5.58</td>
+    <td>0.22</td><td>0.25</td><td>0.21</td><td>0.22</td><td>0.19</td><td>0.73</td><td>4.87</td>
+  </tr>
+  <tr>
+    <td>Ovis-U1</td>
+    <td align="center">2.4B+1.2B</td>
+    <td>0.37</td><td>0.34</td><td>0.32</td><td>0.32</td><td>0.33</td><td>0.72</td><td>5.39</td>
+    <td>0.39</td><td>0.41</td><td>0.38</td><td>0.39</td><td>0.33</td><td>0.74</td><td>4.75</td>
+  </tr>
+  <tr>
+    <td>BAGEL</td>
+    <td align="center">7B+7B</td>
+    <td>0.61</td><td>0.59</td><td>0.52</td><td>0.54</td><td>0.54</td><td>0.74</td><td>5.79</td>
+    <td>0.53</td><td>0.58</td><td>0.53</td><td>0.55</td><td>0.51</td><td>0.78</td><td>5.25</td>
+  </tr>
+  <tr>
+    <td><strong>InternVL-U (Ours)</strong></td>
+    <td align="center">2B+1.7B</td>
+    <td>0.77</td><td>0.74</td><td>0.70</td><td>0.71</td><td>0.71</td><td>0.76</td><td>5.79</td>
+    <td>0.74</td><td>0.72</td><td>0.69</td><td>0.70</td><td>0.72</td><td>0.79</td><td>5.14</td>
+  </tr>
+</tbody>
+</table>
+</div>
+
+
+<div style="max-width:1050px; margin:auto;">
+<table>
+<thead>
+  <tr>
+    <th rowspan="2" align="left">Models</th>
+    <th rowspan="2" align="center"># Params</th>
+    <th colspan="6" align="center">Real</th>
+    <th colspan="6" align="center">Virtual</th>
+  </tr>
+  <tr>
+    <th>TA</th>
+    <th>TP</th>
+    <th>SI</th>
+    <th>LR</th>
+    <th>VC</th>
+    <th>Avg</th>
+    <th>TA</th>
+    <th>TP</th>
+    <th>SI</th>
+    <th>LR</th>
+    <th>VC</th>
+    <th>Avg</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td colspan="14"><strong><em>Generation Models</em></strong></td>
+  </tr>
+  <tr>
+    <td>Qwen-Image-Edit</td>
+    <td align="center">20B</td>
+    <td>0.93</td><td>0.85</td><td>0.77</td><td>0.55</td><td>0.78</td><td>0.80</td>
+    <td>0.60</td><td>0.82</td><td>0.91</td><td>0.81</td><td>0.74</td><td>0.76</td>
+  </tr>
+  <tr>
+    <td>GPT-Image-1.5</td>
+    <td align="center">-</td>
+    <td>0.97</td><td>0.94</td><td>0.86</td><td>0.79</td><td>0.92</td><td>0.91</td>
+    <td>0.85</td><td>0.93</td><td>0.95</td><td>0.92</td><td>0.83</td><td>0.88</td>
+  </tr>
+  <tr>
+    <td>Nano Banana Pro</td>
+    <td align="center">-</td>
+    <td>0.96</td><td>0.95</td><td>0.85</td><td>0.86</td><td>0.92</td><td>0.91</td>
+    <td>0.87</td><td>0.92</td><td>0.96</td><td>0.93</td><td>0.87</td><td>0.92</td>
+  </tr>
+  <tr>
+    <td colspan="14"><strong><em>Unified Models</em></strong></td>
+  </tr>
+  <tr>
+    <td>Lumina-DiMOO</td>
+    <td align="center">8B</td>
+    <td>0.16</td><td>0.04</td><td>0.04</td><td>0.02</td><td>0.06</td><td>0.08</td>
+    <td>0.02</td><td>0.05</td><td>0.19</td><td>0.07</td><td>0.03</td><td>0.10</td>
+  </tr>
+  <tr>
+    <td>Ovis-U1</td>
+    <td align="center">2.4B+1.2B</td>
+    <td>0.29</td><td>0.11</td><td>0.11</td><td>0.08</td><td>0.20</td><td>0.17</td>
+    <td>0.04</td><td>0.16</td><td>0.35</td><td>0.18</td><td>0.15</td><td>0.22</td>
+  </tr>
+  <tr>
+    <td>BAGEL</td>
+    <td align="center">7B+7B</td>
+    <td>0.68</td><td>0.61</td><td>0.38</td><td>0.34</td><td>0.59</td><td>0.53</td>
+    <td>0.36</td><td>0.52</td><td>0.69</td><td>0.64</td><td>0.40</td><td>0.54</td>
+  </tr>
+  <tr>
+    <td><strong>InternVL-U (Ours)</strong></td>
+    <td align="center">2B+1.7B</td>
+    <td>0.94</td><td>0.91</td><td>0.72</td><td>0.73</td><td>0.75</td><td>0.89</td>
+    <td>0.88</td><td>0.87</td><td>0.90</td><td>0.78</td><td>0.57</td><td>0.79</td>
+  </tr>
+</tbody>
+</table>
+</div>
+
+</details>
+
+## 🛠️ Quick Start
+
+### 📂 1. Data Preparation
+You can download images from [this page](https://huggingface.co/collections/OpenGVLab/TextEdit).  The TextEdit benchmark data is organized under `data/` by and category:
+- **Virtual** (categories `1.x.x`): Synthetic/virtual scene images
+- **Real** (categories `2.x`): Real-world scene images
+
+
+
+Evaluation prompts are provided under `eval_prompts/` in two subsets:
+| Subset | Directory | Description |
+|--------|-----------|-------------|
+| **Fullset** | `eval_prompts/fullset/` | Complete benchmark with all samples |
+| **Miniset (500)** | `eval_prompts/miniset/` | 500-sample subset uniformly sampled from the fullset |
+
+Each `.jsonl` file contains per-sample fields: `id`, `prompt`, `original_image`, `gt_image`, `source_text`, `target_text`, `gt_caption`.
+
+### 🤖 2. Model Output Preparation
+You need to use your model to perform image editing inference process. Please organize the outputs in the folder structure shown below to facilitate evaluation.
+```
+output/
+├── internvl-u/                      # Your Model Name
+│   ├── 1.1.1                        # Category Name
+│       ├── 1007088003726.0.jpg      # Model Output Images
+│       ├── 1013932004096.0.jpg          
+│       ├── ...     
+│   ├── 1.1.2  
+│   ├── 1.1.3             
+│   ├── ...           
+│   └── 2.7 
+```
+
+### 📏 3. Model Evaluation
+#### 3.1 Classic Metrics Evaluation
+Classic metrics evaluate text editing quality using **OCR-based text accuracy**, **image-text alignment**, and **aesthetic quality**. All metrics are reported separately for **Virtual** and **Real** splits.
+
+#### Evaluated Metrics
+
+| Abbreviation | Metric | Description |
+|:---:|---|---|
+| **OA** | OCR Accuracy | Whether the target text is correctly rendered in the editing region |
+| **OP** | OCR Precision | Precision of text content (target + background) in the generated image |
+| **OR** | OCR Recall | Recall of text content (target + background) in the generated image |
+| **F1** | OCR F1 | Harmonic mean of OCR Precision and Recall |
+| **NED** | Normalized Edit Distance | ROI-aware normalized edit distance between target and generated text |
+| **CLIP** | CLIPScore | CLIP-based image-text alignment score |
+| **AES** | Aesthetic Score | Predicted aesthetic quality score of the generated image |
+
+#### Usage
+
+Evaluation scripts are provided separately for **fullset** and **miniset**:
+- `eval_scripts/classic_metrics_eval_full.sh` — evaluate on the full benchmark
+- `eval_scripts/classic_metrics_eval_mini.sh` — evaluate on the 500-sample miniset
+
+**Step 1. Modify the contents of the configure script according to your project directory.** (e.g., `eval_scripts/classic_metrics_eval_full.sh`):
+
+```bash
+MODELS="model-a,model-b,model-c"                    # Comma-separated list of model names to be evaluated
+
+path="your_project_path_here"
+CACHE_DIR="$path/TextEdit/checkpoint"               # Directory for all model checkpoints (OCR, CLIP, etc.)
+
+BENCHMARK_DIR="$path/TextEdit/eval_prompts/fullset"
+GT_ROOT_DIR="$path/TextEdit/data"                   # Root path for original & GT images
+MODEL_OUTPUT_ROOT="$path/TextEdit/output"           # Root path for model infer outputs
+OUTPUT_DIR="$path/TextEdit/result/classic_fullset"  # Evaluation result root path for classic metric
+```
+
+> **Note:** All required model checkpoints (PaddleOCR, CLIP, aesthetic model, etc.) should be placed under the **`CACHE_DIR`** directory.
+
+**Step 2.Run evaluation shell script to evaluate your model output.**
+
+```bash
+# Fullset evaluation
+bash eval_scripts/classic_metrics_eval_full.sh
+
+# Miniset evaluation
+bash eval_scripts/classic_metrics_eval_mini.sh
+```
+
+Results are saved as `{model_name}.json` under the output directory, containing per-sample scores and aggregated metrics for both **Virtual** and **Real** splits.
+
+---
+#### 3.2 VLM-based Metrics Evaluation
+
+Our VLM-based evaluation uses **Gemini-3-Pro-Preview** as an expert judge to score text editing quality across five fine-grained dimensions. The evaluation is a **two-step pipeline**.
+
+#### Evaluated Metrics
+
+| Abbreviation | Metric | Description |
+|:---:|---|---|
+| **TA** | Text Accuracy | Spelling correctness and completeness of the target text (1–5) |
+| **TP** | Text Preservation | Preservation of non-target background text (1–5) |
+| **SI** | Scene Integrity | Geometric stability of non-edited background areas (1–5) |
+| **LR** | Local Realism | Inpainting quality, edge cleanness, and seamlessness (1–5) |
+| **VC** | Visual Coherence | Style matching (font, lighting, shadow, texture harmony) (1–5) |
+| **Avg** | Weighted Average | Weighted average of all five dimensions (default weights: 0.4 / 0.3 / 0.1 / 0.1 / 0.1) |
+
+All raw scores (1–5) are normalized to 0–1 for reporting. A **cutoff mechanism** is available: if TA (Q1) < 4, the remaining dimensions are set to 0, reflecting that a failed text edit invalidates other quality dimensions.
+
+#### Step 1: Gemini API Evaluation
+
+Send (Original Image, GT Image, Edited Image) triplets to the Gemini API for scoring.
+
+Configure and run `eval_scripts/vlm_metrics_eval_step1.sh`:
+
+```bash
+API_KEY="your_gemini_api_key_here"
+BASE_URL="your_gemini_api_base_url_here"
+
+python eval_pipeline/vlm_metrics_eval_step1.py \
+  --input_data_dir <your_path>/TextEdit/eval_prompts/fullset \
+  --model_output_root <your_path>/TextEdit/output \
+  --gt_data_root <your_path>/TextEdit/data \
+  --output_base_dir <your_path>/TextEdit/result/vlm_gemini_full_answers \
+  --model_name "gemini-3-pro-preview" \
+  --models "model-a,model-b,model-c" \
+  --api_key "$API_KEY" \
+  --base_url "$BASE_URL" \
+  --num_workers 64
+```
+
+Per-model `.jsonl` answer files are saved under the `output_base_dir`.
+
+#### Step 2: Score Aggregation & Report
+
+Aggregate the per-sample Gemini responses into a final report.
+
+Configure and run `eval_scripts/vlm_metrics_eval_step2.sh`:
+
+```bash
+# Fullset report
+python eval_pipeline/vlm_metrics_eval_step2.py \
+  --answer_dir <your_path>/TextEdit/result/vlm_gemini_full_answers \
+  --output_file <your_path>/TextEdit/result/gemini_report_fullset.json \
+  --weights 0.4 0.3 0.1 0.1 0.1 \
+  --enable_cutoff
+
+# Miniset report
+python eval_pipeline/vlm_metrics_eval_step2.py \
+  --answer_dir <your_path>/TextEdit/result/vlm_gemini_mini_answers \
+  --output_file <your_path>/TextEdit/result/gemini_report_miniset.json \
+  --weights 0.4 0.3 0.1 0.1 0.1 \
+  --enable_cutoff
+```
+
+**Key parameters:**
+- `--weights`: Weights for Q1–Q5 (default: `0.4 0.3 0.1 0.1 0.1`).
+- `--enable_cutoff`: Enable cutoff mechanism — if Q1 < 4, set Q2–Q5 to 0.
+
+The output includes a JSON report, a CSV table, and a Markdown-formatted leaderboard printed to the console.
+
+---
+
+## 🎨 Visualization Ouput Example
+ <img src="assets/output.jpg" width="100%">
+ 
+## Citation
+If you find TextEdit Bench useful, please cite using this BibTeX.