Update README.md

README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+
+task_categories:
+- text-to-image
+language:
+- en
+- zh
+- fr
+tags:
+- vision
+- color
+- evaluation
+- diagnostic
+- AI-Obedience
+pretty_name: VIOLIN
+size_categories:
+- 10K<n<100K
+---
+
+# VIOLIN: Visual Instruction-based Color Evaluation
+
+**VIOLIN** (VIsual Obedience Level-4 EvaluatIoN) is a diagnostic benchmark designed to assess the **Level-4 Instructional Obedience** of text-to-image generative models. 
+
+While state-of-the-art models can render complex semantic scenes (e.g., "Cyberpunk cityscapes"), they often fail at the most fundamental deterministic tasks: generating a perfectly pure, texture-less color image. VIOLIN provides a rigorous framework to measure this "Paradox of Simplicity."
+
+
+
+## 🧪 Key Scientific Insights
+Our research identifies two primary obstacles in current generative AI:
+- **Aesthetic Inertia**: The tendency of models to prioritize visual richness and textures over strict instructional adherence, even when "pure color" or "no texture" is explicitly requested.
+- **Semantic Gravity**: The bias where models follow instructions better when they align with common visual knowledge but fail when context is random or conflicting.
+
+## 📊 Dataset Structure
+The dataset comprises over 42,000 text-image pairs across 6 variations:
+
+| Variation | Description | Evaluation Focus |
+| :--- | :--- | :--- |
+| **Variation 1** | Single Color Block | Basic pixel-level precision (ISCC-NBS) |
+| **Variation 2** | Two-block Split | Spatial layout and vertical/horizontal split |
+| **Variation 3** | Four-quadrant Split | Complex spatial reasoning and contrast |
+| **Variation 4** | Fuzzy Color | Bounded constraints and flexibility |
+| **Variation 5** | Multilingual | Robustness across English, Chinese, and French |
+| **Variation 6** | Color Spaces | Cross-format understanding (Hex, RGB, HSL) |
+
+
+
+## 📐 Evaluation Metrics
+We propose a dual-metric approach for evaluating "Minimum Viable Obedience":
+1. **Color Precision**: Measuring the ΔE (CIEDE2000) or Euclidean distance between the generated pixels and the ground truth.
+2. **Color Purity**: Assessing the presence of artifacts, gradients, or unintended textures using variance-based analysis.
+
+## 📁 How to Use
+You can load the dataset directly via the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Perkzi/VIOLIN")