README.md

---
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")