Datasets:

Perkzi
/

VIOLIN

license: cc-by-4.0
task_categories:
  - text-to-image
language:
  - en
tags:
  - vision
  - evaluation
  - diagnostic
  - AI-Obedience
pretty_name: VIOLIN
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: test
        path: violin-test.parquet
rai:
  dataLimitations: >-
    This dataset is focused on specific color/shape combinations and may not
    cover all real-world visual scenarios.
  dataBiases: >-
    The images are synthetic and may reflect biases inherent in the generative
    models used.
  personalSensitiveInformation: None. The dataset contains no PII (Personally Identifiable Information).
  dataUseCases: >-
    Research on visual reasoning and color-object association in multimodal
    models.
  dataSocialImpact: >-
    Provides a benchmark for evaluating model alignment with human-centric
    visual concepts.
  hasSyntheticData: true

VIOLIN: VIsual Obedience Level-4 EvaluatIoN

Motivation

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."

benchmark

📊 Dataset Structure

Task	Description	Metrics
1. Color purity (single block)	Full-frame uniform color from ISCC–NBS Level-2.	d_{rgb_ed}, d_{lab_00}, d_sd, d_ced, d_hf
2. Color purity (two blocks)	Fill uniform color in two regions.	d_{rgb_ed}, d_{lab_00}, d_sd, d_ced, d_hf
3. Geometric shape	Render a specified shape (e.g., a circle) at a prompt-defined position and scale.	d_iou, d_dist, d_size, d_shape, d_pure
4. Image masking	Apply mask on image. Based on the image in TencentARC BrushNet.	d_iou, d_biou, d_leak, d_dist, d_edge

📁 How to Use

You can load the dataset directly via the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset("Perkzi/VIOLIN", split="test")
print(dataset[0])

📜 Citation

If you find this dataset or our research helpful, please consider citing our paper:

@article{li2026exploring,
  title={Exploring the AI Obedience: Why is Generating a Pure Color Image Harder than CyberPunk?},
  author={Li, Hongyu and Liu, Kuan and Chen, Yuan and Hu, Juntao and Lu, Huimin and Chen, Guanjie and Liu, Xue and Lu, Guangming and Huang, Hong},
  journal={arXiv preprint arXiv:2603.00166},
  year={2026}
}