Datasets:
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Download README.md from Dragoniss/ControlArt-Bench: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/datasets/Dragoniss/ControlArt-Bench/resolve/main/README.md
- Command line
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hf download hf://datasets/Dragoniss/ControlArt-Bench/README.md
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curl -L -o README.md https://huggingface.co/datasets/Dragoniss/ControlArt-Bench/resolve/main/README.md
1.38 kB
metadata
pretty_name: ControlArt-Bench
tags:
- image-retouching
- image-editing
- visual-focus
- benchmark
ControlArt-Bench
ControlArt-Bench is a benchmark for evaluating intent-driven image retouching and visual focus enhancement, introduced with EyeControl.
Dataset Contents
The dataset contains 200 evaluation samples.
Each sample includes:
- a input image.
- a user intent masks.
- a retouching instructions (we support a long instruction and a short instruction).
- a reference image.
Directory Structure
Replace this example with the actual dataset layout:
.
├── inputs/
├── masks/
├── prompts/
├── targets/
└── README.md
Download
pip install -U huggingface_hub
hf download Dragoniss/ControlArt-Bench \
--repo-type dataset \
--local-dir datasets/ControlArt-Bench
Evaluation
See evaluation details in https://github.com/DragonisCV/EyeControl/blob/main/docs/evaluation.md
License
Citation
@article{qin2026dotting,
title={Dotting the Eye: An Intent-Driven Image Retouching Agent for Visual Focus Enhancement},
author={Qin, Chujie and Zhang, Zilong and Chang, Zewei and Guo, Chunle and Wang, Ruixing and Hu, Tao and Cheng, Ming-Ming and Li, Chongyi},
journal={arXiv preprint arXiv:2609.01148},
year={2026},
url={https://arxiv.org/abs/2609.01148}
}