Datasets:

AliHome3D
/

SA-BENCH

license: apache-2.0
pretty_name: SA-BENCH
language:
  - en
task_categories:
  - image-classification
size_categories:
  - 10K<n<100K
tags:
  - image
  - image-quality-assessment
  - aesthetics
  - spatial-aesthetics
  - interior-design
  - benchmark
configs:
  - config_name: distortion
    data_files:
      - split: train
        path: annotations/distortion_2k_train.csv
      - split: test
        path: annotations/distortion_2k_test.csv
      - split: full
        path: annotations/distortion_2k_full.csv
  - config_name: harmony
    data_files:
      - split: train
        path: annotations/harmony_7k_train.csv
      - split: test
        path: annotations/harmony_7k_test.csv
      - split: full
        path: annotations/harmony_7k_full.csv
  - config_name: layout
    data_files:
      - split: train
        path: annotations/layout_6k_train.csv
      - split: test
        path: annotations/layout_6k_test.csv
      - split: full
        path: annotations/layout_6k_full.csv
  - config_name: lighting
    data_files:
      - split: train
        path: annotations/lighting_3k_train.csv
      - split: test
        path: annotations/lighting_3k_test.csv
      - split: full
        path: annotations/lighting_3k_full.csv

SA-BENCH

SA-BENCH is the benchmark dataset released with “Beyond Pixels: Benchmarking and Reward-Based Assessing Framework for Visual Spatial Aesthetics.”

It evaluates the spatial aesthetics of interior images along four dimensions:

distortion
harmony
layout
lighting

The dataset contains 17,768 annotated examples across four spatial-aesthetic dimensions, with image assets and human annotations for training and evaluation.

Dataset Details

Dataset Description

SA-BENCH is designed for image quality and aesthetic assessment of interior scenes. It focuses on spatial aesthetics rather than generic image appeal, and provides dimension-specific annotations for:

distortion: geometric distortion, deformation, alignment errors, and material realism
harmony: style consistency, color coordination, and overall visual coherence
layout: spatial arrangement, balance, and positional relationships of key elements
lighting: illumination quality, shadow realism, light-source consistency, and atmosphere

Dataset Sources

Repository: this Hugging Face dataset repository
Associated paper: Beyond Pixels: Benchmarking and Reward-Based Assessing Framework for Visual Spatial Aesthetics
Code and model release: SA-IQA

Dataset Structure

SA-BENCH/
├── LICENSE
├── README.md
├── annotations/
│   ├── distortion_2k_train.csv
│   ├── distortion_2k_test.csv
│   ├── distortion_2k_full.csv
│   ├── harmony_7k_train.csv
│   ├── harmony_7k_test.csv
│   ├── harmony_7k_full.csv
│   ├── layout_6k_train.csv
│   ├── layout_6k_test.csv
│   ├── layout_6k_full.csv
│   ├── lighting_3k_train.csv
│   ├── lighting_3k_test.csv
│   ├── lighting_3k_full.csv
│   └── *_prompt{1,2,3,4}.jsonl
└── images/
    ├── distortion/images/
    ├── harmony/images/
    ├── layout/images/
    └── lighting/images/

Data Fields

The CSV annotation files contain:

id: image identifier
{dimension}_score_*: individual human annotation scores
{dimension}_score_mos: mean opinion score
{dimension}_score_mos_int: integer-rounded MOS label used for prompt-response generation
model: source generation model, when available for that dimension

The JSONL prompt files contain:

query: prompt text
response: target textual quality label
images: image path list used by the accompanying SA-IQA codebase

The JSONL images values intentionally keep the SA-BENCH/ prefix, for example SA-BENCH/images/distortion/images/distortion_1025.jpg. This matches the expected layout when the dataset directory is used together with the SA-IQA code from the parent project directory. When loading files from inside the Hugging Face dataset repository root directly, strip the leading SA-BENCH/ prefix or prepend the parent directory accordingly.

Data Splits

Each dimension provides train, test, and full CSV splits:

Dimension	Subset	Description
distortion	2,226	Spatial distortion quality
harmony	6,741	Style and color harmony quality
layout	5,556	Spatial layout quality
lighting	3,245	Lighting quality

Together they form a 17,768-example benchmark.

Usage

The CSV files can be loaded directly through the Hugging Face Dataset Viewer using the metadata configurations above. Prompt-based JSONL files are also included for reproducibility and direct use with SA-IQA training/evaluation scripts.

For standard evaluation in this release, use the prompt4 files with the released sa-iqa-prompt4 model.

Intended Use

SA-BENCH is intended for:

non-commercial research on image quality assessment
benchmarking spatial aesthetic assessment methods
training and evaluating multimodal models for interior-image quality prediction
reward-model research for image generation and selection

Limitations

The dataset focuses on interior-scene imagery and may not generalize to portraits, landscapes, or general artistic images.
Scores reflect the annotation protocol used for this benchmark and should not be treated as universal aesthetic judgments.
Users should evaluate fairness, safety, and domain suitability before applying models trained on this dataset to new data.

License

SA-BENCH is released under the Apache License 2.0. See LICENSE for the full license text.

Citation

If you use SA-BENCH, please cite:

@inproceedings{gao2025beyond,
  title={Beyond Pixels: Benchmarking and Reward-Based Assessing Framework for Visual Spatial Aesthetics},
  author={Gao, Yuan and Song, Jin and Fei, Yiyun and Li, Gongzhe and Yang, Ruigao},
  booktitle={CVPR 2025 Workshop},
  year={2025}
}