--- license: apache-2.0 pretty_name: SA-BENCH language: - en task_categories: - image-classification size_categories: - 10K **Dataset Viewer Note:** The Hugging Face Dataset Viewer may report a larger aggregate row count because `train`, `test`, and `full` CSV files are exposed as separate splits. The benchmark itself contains **17,768 examples**. ## 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 ```text 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: ```bibtex @InProceedings{Gao_2026_CVPR, author = {Gao, Yuan and Song, Jin and Fei, Yiyun and Li, Gongzhe and Yang, Ruigao}, title = {Beyond Pixels: Benchmarking and Reward-Based Assessing Framework for Visual Spatial Aesthetics}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2026}, pages = {1819-1829} } ```