| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - glaze |
| - benchmark |
| pretty_name: GlazyBench |
| size_categories: |
| - 10K<n<100K |
| --- |
| # GLAZE Benchmark for Hugging Face |
| [](https://arxiv.org/abs/2605.06641) |
| [](https://github.com/ziazhai/GlazyBench) |
|
|
| This folder is a publication-ready export of the benchmark assets that are currently treated as canonical in this repository. |
|
|
| It is organized into two benchmark tracks: |
|
|
| - `property_prediction/`: fixed train/test split for glaze property prediction. |
| - `image_generation/`: fixed train/test split for glaze image generation with paired metadata and conditioning signals. |
|
|
| It also contains an optional source-information layer for users who want to extract additional fields themselves: |
|
|
| - `source_records/`: one merged JSON file per packaged sample, plus filtered HTML-derived metadata. |
| - `raw_html/`: raw Glazy HTML pages for the packaged sample IDs only. |
| - `tools/`: small utilities that operate directly on the exported Hugging Face package. |
| - `baselines/`: minimal executable reference baselines for both benchmark tracks. |
|
|
| ## Included benchmark assets |
|
|
| ### 1. Property prediction benchmark |
|
|
| Canonical source: `data/` |
|
|
| Tasks: |
|
|
| - `transparency`: 4-class classification |
| - Labels: `Opaque`, `Semi-opaque`, `Translucent`, `Transparent` |
| - `surface`: 9-class classification |
| - Labels: `Glossy`, `Semi-glossy`, `Satin`, `Satin-matte`, `Matte`, `Semi-matte`, `Smooth Matte`, `Dry Matte`, `Stony Matte` |
| - `color_family`: 9-class classification |
| - Labels: `Black`, `Blue`, `Gray`, `Green`, `Orange`, `Purple`, `Red`, `White`, `Yellow` |
| - `color_rgb`: RGB regression target derived from the same canonical target file |
|
|
| Current canonical split sizes: |
|
|
| - Train: 16,781 examples |
| - Test: 4,903 examples |
|
|
| Per-task labeled coverage in the current canonical files: |
|
|
| - Train transparency: 9,023 |
| - Test transparency: 3,322 |
| - Train surface: 9,378 |
| - Test surface: 3,730 |
| - Train color family: 16,781 |
| - Test color family: 4,903 |
|
|
| ### 2. Image generation benchmark |
|
|
| Canonical source: `image_gen/` |
|
|
| This track contains images plus structured metadata for conditional image generation. |
|
|
| Current canonical split sizes: |
|
|
| - Train: 4,490 examples |
| - Test: 443 examples |
|
|
| Per-task labeled coverage in the current canonical files: |
|
|
| - Train transparency: 2,968 |
| - Test transparency: 344 |
| - Train surface: 3,381 |
| - Test surface: 331 |
| - Train color family: 4,483 |
| - Test color family: 443 |
|
|
| ## Intentionally excluded from this export |
|
|
| These files exist in the repository but are not part of the public benchmark package because they are historical backups, intermediate variants, or analysis-side artifacts: |
|
|
| - `data/raw_data/` |
| - `data/train/targets_filtered_by_models.json` |
| - `data/train/targets_filtered_voting.json` |
| - `data/sample_types_train.csv` |
| - `data/sample_type_report.md` |
| - training logs, model outputs, checkpoints, and analysis-only files |
|
|
| The rule used here is simple: if a file is not part of the canonical benchmark split consumed by current benchmark code or benchmark-facing documentation, it is excluded. |
|
|
| ## Optional source-information layer |
|
|
| Some fields in the canonical metadata are intentionally lightweight. In particular, page-derived attributes such as title, author, author URL, and long-form description are richer in the original HTML pages than in the benchmark-facing `metadata.json` files. |
|
|
| To support downstream custom extraction without changing the canonical splits, the package may include: |
|
|
| - `source_records/by_id/<id>.json`: merged per-sample record assembled from packaged recipe, target, metadata, and parsed HTML metadata. |
| - `source_records/html_metadata.json`: filtered HTML-derived metadata for packaged sample IDs. |
| - `raw_html/<id>.html`: original raw HTML page for the packaged sample ID. |
| - `tools/read_source_record.py`: convenience CLI for reading sample records and raw HTML from the exported package itself. |
|
|
| These supplemental files are aligned to packaged sample IDs and are meant for provenance and user-defined parsing, not as the primary benchmark interface. |
|
|
| ## Minimal baselines included |
|
|
| To make the package directly usable after download, this export also includes dependency-light baseline scripts: |
|
|
| - `baselines/property_prediction_baseline.py`: majority-class baseline for `transparency`, `surface`, and `color_family`, plus a mean-RGB baseline for `color_rgb`. |
| - `baselines/image_generation_baseline.py`: nearest-train-sample retrieval baseline using RGB distance. |
|
|
| Run them from the repository root with: |
|
|
| ```bash |
| python huggingface/baselines/property_prediction_baseline.py |
| python huggingface/baselines/image_generation_baseline.py |
| ``` |
|
|
| See `baselines/README.md` for output details and optional flags. |
|
|
| ## Publication note |
|
|
| This folder organizes the benchmark for sharing, but it does not assert a license on behalf of the original data sources. Before uploading to Hugging Face, confirm that the Glazy-derived metadata and images are cleared for redistribution under your intended release terms. |
|
|
| ## Recommended Hugging Face presentation |
|
|
| If you publish this as a dataset repository, keep the two subfolders as two benchmark configurations inside one dataset card: |
|
|
| - `property_prediction` |
| - `image_generation` |
|
|
| This makes the public package match the benchmark structure already used in this repository. |
