| # Image Generation Benchmark | |
| This subfolder contains the canonical train/test split for the glaze image generation benchmark. | |
| ## Files | |
| - `train/images/` | |
| - `train/metadata.json` | |
| - `train/recipes.json` | |
| - `train/targets.json` | |
| - `train/sample_ids.json` | |
| - `test/images/` | |
| - `test/metadata.json` | |
| - `test/recipes.json` | |
| - `test/targets.json` | |
| - `test/sample_ids.json` | |
| - `dataset_statistics.json` | |
| ## Current canonical counts | |
| - Train total: 4,490 | |
| - Test total: 443 | |
| - Train transparency labels: 2,968 | |
| - Test transparency labels: 344 | |
| - Train surface labels: 3,381 | |
| - Test surface labels: 331 | |
| - Train color family labels: 4,483 | |
| - Test color family labels: 443 | |
| ## Benchmark usage | |
| This track supports conditional image generation from glaze-related conditioning signals, including: | |
| - RGB color targets | |
| - transparency labels when available | |
| - surface labels when available | |
| - recipe composition and ingredient lists | |
| - firing condition metadata when available | |
| ## Notes | |
| - The image generation benchmark is separate from the larger property prediction benchmark. | |
| - `metadata.json` is the richest entry point for multimodal loading because it links images, attributes, and recipe-side information. | |
| - Missing `transparency` or `surface` values should be treated as unlabeled rather than negative labels. | |
| - A minimal retrieval-style reference is provided in `../baselines/image_generation_baseline.py`. |