File size: 1,429 Bytes
234f610 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | # 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`. |