image_generation/README.md

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.