Train, Test, Re-evaluate: Schedule-Sensitive Evaluation of Generative Data for Hand Detection
Generated (or synthetic) image data is increasingly used to augment or replace real training datasets when target imagery is scarce, expensive, or biased. For hand detection, particularly in occupational safety settings, public datasets mostly contain bare hands. This under-represents the variation in hand appearance introduced by gloves, tattoos, jewelry, and other personal protective equipment, creating a distribution shift that safety-critical applications encounter at deployment. We test whether generative inpainting, editing only the hand region of a real photograph to introduce accessories, can close this shift gap and improve detection of real hands at deployment. On a paired dataset of real images and their synthetic counterparts, we evaluate YOLOv8n hand detectors across six experiments (A-F), four of which involve training (A, C, D, E) under three random seeds each, evaluate them on a real test set and on a real-gloves-only test split, and report the mean average precision (mAP) at two overlap thresholds (mAP@0.5 and mAP@0.5:0.95) along with paired statistical tests. A two-stage experiment: train on real U synthetic data, then fine-tune the resulting weights on real-only at a lower learning rate, directionally improves mAP@0.5 compared to the real-only baseline model on the standard real test set, and narrows the real-gloves out-of-distribution gap. Another three-stage experiment preserves box-tightness best, achieving the highest mAP@0.5:0.95 among experiments in the study. The synthetic-data utility for safety-critical hand detection depends on the training procedure, and simple multi-stage experiments extract substantial real-deployment benefit from inpainted accessory data.