YOLOv8 trained on synthetic images identifies 2D colloidal assemblies at 97–99% on synthetic data but shows a 43.1% average error on real micrographs, from 20% for spheres to 58.5% for cuboids
The authors built synthetic datasets of roughly 135–155 images each for spherical, ellipsoidal, cuboid, and rod-like colloidal particles, manually annotated into five classes (isolated particles, dimers, chains, loops, clusters), trained four YOLOv8-seg models with polygonal instance segmentation, obtained about 97–99% accuracy on synthetic test sets, and found that transfer to 40 experimental micrographs (ten per shape) degraded sharply, with an average relative error of 43.1% against the synthetic benchmark: 20% for spheres, 44.7% for ellipsoids, 49.2% for rods, and 58.5% for cuboids; the datasets and trained models are openly available and integrated into the isanm.space information system.