Case study: Songdo Vision OBB¶
HBB2OBB produced Songdo Vision OBB, the oriented-box release of the Songdo Vision v2 aerial vehicle dataset: 274,190 oriented boxes over 5,419 frames of 4K drone imagery from Songdo, South Korea, one per horizontal box in the source dataset and carrying its class and identifier.
The hyperparameters were selected by hbb2obb-optimize against 50 manually annotated frames that
share no image with the converted set: 11 SAM model sets over 495 grid points, 30.2 h on one 24 GB
card.
| Songdo Vision OBB | |
|---|---|
| 🎯 Selected configuration | sam_l alone, --imgsz 1024 -sf 0.02 -okp -0.1 -fr 0.1 |
| 📐 Mean / median IoU vs. manual OBBs | 0.88679 / 0.91759 |
| 📊 Share at IoU >= 0.75 / 0.90 | 92.06% / 61.51% |
| 🧭 Orientation error, 90th percentile | 3.50 deg |
| 🔁 Fallbacks to the source HBB | 0 of 4,245 on the validation frames, 0 of 274,190 in the release |
Two findings from that search are worth carrying to another dataset. Closing the mask beat both
alternatives in 165 of 165 comparable cells, on mean IoU and on the share above IoU 0.9 alike, so
-okp is worth setting negative before it is worth tuning. And majority-vote ensembling of SAM
variants did not improve the conversion: the objective could not separate five checkpoints from
one, and the accuracy-against-compute front had two points on it rather than a curve.
The full study, every grid point and the figures behind it ship with the dataset as
hbb2obb_benchmark/, and re-run from the config beside them without this repository.
The study used hbb2obb-optimize with a benchmark file, and hbb2obb-analyze reproduces the breakdown tables published with the dataset; the analysis guide shows two of them.