Optical pretrain, SAR finetune, twelve epochs.

SSDD (Zhang et al.) is 1,160 SAR ship chips (~190–688 px), RadarSat-2 / TerraSAR-X / Sentinel-1, 1–15 m. Official split: file numbers whose last digit is 1 or 9 are test (~232); the rest are train (~928). oriented-det v0.3 loads it natively (dataset.format: ssdd). A full Faster R-CNN 1× finetune from the DOTA Hub reached 90.34% mAP50 on held-out test. There is no SSDD Hub zoo.

SSDD offshore test chip — official RBox ground truth (research illustration; chips stay with the authors)

SSDD offshore test chip — official RBox ground truth (research illustration; chips stay with the authors)


The dataset Link to heading

Single class ship. Native loader discovers VOC XML (rotated_bndbox / robndbox), then COCO, then DOTA. Point data_root at Official-SSDD-OPEN (walks into RBox_SSDD/voc_style). Grayscale SAR is loaded as RGB (channel repeat). Chips fit a 608 keep-ratio canvas — do not tile.

SplitChipsIn training?
last-digit train~928Yes
last-digit test~232No
test inshore / offshore46 / 186subsets of test
odet train --config configs/rotated_faster_rcnn/ssdd_le90_1x.json
make eval-val EXPERIMENT=runs/rotated_faster_rcnn/<timestamp>

Notebook: notebooks/ssdd_finetune_tutorial.ipynb (1-epoch smoke and full 1×). Optional: odet ssdd-to-dota.

SSDD inshore test chip — official RBox ground truth

SSDD inshore test chip — official RBox ground truth


Local 1× Faster R-CNN: 90.34% Link to heading

From hf://rotated_faster_rcnn_dota_le90_1x (1-way ship head re-init), RTX 3090 Ti, 15 m (runs/rotated_faster_rcnn/20260918-130546). Score ≥ 0.05, rotated IoU 0.50, NMS IoU 0.10. Report: docs/eval-reports/rotated_faster_rcnn_ssdd_le90_1x/.

EpochTrain lossIn-train val mAP50 (score ≥ 0.3)
40.29279.86%
80.26080.73%
120.23590.41%

make eval-val (score ≥ 0.05) is 90.34% — same checkpoint, slightly different floor. Best-F1 deploy threshold on that sweep is 0.40 (P 0.94 / R 0.91 / F1 0.93). Mean best IoU vs GT is 0.74.

Precision–recall on SSDD held-out test (Rotated Faster R-CNN 1×)

Precision–recall on SSDD held-out test (Rotated Faster R-CNN 1×)

Threshold sweep (precision, recall, F1) on SSDD held-out test

Threshold sweep (precision, recall, F1) on SSDD held-out test

Literature Faster R-CNN on SSDD sits around ~89% (Guo et al., Sensors 2021: 88.96% overall). Treat <80% overall as a failed train. The notebook 1-epoch smoke will not hit 90%. Inshore / offshore subsets were not scored separately on this run — the pictures above are the contrast, not a split table.


What this shows Link to heading

You do not start SAR detection from random weights. You start from an oriented optical zoo, swap the head, and qualify on a real holdout. We do not redistribute the chips and we do not ship a SAR zoo. Weather-independent ships on your SAR still need your license and your test set.



Written on September 28, 2026 by Jeff Faudi. Link to heading