Three weeks after v0.2.0, Oriented-Det v0.3.1 is on PyPI and tagged on GitHub. This is the dataset + deploy chapter (0.3.0) plus a same-day patch: Rotated RetinaNet Hub is OBB, odet export is a first-class CLI, and deploy sidecars match Hub class lists. The four ResNet-FPN detector families stay. What landed is four native loaders, an HRSC Hub 3× zoo, a DOTA zoo you should advertise from 1×, and ONNX export.
Upgrade Link to heading
pip install -U oriented-det
# or pin:
pip install oriented-det==0.3.1
PyTorch is still installed separately for your platform (pytorch.org). Weights stay on Hugging Face at dl4eo/oriented-det-pretrained. For ONNX export from a checkout: uv pip install -e ".[export]" then odet export --help.
Headline: four loaders, not a fifth detector Link to heading
| Dataset | Modality | In v0.3 | Hub zoo? | Published number |
|---|---|---|---|---|
| HRSC2016 | Optical ships | Native XML + ImageSets | Yes — three 3× | Held-out test 90.41% / 88.77% / 88.34% |
| FAIR1M | Optical, 37-class | Native XML + convert/tile | No (CC BY-NC-SA dump) | Local Faster R-CNN 1× tiled-val 36.70% |
| SSDD | SAR ships | Native VOC / COCO / DOTA | No | Local Faster R-CNN 1× held-out 90.34% |
| HRSID | SAR ships | Native COCO polygons | No | Local Faster R-CNN 1× held-out 78.55% |
DOTA remains the pretrain zoo. HRSC is the published small-data ship zoo. FAIR1M / SSDD / HRSID are train locally from the matching DOTA 1× Hub checkpoint. Full write-ups: HRSC already live, FAIR1M 24 Sep, SSDD 28 Sep, HRSID 1 Oct, Docker 5 Oct, ONNX 8 Oct.
DOTA Hub: advertise and finetune from 1× Link to heading
All eight advertised DOTA Hub slugs quote official Task 1 (hidden test). Recipes train on trainval, so local make eval-val is a leaky monitor — do not quote it as the zoo number.
| Model | 1× AP50 | 3× AP50 | 1× AP75 | 3× AP75 |
|---|---|---|---|---|
| Oriented R-CNN | 76.73% | 74.88% | 50.24 | 51.23 |
| Rotated Faster R-CNN | 74.42% | 74.48% | 41.90 | 45.39 |
| Rotated FCOS | 73.07% | 72.91% | 40.40 | 45.39 |
| Rotated RetinaNet (OBB) | 71.72% | 73.89% | 43.46 | 47.11 |
Advertise and finetune from 1× for Oriented R-CNN, Faster R-CNN, and FCOS. 3× Task 1 AP50 is a drop or a wash; the 3× gain is box tightness (AP75). RetinaNet 3× is the AP50 exception. Versus MMRotate 1×: Oriented R-CNN +1.04, Faster R-CNN +1.02, FCOS +1.79, RetinaNet +3.30 vs OBB 68.42. Circum-HBB from 0.3.0 lives at rotated_retinanet_dota_le90_{1x,3x}_hbb (67.87% / 70.70%).
Split decoder ring Link to heading
| Dataset | What make eval-val scores | Held-out? |
|---|---|---|
| DOTA | val tiles that were also in train | No (leaky). Real test: Task 1 |
| HRSC / FAIR1M / SSDD / HRSID | official test or val | Yes |
Do not call HRSC, FAIR1M, SSDD, or HRSID eval-val leaky. That word is DOTA-only.
ONNX export Link to heading
uv pip install -e ".[export]"
make export-onnx # default: Rotated FCOS DOTA 1× Hub
odet export demo # same CLI as python -m export
Pre-NMS ONNX plus Python preprocess / rotated NMS for Rotated FCOS, Oriented R-CNN, and Rotated Faster R-CNN. Consumers need numpy, Pillow, and ONNX Runtime — no PyTorch, no oriented-det. Supported canvas is fixed 1024×1024 (DOTA tiles). Out of this graph: keep_ratio (HRSC / SSDD / HRSID), sliding-window tiling, RetinaNet detect. Docker Tile Geo Process walkthrough on 5 Oct; ONNX walkthrough on 8 Oct.
Also shipped Link to heading
resize_mode: keep_ratio+pad_size_divisor(HRSC / SSDD / HRSID whole-image canvas)- Random rotate train aug (
PolyRandomRotate-style) - Diagonal-flip θ fix (MMRotate keeps θ; we had applied
π − θ) odet dota-submitfor official Task 1 zips- Deploy floors = eval-val global F1 − 0.05 (RetinaNet OBB 0.25; HBB 0.35); final NMS 0.1 on DOTA / HRSC recipes
- 0.3.1: un-suffixed RetinaNet Hub slugs are OBB;
odet export; deploygenerate_description.pytreats a DOTA class list as v1 by set equality (Hub sidecars are alphabetical)
License, again Link to heading
Apache 2.0 covers oriented-det’s code. It does not cover DOTA, HRSC, FAIR1M, SSDD, or HRSID pixels. See the licensing note. Production detectors still need imagery you are allowed to train on.
What’s next (this series) Link to heading
| Date | Post |
|---|---|
| Thu 24 Sep | FAIR1M — why 36.70% is in band |
| Mon 28 Sep | SSDD — optical DOTA → SAR in 12 epochs |
| Thu 1 Oct | HRSID — 78.55% rotated AP50 vs Wei HBB |
| Mon 5 Oct | Docker — Tile Geo Process, GeoJSON out |
| Thu 8 Oct | ONNX — export without PyTorch on the infer box |
Public roadmap after that: v0.4 speed tier (RTMDet-R, native YOLO-OBB).
Links Link to heading
- Release notes: github.com/DL4EO/oriented-det/releases/tag/v0.3.1
- PyPI: pypi.org/project/oriented-det/0.3.1
- Documentation: dl4eo.github.io/oriented-det
- Pretrained zoo: huggingface.co/dl4eo/oriented-det-pretrained
- Previous: Lessons learned on DOTA · HRSC2016 · v0.2.0
- Next: FAIR1M (24 Sep)