Changelog¶

All notable changes to OrientedDet will be documented in this file.

[Unreleased]¶

ProbIoU ROI regression for Rotated Faster R-CNN (roi_box_reg_main_loss_type: probiou + Smooth L1 aux).
Hub slugs rotated_faster_rcnn_dota_le90_3x (83.42% eval-val mAP50) and rotated_faster_rcnn_dota_le90_1x (77.57% eval-val mAP50).
dataset.train_includes_val config flag (Airbus Playground: train on all folds; val fold for monitoring only).
Source provenance metadata in training runs (git_commit, package version, config hash).
Eval reports under docs/eval-reports/; make eval-val full-tile protocol documented.

ROI regression loss: encoded-space Smooth L1 on all 5 channels (MMRotate), replacing radian periodic angle loss that under-weighted angle gradients vs MMRotate.
Oriented R-CNN: MMDet avg_factor for midpoint RPN and oriented ROI losses; training RPN proposals no longer score-filtered; ROI matching defaults to rotated IoU (roi_use_hbb_for_matching: false); oriented RoIAlign uses first 4 FPN levels only.
Rotated RetinaNet: separate cls/reg 4-conv towers with 3×3 prediction heads; P6/P7 via LastLevelP6P7 on C5; rotated IoU assignment; encoded L1 reg loss with avg_factor normalization.

RetinaNet checkpoints from before this release are incompatible (head.convs / 1×1 heads / extra_fpn_conv removed). Re-train or use Hub weights published after this change.

Core geometry (Polygon, QBox, RBox) and transforms
Rotated IoU, NMS, and optional GPU kernels
DOTA loader, tiling, augmentations, oriented mAP
Airbus Playground CSV dataset support
Oriented R-CNN, Rotated Faster R-CNN, Rotated RetinaNet
JSON config training via odet train
Pretrained weights on Hugging Face Hub (dl4eo/oriented-det-pretrained), including Oriented R-CNN 1× DOTA le90 (74.79% eval-val mAP50) and Oriented R-CNN 3× DOTA le90 (79.40% eval-val mAP50)
MkDocs user guide and API reference