ReDet implements a rotation-equivariant detector for object detection in aerial images, addressing the challenge of arbitrary object orientations. It incorporates rotation-equivariant networks to extract orientation-aware features, reducing redundant parameters compared to standard CNN approaches. The method uses rotation-invariant RoI alignment to produce consistent features regardless of object orientation. This is a research implementation built on mmdetection for evaluating aerial object detection performance.