https://deeplearning.earth/tags/rotated-faster-rcnn/Recent content in Rotated-Faster-Rcnn on DeepLearning.EarthHugo -- gohugo.ioen-usSat, 11 Jul 2026 11:00:00 +0700https://deeplearning.earth/posts/2026-07-11_oriented-det_v0_1_1_prob_iou_mmrotate_parity_and_the_updated_zoo/Sat, 11 Jul 2026 09:00:00 +0700https://deeplearning.earth/posts/2026-07-11_oriented-det_v0_1_1_prob_iou_mmrotate_parity_and_the_updated_zoo/Six weeks after v0.1.0, Oriented-Det v0.1.1 is on PyPI and tagged on GitHub. This is the release that packages the ProbIoU work, closes several MMRotate parity gaps in the training stack, and publishes the full eval-val protocol we have been using internally. If you already read Rotated Faster R-CNN on DOTA without custom CUDA, you have seen the technical story behind the headline number. This post is the release note: what changed, how to upgrade, and what to watch for.https://deeplearning.earth/posts/2026-07-10_rotated_faster_rcnn_probiou_dota/Fri, 10 Jul 2026 09:00:00 +0700https://deeplearning.earth/posts/2026-07-10_rotated_faster_rcnn_probiou_dota/Satellite scene with oriented bounding boxes Oriented object detection on satellite imagery lives or dies on rotated IoU — the overlap between two arbitrarily angled rectangles. Frameworks like MMRotate ship exact, CUDA-accelerated rotated IoU for training and inference. That is fast and precise, but it ties you to a heavy stack: MMCV custom ops, version pins, and compiled extensions that are painful to ship in a lean research codebase. OrientedDet takes a different path for v1: a full Python / PyTorch detector with no custom CUDA kernels and no MMCV runtime dependency.