Tag: Rotated-Fcos

  • Which oriented detector should you train?

    Start with Rotated RTMDet-S (77.60% on DOTA, tight-box score 52.01). Switch to Oriented R-CNN for a diagonal row of long vehicles, Faster R-CNN when finding the object matters more, or FCOS for that row on a shorter ResNet train.

  • Does MMRotate Faster R-CNN also mess up the bus lot?

    Official MMRotate Rotated Faster R-CNN 1× is as messy as OrientedDet Faster R-CNN on the DOTA bus-lot demo tile. The failure is architectural and rare (dense ~45° objects). Oriented R-CNN is clean but heavy to finetune; FCOS follows the heading on this tile. RTMDet was not re-run here.

  • OSSDD — Sentinel-1 ships, finetuned with RTMDet, Faster R-CNN, and FCOS

    OSSDD is Sentinel-1 GRD ship chips with rotated boxes. VH 1× finetunes from the DOTA hubs. Held-out test AP50 at IoU 0.5 and score 0.3: RTMDet-S 88.54%, Rotated Faster R-CNN 70.53%, Rotated FCOS 52.70%. The paper mAP on these rotated boxes is 69.09% / 60.10% / 62.65%. No OSSDD Hub.

  • ONNX export in oriented-det — inference without PyTorch

    oriented-det v0.3 ships pre-NMS ONNX export for Rotated FCOS, Oriented R-CNN, and Faster R-CNN. Consumers run ONNX Runtime + numpy + Pillow. Fixed 1024 canvas; keep_ratio and sliding windows stay out of graph.

  • Rotated FCOS vs Oriented R-CNN on macOS

    Hands-on Apple Silicon comparison of Hub 1× DOTA checkpoints — Rotated FCOS (73.07% official Task 1) vs Oriented R-CNN (76.73%) — MPS latency, 1× training wall on NVIDIA L4, score thresholds, and side-by-side detections.

  • Oriented-Det v0.2.0 — Rotated FCOS, decoded rIoU, and a four-family zoo

    Oriented-det v0.2.0 is on PyPI — Rotated FCOS joins the zoo as the balanced one-stage detector, with a decoded rIoU 1× Hub checkpoint at 73.07% official DOTA Task 1, and the same Apache 2.0 stack.