FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection

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Deep LearningFCOSR
Overview

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection
arXiv preprint (arXiv:2111.10780).

This implement is modified from mmdetection. We also refer to the codes of ReDet, PIoU, and ProbIoU.

In the process of implementation, we find that only Python code processing will produce huge memory overhead on Nvidia devices. Therefore, we directly write the label assignment module proposed in this paper in the form of CUDA extension of Pytorch. The program could not work effectively when we migrate it to cuda 11 (only support cuda10). By applying CUDA expansion, the memory utilization is improved and a lot of unnecessary calculations are reduced. We also try to train FCOSR-M on 2080ti (4 images per device), which can basically fill memory of graphics card.

Install

Please refer to install.md for installation and dataset preparation.

Getting Started

Please see get_started.md for the basic usage.

Model Zoo

benchmark

The password of baiduPan is ABCD

FCOSR serise DOTA 1.0 result.FPS(2080ti) Detail

Model backbone MS Sched. Param. Input GFLOPs FPS mAP download
FCOSR-S Mobilenet v2 - 3x 7.32M 1024×1024 101.42 23.7 74.05 model/cfg
FCOSR-S Mobilenet v2 ✓ 3x 7.32M 1024×1024 101.42 23.7 76.11 model/cfg
FCOSR-M ResNext50-32x4 - 3x 31.4M 1024×1024 210.01 14.6 77.15 model/cfg
FCOSR-M ResNext50-32x4 ✓ 3x 31.4M 1024×1024 210.01 14.6 79.25 model/cfg
FCOSR-L ResNext101-64x4 - 3x 89.64M 1024×1024 445.75 7.9 77.39 model/cfg
FCOSR-L ResNext101-64x4 ✓ 3x 89.64M 1024×1024 445.75 7.9 78.80 model/cfg

FCOSR serise DOTA 1.5 result. FPS(2080ti) Detail

Model backbone MS Sched. Param. Input GFLOPs FPS mAP download
FCOSR-S Mobilenet v2 - 3x 7.32M 1024×1024 101.42 23.7 66.37 model/cfg
FCOSR-S Mobilenet v2 ✓ 3x 7.32M 1024×1024 101.42 23.7 73.14 model/cfg
FCOSR-M ResNext50-32x4 - 3x 31.4M 1024×1024 210.01 14.6 68.74 model/cfg
FCOSR-M ResNext50-32x4 ✓ 3x 31.4M 1024×1024 210.01 14.6 73.79 model/cfg
FCOSR-L ResNext101-64x4 - 3x 89.64M 1024×1024 445.75 7.9 69.96 model/cfg
FCOSR-L ResNext101-64x4 ✓ 3x 89.64M 1024×1024 445.75 7.9 75.41 model/cfg

FCOSR serise HRSC2016 result. FPS(2080ti)

Model backbone Rot. Sched. Param. Input GFLOPs FPS AP50(07) AP75(07) AP50(12) AP75(12) download
FCOSR-S Mobilenet v2 ✓ 40k iters 7.29M 800×800 61.57 35.3 90.08 76.75 92.67 75.73 model/cfg
FCOSR-M ResNext50-32x4 ✓ 40k iters 31.37M 800×800 127.87 26.9 90.15 78.58 94.84 81.38 model/cfg
FCOSR-L ResNext101-64x4 ✓ 40k iters 89.61M 800×800 271.75 15.1 90.14 77.98 95.74 80.94 model/cfg

Lightweight FCOSR test result on Jetson Xavier NX (DOTA 1.0 single-scale). Detail

Model backbone Head channels Sched. Param Size Input GFLOPs FPS mAP onnx TensorRT
FCOSR-lite Mobilenet v2 256 3x 6.9M 51.63MB 1024×1024 101.25 7.64 74.30 Wait rtr
FCOSR-tiny Mobilenet v2 128 3x 3.52M 23.2MB 1024×1024 35.89 10.68 73.93 Wait rtr
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