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Yolov5 for Oriented Object Detection

图片 train_batch0.jpg results.png

Results and Models

The results on DOTA_subsize1024_gap200_rate1.0 test-dev set are shown in the table below. (password: yolo)

Model
(download link)
Size
(pixels)
TTA
(multi-scale/
rotate testing)
OBB mAPtest
0.5
DOTAv1.0
OBB mAPtest
0.5
DOTAv1.5
OBB mAPtest
0.5
DOTAv2.0
Speed
CPU b1
(ms)
Speed
2080Ti b1
(ms)
Speed
2080Ti b16
(ms)
params
(M)
FLOPs
@640 (B)
yolov5m [baidu/google] 1024 × 77.3 73.2 58.0 328.2 16.9 11.3 21.6 50.5
yolov5s [baidu] 1024 × 76.8 - - - 15.6 - 7.5 17.5
yolov5n [baidu] 1024 × 73.3 - - - 15.2 - 2.0 5.0

Getting Started

The tutorial for setting environment, data preparation, and training Yolov5_obb model
The data annotation tutorial
https://github.com/chinakook/labelImg2 - oriented box annotation tool

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yolov5 (Oriented Object Detection) with updated anchors

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  • Python 59.7%
  • C++ 29.8%
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  • Cuda 3.8%
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