Yolov5 for Oriented Object Detection

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 mAP^{test ^0.5 DOTAv1.0}	OBB mAP^{test ^0.5 DOTAv1.5}	OBB mAP^{test ^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

Name		Name	Last commit message	Last commit date
Latest commit History 116 Commits
DOTA_devkit		DOTA_devkit
data		data
dataset/dataset_demo		dataset/dataset_demo
docs		docs
models		models
sh		sh
tools		tools
utils		utils
Arial.ttf		Arial.ttf
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
detect.py		detect.py
export.py		export.py
hubconf.py		hubconf.py
requirements.txt		requirements.txt
setup.cfg		setup.cfg
test.txt		test.txt
train.py		train.py
tutorial.ipynb		tutorial.ipynb
val.py		val.py