Object Detection

YOLOv7

Model	State	Test Size	\(AP^{val}\)	\(AP_{50}^{val}\)	\(AP_{75}^{val}\)	Param.	FLOPs
YOLOv7	🔧	640	51.4%	69.7%	55.9%	—	—
YOLOv7-X	🔧	640	53.1%	71.2%	57.8%	—	—
YOLOv7-W6	🔧	1280	54.9%	72.6%	60.1%	—	—
YOLOv7-E6	🔧	1280	56.0%	73.5%	61.2%	—	—
YOLOv7-D6	🔧	1280	56.6%	74.0%	61.8%	—	—
YOLOv7-E6E	🔧	1280	56.8%	74.4%	62.1%	—	—

YOLOv9

The trained models were utilized from the original repository Link

Model	State	Test Size	\(AP^{val}\)	\(AP_{50}^{val}\)	\(AP_{75}^{val}\)	Param.	FLOPs
YOLOv9-T	🔧	640	—	—	—	—	—
YOLOv9-S	✅	640	46.8%	63.4%	50.7%	7.1M	26.4G
YOLOv9-M	✅	640	51.4%	68.1%	56.1%	20.0M	76.3G
YOLOv9-C	✅	640	53.0%	70.2%	57.8%	25.3M	102.1G
YOLOv9-E	🔧	640	55.6%	72.8%	60.6%	57.3M	189.0G

Architecture

graph LR
subgraph BackBone
B1-->B2;
B2-->B3;
B3-->B4;
B4-->B5;
end

subgraph FPN
B3-->N3;
B4-->N4;
B5-->N5;
N5-->N4;
N4-->N3;
end

subgraph PAN
P3-->P4;
P4-->P5;
N3-->P3;
N4-->P4;
N5-->P5;
end

P3-->Main_Head;
P4-->Main_Head;
P5-->Main_Head;

subgraph Aux
B3-->R3;
B4-->R4;
B5-->R5;
R3-->A3;
R4-->A3;
R4-->A4;
R5-->A3;
R5-->A4;
R5-->A5;
end
A3-->Auxiliary_Head;
A4-->Auxiliary_Head;
A5-->Auxiliary_Head;