Table 6. Summary of YO-CNN.

S.no	From	n	Parameters	Module	Arguments
0	−1	1	3,520	models.common.Conv	[3, 32, 6, 2, 2]
1	−1	1	18,560	models.common.Conv	[32, 64, 3, 2]
2	−1	1	18,816	models.common.C3	[64, 64, 1]
3	−1	1	73,984	models.common.Conv	[64, 128, 3, 2]
4	−1	2	115,712	models.common.C3	[128, 128, 2]
5	−1	1	295,424	models.common.Conv	[128, 256, 3, 2]
6	−1	3	625,152	models.common.C3	[256, 256, 3]
7	−1	1	1,180,672	models.common.Conv	[256, 512, 3, 2]
8	−1	1	1,182,720	models.common.C3	[512, 512, 1]
9	−1	1	656,896	models.common.SPPF	[512, 512, 5]
10	−1	1	131,584	models.common.Conv	[512, 256, 1, 1]
11	−1	1	0	torch.nn.modules.upsampling.Upsample	[None, 2, ’nearest’]
12	[−1, 6]	1	0	models.common.Concat	[1]
13	−1	1	361,984	models.common.C3	[512, 256, 1, False]
14	−1	1	33,024	models.common.Conv	[256, 128, 1, 1]
15	−1	1	0	torch.nn.modules.upsampling.Upsample	[None, 2, ’nearest’]
16	[−1, 4]	1	0	models.common.Concat	[1]
17	−1	1	90,880	models.common.C3	[256, 128, 1, False]
18	-1	1	147,712	models.common.Conv	[128, 128, 3, 2]
19	[−1, 14]	1	0	models.common.Concat	[1]
20	−1	1	296,448	models.common.C3	[256, 256, 1, False]
21	−1	1	590,336	models.common.Conv	[256, 256, 3, 2]
22	[−1, 10]	1	0	models.common.Concat	[1]
23	−1	1	1,182,720	models.common.C3	[512, 512, 1, False]
24	[17, 20, 23]	1	59,334	models.yolo.Detect	[17, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]

Notes.

Model Summary: 270 layers, 7,065,478 parameters, 7,065,478 gradients, 16.0 GFLOPs.