【YOLOv5/v7改进系列】引入CoordConv—

一、导言

与标准卷积层相比，CoordConv 的主要区别在于它显式地考虑了位置信息。在标准卷积中，卷积核在输入上滑动时，仅关注局部区域的像素强度，而忽略其绝对位置。CoordConv 通过在输入特征图中添加坐标信息，使得卷积核能够“知道”每个像素的确切位置，这在某些任务中（如需要精确定位的任务）可能是有益的。例如，在游戏环境中的物体定位、图像中的目标检测或者强化学习中的策略学习等场景下，位置信息可能对模型的表现有显著影响。

文章探讨了卷积神经网络（CNN）在处理坐标变换任务时的一个令人惊讶的失败案例，并提出了一种解决方案，即CoordConv。以下是对该论文优点和缺点的分析：

优点：

创新性：
提出了一个新颖的问题，即CNN在学习从坐标空间到像素空间的映射时存在显著的缺陷。CoordConv的概念为CNN提供了一个简单的补救措施，通过额外的坐标通道使卷积层能够访问其输入坐标。
实证研究：
作者通过一系列实验展示了CNN在处理坐标变换问题上的局限性，以及CoordConv如何解决这些问题。展示了CoordConv在不同任务上（如GAN、Faster R-CNN、Atari游戏）的性能提升，这表明其适用范围广泛。
效率与性能：
CoordConv模型不仅在训练速度上远超传统CNN（150倍快），而且参数量也大大减少（10-100倍少），同时实现了完美的一般化能力。
代码公开：
为了便于其他研究人员复现实验结果并利用CoordConv，作者公开了实现代码。

缺点：

潜在的普遍性：
尽管CoordConv在特定任务中表现出色，但其是否能在所有涉及坐标变换或位置敏感的任务中普遍有效，仍需进一步研究。
对现有任务的影响：
论文提到，CoordConv可能改善了某些任务的表现，但没有深入讨论它是否会在所有相关领域都带来同样的改进，或者是否有可能在某些情况下反而产生负面影响。
应用的局限性：
CoordConv虽然在论文中提及的几个任务上有效，但它的长期影响和在更多复杂场景下的适应性仍需观察。

总的来说，这篇论文提出了一个重要的洞见，即即使在直观上适合使用CNN的任务中也可能存在陷阱，而CoordConv是一个有效的解决方案。然而，对于其更广泛的适用性和理论基础，可能还需要更多的研究来验证和扩展。

二、准备工作

首先在YOLOv5/v7的models文件夹下新建文件coordconv.py，导入如下代码

from models.common import *# https://arxiv.org/pdf/1807.03247
# ------coordconv-----------------------------------------------------
class AddCoords(nn.Module):def __init__(self, with_r=False):super().__init__()self.with_r = with_rdef forward(self, input_tensor):"""Args:input_tensor: shape(batch, channel, x_dim, y_dim)"""batch_size, _, x_dim, y_dim = input_tensor.size()xx_channel = torch.arange(x_dim).repeat(1, y_dim, 1)yy_channel = torch.arange(y_dim).repeat(1, x_dim, 1).transpose(1, 2)xx_channel = xx_channel.float() / (x_dim - 1)yy_channel = yy_channel.float() / (y_dim - 1)xx_channel = xx_channel * 2 - 1yy_channel = yy_channel * 2 - 1xx_channel = xx_channel.repeat(batch_size, 1, 1, 1).transpose(2, 3)yy_channel = yy_channel.repeat(batch_size, 1, 1, 1).transpose(2, 3)ret = torch.cat([input_tensor,xx_channel.type_as(input_tensor),yy_channel.type_as(input_tensor)], dim=1)if self.with_r:rr = torch.sqrt(torch.pow(xx_channel.type_as(input_tensor) - 0.5, 2) + torch.pow(yy_channel.type_as(input_tensor) - 0.5,2))ret = torch.cat([ret, rr], dim=1)return retclass CoordConv(nn.Module):def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, with_r=False):super().__init__()self.addcoords = AddCoords(with_r=with_r)in_channels += 2if with_r:in_channels += 1self.conv = Conv(in_channels, out_channels, k=kernel_size, s=stride)def forward(self, x):x = self.addcoords(x)x = self.conv(x)return x# -----------------------------------------------------------

其次在在YOLOv5/v7项目文件下的models/yolo.py中在文件首部添加代码

from models.coordconv import CoordConv

并搜索def parse_model(d, ch)

定位到如下行添加以下代码

CoordConv,

三、YOLOv7-tiny改进工作

完成二后，在YOLOv7项目文件下的models文件夹下创建新的文件yolov7-tiny-coordconv.yaml，导入如下代码。

# parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple# anchors
anchors:- [10,13, 16,30, 33,23]  # P3/8- [30,61, 62,45, 59,119]  # P4/16- [116,90, 156,198, 373,326]  # P5/32# yolov7-tiny backbone
backbone:# [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True[[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]],  # 0-P1/2[-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]],  # 1-P2/4[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 7[-1, 1, MP, []],  # 8-P3/8[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 14[-1, 1, MP, []],  # 15-P4/16[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 21[-1, 1, MP, []],  # 22-P5/32[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 28]# yolov7-tiny head
head:[[-1, 1, v7tiny_SPP, [256]], # 29[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4[[-1, -2], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 39[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3[[-1, -2], 1, Concat, [1]],[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 49[-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]],[[-1, 39], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 57[-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]],[[-1, 29], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, CoordConv, [128, 3, 1]],[-1, 1, CoordConv, [128, 3, 1]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 65[49, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[57, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[65, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[66, 67, 68], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)]

from  n    params  module                                  arguments                     0                -1  1       928  models.common.Conv                      [3, 32, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]2                -1  1      2112  models.common.Conv                      [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]3                -2  1      2112  models.common.Conv                      [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]4                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]5                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]6  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           7                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]8                -1  1         0  models.common.MP                        []                            9                -1  1      4224  models.common.Conv                      [64, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]10                -2  1      4224  models.common.Conv                      [64, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]11                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]12                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]13  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]15                -1  1         0  models.common.MP                        []                            16                -1  1     16640  models.common.Conv                      [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]17                -2  1     16640  models.common.Conv                      [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]18                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]19                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]20  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           21                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]22                -1  1         0  models.common.MP                        []                            23                -1  1     66048  models.common.Conv                      [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]24                -2  1     66048  models.common.Conv                      [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]25                -1  1    590336  models.common.Conv                      [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]26                -1  1    590336  models.common.Conv                      [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]27  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           28                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]29                -1  1    657408  models.common.v7tiny_SPP                [512, 256]                    30                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]31                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          32                21  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]33          [-1, -2]  1         0  models.common.Concat                    [1]                           34                -1  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]35                -2  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]36                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]37                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]38  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           39                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]40                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]41                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          42                14  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]43          [-1, -2]  1         0  models.common.Concat                    [1]                           44                -1  1      4160  models.common.Conv                      [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]45                -2  1      4160  models.common.Conv                      [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]46                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]47                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]48  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           49                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]50                -1  1     73984  models.common.Conv                      [64, 128, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]51          [-1, 39]  1         0  models.common.Concat                    [1]                           52                -1  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]53                -2  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]54                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]55                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]56  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           57                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]58                -1  1    295424  models.common.Conv                      [128, 256, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]59          [-1, 29]  1         0  models.common.Concat                    [1]                           60                -1  1     65792  models.common.Conv                      [512, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]61                -2  1     65792  models.common.Conv                      [512, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]62                -1  1    150016  models.coordconv.CoordConv              [128, 128, 3, 1]              63                -1  1    150016  models.coordconv.CoordConv              [128, 128, 3, 1]              64  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           65                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]66                49  1     73984  models.common.Conv                      [64, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]67                57  1    295424  models.common.Conv                      [128, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]68                65  1   1180672  models.common.Conv                      [256, 512, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]69      [66, 67, 68]  1     17132  models.yolo.IDetect                     [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]Model Summary: 264 layers, 6019596 parameters, 6019596 gradients, 13.2 GFLOPS

运行后若打印出如上文本代表改进成功。

四、YOLOv5s改进工作

完成二后，在YOLOv5项目文件下的models文件夹下创建新的文件yolov5s-coordconv.yaml，导入如下代码。

# Parameters
nc: 1  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:- [10,13, 16,30, 33,23]  # P3/8- [30,61, 62,45, 59,119]  # P4/16- [116,90, 156,198, 373,326]  # P5/32# YOLOv5 v6.0 backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2[-1, 1, Conv, [128, 3, 2]],  # 1-P2/4[-1, 3, C3, [128]],[-1, 1, Conv, [256, 3, 2]],  # 3-P3/8[-1, 6, C3, [256]],[-1, 1, Conv, [512, 3, 2]],  # 5-P4/16[-1, 9, C3, [512]],[-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32[-1, 3, C3, [1024]],[-1, 1, SPPF, [1024, 5]],  # 9]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]],  # cat backbone P4[-1, 3, C3, [512, False]],  # 13[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]],  # cat backbone P3[-1, 3, C3, [256, False]],  # 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 14], 1, Concat, [1]],  # cat head P4[-1, 3, C3, [512, False]],  # 20 (P4/16-medium)[-1, 1, CoordConv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]],  # cat head P5[-1, 3, C3, [1024, False]],  # 23 (P5/32-large)[[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

from  n    params  module                                  arguments                     0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                2                -1  1     18816  models.common.C3                        [64, 64, 1]                   3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               4                -1  2    115712  models.common.C3                        [128, 128, 2]                 5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              6                -1  3    625152  models.common.C3                        [256, 256, 3]                 7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 10                -1  1    131584  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    361984  models.common.C3                        [512, 256, 1, False]          14                -1  1     33024  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     90880  models.common.C3                        [256, 128, 1, False]          18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              19          [-1, 14]  1         0  models.common.Concat                    [1]                           20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          21                -1  1    594944  models.coordconv.CoordConv              [256, 256, 3, 2]              22          [-1, 10]  1         0  models.common.Concat                    [1]                           23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          24      [17, 20, 23]  1     16182  models.yolo.Detect                      [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]Model Summary: 272 layers, 7026934 parameters, 7026934 gradients, 15.9 GFLOPs

运行后若打印出如上文本代表改进成功。

五、YOLOv5n改进工作

完成二后，在YOLOv5项目文件下的models文件夹下创建新的文件yolov5n-coordconv.yaml，导入如下代码。

# Parameters
nc: 1  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.25  # layer channel multiple
anchors:- [10,13, 16,30, 33,23]  # P3/8- [30,61, 62,45, 59,119]  # P4/16- [116,90, 156,198, 373,326]  # P5/32# YOLOv5 v6.0 backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2[-1, 1, Conv, [128, 3, 2]],  # 1-P2/4[-1, 3, C3, [128]],[-1, 1, Conv, [256, 3, 2]],  # 3-P3/8[-1, 6, C3, [256]],[-1, 1, Conv, [512, 3, 2]],  # 5-P4/16[-1, 9, C3, [512]],[-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32[-1, 3, C3, [1024]],[-1, 1, SPPF, [1024, 5]],  # 9]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]],  # cat backbone P4[-1, 3, C3, [512, False]],  # 13[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]],  # cat backbone P3[-1, 3, C3, [256, False]],  # 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 14], 1, Concat, [1]],  # cat head P4[-1, 3, C3, [512, False]],  # 20 (P4/16-medium)[-1, 1, CoordConv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]],  # cat head P5[-1, 3, C3, [1024, False]],  # 23 (P5/32-large)[[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

from  n    params  module                                  arguments                     0                -1  1      1760  models.common.Conv                      [3, 16, 6, 2, 2]              1                -1  1      4672  models.common.Conv                      [16, 32, 3, 2]                2                -1  1      4800  models.common.C3                        [32, 32, 1]                   3                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                4                -1  2     29184  models.common.C3                        [64, 64, 2]                   5                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               6                -1  3    156928  models.common.C3                        [128, 128, 3]                 7                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              8                -1  1    296448  models.common.C3                        [256, 256, 1]                 9                -1  1    164608  models.common.SPPF                      [256, 256, 5]                 10                -1  1     33024  models.common.Conv                      [256, 128, 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     90880  models.common.C3                        [256, 128, 1, False]          14                -1  1      8320  models.common.Conv                      [128, 64, 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     22912  models.common.C3                        [128, 64, 1, False]           18                -1  1     36992  models.common.Conv                      [64, 64, 3, 2]                19          [-1, 14]  1         0  models.common.Concat                    [1]                           20                -1  1     74496  models.common.C3                        [128, 128, 1, False]          21                -1  1    150016  models.coordconv.CoordConv              [128, 128, 3, 2]              22          [-1, 10]  1         0  models.common.Concat                    [1]                           23                -1  1    296448  models.common.C3                        [256, 256, 1, False]          24      [17, 20, 23]  1      8118  models.yolo.Detect                      [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [64, 128, 256]]Model Summary: 272 layers, 1767574 parameters, 1767574 gradients, 4.2 GFLOPs