本文所涉及到的yolov5网络为6.1版本（6.0-6.2均适用）

yolov5加入注意力机制模块的三个标准步骤（适用于本文中的任何注意力机制）

1.common.py中加入注意力机制模块

2.yolo.py中增加对应的注意力机制关键字

3.yaml文件中添加相应模块

注：所有注意力机制的添加方法都是一致的，加入注意力机制是否有效的关键在于注意力机制添加的位置，本文提供两种常用常用方法。

注：需要下列所有注意力机制已经改好的代码版本及yaml文件（到手即用），请私聊我（免费）

目录

1.CBAM注意力机制

2.SE注意力机制

3.ECA注意力注意力机制

4.CA注意力注意力机制

5.SimAM注意力机制

6.ShuffleAttention注意力机制

7.CrissCrossAttention注意力机制

1.CBAM注意力机制

class ChannelAttention(nn.Module):def __init__(self, in_planes, ratio=16):super(ChannelAttention, self).__init__()self.avg_pool = nn.AdaptiveAvgPool2d(1)self.max_pool = nn.AdaptiveMaxPool2d(1) self.f1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False)self.relu = nn.ReLU()self.f2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False)self.sigmoid = nn.Sigmoid() def forward(self, x):avg_out = self.f2(self.relu(self.f1(self.avg_pool(x))))max_out = self.f2(self.relu(self.f1(self.max_pool(x))))out = self.sigmoid(avg_out + max_out)return outclass SpatialAttention(nn.Module):def __init__(self, kernel_size=7):super(SpatialAttention, self).__init__() assert kernel_size in (3, 7), 'kernel size must be 3 or 7'padding = 3 if kernel_size == 7 else 1 self.conv = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)self.sigmoid = nn.Sigmoid() def forward(self, x):avg_out = torch.mean(x, dim=1, keepdim=True)max_out, _ = torch.max(x, dim=1, keepdim=True)x = torch.cat([avg_out, max_out], dim=1)x = self.conv(x)return self.sigmoid(x)class CBAM(nn.Module):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, ratio=16, kernel_size=7):# ch_in, ch_out, number, shortcut, groups, expansionsuper(CBAM, self).__init__()# c_ = int(c2 * e)# hidden channels# self.cv1 = Conv(c1, c_, 1, 1)# self.cv2 = Conv(c1, c_, 1, 1)# self.cv3 = Conv(2 * c_, c2, 1)# self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])self.channel_attention = ChannelAttention(c1, ratio)self.spatial_attention = SpatialAttention(kernel_size) # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)]) def forward(self, x):out = self.channel_attention(x) * x# print('outchannels:{}'.format(out.shape))out = self.spatial_attention(out) * outreturn out

以上代码需要添加在models文件夹下的common.py文件中，具体添加位置如果找不准可以选择common.py文件的最底端（最稳妥的做法，肯定不会错），或者C3模块后面（方便查找）。

第二步，需要更改models文件夹下的yolo.py文件。可以直接ctrl+F 然后查找parse_model关键字，定位到parse_model函数，你会发现有一段这样的代码

 if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):c1, c2 = ch[f], args[0]if c2 != no:# if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:args.insert(2, n)# number of repeatsn = 1

我们仅需在第1行和第8行末尾添加CBAM即可，具体做法如下

if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, CBAM):c1, c2 = ch[f], args[0]if c2 != no:# if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x, CBAM]:args.insert(2, n)# number of repeatsn = 1

第三步，就是最为关键的改动yaml文件了，我们以yolov5s.yaml为例进行改进，这里仅截取关键部分，未截取部分则不做改动。

第一个版本是将CBAM放在backbone部分的最末端，这样可以使注意力机制看到整个backbone部分的特征图，将具有全局视野，类似于一个小transformer结构。

# YOLOv5 v6.0 backbonebackbone:# [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 [-1, 3, CBAM, [1024]], # 10]# YOLOv5 v6.0 headhead:[[-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]],# 14 [-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]],# 18 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 15], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 21 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 11], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 24 (P5/32-large) [[18, 21, 24], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

第二个版本是将CBAM放在backbone部分每个C3模块的后面，这样可以使注意力机制看到局部的特征，每层进行一次注意力，可以分担学习压力。

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, 3, CBAM, [128]], # 3 [-1, 1, Conv, [256, 3, 2]],# 4-P3/8 [-1, 6, C3, [256]],[-1, 3, CBAM, [256]],[-1, 1, Conv, [512, 3, 2]],# 7-P4/16 [-1, 9, C3, [512]], [-1, 3, CBAM, [512]],[-1, 1, Conv, [1024, 3, 2]],#10 -P5/32 [-1, 3, C3, [1024]], [-1, 3, CBAM, [1024]],[-1, 1, SPPF, [1024, 5]],# 13]# YOLOv5 v6.0 headhead:[[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 9], 1, Concat, [1]],# cat backbone P4 [-1, 3, C3, [512, False]],# 17 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]],# cat backbone P3 [-1, 3, C3, [256, False]],# 21 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 18], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 24 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 14], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 27 (P5/32-large) [[21, 24, 27], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

2.SE注意力机制

同理，首先将下方代码添加在models文件夹下的common.py文件中，具体添加位置如果找不准可以选择common.py文件的最底端（最稳妥的做法，肯定不会错），或者C3模块后面（方便查找）。

class SE(nn.Module):def __init__(self, c1, c2, r=16):super(SE, self).__init__()self.avgpool = nn.AdaptiveAvgPool2d(1)self.l1 = nn.Linear(c1, c1 // r, bias=False)self.relu = nn.ReLU(inplace=True)self.l2 = nn.Linear(c1 // r, c1, bias=False)self.sig = nn.Sigmoid()def forward(self, x):b, c, _, _ = x.size()y = self.avgpool(x).view(b, c)y = self.l1(y)y = self.relu(y)y = self.l2(y)y = self.sig(y)y = y.view(b, c, 1, 1)return x * y.expand_as(x)

第二步，需要更改models文件夹下的yolo.py文件。可以直接ctrl+F 然后查找parse_model关键字，定位到parse_model函数，你会发现有一段这样的代码

 if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):c1, c2 = ch[f], args[0]if c2 != no:# if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:args.insert(2, n)# number of repeatsn = 1

我们仅需在第1行和第8行末尾添加SE即可，具体做法如下

if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, SE):c1, c2 = ch[f], args[0]if c2 != no:# if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x, SE]:args.insert(2, n)# number of repeatsn = 1

第三步，就是最为关键的改动yaml文件了，我们以yolov5s.yaml为例进行改进，这里仅截取关键部分，未截取部分则不做改动。

第一个版本是将SE放在backbone部分的最末端，这样可以使注意力机制看到整个backbone部分的特征图，将具有全局视野，类似于一个小transformer结构。

# YOLOv5 v6.0 backbonebackbone:# [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 [-1, 3, SE, [1024]], # 10]# YOLOv5 v6.0 headhead:[[-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]],# 14 [-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]],# 18 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 15], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 21 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 11], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 24 (P5/32-large) [[18, 21, 24], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

第二个版本是将SE放在backbone部分每个C3模块的后面，这样可以使注意力机制看到局部的特征，每层进行一次注意力，可以分担学习压力。

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, 3, SE, [128]], # 3 [-1, 1, Conv, [256, 3, 2]],# 4-P3/8 [-1, 6, C3, [256]],[-1, 3, SE, [256]],[-1, 1, Conv, [512, 3, 2]],# 7-P4/16 [-1, 9, C3, [512]], [-1, 3, SE, [512]],[-1, 1, Conv, [1024, 3, 2]],#10 -P5/32 [-1, 3, C3, [1024]], [-1, 3, SE, [1024]],[-1, 1, SPPF, [1024, 5]],# 13]# YOLOv5 v6.0 headhead:[[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 9], 1, Concat, [1]],# cat backbone P4 [-1, 3, C3, [512, False]],# 17 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]],# cat backbone P3 [-1, 3, C3, [256, False]],# 21 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 18], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 24 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 14], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 27 (P5/32-large) [[21, 24, 27], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

3.ECA注意力注意力机制

同理，首先将下方代码添加在models文件夹下的common.py文件中，具体添加位置如果找不准可以选择common.py文件的最底端（最稳妥的做法，肯定不会错），或者C3模块后面（方便查找）。

class h_sigmoid(nn.Module):def __init__(self, inplace=True):super(h_sigmoid, self).__init__()self.relu = nn.ReLU6(inplace=inplace)def forward(self, x):return self.relu(x + 3) / 6class h_swish(nn.Module):def __init__(self, inplace=True):super(h_swish, self).__init__()self.sigmoid = h_sigmoid(inplace=inplace)def forward(self, x):return x * self.sigmoid(x)class CA(nn.Module):def __init__(self, inp, oup, reduction=32):super(CA, self).__init__()self.pool_h = nn.AdaptiveAvgPool2d((None, 1))self.pool_w = nn.AdaptiveAvgPool2d((1, None))mip = max(8, inp // reduction)self.conv1 = nn.Conv2d(inp, mip, kernel_size=1, stride=1, padding=0)self.bn1 = nn.BatchNorm2d(mip)self.act = h_swish()self.conv_h = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)self.conv_w = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)def forward(self, x):identity = xn, c, h, w = x.size()x_h = self.pool_h(x)x_w = self.pool_w(x).permute(0, 1, 3, 2)y = torch.cat([x_h, x_w], dim=2)y = self.conv1(y)y = self.bn1(y)y = self.act(y)x_h, x_w = torch.split(y, [h, w], dim=2)x_w = x_w.permute(0, 1, 3, 2)a_h = self.conv_h(x_h).sigmoid()a_w = self.conv_w(x_w).sigmoid()out = identity * a_w * a_hreturn out

ECA注意力机制比较特殊，不需要改动models文件夹下的yolo.py文件，可直接使用。

第三步，就是最为关键的改动yaml文件了，我们以yolov5s.yaml为例进行改进，这里仅截取关键部分，未截取部分则不做改动。

第一个版本是将ECA放在backbone部分的最末端，这样可以使注意力机制看到整个backbone部分的特征图，将具有全局视野，类似于一个小transformer结构。

# YOLOv5 v6.0 backbonebackbone:# [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 [-1, 3, SE, [1024]], # 10]# YOLOv5 v6.0 headhead:[[-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]],# 14 [-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]],# 18 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 15], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 21 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 11], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 24 (P5/32-large) [[18, 21, 24], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

第二个版本是将ECA放在backbone部分每个C3模块的后面，这样可以使注意力机制看到局部的特征，每层进行一次注意力，可以分担学习压力。

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, 3, SE, [128]], # 3 [-1, 1, Conv, [256, 3, 2]],# 4-P3/8 [-1, 6, C3, [256]],[-1, 3, SE, [256]],[-1, 1, Conv, [512, 3, 2]],# 7-P4/16 [-1, 9, C3, [512]], [-1, 3, SE, [512]],[-1, 1, Conv, [1024, 3, 2]],#10 -P5/32 [-1, 3, C3, [1024]], [-1, 3, SE, [1024]],[-1, 1, SPPF, [1024, 5]],# 13]# YOLOv5 v6.0 headhead:[[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 9], 1, Concat, [1]],# cat backbone P4 [-1, 3, C3, [512, False]],# 17 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]],# cat backbone P3 [-1, 3, C3, [256, False]],# 21 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 18], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 24 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 14], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 27 (P5/32-large) [[21, 24, 27], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

4.CA注意力注意力机制

同理，首先将下方代码添加在models文件夹下的common.py文件中，具体添加位置如果找不准可以选择common.py文件的最底端（最稳妥的做法，肯定不会错），或者C3模块后面（方便查找）。

class ECA(nn.Module):"""Constructs a ECA module.Args:channel: Number of channels of the input feature mapk_size: Adaptive selection of kernel size"""def __init__(self, channel, k_size=3):super(ECA, self).__init__()self.avg_pool = nn.AdaptiveAvgPool2d(1)self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)self.sigmoid = nn.Sigmoid()def forward(self, x):# feature descriptor on the global spatial informationy = self.avg_pool(x)# Two different branches of ECA moduley = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)# Multi-scale information fusiony = self.sigmoid(y)x= x*y.expand_as(x)return x * y.expand_as(x)

第二步，需要更改models文件夹下的yolo.py文件。可以直接ctrl+F 然后查找parse_model关键字，定位到parse_model函数，你会发现有一段这样的代码

 if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):c1, c2 = ch[f], args[0]if c2 != no:# if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:args.insert(2, n)# number of repeatsn = 1

我们仅需在第1行和第8行末尾添加SE即可，具体做法如下

if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, SE):c1, c2 = ch[f], args[0]if c2 != no:# if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x, SE]:args.insert(2, n)# number of repeatsn = 1

第一个版本是将CA放在backbone部分的最末端，这样可以使注意力机制看到整个backbone部分的特征图，将具有全局视野，类似于一个小transformer结构。

# YOLOv5 v6.0 backbonebackbone:# [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 [-1, 3, CA, [1024]], # 10]# YOLOv5 v6.0 headhead:[[-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]],# 14 [-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]],# 18 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 15], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 21 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 11], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 24 (P5/32-large) [[18, 21, 24], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

第二个版本是将CA放在backbone部分每个C3模块的后面，这样可以使注意力机制看到局部的特征，每层进行一次注意力，可以分担学习压力。

# YOLOv5 v6.0 backbonebackbone:# [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, 3, CA, [128]], # 3 [-1, 1, Conv, [256, 3, 2]],# 4-P3/8 [-1, 6, C3, [256]],[-1, 3, CA, [256]],[-1, 1, Conv, [512, 3, 2]],# 7-P4/16 [-1, 9, C3, [512]], [-1, 3, CA, [512]],[-1, 1, Conv, [1024, 3, 2]],#10 -P5/32 [-1, 3, C3, [1024]], [-1, 3, CA, [1024]],[-1, 1, SPPF, [1024, 5]],# 13]# YOLOv5 v6.0 headhead:[[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 9], 1, Concat, [1]],# cat backbone P4 [-1, 3, C3, [512, False]],# 17 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]],# cat backbone P3 [-1, 3, C3, [256, False]],# 21 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 18], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 24 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 14], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 27 (P5/32-large) [[21, 24, 27], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

5.SimAM注意力机制

同理，首先将下方代码添加在models文件夹下的common.py文件中，具体添加位置如果找不准可以选择common.py文件的最底端（最稳妥的做法，肯定不会错），或者C3模块后面（方便查找）。

class SimAM(torch.nn.Module):def __init__(self, channels = None,out_channels = None, e_lambda = 1e-4):super(SimAM, self).__init__()self.activaton = nn.Sigmoid()self.e_lambda = e_lambdadef forward(self, x):b, c, h, w = x.size()n = w * h - 1x_minus_mu_square = (x - x.mean(dim=[2,3], keepdim=True)).pow(2)y = x_minus_mu_square / (4 * (x_minus_mu_square.sum(dim=[2,3], keepdim=True) / n + self.e_lambda)) + 0.5return x * self.activaton(y)

第二步，需要更改models文件夹下的yolo.py文件。可以直接ctrl+F 然后查找parse_model关键字，定位到parse_model函数，你会发现有一段这样的代码

 if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):c1, c2 = ch[f], args[0]if c2 != no:# if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:args.insert(2, n)# number of repeatsn = 1

我们仅需在第1行和第8行末尾添加SimAM即可，具体做法如下

if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, SimAM):c1, c2 = ch[f], args[0]if c2 != no:# if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x, SimAM]:args.insert(2, n)# number of repeatsn = 1

第一个版本是将SimAM放在backbone部分的最末端，这样可以使注意力机制看到整个backbone部分的特征图，将具有全局视野，类似于一个小transformer结构。

# YOLOv5 v6.0 backbonebackbone:# [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 [-1, 3, SimAM, [1024]], # 10]# YOLOv5 v6.0 headhead:[[-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]],# 14 [-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]],# 18 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 15], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 21 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 11], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 24 (P5/32-large) [[18, 21, 24], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

第二个版本是将SimAM放在backbone部分每个C3模块的后面，这样可以使注意力机制看到局部的特征，每层进行一次注意力，可以分担学习压力。

# YOLOv5 v6.0 backbonebackbone:# [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, 3, SimAM, [128]], # 3 [-1, 1, Conv, [256, 3, 2]],# 4-P3/8 [-1, 6, C3, [256]],[-1, 3, SimAM, [256]],[-1, 1, Conv, [512, 3, 2]],# 7-P4/16 [-1, 9, C3, [512]], [-1, 3, SimAM, [512]],[-1, 1, Conv, [1024, 3, 2]],#10 -P5/32 [-1, 3, C3, [1024]], [-1, 3, SimAM, [1024]],[-1, 1, SPPF, [1024, 5]],# 13]# YOLOv5 v6.0 headhead:[[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 9], 1, Concat, [1]],# cat backbone P4 [-1, 3, C3, [512, False]],# 17 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]],# cat backbone P3 [-1, 3, C3, [256, False]],# 21 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 18], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 24 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 14], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 27 (P5/32-large) [[21, 24, 27], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

6.ShuffleAttention注意力机制

同理，首先将下方代码添加在models文件夹下的common.py文件中，具体添加位置如果找不准可以选择common.py文件的最底端（最稳妥的做法，肯定不会错），或者C3模块后面（方便查找）。

class ShuffleAttention(nn.Module):def __init__(self, channel=512,reduction=16,G=8):super().__init__()self.G=Gself.channel=channelself.avg_pool = nn.AdaptiveAvgPool2d(1)self.gn = nn.GroupNorm(channel // (2 * G), channel // (2 * G))self.cweight = torch.ones(1, channel // (2 * G), 1, 1)self.cbias = torch.ones(1, channel // (2 * G), 1, 1)self.sweight = torch.ones(1, channel // (2 * G), 1, 1)self.sbias = torch.ones(1, channel // (2 * G), 1, 1)self.sigmoid=nn.Sigmoid()@staticmethoddef channel_shuffle(x, groups):b, c, h, w = x.shapex = x.reshape(b, groups, -1, h, w)x = x.permute(0, 2, 1, 3, 4)# flattenx = x.reshape(b, -1, h, w)return xdef forward(self, x):b, c, h, w = x.size()#group into subfeaturesx=x.view(b*self.G,-1,h,w) #bs*G,c//G,h,w#channel_splitx_0,x_1=x.chunk(2,dim=1) #bs*G,c//(2*G),h,w#channel attentionx_channel=self.avg_pool(x_0) #bs*G,c//(2*G),1,1x_channel=self.cweight*x_channel+self.cbias #bs*G,c//(2*G),1,1x_channel=x_0*self.sigmoid(x_channel)#spatial attentionx_spatial=self.gn(x_1) #bs*G,c//(2*G),h,wx_spatial=self.sweight*x_spatial+self.sbias #bs*G,c//(2*G),h,wx_spatial=x_1*self.sigmoid(x_spatial) #bs*G,c//(2*G),h,w# concatenate along channel axisout=torch.cat([x_channel,x_spatial],dim=1)#bs*G,c//G,h,wout=out.contiguous().view(b,-1,h,w)# channel shuffleout = self.channel_shuffle(out, 2)return out

第二步，需要更改models文件夹下的yolo.py文件。可以直接ctrl+F 然后查找parse_model关键字，定位到parse_model函数，你会发现有一段这样的代码

 if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):c1, c2 = ch[f], args[0]if c2 != no:# if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:args.insert(2, n)# number of repeatsn = 1

我们仅需在第1行和第8行末尾添加ShuffleAttention即可，具体做法如下

if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, ShuffleAttention):c1, c2 = ch[f], args[0]if c2 != no:# if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x, ShuffleAttention]:args.insert(2, n)# number of repeatsn = 1

第一个版本是将ShuffleAttention放在backbone部分的最末端，这样可以使注意力机制看到整个backbone部分的特征图，将具有全局视野，类似于一个小transformer结构。

# YOLOv5 v6.0 backbonebackbone:# [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 [-1, 3, ShuffleAttention, [1024]], # 10]# YOLOv5 v6.0 headhead:[[-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]],# 14 [-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]],# 18 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 15], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 21 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 11], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 24 (P5/32-large) [[18, 21, 24], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

第二个版本是将ShuffleAttention放在backbone部分每个C3模块的后面，这样可以使注意力机制看到局部的特征，每层进行一次注意力，可以分担学习压力。

# YOLOv5 v6.0 backbonebackbone:# [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, 3, ShuffleAttention, [128]], # 3 [-1, 1, Conv, [256, 3, 2]],# 4-P3/8 [-1, 6, C3, [256]],[-1, 3, ShuffleAttention, [256]],[-1, 1, Conv, [512, 3, 2]],# 7-P4/16 [-1, 9, C3, [512]], [-1, 3, ShuffleAttention, [512]],[-1, 1, Conv, [1024, 3, 2]],#10 -P5/32 [-1, 3, C3, [1024]], [-1, 3, ShuffleAttention, [1024]],[-1, 1, SPPF, [1024, 5]],# 13]# YOLOv5 v6.0 headhead:[[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 9], 1, Concat, [1]],# cat backbone P4 [-1, 3, C3, [512, False]],# 17 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]],# cat backbone P3 [-1, 3, C3, [256, False]],# 21 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 18], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 24 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 14], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 27 (P5/32-large) [[21, 24, 27], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

7.CrissCrossAttention注意力机制

同理，首先将下方代码添加在models文件夹下的common.py文件中，具体添加位置如果找不准可以选择common.py文件的最底端（最稳妥的做法，肯定不会错），或者C3模块后面（方便查找）。

def INF(B,H,W): return -torch.diag(torch.tensor(float("inf")).repeat(H),0).unsqueeze(0).repeat(B*W,1,1).cuda()class CrissCrossAttention(nn.Module):""" Criss-Cross Attention Module"""def __init__(self, in_dim, out_channels, none):super(CrissCrossAttention,self).__init__()self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)self.softmax = nn.Softmax(dim=3)self.INF = INFself.gamma = nn.Parameter(torch.zeros(1))def forward(self, x):m_batchsize, _, height, width = x.size()proj_query = self.query_conv(x)proj_query_H = proj_query.permute(0,3,1,2).contiguous().view(m_batchsize*width,-1,height).permute(0, 2, 1)proj_query_W = proj_query.permute(0,2,1,3).contiguous().view(m_batchsize*height,-1,width).permute(0, 2, 1)proj_key = self.key_conv(x)proj_key_H = proj_key.permute(0,3,1,2).contiguous().view(m_batchsize*width,-1,height)proj_key_W = proj_key.permute(0,2,1,3).contiguous().view(m_batchsize*height,-1,width)proj_value = self.value_conv(x)proj_value_H = proj_value.permute(0,3,1,2).contiguous().view(m_batchsize*width,-1,height)proj_value_W = proj_value.permute(0,2,1,3).contiguous().view(m_batchsize*height,-1,width)energy_H = (torch.bmm(proj_query_H, proj_key_H)+self.INF(m_batchsize, height, width)).view(m_batchsize,width,height,height).permute(0,2,1,3)energy_W = torch.bmm(proj_query_W, proj_key_W).view(m_batchsize,height,width,width)concate = self.softmax(torch.cat([energy_H, energy_W], 3))att_H = concate[:,:,:,0:height].permute(0,2,1,3).contiguous().view(m_batchsize*width,height,height)#print(concate)#print(att_H) att_W = concate[:,:,:,height:height+width].contiguous().view(m_batchsize*height,width,width)out_H = torch.bmm(proj_value_H, att_H.permute(0, 2, 1)).view(m_batchsize,width,-1,height).permute(0,2,3,1)out_W = torch.bmm(proj_value_W, att_W.permute(0, 2, 1)).view(m_batchsize,height,-1,width).permute(0,2,1,3)#print(out_H.size(),out_W.size())return self.gamma*(out_H + out_W) + x

第二步，需要更改models文件夹下的yolo.py文件。可以直接ctrl+F 然后查找parse_model关键字，定位到parse_model函数，你会发现有一段这样的代码

 if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):c1, c2 = ch[f], args[0]if c2 != no:# if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:args.insert(2, n)# number of repeatsn = 1

我们仅需在第1行和第8行末尾添加CrissCrossAttention即可，具体做法如下

if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3new, C3new2, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, CrissCrossAttention):c1, c2 = ch[f], args[0]if c2 != no:# if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3new, C3new2, C3TR, C3Ghost, C3x, CrissCrossAttention]:args.insert(2, n)# number of repeatsn = 1

第一个版本是将CrissCrossAttention放在backbone部分的最末端，这样可以使注意力机制看到整个backbone部分的特征图，将具有全局视野，类似于一个小transformer结构。

# YOLOv5 v6.0 backbonebackbone:# [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 [-1, 3, CrissCrossAttention, [1024]], # 10]# YOLOv5 v6.0 headhead:[[-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]],# 14 [-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]],# 18 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 15], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 21 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 11], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 24 (P5/32-large) [[18, 21, 24], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

第二个版本是将CrissCrossAttention放在backbone部分每个C3模块的后面，这样可以使注意力机制看到局部的特征，每层进行一次注意力，可以分担学习压力。

# YOLOv5 v6.0 backbonebackbone:# [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, 3, CrissCrossAttention, [128]], # 3 [-1, 1, Conv, [256, 3, 2]],# 4-P3/8 [-1, 6, C3, [256]],[-1, 3, CrissCrossAttention, [256]],[-1, 1, Conv, [512, 3, 2]],# 7-P4/16 [-1, 9, C3, [512]], [-1, 3, CrissCrossAttention, [512]],[-1, 1, Conv, [1024, 3, 2]],#10 -P5/32 [-1, 3, C3, [1024]], [-1, 3, CrissCrossAttention, [1024]],[-1, 1, SPPF, [1024, 5]],# 13]# YOLOv5 v6.0 headhead:[[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 9], 1, Concat, [1]],# cat backbone P4 [-1, 3, C3, [512, False]],# 17 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]],# cat backbone P3 [-1, 3, C3, [256, False]],# 21 (P3/8-small) [-1, 1, Conv, [256, 3, 2]], [[-1, 18], 1, Concat, [1]],# cat head P4 [-1, 3, C3, [512, False]],# 24 (P4/16-medium) [-1, 1, Conv, [512, 3, 2]], [[-1, 14], 1, Concat, [1]],# cat head P5 [-1, 3, C3, [1024, False]],# 27 (P5/32-large) [[21, 24, 27], 1, Detect, [nc, anchors]],# Detect(P3, P4, P5)]

如需单独辅导改进（有偿） 可添加博主vx：Wansit99