yolov5改进之加入CBAM,SE,ECA,CA,SimAM,ShuffleAttention,Criss-CrossAttention,CrissCrossAttention多种注意力机制

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）

common.py中加入注意力机制模块

yolo.py中增加对应的注意力机制关键字

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 out  class 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 convolutions    def __init__(self, c1, c2, ratio=16, kernel_size=7):  # ch_in, ch_out, number, shortcut, groups, expansion        super(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) * out        return 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 output                c2 = make_divisible(c2 * gw, 8)            args = [c1, c2, *args[1:]]            if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:                args.insert(2, n)  # number of repeats                n = 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 output                c2 = 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 repeats                n = 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 output                c2 = make_divisible(c2 * gw, 8)            args = [c1, c2, *args[1:]]            if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:                args.insert(2, n)  # number of repeats                n = 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 output                c2 = 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 repeats                n = 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 = x        n, 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_h        return 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 map        k_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 information        y = self.avg_pool(x)        # Two different branches of ECA module        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)        # Multi-scale information fusion        y = 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 output                c2 = make_divisible(c2 * gw, 8)            args = [c1, c2, *args[1:]]            if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:                args.insert(2, n)  # number of repeats                n = 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 output                c2 = 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 repeats                n = 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_lambda    def forward(self, x):        b, c, h, w = x.size()                n = w * h - 1        x_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)) + 0.5        return 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 output                c2 = make_divisible(c2 * gw, 8)            args = [c1, c2, *args[1:]]            if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:                args.insert(2, n)  # number of repeats                n = 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 output                c2 = 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 repeats                n = 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=G        self.channel=channel        self.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()    @staticmethod    def channel_shuffle(x, groups):        b, c, h, w = x.shape        x = x.reshape(b, groups, -1, h, w)        x = x.permute(0, 2, 1, 3, 4)        # flatten        x = x.reshape(b, -1, h, w)        return x    def forward(self, x):        b, c, h, w = x.size()                #group into subfeatures        x=x.view(b*self.G,-1,h,w) #bs*G,c//G,h,w        #channel_split        x_0,x_1=x.chunk(2,dim=1) #bs*G,c//(2*G),h,w        #channel attention        x_channel=self.avg_pool(x_0) #bs*G,c//(2*G),1,1        x_channel=self.cweight*x_channel+self.cbias #bs*G,c//(2*G),1,1        x_channel=x_0*self.sigmoid(x_channel)        #spatial attention        x_spatial=self.gn(x_1) #bs*G,c//(2*G),h,w        x_spatial=self.sweight*x_spatial+self.sbias #bs*G,c//(2*G),h,w        x_spatial=x_1*self.sigmoid(x_spatial) #bs*G,c//(2*G),h,w        # concatenate along channel axis        out=torch.cat([x_channel,x_spatial],dim=1)  #bs*G,c//G,h,w        out=out.contiguous().view(b,-1,h,w)        # channel shuffle        out = 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 output                c2 = make_divisible(c2 * gw, 8)            args = [c1, c2, *args[1:]]            if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:                args.insert(2, n)  # number of repeats                n = 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 output                c2 = 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 repeats                n = 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 = INF        self.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 output                c2 = make_divisible(c2 * gw, 8)            args = [c1, c2, *args[1:]]            if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:                args.insert(2, n)  # number of repeats                n = 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 output                c2 = 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 repeats                n = 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

来源地址：https://blog.csdn.net/Wansit/article/details/128783023