YOLOv8改进 | 模块缝合 | C2f 融合RFAConv和CBAM注意力机制【二次融合小白必备】

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空间注意力虽提高卷积神经网络性能，但有局限。本文介绍了感受野注意力（RFA）机制并融合CBAM注意力机制，解决大尺寸卷积核参数共享问题。RFA关注感受野空间特征，为大型卷积核提供有效权重。RFAConv操作几乎不增加计算成本，显著提升网络性能。文章在介绍主要的原理后，将手把手教学如何进行模块的代码添加和修改，并将修改后的完整代码放在文章的最后，方便大家一键运行，小白也可轻松上手实践。以帮助您更好地学习深度学习目标检测YOLO系列的挑战。

专栏地址：YOLOv8改进——更新各种有效涨点方法——点击即可跳转

1.原理

2. 将C2f_RFCBAMConv添加到yolov8网络中

2.1 C2f_RFCBAMConv代码实现

2.2 C2f_RFCBAMConv的神经网络模块代码解析

2.3 更改init.py文件

2.4 添加yaml文件

2.5 注册模块

2.6 执行程序

3. 完整代码分享

4. GFLOPs

5. 进阶

6. 总结

1.原理

论文地址：RFAConv: Innovating Spatial Attention and Standard Convolutional Operation——点击即可跳转

官方代码：官方代码仓库——点击即可跳转

RFAConv（受体场注意卷积）是一种新颖的卷积运算，旨在解决标准卷积和现有空间注意机制的局限性，特别是在参数共享和大型卷积核方面。

RFAConv 背后的关键原则：

受体场空间特征：与专注于单个空间特征的传统空间注意不同，RFAConv 强调受体场空间特征，这些特征是根据卷积核的大小动态生成的。这种方法通过关注受体场内不同特征的重要性来增强特征提取。
解决参数共享问题：在标准卷积中，内核参数在整个输入中共享，限制了网络跨空间位置捕获不同信息的能力。RFAConv 通过将注意力机制与卷积相结合来解决此问题，为每个受体场创建非共享参数。
注意力机制集成：RFAConv 集成了一种注意力机制，该机制为接受场中的每个特征分配重要性，使网络能够专注于最重要的信息。此过程避免了 CBAM 和 CA 等传统注意力机制的局限性，这些机制在不同空间区域之间共享注意力权重。
高效轻量：尽管引入了注意力机制，但 RFAConv 仅增加了极少的计算开销和参数。它还使用组卷积等技术来高效提取接受场空间特征，使其适用于实时应用。
性能提升：通过解决空间注意力和卷积参数共享的局限性，RFAConv 增强了神经网络在分类、对象检测和分割等任务中的性能，在许多情况下优于 CBAM 和 CA 等其他基于注意力的方法。

综上所述，RFAConv 通过关注感受野空间特征进行创新，提供了一种更灵活、更强大的方法来替代标准卷积，同时保持效率并提高网络性能。

2. 将C2f_RFCBAMConv添加到yolov8网络中

2.1 C2f_RFCBAMConv代码实现

关键步骤一: 将下面代码粘贴到在/ultralytics/ultralytics/nn/modules/block.py中，并在该文件的__all__中添加“C2f_RFCBAMConv”


from einops import rearrange
 
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) / 6
 
class 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 RFAConv(nn.Module):
    def __init__(self,in_channel,out_channel,kernel_size,stride=1):
        super().__init__()
        self.kernel_size = kernel_size
 
        self.get_weight = nn.Sequential(nn.AvgPool2d(kernel_size=kernel_size, padding=kernel_size // 2, stride=stride),
                                        nn.Conv2d(in_channel, in_channel * (kernel_size ** 2), kernel_size=1, groups=in_channel,bias=False))
        self.generate_feature = nn.Sequential(
            nn.Conv2d(in_channel, in_channel * (kernel_size ** 2), kernel_size=kernel_size,padding=kernel_size//2,stride=stride, groups=in_channel, bias=False),
            nn.BatchNorm2d(in_channel * (kernel_size ** 2)),
            nn.ReLU())
       
        # self.conv = nn.Sequential(nn.Conv2d(in_channel, out_channel, kernel_size=kernel_size, stride=kernel_size),
        #                           nn.BatchNorm2d(out_channel),
        #                           nn.ReLU())
        self.conv = Conv(in_channel, out_channel, k=kernel_size, s=kernel_size, p=0)
 
    def forward(self,x):
        b,c = x.shape[0:2]
        weight =  self.get_weight(x)
        h,w = weight.shape[2:]
        weighted = weight.view(b, c, self.kernel_size ** 2, h, w).softmax(2)  # b c*kernel**2,h,w ->  b c k**2 h w 
        feature = self.generate_feature(x).view(b, c, self.kernel_size ** 2, h, w)  #b c*kernel**2,h,w ->  b c k**2 h w  
        weighted_data = feature * weighted
        conv_data = rearrange(weighted_data, 'b c (n1 n2) h w -> b c (h n1) (w n2)', n1=self.kernel_size, # b c k**2 h w ->  b c h*k w*k
                              n2=self.kernel_size)
        return self.conv(conv_data)
 
 
class SE(nn.Module):
    def __init__(self, in_channel, ratio=16):
        super(SE, self).__init__()
        self.gap = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Sequential(
            nn.Linear(in_channel, ratio, bias=False),  # 从 c -> c/r
            nn.ReLU(),
            nn.Linear(ratio, in_channel, bias=False),  # 从 c/r -> c
            nn.Sigmoid()
        )
 
    def forward(self, x):
            b, c= x.shape[0:2]
            y = self.gap(x).view(b, c)
            y = self.fc(y).view(b, c,1, 1)
            return y
 
 
class RFCBAMConv(nn.Module):
    def __init__(self,in_channel,out_channel,kernel_size=3,stride=1):
        super().__init__()
        if kernel_size % 2 == 0:
            assert("the kernel_size must be  odd.")
        self.kernel_size = kernel_size
        self.generate = nn.Sequential(nn.Conv2d(in_channel,in_channel * (kernel_size**2),kernel_size,padding=kernel_size//2,
                                                stride=stride,groups=in_channel,bias =False),
                                      nn.BatchNorm2d(in_channel * (kernel_size**2)),
                                      nn.ReLU()
                                      )
        self.get_weight = nn.Sequential(nn.Conv2d(2,1,kernel_size=3,padding=1,bias=False),nn.Sigmoid())
        self.se = SE(in_channel)
 
        # self.conv = nn.Sequential(nn.Conv2d(in_channel,out_channel,kernel_size,stride=kernel_size),nn.BatchNorm2d(out_channel),nn.ReLu())
        self.conv = Conv(in_channel, out_channel, k=kernel_size, s=kernel_size, p=0)
        
    def forward(self,x):
        b,c = x.shape[0:2]
        channel_attention =  self.se(x)
        generate_feature = self.generate(x)
 
        h,w = generate_feature.shape[2:]
        generate_feature = generate_feature.view(b,c,self.kernel_size**2,h,w)
        
        generate_feature = rearrange(generate_feature, 'b c (n1 n2) h w -> b c (h n1) (w n2)', n1=self.kernel_size,
                              n2=self.kernel_size)
        
        unfold_feature = generate_feature * channel_attention
        max_feature,_ = torch.max(generate_feature,dim=1,keepdim=True)
        mean_feature = torch.mean(generate_feature,dim=1,keepdim=True)
        receptive_field_attention = self.get_weight(torch.cat((max_feature,mean_feature),dim=1))
        conv_data = unfold_feature  * receptive_field_attention
        return self.conv(conv_data)
 
class RFCAConv(nn.Module):
    def __init__(self, inp, oup, kernel_size, stride=1, reduction=32):
        super(RFCAConv, self).__init__()
        self.kernel_size = kernel_size
        self.generate = nn.Sequential(nn.Conv2d(inp,inp * (kernel_size**2),kernel_size,padding=kernel_size//2,
                                                stride=stride,groups=inp,
                                                bias =False),
                                      nn.BatchNorm2d(inp * (kernel_size**2)),
                                      nn.ReLU()
                                      )
        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, inp, kernel_size=1, stride=1, padding=0)
        self.conv_w = nn.Conv2d(mip, inp, kernel_size=1, stride=1, padding=0)
        self.conv = nn.Sequential(nn.Conv2d(inp,oup,kernel_size,stride=kernel_size))
        
 
    def forward(self, x):
        b,c = x.shape[0:2]
        generate_feature = self.generate(x)
        h,w = generate_feature.shape[2:]
        generate_feature = generate_feature.view(b,c,self.kernel_size**2,h,w)
        
        generate_feature = rearrange(generate_feature, 'b c (n1 n2) h w -> b c (h n1) (w n2)', n1=self.kernel_size,
                              n2=self.kernel_size)
        
        x_h = self.pool_h(generate_feature)
        x_w = self.pool_w(generate_feature).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) 
        
        h,w = generate_feature.shape[2:]
        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()
        return self.conv(generate_feature * a_w * a_h)
 
class Bottleneck_RFAConv(Bottleneck):
    """Standard bottleneck with RFAConv."""
 
    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):  # ch_in, ch_out, shortcut, groups, kernels, expand
        super().__init__(c1, c2, shortcut, g, k, e)
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, k[0], 1)
        self.cv2 = RFAConv(c_, c2, k[1])
 
class C3_RFAConv(C3):
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)  # hidden channels
        self.m = nn.Sequential(*(Bottleneck_RFAConv(c_, c_, shortcut, g, k=(1, 3), e=1.0) for _ in range(n)))
 
class C2f_RFAConv(C2f):
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        self.m = nn.ModuleList(Bottleneck_RFAConv(self.c, self.c, shortcut, g, k=(3, 3), e=1.0) for _ in range(n))
 
class Bottleneck_RFCBAMConv(Bottleneck):
    """Standard bottleneck with RFCBAMConv."""
 
    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):  # ch_in, ch_out, shortcut, groups, kernels, expand
        super().__init__(c1, c2, shortcut, g, k, e)
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, k[0], 1)
        self.cv2 = RFCBAMConv(c_, c2, k[1])
 
class C3_RFCBAMConv(C3):
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)  # hidden channels
        self.m = nn.Sequential(*(Bottleneck_RFCBAMConv(c_, c_, shortcut, g, k=(1, 3), e=1.0) for _ in range(n)))
 
class C2f_RFCBAMConv(C2f):
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        self.m = nn.ModuleList(Bottleneck_RFCBAMConv(self.c, self.c, shortcut, g, k=(3, 3), e=1.0) for _ in range(n))

2.2 C2f_RFCBAMConv的神经网络模块代码解析

C2f_RFCBAMConv 类通过将 CBAM（卷积块注意力机制）集成到一个瓶颈结构中，扩展了 C2f 模块。

主要组成部分：

C2f 模块：
- C2f 是YOLOv8的模块，允许在神经网络中灵活组合通道。通常，它会将输入分成多个分支进行处理，最后将它们合并。
- 在这里，它使用了 Bottleneck_RFCBAMConv 结构，这个结构包含了多个 Bottleneck_RFCBAMConv 块。
Bottleneck_RFCBAMConv：
- 这是一个集成了 CBAM 机制的瓶颈块，其中 "RFCBAMConv" 代表“带 CBAM 的残差特征卷积”。
- Bottleneck（瓶颈块）：执行典型的瓶颈变换，首先减少通道数，然后再扩展。这通常用于残差网络（ResNet）中。
- RFCBAMConv：假设 RFCBAMConv 集成了 CBAM，一种通过通道注意力和空间注意力来增强特征表示的机制。

CBAM（卷积块注意力机制）：

CBAM 是一种注意力机制，通过关注重要的空间位置和特征通道来增强特征表达。它由两个主要部分组成：

通道注意力：通过学习每个通道的权重，优先处理重要的通道。通常通过全局池化然后通过一个小型神经网络来计算注意力权重。
空间注意力：通过在特征图的空间维度（高度和宽度）上应用注意力，专注于相关的空间位置。

C2f_RFCBAMConv 的流程：

输入处理：首先将输入通道 c1 传入 Conv 层（cv1），通道数被减少到中间通道数 c_。
RFCBAMConv 应用：然后将减少后的特征图传入 RFCBAMConv 块，在其中应用 CBAM 注意力机制。该块会先应用通道和空间注意力，然后进行卷积操作。
输出：在注意力机制作用后，生成处理后的输出特征图。

因此，C2f_RFCBAMConv 将基于 CBAM 的瓶颈注意力机制集成到 C2f 架构中，增强了卷积过程中对特征提取的通道和空间聚焦。

2.3 更改init.py文件

关键步骤二：修改modules文件夹下的__init__.py文件，先导入函数

然后在下面的__all__中声明函数

2.4 添加yaml文件

关键步骤三：在/ultralytics/ultralytics/cfg/models/v8下面新建文件yolov8_C2f_RFCBAMConv.yaml文件，粘贴下面的内容

OD【目标检测】

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
nc: 80  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPs
  s: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs
  m: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
  l: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
  x: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs

# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, RFCBAMConv, [64, 3, 2]]  # 0-P1/2
  - [-1, 1, RFCBAMConv, [128, 3, 2]]  # 1-P2/4
  - [-1, 3, C2f_RFCBAMConv, [128, True]]
  - [-1, 1, RFCBAMConv, [256, 3, 2]]  # 3-P3/8
  - [-1, 6, C2f_RFCBAMConv, [256, True]]
  - [-1, 1, RFCBAMConv, [512, 3, 2]]  # 5-P4/16
  - [-1, 6, C2f_RFCBAMConv, [512, True]]
  - [-1, 1, RFCBAMConv, [1024, 3, 2]]  # 7-P5/32
  - [-1, 3, C2f_RFCBAMConv, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]]  # 9

# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f_RFCBAMConv, [512]]  # 12

  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 4], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f_RFCBAMConv, [256]]  # 15 (P3/8-small)

  - [-1, 1, RFCBAMConv, [256, 3, 2]]
  - [[-1, 12], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f_RFCBAMConv, [512]]  # 18 (P4/16-medium)

  - [-1, 1, RFCBAMConv, [512, 3, 2]]
  - [[-1, 9], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f_RFCBAMConv, [1024]]  # 21 (P5/32-large)

  - [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)

Seg【语义分割】

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
nc: 80  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPs
  s: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs
  m: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
  l: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
  x: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs

# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, RFCBAMConv, [64, 3, 2]]  # 0-P1/2
  - [-1, 1, RFCBAMConv, [128, 3, 2]]  # 1-P2/4
  - [-1, 3, C2f_RFCBAMConv, [128, True]]
  - [-1, 1, RFCBAMConv, [256, 3, 2]]  # 3-P3/8
  - [-1, 6, C2f_RFCBAMConv, [256, True]]
  - [-1, 1, RFCBAMConv, [512, 3, 2]]  # 5-P4/16
  - [-1, 6, C2f_RFCBAMConv, [512, True]]
  - [-1, 1, RFCBAMConv, [1024, 3, 2]]  # 7-P5/32
  - [-1, 3, C2f_RFCBAMConv, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]]  # 9

# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f_RFCBAMConv, [512]]  # 12

  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 4], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f_RFCBAMConv, [256]]  # 15 (P3/8-small)

  - [-1, 1, RFCBAMConv, [256, 3, 2]]
  - [[-1, 12], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f_RFCBAMConv, [512]]  # 18 (P4/16-medium)

  - [-1, 1, RFCBAMConv, [512, 3, 2]]
  - [[-1, 9], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f_RFCBAMConv, [1024]]  # 21 (P5/32-large)

  - [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)

温馨提示：因为本文只是对yolov8基础上添加模块，如果要对yolov8n/l/m/x进行添加则只需要指定对应的depth_multiple 和 width_multiple。不明白的同学可以看这篇文章： yolov8yaml文件解读——点击即可跳转

# YOLOv8n
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.25  # layer channel multiple
max_channels: 1024 # max_channels
 
# YOLOv8s
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
max_channels: 1024 # max_channels
 
# YOLOv8l 
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple
max_channels: 512 # max_channels
 
# YOLOv8m
depth_multiple: 0.67  # model depth multiple
width_multiple: 0.75  # layer channel multiple
max_channels: 768 # max_channels
 
# YOLOv8x
depth_multiple: 1.33  # model depth multiple
width_multiple: 1.25  # layer channel multiple
max_channels: 512 # max_channels

2.5 注册模块

关键步骤四：在task.py的parse_model函数中注册

2.6 执行程序

在train.py中，将model的参数路径设置为yolov8_C2f_RFCBAMConv.yaml的路径

建议大家写绝对路径，确保一定能找到

from ultralytics import YOLO
import warnings
warnings.filterwarnings('ignore')
from pathlib import Path
 
if __name__ == '__main__':
 
 
    # 加载模型
    model = YOLO("ultralytics/cfg/v8/yolov8.yaml")  # 你要选择的模型yaml文件地址
    # Use the model
    results = model.train(data=r"你的数据集的yaml文件地址",
                          epochs=100, batch=16, imgsz=640, workers=4, name=Path(model.cfg).stem)  # 训练模型

🚀运行程序，如果出现下面的内容则说明添加成功🚀

                   from  n    params  module                                       arguments
  0                  -1  1       875  ultralytics.nn.modules.block.RFCBAMConv      [3, 16, 3, 2]
  1                  -1  1      6786  ultralytics.nn.modules.block.RFCBAMConv      [16, 32, 3, 2]
  2                  -1  1      9474  ultralytics.nn.modules.block.C2f_RFCBAMConv  [32, 32, 1, True]
  3                  -1  1     22770  ultralytics.nn.modules.block.RFCBAMConv      [32, 64, 3, 2]
  4                  -1  2     58084  ultralytics.nn.modules.block.C2f_RFCBAMConv  [64, 64, 2, True]
  5                  -1  1     82386  ultralytics.nn.modules.block.RFCBAMConv      [64, 128, 3, 2]
  6                  -1  2    214436  ultralytics.nn.modules.block.C2f_RFCBAMConv  [128, 128, 2, True]
  7                  -1  1    312210  ultralytics.nn.modules.block.RFCBAMConv      [128, 256, 3, 2]
  8                  -1  1    477074  ultralytics.nn.modules.block.C2f_RFCBAMConv  [256, 256, 1, True]
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 12                  -1  1    156626  ultralytics.nn.modules.block.C2f_RFCBAMConv  [384, 128, 1]
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 15                  -1  1     41458  ultralytics.nn.modules.block.C2f_RFCBAMConv  [192, 64, 1]
 16                  -1  1     45394  ultralytics.nn.modules.block.RFCBAMConv      [64, 64, 3, 2]
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 18                  -1  1    132050  ultralytics.nn.modules.block.C2f_RFCBAMConv  [192, 128, 1]
 19                  -1  1    164498  ultralytics.nn.modules.block.RFCBAMConv      [128, 128, 3, 2]
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]
 21                  -1  1    509842  ultralytics.nn.modules.block.C2f_RFCBAMConv  [384, 256, 1]
 22        [15, 18, 21]  1    897664  ultralytics.nn.modules.head.Detect           [80, [64, 128, 256]]
YOLOv8_C2f_RFCBAMConv summary: 480 layers, 3296235 parameters, 3296219 gradients

3. 完整代码分享

https://pan.baidu.com/s/1NOYgkQJjZfyBh4igJAZNqw?pwd=7hxv

提取码: 7hxv

4. GFLOPs

关于GFLOPs的计算方式可以查看：百面算法工程师 | 卷积基础知识——Convolution

未改进的YOLOv8nGFLOPs

改进后的GFLOPs

~~手里的没有卡了，需要的同学自己测一下吧~~

5. 进阶

可以与其他的注意力机制或者损失函数等结合，进一步提升检测效果

6. 总结

C2f_RFCBAMConv 是一个自定义神经网络模块，它将 CBAM（卷积块注意力机制）集成到 C2f 结构的瓶颈块中，增强了模型的特征提取能力。它首先通过瓶颈层减少输入通道数，再应用 CBAM 注意力机制，该机制通过通道注意力和空间注意力分别关注重要的特征通道和空间位置，最终提高模型在处理复杂图像时的特征表达能力。整个过程包括先对输入进行卷积处理，然后使用 CBAM 进行特征增强，最后输出增强后的特征图，进一步提高了网络的表达能力和精度。