YOLOv8最新改进系列：YOLOv8融合BoTNet模块，融合CNN+自然语言处理技术的优势，有效提升检测效果！

YOLOv8最新改进系列：YOLOv8融合BoTNet模块

YOLOv8主干特征提取网络为卷积神经网络，卷积神经网络具有平移不变性和局部性，缺乏全局建模长距离建模的能力，引入自然语言处理任务中的Transformer可以形成CNN+TransFormer架构，充分融合两者的优点，提高目标检测效果。

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一、 BoTNet简介

论文地址：https://arxiv.org/abs/2101.11605

BoTNet是一种简单但功能强大的主干网络，该架构将注意力模块纳入了包括图像分类，目标检测，实例分割等多种计算机视觉任务。通过仅将ResNet的最后三个的卷积层替换成MHSA层，并不进行其他改变，在墓边检测方面显著改善了极限，同时减少了参数两，从而使得延迟最小化。
Transformer中的MHSA和BoTNet中的MHSA的区别：

归一化，Transformer使用 Layer Normalization，而BoTNet使用 Batch Normalization。
非线性激活，Transformer仅仅使用一个非线性激活在FPN block模块中，BoTNet使用了3个非线性激活。
输出投影，Transformer中的MHSA包含一个输出投影，BoTNet则没有。
优化器，Transformer使用Adam优化器训练，BoTNet使用sgd+ momentum

二、改进方法

2.1 修改YAML(不知道在哪改动的，见我博客之前的文章，均有详细介绍！)

# 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, Conv, [64, 3, 2]]  # 0-P1/2
  - [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4
  - [-1, 3, C2f, [128, True]]
  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
  - [-1, 6, C2f, [256, True]]
  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
  - [-1, 6, C2f, [512, True]]
  - [-1, 1, GhostConv, [1024, 3, 2]]  # 7-P5/32
  - [-1, 3, C2f, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]]  # 9
  - [-1, 1, BoTNet, [1024]]
#详细的各类改进方法和流程操作，请关注B站博主：AI学术叫叫兽 
#详细的各类改进方法和流程操作，请关注B站博主：AI学术叫叫兽 
# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [512]]  # 12

  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 4], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f, [256]]  # 15 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 12], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 18 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 9], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 21 (P5/32-large)

  - [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)
#详细的各类改进方法和流程操作，请关注B站博主：AI学术叫叫兽 

2.2 新建BoTNet模块代码

核心代码如下：

class MHSA(nn.Module):
    def __init__(self, n_dims, width=14, height=14, heads=4, pos_emb=False):
        super(MHSA, self).__init__()
 
        self.heads = heads
        self.query = nn.Conv2d(n_dims, n_dims, kernel_size=1)
        self.key = nn.Conv2d(n_dims, n_dims, kernel_size=1)
        self.value = nn.Conv2d(n_dims, n_dims, kernel_size=1)
        self.pos = pos_emb
        if self.pos:
            self.rel_h_weight = nn.Parameter(torch.randn([1, heads, (n_dims) // heads, 1, int(height)]),
                                             requires_grad=True)
            self.rel_w_weight = nn.Parameter(torch.randn([1, heads, (n_dims) // heads, int(width), 1]),
                                             requires_grad=True)
        self.softmax = nn.Softmax(dim=-1)
 
    def forward(self, x):
        n_batch, C, width, height = x.size()
        q = self.query(x).view(n_batch, self.heads, C // self.heads, -1)
        k = self.key(x).view(n_batch, self.heads, C // self.heads, -1)
        v = self.value(x).view(n_batch, self.heads, C // self.heads, -1)
        # print('q shape:{},k shape:{},v shape:{}'.format(q.shape,k.shape,v.shape))  #1,4,64,256
        content_content = torch.matmul(q.permute(0, 1, 3, 2), k)  # 1,C,h*w,h*w
        # print("qkT=",content_content.shape)
        c1, c2, c3, c4 = content_content.size()
        if self.pos:
            # print("old content_content shape",content_content.shape) #1,4,256,256
            content_position = (self.rel_h_weight + self.rel_w_weight).view(1, self.heads, C // self.heads, -1).permute(
                0, 1, 3, 2)  # 1,4,1024,64
 
            content_position = torch.matmul(content_position, q)  # ([1, 4, 1024, 256])
            content_position = content_position if (
                        content_content.shape == content_position.shape) else content_position[:, :, :c3, ]
            assert (content_content.shape == content_position.shape)
            # print('new pos222-> shape:',content_position.shape)
            # print('new content222-> shape:',content_content.shape)
            energy = content_content + content_position
        else:
            energy = content_content
        attention = self.softmax(energy)
        out = torch.matmul(v, attention.permute(0, 1, 3, 2))  # 1,4,256,64
        out = out.view(n_batch, C, width, height)
        return out

2.3 修改tasks.py

找到下述代码，并替换即可。

        if m in (Classify, Conv, GGhostRegNet, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
                 BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, RepC3, SEAttention,ContextAggregation, BoTNet):
            c1, c2 = ch[f], args[0]

三、跑通示例（#详细的各类改进方法和流程操作以及跑通的改进源码，请关注B站博主：AI学术叫叫兽 ）

修改完成，收工！

AI学术叫叫兽er在这！家人们，给我遥遥领先！！！
AI学术叫叫兽er在这！家人们，给我遥遥领先！！！
AI学术叫叫兽er在这！家人们，给我遥遥领先！！！