爆改YOLOv8 | yolov8添加MSDA注意力机制

1,本文介绍

MSDA(多尺度扩张注意力)模块通过自注意力机制在不同尺度上有效地捕捉特征的稀疏性。它首先通过线性投影生成特征图 (X) 的查询、键和值。然后,将特征图的通道划分为 (n) 个头部,在每个头部中使用不同的扩张率进行多尺度的自注意力操作。具体来说,MSDA按以下步骤操作:对每个头部 (i) 进行自注意力处理,并将所有头部的输出连接在一起,之后通过线性层进行特征融合。通过为不同头部设置不同的扩张率,MSDA可以在关注的接收域内有效地聚合多尺度的语义信息,同时在避免复杂操作和额外计算成本的情况下,减少了自注意力机制的冗余。

MSDA模块的主要改进包括:

  1. 多尺度特征提取:通过不同头部的自注意力机制,MSDA能够捕捉到不同尺度的语义信息,这对于理解图像的不同抽象层次非常重要。

  2. 稀疏性利用:MSDA利用自注意力机制在不同尺度上的稀疏性,降低了计算冗余,同时保持了良好的性能。

  3. 头部通道分离:MSDA将特征图的通道分割为多个头部,每个头部处理不同的特征子集,这样可以并行处理,提升模型的学习能力和效率。

  4. 不同扩张率:通过在不同头部设置不同的扩张率,MSDA能够在各个头部关注不同尺度的特征,从而更全面地捕捉图像中的信息。

  5. 特征聚合:MSDA将各个头部的输出通过连接操作合并,并通过线性层进行特征聚合,整合各个头部学习到的信息,得到更丰富的特征表示。

关于MSDA的详细介绍可以看论文:https://arxiv.org/pdf/2302.01791.pdf

本文将讲解如何将MSDA融合进yolov8

话不多说,上代码!

2,将MSDA融合进YOLOv8

2.1 步骤一

首先找到如下的目录'ultralytics/nn/modules',然后在这个目录下创建一个MSDA.py文件,文件名字可以根据你自己的习惯起,然后将MSDA的核心代码复制进去。

import torch
import torch.nn as nn
from functools import partial
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg
 
class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)
 
    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x
 
 
class DilateAttention(nn.Module):
    "Implementation of Dilate-attention"
    def __init__(self, head_dim, qk_scale=None, attn_drop=0, kernel_size=3, dilation=1):
        super().__init__()
        self.head_dim = head_dim
        self.scale = qk_scale or head_dim ** -0.5
        self.kernel_size=kernel_size
        self.unfold = nn.Unfold(kernel_size, dilation, dilation*(kernel_size-1)//2, 1)
        self.attn_drop = nn.Dropout(attn_drop)
 
    def forward(self,q,k,v):
        #B, C//3, H, W
        B,d,H,W = q.shape
        q = q.reshape([B, d//self.head_dim, self.head_dim, 1 ,H*W]).permute(0, 1, 4, 3, 2)  # B,h,N,1,d
        k = self.unfold(k).reshape([B, d//self.head_dim, self.head_dim, self.kernel_size*self.kernel_size, H*W]).permute(0, 1, 4, 2, 3)  #B,h,N,d,k*k
        attn = (q @ k) * self.scale  # B,h,N,1,k*k
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        v = self.unfold(v).reshape([B, d//self.head_dim, self.head_dim, self.kernel_size*self.kernel_size, H*W]).permute(0, 1, 4, 3, 2)  # B,h,N,k*k,d
        x = (attn @ v).transpose(1, 2).reshape(B, H, W, d)
        return x
 
 
class MultiDilatelocalAttention(nn.Module):
    "Implementation of Dilate-attention"
 
    def __init__(self, dim, num_heads=8, qkv_bias=True, qk_scale=None,
                 attn_drop=0.,proj_drop=0., kernel_size=3, dilation=[1, 2, 3, 4]):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.dilation = dilation
        self.kernel_size = kernel_size
        self.scale = qk_scale or head_dim ** -0.5
        self.num_dilation = len(dilation)
        assert num_heads % self.num_dilation == 0, f"num_heads{num_heads} must be the times of num_dilation{self.num_dilation}!!"
        self.qkv = nn.Conv2d(dim, dim * 3, 1, bias=qkv_bias)
        self.dilate_attention = nn.ModuleList(
            [DilateAttention(head_dim, qk_scale, attn_drop, kernel_size, dilation[i])
             for i in range(self.num_dilation)])
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
 
    def forward(self, x):
        B, C, H, W = x.shape
        # x = x.permute(0, 3, 1, 2)# B, C, H, W
        y = x.clone()
        qkv = self.qkv(x).reshape(B, 3, self.num_dilation, C//self.num_dilation, H, W).permute(2, 1, 0, 3, 4, 5)
        #num_dilation,3,B,C//num_dilation,H,W
        y1 = y.reshape(B, self.num_dilation, C//self.num_dilation, H, W).permute(1, 0, 3, 4, 2 )
        # num_dilation, B, H, W, C//num_dilation
        for i in range(self.num_dilation):
            y1[i] = self.dilate_attention[i](qkv[i][0], qkv[i][1], qkv[i][2])# B, H, W,C//num_dilation
        y2 = y1.permute(1, 2, 3, 0, 4).reshape(B, H, W, C)
        y3 = self.proj(y2)
        y4 = self.proj_drop(y3).permute(0, 3, 1, 2)
        return y4
 
 
class DilateBlock(nn.Module):
    "Implementation of Dilate-attention block"
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False,qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0.,act_layer=nn.GELU, norm_layer=nn.LayerNorm, kernel_size=3, dilation=[1, 2, 3],
                 cpe_per_block=False):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.mlp_ratio = mlp_ratio
        self.kernel_size = kernel_size
        self.dilation = dilation
        self.cpe_per_block = cpe_per_block
        if self.cpe_per_block:
            self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
        self.norm1 = norm_layer(dim)
        self.attn = MultiDilatelocalAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
                                                attn_drop=attn_drop, kernel_size=kernel_size, dilation=dilation)
 
        self.drop_path = DropPath(
            drop_path) if drop_path > 0. else nn.Identity()
 
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
                       act_layer=act_layer, drop=drop)
 
    def forward(self, x):
        if self.cpe_per_block:
            x = x + self.pos_embed(x)
        x = x.permute(0, 2, 3, 1)
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        x = x.permute(0, 3, 1, 2)
        #B, C, H, W
        return x
 
 
class GlobalAttention(nn.Module):
    "Implementation of self-attention"
 
    def __init__(self, dim,  num_heads=8, qkv_bias=False,
                 qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5
 
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
 
    def forward(self, x):
        B, H, W, C = x.shape
        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads,
                                  C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
 
        x = (attn @ v).transpose(1, 2).reshape(B, H, W, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x
 
 
class GlobalBlock(nn.Module):
    """
    Implementation of Transformer
    """
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False,qk_scale=None, drop=0.,
                 attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
                 cpe_per_block=False):
        super().__init__()
        self.cpe_per_block = cpe_per_block
        if self.cpe_per_block:
            self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
        self.norm1 = norm_layer(dim)
        self.attn = GlobalAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias,
                              qk_scale=qk_scale, attn_drop=attn_drop)
 
        self.drop_path = DropPath(
            drop_path) if drop_path > 0. else nn.Identity()
 
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
                       act_layer=act_layer, drop=drop)
 
    def forward(self, x):
        if self.cpe_per_block:
            x = x + self.pos_embed(x)
        x = x.permute(0, 2, 3, 1)
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        x = x.permute(0, 3, 1, 2)
        return x
 
 
class PatchEmbed(nn.Module):
    """Image to Patch Embedding.
    """
    def __init__(self, img_size=224, in_chans=3, hidden_dim=16,
                 patch_size=4, embed_dim=96, patch_way=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.num_patches = patches_resolution[0] * patches_resolution[1]
        self.img_size = img_size
        assert patch_way in ['overlaping', 'nonoverlaping', 'pointconv'],\
            "the patch embedding way isn't exist!"
        if patch_way == "nonoverlaping":
            self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        elif patch_way == "overlaping":
            self.proj = nn.Sequential(
                nn.Conv2d(in_chans, hidden_dim, kernel_size=3, stride=1,
                          padding=1, bias=False),  # 224x224
                nn.BatchNorm2d(hidden_dim),
                nn.GELU( ),
                nn.Conv2d(hidden_dim, int(hidden_dim*2), kernel_size=3, stride=2,
                          padding=1, bias=False),  # 112x112
                nn.BatchNorm2d(int(hidden_dim*2)),
                nn.GELU( ),
                nn.Conv2d(int(hidden_dim*2), int(hidden_dim*4), kernel_size=3, stride=1,
                          padding=1, bias=False),  # 112x112
                nn.BatchNorm2d(int(hidden_dim*4)),
                nn.GELU( ),
                nn.Conv2d(int(hidden_dim*4), embed_dim, kernel_size=3, stride=2,
                          padding=1, bias=False),  # 56x56
            )
        else:
            self.proj = nn.Sequential(
                nn.Conv2d(in_chans, hidden_dim, kernel_size=3, stride=2,
                          padding=1, bias=False),  # 112x112
                nn.BatchNorm2d(hidden_dim),
                nn.GELU( ),
                nn.Conv2d(hidden_dim, int(hidden_dim*2), kernel_size=1, stride=1,
                          padding=0, bias=False),  # 112x112
                nn.BatchNorm2d(int(hidden_dim*2)),
                nn.GELU( ),
                nn.Conv2d(int(hidden_dim*2), int(hidden_dim*4), kernel_size=3, stride=2,
                          padding=1, bias=False),  # 56x56
                nn.BatchNorm2d(int(hidden_dim*4)),
                nn.GELU( ),
                nn.Conv2d(int(hidden_dim*4), embed_dim, kernel_size=1, stride=1,
                          padding=0, bias=False),   # 56x56
            )
 
    def forward(self, x):
        B, C, H, W = x.shape
        # FIXME look at relaxing size constraints
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x)  # B, C, H, W
        return x
 
 
class PatchMerging(nn.Module):
    """ Patch Merging Layer.
    """
    def __init__(self, in_channels, out_channels, merging_way, cpe_per_satge, norm_layer=nn.BatchNorm2d):
        super().__init__()
        assert merging_way in ['conv3_2', 'conv2_2', 'avgpool3_2', 'avgpool2_2'], \
            "the merging way is not exist!"
        self.cpe_per_satge = cpe_per_satge
        if merging_way == 'conv3_2':
            self.proj = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1),
                norm_layer(out_channels),
            )
        elif merging_way == 'conv2_2':
            self.proj = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=2, stride=2, padding=0),
                norm_layer(out_channels),
            )
        elif merging_way == 'avgpool3_2':
            self.proj = nn.Sequential(
                nn.AvgPool2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1),
                norm_layer(out_channels),
            )
        else:
            self.proj = nn.Sequential(
                nn.AvgPool2d(in_channels, out_channels, kernel_size=2, stride=2, padding=0),
                norm_layer(out_channels),
            )
        if self.cpe_per_satge:
            self.pos_embed = nn.Conv2d(out_channels, out_channels, 3, padding=1, groups=out_channels)
 
    def forward(self, x):
        #x: B, C, H ,W
        x = self.proj(x)
        if self.cpe_per_satge:
            x = x + self.pos_embed(x)
        return x
 
 
class Dilatestage(nn.Module):
    """ A basic Dilate Transformer layer for one stage.
    """
    def __init__(self, dim, depth, num_heads, kernel_size, dilation,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0.,
                 attn_drop=0., drop_path=0., act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm, cpe_per_satge=False, cpe_per_block=False,
                 downsample=True, merging_way=None):
 
        super().__init__()
        # build blocks
        self.blocks = nn.ModuleList([
            DilateBlock(dim=dim, num_heads=num_heads,
                        kernel_size=kernel_size, dilation=dilation,
                        mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                        qk_scale=qk_scale, drop=drop, attn_drop=attn_drop,
                        drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                        norm_layer=norm_layer, act_layer=act_layer, cpe_per_block=cpe_per_block)
            for i in range(depth)])
 
        # patch merging layer
        self.downsample = PatchMerging(dim, int(dim * 2), merging_way, cpe_per_satge) if downsample else nn.Identity()
 
    def forward(self, x):
        for blk in self.blocks:
            x = blk(x)
        x = self.downsample(x)
        return x
 
 
class Globalstage(nn.Module):
    """ A basic Transformer layer for one stage."""
    def __init__(self, dim, depth, num_heads, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                 drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,
                 cpe_per_satge=False, cpe_per_block=False,
                 downsample=True, merging_way=None):
 
        super().__init__()
        # build blocks
        self.blocks = nn.ModuleList([
            GlobalBlock(dim=dim, num_heads=num_heads,
                        mlp_ratio=mlp_ratio,qkv_bias=qkv_bias,
                        qk_scale=qk_scale, drop=drop, attn_drop=attn_drop,
                        drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                        norm_layer=norm_layer, act_layer=act_layer, cpe_per_block=cpe_per_block)
            for i in range(depth)])
 
        # patch merging layer
        self.downsample = PatchMerging(dim, int(dim*2), merging_way, cpe_per_satge) if downsample else nn.Identity()
 
    def forward(self, x):
        for blk in self.blocks:
            x = blk(x)
        x = self.downsample(x)
        return x
 
 
class Dilateformer(nn.Module):
    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dim=96,
                 depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], kernel_size=3, dilation=[1, 2, 3],
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.1,
                 norm_layer=partial(nn.LayerNorm, eps=1e-6),
                 merging_way='conv3_2',
                 patch_way='overlaping',
                 dilate_attention=[True, True, False, False],
                 downsamples=[True, True, True, False],
                 cpe_per_satge=False, cpe_per_block=True):
        super().__init__()
        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
 
        #patch embedding
        self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size,
                                      in_chans=in_chans, embed_dim=embed_dim, patch_way=patch_way)
        dpr = [x.item() for x in torch.linspace(0, drop_path, sum(depths))]
        self.stages = nn.ModuleList()
        for i_layer in range(self.num_layers):
            if dilate_attention[i_layer]:
                stage = Dilatestage(dim=int(embed_dim * 2 ** i_layer),
                                    depth=depths[i_layer],
                                    num_heads=num_heads[i_layer],
                                    kernel_size=kernel_size,
                                    dilation=dilation,
                                    mlp_ratio=self.mlp_ratio,
                                    qkv_bias=qkv_bias, qk_scale=qk_scale,
                                    drop=drop, attn_drop=attn_drop,
                                    drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                                    norm_layer=norm_layer,
                                    downsample=downsamples[i_layer],
                                    cpe_per_block=cpe_per_block,
                                    cpe_per_satge=cpe_per_satge,
                                    merging_way=merging_way
                                    )
            else:
                stage = Globalstage(dim=int(embed_dim * 2 ** i_layer),
                                    depth=depths[i_layer],
                                    num_heads=num_heads[i_layer],
                                    mlp_ratio=self.mlp_ratio,
                                    qkv_bias=qkv_bias, qk_scale=qk_scale,
                                    drop=drop, attn_drop=attn_drop,
                                    drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                                    norm_layer=norm_layer,
                                    downsample=downsamples[i_layer],
                                    cpe_per_block=cpe_per_block,
                                    cpe_per_satge=cpe_per_satge,
                                    merging_way=merging_way
                                    )
            self.stages.append(stage)
        self.norm = norm_layer(self.num_features)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
 
        self.apply(self._init_weights)
 
    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
 
    @torch.jit.ignore
    def no_weight_decay(self):
        return {'absolute_pos_embed'}
 
    def forward_features(self, x):
        x = self.patch_embed(x)
        for stage in self.stages:
            x = stage(x)
 
        x = x.flatten(2).transpose(1, 2)
        x = self.norm(x)  # B L C
        x = self.avgpool(x.transpose(1, 2))  # B C 1
        x = torch.flatten(x, 1)
        return x
 
    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x
 
 
@register_model
def dilateformer_tiny(pretrained=True, **kwargs):
    model = Dilateformer(depths=[2, 2, 6, 2], embed_dim=72, num_heads=[ 3, 6, 12, 24 ], **kwargs)
    model.default_cfg = _cfg()
    return model
 
 
@register_model
def dilateformer_small(pretrained=True, **kwargs):
    model = Dilateformer(depths=[3, 5, 8, 3], embed_dim=72, num_heads=[ 3, 6, 12, 24 ],  **kwargs)
    model.default_cfg = _cfg()
    return model
 
 
@register_model
def dilateformer_base(pretrained=True, **kwargs):
    model = Dilateformer(depths=[4, 8, 10, 3], embed_dim=96, num_heads=[ 3, 6, 12, 24 ],  **kwargs)
    model.default_cfg = _cfg()
    return model
 
 
 
 
 
if __name__ == "__main__":
    x = torch.rand([1, 3, 224,224])
    m = dilateformer_tiny(pretrained=False)
    y = m(x)
    print(y.shape)

2.2 步骤二

在tasks.py中注册我们的MSDA模块。 如下图所示

爆改YOLOv8 | yolov8添加MSDA注意力机制_第1张图片

2.3 步骤三

在parse_model中添加如下红框标注代码

爆改YOLOv8 | yolov8添加MSDA注意力机制_第2张图片

到此注册成功,复制后面的yaml文件直接运行即可

关于msda添加的位置有两种方案,大家可以自行选择

yaml文件1


# 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, Conv, [1024, 3, 2]]  # 7-P5/32
  - [-1, 3, C2f, [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, [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)
  - [-1, 1, MultiDilatelocalAttention, []]  # 22
 
  - [[15, 18, 22], 1, Detect, [nc]]  # Detect(P3, P4, P5)

yaml文件2

# 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, Conv, [1024, 3, 2]]  # 7-P5/32
  - [-1, 3, C2f, [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, [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, MultiDilatelocalAttention, []]  # 16
 
  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 12], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 19 (P4/16-medium)
  - [-1, 1, MultiDilatelocalAttention, []]  # 20
 
  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 9], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 23 (P5/32-large)
  - [-1, 1, MultiDilatelocalAttention, []]  # 24
 
  - [[16, 20, 24], 1, Detect, [nc]]  # Detect(P3, P4, P5)

# 关于MSDA添加的位置可以自行调试,针对不同数据集位置不同,效果不同

不知不觉已经看完了哦,动动小手留个点赞吧--_--

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