YOLO11改进 | 主干网络 | 将backbone替换为Swin-Transformer结构【论文必备】

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💡💡💡本专栏所有程序均经过测试，可成功执行💡💡💡

本文给大家带来的教程是将YOLO11的backbone替换为Swin-Transformer结构来提取特征。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：YOLO11入门 + 改进涨点——点击即可跳转欢迎订阅

1.论文

2. 将Swin-Transformer添加到yolo11网络中

2.1 Swin-Transformer代码实现

2.2 Swin-Transformer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 在task.py中进行注册

2.6 执行程序

3.修改后的网络结构图

4. 完整代码分享

5. GFLOPs

6. 进阶

7.总结

1.论文

论文地址：Swin-Transformer点击即可跳转

官方代码：Swin-Transformer官方代码仓库点击即可跳转

2. 将Swin-Transformer添加到yolo11网络中

2.1 Swin-Transformer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


#swin transformer
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from typing import Optionaldef drop_path_f(x, drop_prob: float = 0., training: bool = False):if drop_prob == 0. or not training:return xkeep_prob = 1 - drop_probshape = (x.shape[0],) + (1,) * (x.ndim - 1)random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)random_tensor.floor_()output = x.div(keep_prob) * random_tensorreturn outputclass DropPath(nn.Module):def __init__(self, drop_prob=None):super(DropPath, self).__init__()self.drop_prob = drop_probdef forward(self, x):return drop_path_f(x, self.drop_prob, self.training)def window_partition(x, window_size: int):B, H, W, C = x.shapex = x.view(B, H // window_size, window_size, W // window_size, window_size, C)# permute: [B, H//Mh, Mh, W//Mw, Mw, C] -> [B, H//Mh, W//Mh, Mw, Mw, C]# view: [B, H//Mh, W//Mw, Mh, Mw, C] -> [B*num_windows, Mh, Mw, C]windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)return windowsdef window_reverse(windows, window_size: int, H: int, W: int):B = int(windows.shape[0] / (H * W / window_size / window_size))# view: [B*num_windows, Mh, Mw, C] -> [B, H//Mh, W//Mw, Mh, Mw, C]x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)# permute: [B, H//Mh, W//Mw, Mh, Mw, C] -> [B, H//Mh, Mh, W//Mw, Mw, C]# view: [B, H//Mh, Mh, W//Mw, Mw, C] -> [B, H, W, C]x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)return xclass 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_featureshidden_features = hidden_features or in_featuresself.fc1 = nn.Linear(in_features, hidden_features)self.act = act_layer()self.drop1 = nn.Dropout(drop)self.fc2 = nn.Linear(hidden_features, out_features)self.drop2 = nn.Dropout(drop)def forward(self, x):x = self.fc1(x)x = self.act(x)x = self.drop1(x)x = self.fc2(x)x = self.drop2(x)return xclass WindowAttention(nn.Module):r""" Window based multi-head self attention (W-MSA) module with relative position bias.It supports both of shifted and non-shifted window.Args:dim (int): Number of input channels.window_size (tuple[int]): The height and width of the window.num_heads (int): Number of attention heads.qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: Trueattn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0proj_drop (float, optional): Dropout ratio of output. Default: 0.0"""def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.):super().__init__()self.dim = dimself.window_size = window_size  # [Mh, Mw]self.num_heads = num_headshead_dim = dim // num_headsself.scale = head_dim ** -0.5# define a parameter table of relative position biasself.relative_position_bias_table = nn.Parameter(torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # [2*Mh-1 * 2*Mw-1, nH]# get pair-wise relative position index for each token inside the windowcoords_h = torch.arange(self.window_size[0])coords_w = torch.arange(self.window_size[1])coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing="ij"))  # [2, Mh, Mw]coords_flatten = torch.flatten(coords, 1)  # [2, Mh*Mw]# [2, Mh*Mw, 1] - [2, 1, Mh*Mw]relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # [2, Mh*Mw, Mh*Mw]relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # [Mh*Mw, Mh*Mw, 2]relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0relative_coords[:, :, 1] += self.window_size[1] - 1relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1relative_position_index = relative_coords.sum(-1)  # [Mh*Mw, Mh*Mw]self.register_buffer("relative_position_index", relative_position_index)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)nn.init.trunc_normal_(self.relative_position_bias_table, std=.02)self.softmax = nn.Softmax(dim=-1)def forward(self, x, mask: Optional[torch.Tensor] = None):"""Args:x: input features with shape of (num_windows*B, Mh*Mw, C)mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None"""# [batch_size*num_windows, Mh*Mw, total_embed_dim]B_, N, C = x.shape# qkv(): -> [batch_size*num_windows, Mh*Mw, 3 * total_embed_dim]# reshape: -> [batch_size*num_windows, Mh*Mw, 3, num_heads, embed_dim_per_head]# permute: -> [3, batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4).contiguous()# [batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)# transpose: -> [batch_size*num_windows, num_heads, embed_dim_per_head, Mh*Mw]# @: multiply -> [batch_size*num_windows, num_heads, Mh*Mw, Mh*Mw]q = q * self.scaleattn = (q @ k.transpose(-2, -1))# relative_position_bias_table.view: [Mh*Mw*Mh*Mw,nH] -> [Mh*Mw,Mh*Mw,nH]relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # [nH, Mh*Mw, Mh*Mw]attn = attn + relative_position_bias.unsqueeze(0)if mask is not None:# mask: [nW, Mh*Mw, Mh*Mw]nW = mask.shape[0]  # num_windows# attn.view: [batch_size, num_windows, num_heads, Mh*Mw, Mh*Mw]# mask.unsqueeze: [1, nW, 1, Mh*Mw, Mh*Mw]attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)attn = attn.view(-1, self.num_heads, N, N)attn = self.softmax(attn)else:attn = self.softmax(attn)attn = self.attn_drop(attn)# @: multiply -> [batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]# transpose: -> [batch_size*num_windows, Mh*Mw, num_heads, embed_dim_per_head]# reshape: -> [batch_size*num_windows, Mh*Mw, total_embed_dim]#x = (attn @ v).transpose(1, 2).reshape(B_, N, C)x = (attn.to(v.dtype) @ v).transpose(1, 2).reshape(B_, N, C)x = self.proj(x)x = self.proj_drop(x)return xclass SwinTransformerBlock(nn.Module):r""" Swin Transformer Block.Args:dim (int): Number of input channels.num_heads (int): Number of attention heads.window_size (int): Window size.shift_size (int): Shift size for SW-MSA.mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: Truedrop (float, optional): Dropout rate. Default: 0.0attn_drop (float, optional): Attention dropout rate. Default: 0.0drop_path (float, optional): Stochastic depth rate. Default: 0.0act_layer (nn.Module, optional): Activation layer. Default: nn.GELUnorm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm"""def __init__(self, dim, num_heads, window_size=7, shift_size=0,mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,act_layer=nn.GELU, norm_layer=nn.LayerNorm):super().__init__()self.dim = dimself.num_heads = num_headsself.window_size = window_sizeself.shift_size = shift_sizeself.mlp_ratio = mlp_ratioassert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"self.norm1 = norm_layer(dim)self.attn = WindowAttention(dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, qkv_bias=qkv_bias,attn_drop=attn_drop, proj_drop=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, attn_mask):H, W = self.H, self.WB, L, C = x.shapeassert L == H * W, "input feature has wrong size"shortcut = xx = self.norm1(x)x = x.view(B, H, W, C)# pad feature maps to multiples of window size# 把 feature map 给 pad 到 window size 的整数倍pad_l = pad_t = 0pad_r = (self.window_size - W % self.window_size) % self.window_sizepad_b = (self.window_size - H % self.window_size) % self.window_sizex = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))_, Hp, Wp, _ = x.shape# cyclic shiftif self.shift_size > 0:shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))else:shifted_x = xattn_mask = None# partition windowsx_windows = window_partition(shifted_x, self.window_size)  # [nW*B, Mh, Mw, C]x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # [nW*B, Mh*Mw, C]# W-MSA/SW-MSAattn_windows = self.attn(x_windows, mask=attn_mask)  # [nW*B, Mh*Mw, C]# merge windowsattn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)  # [nW*B, Mh, Mw, C]shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # [B, H', W', C]# reverse cyclic shiftif self.shift_size > 0:x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))else:x = shifted_xif pad_r > 0 or pad_b > 0:# 把前面pad的数据移除掉x = x[:, :H, :W, :].contiguous()x = x.view(B, H * W, C)# FFNx = shortcut + self.drop_path(x)x = x + self.drop_path(self.mlp(self.norm2(x)))return xclass SwinStage(nn.Module):"""A basic Swin Transformer layer for one stage.Args:dim (int): Number of input channels.depth (int): Number of blocks.num_heads (int): Number of attention heads.window_size (int): Local window size.mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: Truedrop (float, optional): Dropout rate. Default: 0.0attn_drop (float, optional): Attention dropout rate. Default: 0.0drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNormdownsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: Noneuse_checkpoint (bool): Whether to use checkpointing to save memory. Default: False."""def __init__(self, dim, c2, depth, num_heads, window_size,mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,drop_path=0., norm_layer=nn.LayerNorm, use_checkpoint=False):super().__init__()assert dim == c2, r"no. in/out channel should be same"self.dim = dimself.depth = depthself.window_size = window_sizeself.use_checkpoint = use_checkpointself.shift_size = window_size // 2# build blocksself.blocks = nn.ModuleList([SwinTransformerBlock(dim=dim,num_heads=num_heads,window_size=window_size,shift_size=0 if (i % 2 == 0) else self.shift_size,mlp_ratio=mlp_ratio,qkv_bias=qkv_bias,drop=drop,attn_drop=attn_drop,drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,norm_layer=norm_layer)for i in range(depth)])def create_mask(self, x, H, W):# calculate attention mask for SW-MSAHp = int(np.ceil(H / self.window_size)) * self.window_sizeWp = int(np.ceil(W / self.window_size)) * self.window_sizeimg_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # [1, Hp, Wp, 1]h_slices = (slice(0, -self.window_size),slice(-self.window_size, -self.shift_size),slice(-self.shift_size, None))w_slices = (slice(0, -self.window_size),slice(-self.window_size, -self.shift_size),slice(-self.shift_size, None))cnt = 0for h in h_slices:for w in w_slices:img_mask[:, h, w, :] = cntcnt += 1mask_windows = window_partition(img_mask, self.window_size)  # [nW, Mh, Mw, 1]mask_windows = mask_windows.view(-1, self.window_size * self.window_size)  # [nW, Mh*Mw]attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)  # [nW, 1, Mh*Mw] - [nW, Mh*Mw, 1]# [nW, Mh*Mw, Mh*Mw]attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))return attn_maskdef forward(self, x):B, C, H, W = x.shapex = x.permute(0, 2, 3, 1).contiguous().view(B, H * W, C)attn_mask = self.create_mask(x, H, W)  # [nW, Mh*Mw, Mh*Mw]for blk in self.blocks:blk.H, blk.W = H, Wif not torch.jit.is_scripting() and self.use_checkpoint:x = checkpoint.checkpoint(blk, x, attn_mask)else:x = blk(x, attn_mask)x = x.view(B, H, W, C)x = x.permute(0, 3, 1, 2).contiguous()return xclass PatchEmbed(nn.Module):def __init__(self, in_c=3, embed_dim=96, patch_size=4, norm_layer=None):super().__init__()patch_size = (patch_size, patch_size)self.patch_size = patch_sizeself.in_chans = in_cself.embed_dim = embed_dimself.proj = nn.Conv2d(in_c, embed_dim, kernel_size=patch_size, stride=patch_size)self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()def forward(self, x):_, _, H, W = x.shape# paddingpad_input = (H % self.patch_size[0] != 0) or (W % self.patch_size[1] != 0)if pad_input:# to pad the last 3 dimensions,# (W_left, W_right, H_top,H_bottom, C_front, C_back)x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1],0, self.patch_size[0] - H % self.patch_size[0],0, 0))x = self.proj(x)B, C, H, W = x.shape# flatten: [B, C, H, W] -> [B, C, HW]# transpose: [B, C, HW] -> [B, HW, C]x = x.flatten(2).transpose(1, 2)x = self.norm(x)# view: [B, HW, C] -> [B, H, W, C]# permute: [B, H, W, C] -> [B, C, H, W]x = x.view(B, H, W, C)x = x.permute(0, 3, 1, 2).contiguous()return xclass PatchMerging(nn.Module):r""" Patch Merging Layer.Args:dim (int): Number of input channels.norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm"""def __init__(self, dim, c2, norm_layer=nn.LayerNorm):super().__init__()assert c2 == (2 * dim), r"no. out channel should be 2 * no. in channel "self.dim = dimself.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)self.norm = norm_layer(4 * dim)def forward(self, x):"""x: B, C, H, W"""B, C, H, W = x.shape# assert L == H * W, "input feature has wrong size"x = x.permute(0, 2, 3, 1).contiguous()# x = x.view(B, H*W, C)# paddingpad_input = (H % 2 == 1) or (W % 2 == 1)if pad_input:# to pad the last 3 dimensions, starting from the last dimension and moving forward.# (C_front, C_back, W_left, W_right, H_top, H_bottom)x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))x0 = x[:, 0::2, 0::2, :]  # [B, H/2, W/2, C]x1 = x[:, 1::2, 0::2, :]  # [B, H/2, W/2, C]x2 = x[:, 0::2, 1::2, :]  # [B, H/2, W/2, C]x3 = x[:, 1::2, 1::2, :]  # [B, H/2, W/2, C]x = torch.cat([x0, x1, x2, x3], -1)  # [B, H/2, W/2, 4*C]x = x.view(B, -1, 4 * C)  # [B, H/2*W/2, 4*C]x = self.norm(x)x = self.reduction(x)  # [B, H/2*W/2, 2*C]x = x.view(B, int(H / 2), int(W / 2), C * 2)x = x.permute(0, 3, 1, 2).contiguous()return x

2.2 Swin-Transformer的神经网络模块代码解析

DropPath：实现随机深度正则化的类，这是一种在训练期间随机丢弃一些残差分支以提高模型泛化能力的技术。
WindowAttention：实现具有相对位置偏差的基于窗口的多头自注意力 (W-MSA)，它处理输入图像的小窗口以有效捕获局部依赖关系。
SwinTransformerBlock：一个 Swin Transformer 块，包括基于窗口的多头自注意力和具有 GELU 激活的前馈网络 (FFN)。它支持移位窗口以提高模型捕获跨窗口连接的能力。
SwinStage：Swin Transformer 的一个阶段，由多个 Swin Transformer 块组成。它包括处理循环移位、窗口分区和注意力应用的逻辑。
PatchEmbed：将输入图像转换为补丁，然后将其线性投影到更高维的嵌入空间，本质上是为转换器层准备输入。
PatchMerging：合并相邻补丁以减少空间维度，这有助于逐步降低特征采样率。

主要特点：

窗口分区和反转函数允许在非重叠窗口中处理输入，与标准自注意力相比，这降低了计算复杂度。
相对位置编码可在不需要固定位置网格的情况下将空间信息添加到注意力机制中。
移位窗口机制允许跨窗口连接并增强模型捕获远程依赖关系的能力。
补丁嵌入和合并转换输入图像并逐步降低空间分辨率，使其对于大输入更有效率。

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/11下面新建文件yolo11_Swin-Transformer.yaml文件，粘贴下面的内容

目标检测

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 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=yolo11n.yaml' will call yolo11.yaml with scale 'n'# [depth, width, max_channels]n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPss: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPsm: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPsl: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPsx: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs# YOLO11n backbone
backbone:# [from, repeats, module, args]- [-1, 1, PatchEmbed, [96, 4]]  # 0 [b, 96, 160, 160]- [-1, 1, SwinStage , [96, 2, 3, 7]]  # 1 [b, 96, 160, 160]- [-1, 1, PatchMerging, [192]]    # 2 [b, 192, 80, 80]- [-1, 1, SwinStage,  [192, 2, 6, 7]]  # 3 --F0-- [b, 192, 80, 80] p3- [-1, 1, PatchMerging, [384]]   # 4 [b, 384, 40, 40]- [-1, 1, SwinStage, [384, 6, 12, 7]] # 5 --F1-- [b, 384, 40, 40] p4- [-1, 1, PatchMerging, [768]]   # 6 [b, 768, 20, 20]- [-1, 1, SwinStage, [768, 2, 24, 7]] # 7 --F2-- [b, 768, 20, 20]- [-1, 1, SPPF, [768, 5]]# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 5], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 3], 1, Concat, [1]] # cat backbone P3- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 11], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 8], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [[14, 17, 20], 1, Detect, [nc]] # Detect(P3, P4, P5)

语义分割

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 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=yolo11n.yaml' will call yolo11.yaml with scale 'n'# [depth, width, max_channels]n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPss: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPsm: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPsl: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPsx: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs# YOLO11n backbone
backbone:# [from, repeats, module, args]- [-1, 1, PatchEmbed, [96, 4]]  # 0 [b, 96, 160, 160]- [-1, 1, SwinStage , [96, 2, 3, 7]]  # 1 [b, 96, 160, 160]- [-1, 1, PatchMerging, [192]]    # 2 [b, 192, 80, 80]- [-1, 1, SwinStage,  [192, 2, 6, 7]]  # 3 --F0-- [b, 192, 80, 80] p3- [-1, 1, PatchMerging, [384]]   # 4 [b, 384, 40, 40]- [-1, 1, SwinStage, [384, 6, 12, 7]] # 5 --F1-- [b, 384, 40, 40] p4- [-1, 1, PatchMerging, [768]]   # 6 [b, 768, 20, 20]- [-1, 1, SwinStage, [768, 2, 24, 7]] # 7 --F2-- [b, 768, 20, 20]- [-1, 1, SPPF, [768, 5]]# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 5], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 3], 1, Concat, [1]] # cat backbone P3- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 11], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 8], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [[14, 17, 20], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)

旋转目标检测

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 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=yolo11n.yaml' will call yolo11.yaml with scale 'n'# [depth, width, max_channels]n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPss: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPsm: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPsl: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPsx: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs# YOLO11n backbone
backbone:# [from, repeats, module, args]- [-1, 1, PatchEmbed, [96, 4]]  # 0 [b, 96, 160, 160]- [-1, 1, SwinStage , [96, 2, 3, 7]]  # 1 [b, 96, 160, 160]- [-1, 1, PatchMerging, [192]]    # 2 [b, 192, 80, 80]- [-1, 1, SwinStage,  [192, 2, 6, 7]]  # 3 --F0-- [b, 192, 80, 80] p3- [-1, 1, PatchMerging, [384]]   # 4 [b, 384, 40, 40]- [-1, 1, SwinStage, [384, 6, 12, 7]] # 5 --F1-- [b, 384, 40, 40] p4- [-1, 1, PatchMerging, [768]]   # 6 [b, 768, 20, 20]- [-1, 1, SwinStage, [768, 2, 24, 7]] # 7 --F2-- [b, 768, 20, 20]- [-1, 1, SPPF, [768, 5]]# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 5], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 3], 1, Concat, [1]] # cat backbone P3- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 11], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 8], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [[14, 17, 20], 1, OBB, [nc, 1]] # Detect(P3, P4, P5)

温馨提示：本文只是对yolo11基础上添加模块，如果要对yolo11n/l/m/x进行添加则只需要指定对应的depth_multiple 和 width_multiple

# YOLO11n
depth_multiple: 0.50  # model depth multiple
width_multiple: 0.25  # layer channel multiple
max_channel：1024# YOLO11s
depth_multiple: 0.50  # model depth multiple
width_multiple: 0.50  # layer channel multiple
max_channel：1024# YOLO11m
depth_multiple: 0.50  # model depth multiple
width_multiple: 1.00  # layer channel multiple
max_channel：512# YOLO11l 
depth_multiple: 1.00  # model depth multiple
width_multiple: 1.00  # layer channel multiple
max_channel：512 # YOLO11x
depth_multiple: 1.00  # model depth multiple
width_multiple: 1.50 # layer channel multiple
max_channel：512

2.5 在task.py中进行注册

关键步骤四：在parse_model函数中进行注册，添加GCNet

先在task.py导入函数

然后在task.py文件下找到parse_model这个函数，如下图，添加

elif m in (PatchMerging,PatchEmbed,SwinStage):c1, c2 = ch[f], args[0]if c2 != nc:c2 = make_divisible(min(c2, max_channels) * width, 8)args = [c1, c2, *args[1:]]

2.6 执行程序

关键步骤五: 在ultralytics文件中新建train.py，将model的参数路径设置为yolo11_Swin-Transformer.yaml的路径即可

from ultralytics import YOLO
import warnings
warnings.filterwarnings('ignore')
from pathlib import Pathif __name__ == '__main__':# 加载模型model = YOLO("ultralytics/cfg/11/yolo11.yaml")  # 你要选择的模型yaml文件地址# Use the modelresults = 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                                       arguments0                  -1  1      1176  ultralytics.nn.modules.block.PatchEmbed      [3, 24, 4]1                  -1  1     15462  ultralytics.nn.modules.block.SwinStage       [24, 24, 2, 3, 7]2                  -1  1      4800  ultralytics.nn.modules.block.PatchMerging    [24, 48]3                  -1  1     58572  ultralytics.nn.modules.block.SwinStage       [48, 48, 2, 6, 7]4                  -1  1     18816  ultralytics.nn.modules.block.PatchMerging    [48, 96]5                  -1  1    683208  ultralytics.nn.modules.block.SwinStage       [96, 96, 6, 12, 7]6                  -1  1     74496  ultralytics.nn.modules.block.PatchMerging    [96, 192]7                  -1  1    897840  ultralytics.nn.modules.block.SwinStage       [192, 192, 2, 24, 7]8                  -1  1     92736  ultralytics.nn.modules.block.SPPF            [192, 192, 5]9                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']10             [-1, 5]  1         0  ultralytics.nn.modules.conv.Concat           [1]11                  -1  1     99008  ultralytics.nn.modules.block.C3k2            [288, 128, 1, False]12                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']13             [-1, 3]  1         0  ultralytics.nn.modules.conv.Concat           [1]14                  -1  1     26976  ultralytics.nn.modules.block.C3k2            [176, 64, 1, False]15                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]16            [-1, 11]  1         0  ultralytics.nn.modules.conv.Concat           [1]17                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]18                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]19             [-1, 8]  1         0  ultralytics.nn.modules.conv.Concat           [1]20                  -1  1    362496  ultralytics.nn.modules.block.C3k2            [320, 256, 1, True]21        [14, 17, 20]  1    464912  ultralytics.nn.modules.head.Detect           [80, [64, 128, 256]]
YOLO11_swin-Transformer summary: 390 layers, 3,071,922 parameters, 3,071,906 gradients, 29.1 GFLOPs

3.修改后的网络结构图

4. 完整代码分享

这个后期补充吧~，先按照步骤来即可

5. 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YOLO11n GFLOPs

改进后的GFLOPs

6. 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

7.总结

通过以上的改进方法，我们成功提升了模型的表现。这只是一个开始，未来还有更多优化和技术深挖的空间。在这里，我想隆重向大家推荐我的专栏——<专栏地址：YOLO11入门 + 改进涨点——点击即可跳转欢迎订阅>。这个专栏专注于前沿的深度学习技术，特别是目标检测领域的最新进展，不仅包含对YOLO11的深入解析和改进策略，还会定期更新来自各大顶会（如CVPR、NeurIPS等）的论文复现和实战分享。

为什么订阅我的专栏？ ——专栏地址：YOLO11入门 + 改进涨点——点击即可跳转欢迎订阅