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YOLO11改进 | 融合改进 | C3k2引入多尺度分支来增强特征表征【全网独家 附结构图】

news 2024/11/5 15:49:54

秋招面试专栏推荐 ：深度学习算法工程师面试问题总结【百面算法工程师】——点击即可跳转

💡💡💡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C3k2替换为融合后的结构来提取特征。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

目录

1.论文

2.  C3k2_DiverseBranchBlock代码实现

2.1 将 C3k2_DiverseBranchBlock添加到YOLO11中

2.2 更改init.py文件

2.3 添加yaml文件

2.4 在task.py中进行注册

2.5 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：Diverse Branch Block: Building a Convolution as an Inception-like Unit——点击即可跳转

官方仓库：官方代码仓库——点击即可跳转

2.  C3k2_DiverseBranchBlock代码实现

2.1 将 C3k2_DiverseBranchBlock添加到YOLO11中

关键步骤一：将下面代码粘贴到在/ultralytics/ultralytics/nn/modules/block.py中

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as npdef transI_fusebn(kernel, bn):gamma = bn.weightstd = (bn.running_var + bn.eps).sqrt()return kernel * ((gamma / std).reshape(-1, 1, 1, 1)), bn.bias - bn.running_mean * gamma / stddef transII_addbranch(kernels, biases):return sum(kernels), sum(biases)def transIII_1x1_kxk(k1, b1, k2, b2, groups):if groups == 1:k = F.conv2d(k2, k1.permute(1, 0, 2, 3))  #b_hat = (k2 * b1.reshape(1, -1, 1, 1)).sum((1, 2, 3))else:k_slices = []b_slices = []k1_T = k1.permute(1, 0, 2, 3)k1_group_width = k1.size(0) // groupsk2_group_width = k2.size(0) // groupsfor g in range(groups):k1_T_slice = k1_T[:, g * k1_group_width:(g + 1) * k1_group_width, :, :]k2_slice = k2[g * k2_group_width:(g + 1) * k2_group_width, :, :, :]k_slices.append(F.conv2d(k2_slice, k1_T_slice))b_slices.append((k2_slice * b1[g * k1_group_width:(g + 1) * k1_group_width].reshape(1, -1, 1, 1)).sum((1, 2, 3)))k, b_hat = transIV_depthconcat(k_slices, b_slices)return k, b_hat + b2def transIV_depthconcat(kernels, biases):return torch.cat(kernels, dim=0), torch.cat(biases)def transV_avg(channels, kernel_size, groups):input_dim = channels // groupsk = torch.zeros((channels, input_dim, kernel_size, kernel_size))k[np.arange(channels), np.tile(np.arange(input_dim), groups), :, :] = 1.0 / kernel_size ** 2return k#   This has not been tested with non-square kernels (kernel.size(2) != kernel.size(3)) nor even-size kernels
def transVI_multiscale(kernel, target_kernel_size):H_pixels_to_pad = (target_kernel_size - kernel.size(2)) // 2W_pixels_to_pad = (target_kernel_size - kernel.size(3)) // 2return F.pad(kernel, [H_pixels_to_pad, H_pixels_to_pad, W_pixels_to_pad, W_pixels_to_pad])def conv_bn(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1,padding_mode='zeros'):conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,stride=stride, padding=padding, dilation=dilation, groups=groups,bias=False, padding_mode=padding_mode)bn_layer = nn.BatchNorm2d(num_features=out_channels, affine=True)se = nn.Sequential()se.add_module('conv', conv_layer)se.add_module('bn', bn_layer)return seclass IdentityBasedConv1x1(nn.Conv2d):def __init__(self, channels, groups=1):super(IdentityBasedConv1x1, self).__init__(in_channels=channels, out_channels=channels, kernel_size=1, stride=1,padding=0, groups=groups, bias=False)assert channels % groups == 0input_dim = channels // groupsid_value = np.zeros((channels, input_dim, 1, 1))for i in range(channels):id_value[i, i % input_dim, 0, 0] = 1self.id_tensor = torch.from_numpy(id_value).type_as(self.weight)nn.init.zeros_(self.weight)def forward(self, input):kernel = self.weight + self.id_tensor.to(self.weight.device).type_as(self.weight)result = F.conv2d(input, kernel, None, stride=1, padding=0, dilation=self.dilation, groups=self.groups)return resultdef get_actual_kernel(self):return self.weight + self.id_tensor.to(self.weight.device)class BNAndPadLayer(nn.Module):def __init__(self,pad_pixels,num_features,eps=1e-5,momentum=0.1,affine=True,track_running_stats=True):super(BNAndPadLayer, self).__init__()self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine, track_running_stats)self.pad_pixels = pad_pixelsdef forward(self, input):output = self.bn(input)if self.pad_pixels > 0:if self.bn.affine:pad_values = self.bn.bias.detach() - self.bn.running_mean * self.bn.weight.detach() / torch.sqrt(self.bn.running_var + self.bn.eps)else:pad_values = - self.bn.running_mean / torch.sqrt(self.bn.running_var + self.bn.eps)output = F.pad(output, [self.pad_pixels] * 4)pad_values = pad_values.view(1, -1, 1, 1)output[:, :, 0:self.pad_pixels, :] = pad_valuesoutput[:, :, -self.pad_pixels:, :] = pad_valuesoutput[:, :, :, 0:self.pad_pixels] = pad_valuesoutput[:, :, :, -self.pad_pixels:] = pad_valuesreturn output@propertydef weight(self):return self.bn.weight@propertydef bias(self):return self.bn.bias@propertydef running_mean(self):return self.bn.running_mean@propertydef running_var(self):return self.bn.running_var@propertydef eps(self):return self.bn.epsclass DiverseBranchBlock(nn.Module):def __init__(self, in_channels, out_channels, kernel_size,stride=1, padding=None, dilation=1, groups=1,internal_channels_1x1_3x3=None,deploy=False, single_init=False):super(DiverseBranchBlock, self).__init__()self.deploy = deployself.nonlinear = Conv.default_actself.kernel_size = kernel_sizeself.out_channels = out_channelsself.groups = groupsif padding is None:padding = autopad(kernel_size, padding, dilation)assert padding == kernel_size // 2if deploy:self.dbb_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,stride=stride,padding=padding, dilation=dilation, groups=groups, bias=True)else:self.dbb_origin = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,stride=stride, padding=padding, dilation=dilation, groups=groups)self.dbb_avg = nn.Sequential()if groups < out_channels:self.dbb_avg.add_module('conv',nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1,stride=1, padding=0, groups=groups, bias=False))self.dbb_avg.add_module('bn', BNAndPadLayer(pad_pixels=padding, num_features=out_channels))self.dbb_avg.add_module('avg', nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=0))self.dbb_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,padding=0, groups=groups)else:self.dbb_avg.add_module('avg', nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=padding))self.dbb_avg.add_module('avgbn', nn.BatchNorm2d(out_channels))if internal_channels_1x1_3x3 is None:internal_channels_1x1_3x3 = in_channels if groups < out_channels else 2 * in_channels  # For mobilenet, it is better to have 2X internal channelsself.dbb_1x1_kxk = nn.Sequential()if internal_channels_1x1_3x3 == in_channels:self.dbb_1x1_kxk.add_module('idconv1', IdentityBasedConv1x1(channels=in_channels, groups=groups))else:self.dbb_1x1_kxk.add_module('conv1',nn.Conv2d(in_channels=in_channels, out_channels=internal_channels_1x1_3x3,kernel_size=1, stride=1, padding=0, groups=groups, bias=False))self.dbb_1x1_kxk.add_module('bn1', BNAndPadLayer(pad_pixels=padding, num_features=internal_channels_1x1_3x3,affine=True))self.dbb_1x1_kxk.add_module('conv2',nn.Conv2d(in_channels=internal_channels_1x1_3x3, out_channels=out_channels,kernel_size=kernel_size, stride=stride, padding=0, groups=groups,bias=False))self.dbb_1x1_kxk.add_module('bn2', nn.BatchNorm2d(out_channels))#   The experiments reported in the paper used the default initialization of bn.weight (all as 1). But changing the initialization may be useful in some cases.if single_init:#   Initialize the bn.weight of dbb_origin as 1 and others as 0. This is not the default setting.self.single_init()def get_equivalent_kernel_bias(self):k_origin, b_origin = transI_fusebn(self.dbb_origin.conv.weight, self.dbb_origin.bn)if hasattr(self, 'dbb_1x1'):k_1x1, b_1x1 = transI_fusebn(self.dbb_1x1.conv.weight, self.dbb_1x1.bn)k_1x1 = transVI_multiscale(k_1x1, self.kernel_size)else:k_1x1, b_1x1 = 0, 0if hasattr(self.dbb_1x1_kxk, 'idconv1'):k_1x1_kxk_first = self.dbb_1x1_kxk.idconv1.get_actual_kernel()else:k_1x1_kxk_first = self.dbb_1x1_kxk.conv1.weightk_1x1_kxk_first, b_1x1_kxk_first = transI_fusebn(k_1x1_kxk_first, self.dbb_1x1_kxk.bn1)k_1x1_kxk_second, b_1x1_kxk_second = transI_fusebn(self.dbb_1x1_kxk.conv2.weight, self.dbb_1x1_kxk.bn2)k_1x1_kxk_merged, b_1x1_kxk_merged = transIII_1x1_kxk(k_1x1_kxk_first, b_1x1_kxk_first, k_1x1_kxk_second,b_1x1_kxk_second, groups=self.groups)k_avg = transV_avg(self.out_channels, self.kernel_size, self.groups)k_1x1_avg_second, b_1x1_avg_second = transI_fusebn(k_avg.to(self.dbb_avg.avgbn.weight.device),self.dbb_avg.avgbn)if hasattr(self.dbb_avg, 'conv'):k_1x1_avg_first, b_1x1_avg_first = transI_fusebn(self.dbb_avg.conv.weight, self.dbb_avg.bn)k_1x1_avg_merged, b_1x1_avg_merged = transIII_1x1_kxk(k_1x1_avg_first, b_1x1_avg_first, k_1x1_avg_second,b_1x1_avg_second, groups=self.groups)else:k_1x1_avg_merged, b_1x1_avg_merged = k_1x1_avg_second, b_1x1_avg_secondreturn transII_addbranch((k_origin, k_1x1, k_1x1_kxk_merged, k_1x1_avg_merged),(b_origin, b_1x1, b_1x1_kxk_merged, b_1x1_avg_merged))def switch_to_deploy(self):if hasattr(self, 'dbb_reparam'):returnkernel, bias = self.get_equivalent_kernel_bias()self.dbb_reparam = nn.Conv2d(in_channels=self.dbb_origin.conv.in_channels,out_channels=self.dbb_origin.conv.out_channels,kernel_size=self.dbb_origin.conv.kernel_size, stride=self.dbb_origin.conv.stride,padding=self.dbb_origin.conv.padding, dilation=self.dbb_origin.conv.dilation,groups=self.dbb_origin.conv.groups, bias=True)self.dbb_reparam.weight.data = kernelself.dbb_reparam.bias.data = biasfor para in self.parameters():para.detach_()self.__delattr__('dbb_origin')self.__delattr__('dbb_avg')if hasattr(self, 'dbb_1x1'):self.__delattr__('dbb_1x1')self.__delattr__('dbb_1x1_kxk')def forward(self, inputs):if hasattr(self, 'dbb_reparam'):return self.nonlinear(self.dbb_reparam(inputs))out = self.dbb_origin(inputs)if hasattr(self, 'dbb_1x1'):out += self.dbb_1x1(inputs)out += self.dbb_avg(inputs)out += self.dbb_1x1_kxk(inputs)return self.nonlinear(out)def init_gamma(self, gamma_value):if hasattr(self, "dbb_origin"):torch.nn.init.constant_(self.dbb_origin.bn.weight, gamma_value)if hasattr(self, "dbb_1x1"):torch.nn.init.constant_(self.dbb_1x1.bn.weight, gamma_value)if hasattr(self, "dbb_avg"):torch.nn.init.constant_(self.dbb_avg.avgbn.weight, gamma_value)if hasattr(self, "dbb_1x1_kxk"):torch.nn.init.constant_(self.dbb_1x1_kxk.bn2.weight, gamma_value)def single_init(self):self.init_gamma(0.0)if hasattr(self, "dbb_origin"):torch.nn.init.constant_(self.dbb_origin.bn.weight, 1.0)class C3k2_DiverseBranchBlock(nn.Module):"""融合了 C3k2 和 DiverseBranchBlock 的模块。"""def __init__(self, c1, c2, n=1, c3k=False, e=0.5, g=1, shortcut=True, dbb_kernel_size=3, dbb_stride=1, dbb_padding=None, dbb_groups=1):"""Initializes a fused module that combines the C3k2 (faster CSP Bottleneck with 2 convolutions)and DiverseBranchBlock."""super().__init__()# 初始化 C3k2 部分self.c3k2 = C3k2(c1, c2, n=n, c3k=c3k, e=e, g=g, shortcut=shortcut)# 初始化 DiverseBranchBlock 部分self.diverse_branch_block = DiverseBranchBlock(c2, c2, dbb_kernel_size, stride=dbb_stride, padding=dbb_padding, groups=dbb_groups)def forward(self, x):# 先经过 C3k2 模块x = self.c3k2(x)# 再经过 DiverseBranchBlock 模块x = self.diverse_branch_block(x)return x

2.2 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

2.3 添加yaml文件

关键步骤三：在/ultralytics/ultralytics/cfg/models/11下面新建文件yolo11_C3k2_DiverseBranchBlock.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, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 2, C3k2, [256, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, C3k2, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 2, C2PSA, [1024]] # 10# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2_DiverseBranchBlock, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [[16, 19, 22], 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, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 2, C3k2, [256, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, C3k2, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 2, C2PSA, [1024]] # 10# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2_DiverseBranchBlock, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [[16, 19, 22], 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, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 2, C3k2, [256, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, C3k2, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 2, C2PSA, [1024]] # 10# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2_DiverseBranchBlock, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [[16, 19, 22], 1, OBB, [nc, 1]] # OBB(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

2.4 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C3k2_DiverseBranchBlock

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C3k2_DiverseBranchBlock

2.5 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_C3k2_DiverseBranchBlock.yaml的路径即可 【注意是在外边的Ultralytics下新建train.py】

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       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]13                  -1  1    456896  ultralytics.nn.modules.block.C3k2_DiverseBranchBlock[384, 128, 1, False]14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]23        [16, 19, 22]  1    464912  ultralytics.nn.modules.head.Detect           [80, [64, 128, 256]]
YOLO11_C3k2_DiverseBranchBlock summary: 339 layers, 2,969,680 parameters, 2,969,664 gradients, 7.7 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

  1. 前沿技术解读：专栏不仅限于YOLO系列的改进，还会涵盖各类主流与新兴网络的最新研究成果，帮助你紧跟技术潮流。

  2. 详尽的实践分享：所有内容实践性也极强。每次更新都会附带代码和具体的改进步骤，保证每位读者都能迅速上手。

  3. 问题互动与答疑：订阅我的专栏后，你将可以随时向我提问，获取及时的答疑。

  4. 实时更新，紧跟行业动态：不定期发布来自全球顶会的最新研究方向和复现实验报告，让你时刻走在技术前沿。

专栏适合人群：

  • 对目标检测、YOLO系列网络有深厚兴趣的同学

  • 希望在用YOLO算法写论文的同学

  • 对YOLO算法感兴趣的同学等


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