wordpress付费站内搜索,佛山网站建设凤软,wordpress的模板在哪里改,域名解析好了怎么做网站本篇文章将介绍一个新的改进模块——SCConv#xff08;小波空间和通道重构卷积#xff09;#xff0c;并阐述如何将其应用于YOLOv11中#xff0c;显著提升模型性能。为了减少YOLOv11模型的空间和通道维度上的冗余#xff0c;我们引入空间和通道重构卷积。首先#xff0c;… 本篇文章将介绍一个新的改进模块——SCConv小波空间和通道重构卷积并阐述如何将其应用于YOLOv11中显著提升模型性能。为了减少YOLOv11模型的空间和通道维度上的冗余我们引入空间和通道重构卷积。首先我们将解析SCConv的工作原理它通过空间重构单元SRU和通道重构单元CRU减少卷积神经网络中的空间和通道冗余。随后我们会详细说明如何将该模块与YOLOv11相结合展示代码实现细节及其使用方法最终展现这一改进对目标检测效果的积极影响。
1. Spatial and Channel reconstruction Convolution(SCConv)结构介绍 SCConv模块由两个核心部分组成空间重建单元 (SRU) 和 通道重建单元 (CRU)。它们按照顺序组合使用首先通过SRU减少空间维度上的冗余然后通过CRU减少通道维度上的冗余。SCConv可以无缝集成到现有的CNN中用于替代标准卷积操作(Li_SCConv_Spatial_and_C…)。
1.1. 空间重建单元 (SRU)
SRU的主要目标是减少空间冗余。其工作流程如下
分离操作SRU通过训练好的参数对输入特征进行加权分离出包含丰富空间信息的特征和不包含太多信息的冗余特征。 通过对特征图使用Group Normalization (GN)提取每个特征图的缩放因子即γγ反映了空间像素的方差值越大说明该特征图包含的空间信息越丰富。基于这些缩放因子SRU将特征图分为两部分一部分包含丰富空间信息另一部分则是较少的信息。重建操作SRU通过交叉重建的方式将分离的特征进行重组提升信息流。该操作不仅减少了冗余还通过将富有信息的特征与低信息的特征组合进一步增强了特征的空间表达能力。
1.2. 通道重建单元 (CRU)
CRU的目标是减少通道维度上的冗余。其流程分为三个步骤 分离 (Split)CRU首先将输入特征图的通道分成两部分分别包含αC个通道和(1-α)C个通道然后通过1×1卷积进行压缩减少计算量。 变换 (Transform)分离后的上半部分特征图通过Group-wise Convolution (GWC) 和**Point-wise Convolution (PWC)**进行变换提取代表性强的高层特征下半部分通过廉价的1×1卷积提取浅层特征作为补充。 融合 (Fuse)通过全局平均池化Pooling和注意力机制CRU将上半部分的高层特征和下半部分的浅层特征进行加权融合得到最终的通道精炼特征。这种融合确保了信息在通道维度上的有效传递和冗余的消除。
2. YOLOv11与SCConv的结合 1. 改进C3k2本文使用SCConv卷积改进C3k2构建C3k2_SCConv模块然后使用C3k2_SCConv替换原有的C3k2这样就可以利用SCConv减少C3k2中的空间和通道的冗余。 2. 在backbone添加SCConv本文将SCConv卷积添加到SPPF模块之前减少backbone中的空间和通道的冗余。通过将空间和通道信息分别优化减少冗余信息从而提升模型的整体表现
3. Spatial and Channel reconstruction Convolution(SCConv)代码部分
import torch
import torch.nn.functional as F
import torch.nn as nnfrom .conv import Conv
from .block import C2f, C3, Bottleneckclass GroupBatchnorm2d(nn.Module):def __init__(self, c_num: int,group_num: int 16,eps: float 1e-10):super(GroupBatchnorm2d, self).__init__()assert c_num group_numself.group_num group_numself.weight nn.Parameter(torch.randn(c_num, 1, 1))self.bias nn.Parameter(torch.zeros(c_num, 1, 1))self.eps epsdef forward(self, x):N, C, H, W x.size()x x.view(N, self.group_num, -1)mean x.mean(dim2, keepdimTrue)std x.std(dim2, keepdimTrue)x (x - mean) / (std self.eps)x x.view(N, C, H, W)return x * self.weight self.biasclass SRU(nn.Module):def __init__(self,oup_channels: int,group_num: int 16,gate_treshold: float 0.5,torch_gn: bool False):super().__init__()self.gn nn.GroupNorm(num_channelsoup_channels, num_groupsgroup_num) if torch_gn else GroupBatchnorm2d(c_numoup_channels, group_numgroup_num)self.gate_treshold gate_tresholdself.sigomid nn.Sigmoid()def forward(self, x):gn_x self.gn(x)w_gamma self.gn.weight / torch.sum(self.gn.weight)w_gamma w_gamma.view(1, -1, 1, 1)reweigts self.sigomid(gn_x * w_gamma)# Gateinfo_mask reweigts self.gate_tresholdnoninfo_mask reweigts self.gate_tresholdx_1 info_mask * gn_xx_2 noninfo_mask * gn_xx self.reconstruct(x_1, x_2)return xdef reconstruct(self, x_1, x_2):x_11, x_12 torch.split(x_1, x_1.size(1) // 2, dim1)x_21, x_22 torch.split(x_2, x_2.size(1) // 2, dim1)return torch.cat([x_11 x_22, x_12 x_21], dim1)class CRU(nn.Module):alpha: 0alpha1def __init__(self,op_channel: int,alpha: float 1 / 2,squeeze_radio: int 2,group_size: int 2,group_kernel_size: int 3,):super().__init__()self.up_channel up_channel int(alpha * op_channel)self.low_channel low_channel op_channel - up_channelself.squeeze1 nn.Conv2d(up_channel, up_channel // squeeze_radio, kernel_size1, biasFalse)self.squeeze2 nn.Conv2d(low_channel, low_channel // squeeze_radio, kernel_size1, biasFalse)# upself.GWC nn.Conv2d(up_channel // squeeze_radio, op_channel, kernel_sizegroup_kernel_size, stride1,paddinggroup_kernel_size // 2, groupsgroup_size)self.PWC1 nn.Conv2d(up_channel // squeeze_radio, op_channel, kernel_size1, biasFalse)# lowself.PWC2 nn.Conv2d(low_channel // squeeze_radio, op_channel - low_channel // squeeze_radio, kernel_size1,biasFalse)self.advavg nn.AdaptiveAvgPool2d(1)def forward(self, x):# Splitup, low torch.split(x, [self.up_channel, self.low_channel], dim1)up, low self.squeeze1(up), self.squeeze2(low)# TransformY1 self.GWC(up) self.PWC1(up)Y2 torch.cat([self.PWC2(low), low], dim1)# Fuseout torch.cat([Y1, Y2], dim1)out F.softmax(self.advavg(out), dim1) * outout1, out2 torch.split(out, out.size(1) // 2, dim1)return out1 out2class ScConv(nn.Module):def __init__(self,op_channel: int,group_num: int 4,gate_treshold: float 0.5,alpha: float 1 / 2,squeeze_radio: int 2,group_size: int 2,group_kernel_size: int 3,):super().__init__()self.SRU SRU(op_channel,group_numgroup_num,gate_tresholdgate_treshold)self.CRU CRU(op_channel,alphaalpha,squeeze_radiosqueeze_radio,group_sizegroup_size,group_kernel_sizegroup_kernel_size)def forward(self, x):x self.SRU(x)x self.CRU(x)return xclass Bottleneck_ScConv(nn.Module):Standard bottleneck.def __init__(self, c1, c2, shortcutTrue, g1, k(3, 3), e0.5):Initializes a standard bottleneck module with optional shortcut connection and configurable parameters.super().__init__()c_ int(c2 * e) # hidden channelsself.cv1 Conv(c1, c_, k[0], 1)self.cv2 ScConv(c2)self.add shortcut and c1 c2def forward(self, x):Applies the YOLO FPN to input data.return x self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))class C3k(C3):C3k is a CSP bottleneck module with customizable kernel sizes for feature extraction in neural networks.def __init__(self, c1, c2, n1, shortcutTrue, g1, e0.5, k3):Initializes the C3k module with specified channels, number of layers, and configurations.super().__init__(c1, c2, n, shortcut, g, e)c_ int(c2 * e) # hidden channels# self.m nn.Sequential(*(RepBottleneck(c_, c_, shortcut, g, k(k, k), e1.0) for _ in range(n)))self.m nn.Sequential(*(Bottleneck_ScConv(c_, c_, shortcut, g, k(k, k), e1.0) for _ in range(n)))# 在c3kTrue时使用Bottleneck_ScConv特征融合为false的时候我们使用普通的Bottleneck提取特征
class C3k2_SC(C2f):Faster Implementation of CSP Bottleneck with 2 convolutions.def __init__(self, c1, c2, n1, c3kFalse, e0.5, g1, shortcutTrue):Initializes the C3k2 module, a faster CSP Bottleneck with 2 convolutions and optional C3k blocks.super().__init__(c1, c2, n, shortcut, g, e)self.m nn.ModuleList(C3k(self.c, self.c, 2, shortcut, g) if c3k else Bottleneck(self.c, self.c, shortcut, g) for _ in range(n))if __name__ __main__:DW ScConv(256)#创建一个输入张量batch_size 8input_tensortorch.randn(batch_size, 256, 64, 64 )#运行模型并打印输入和输出的形状output_tensor DW(input_tensor)print(Input shape:,input_tensor.shape)print(0utput shape:,output_tensor.shape)4. 将SCConv引入到YOLOv11中
第一: 将下面的核心代码复制到D:\bilibili\model\YOLO11\ultralytics-main\ultralytics\nn路径下如下图所示。 第二在task.py中导入SCConv包 第三在task.py中的模型配置部分下面代码
第一个改进需修改的地方 第二个改进需修改的地方
elif m is ScConv:args [ch[f]]第四将模型配置文件复制到YOLOV11.YAMY文件中
第一个修改的配置文件
# 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. modelyolo11n.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_SC, [256, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2_SC, [512, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2_SC, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, C3k2_SC, [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_SC, [512, False]] # 13- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 2, C3k2_SC, [256, False]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2_SC, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2_SC, [1024, True]] # 22 (P5/32-large)- [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)第二个修改的配置文件
# 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. modelyolo11n.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, ScConv, []]- [-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, [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, 14], 1, Concat, [1]] # cat head P4- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 11], 1, Concat, [1]] # cat head P5- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)- [[17, 20, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)第五运行成功 from ultralytics.models import NAS, RTDETR, SAM, YOLO, FastSAM, YOLOWorldif __name____main__:# 使用自己的YOLOv11.yamy文件搭建模型并加载预训练权重训练模型model YOLO(rD:\bilibili\model\YOLO11\ultralytics-main\ultralytics\cfg\models\11\yolo11_SConv.yaml)\.load(rD:\bilibili\model\YOLO11\ultralytics-main\yolo11n.pt) # build from YAML and transfer weightsresults model.train(datarD:\bilibili\model\ultralytics-main\ultralytics\cfg\datasets\VOC_my.yaml,epochs100, imgsz640, batch8)
