1，本文介绍
本文介绍了一种改进机制，通过引入 YOLOv10 的 C2fCIB 模块来提升 YOLOv8 的性能。C2fCIB 模块中的 CIB（Compact Inverted Bottleneck）结构采用了高效的深度卷积进行空间特征混合，并使用点卷积进行通道特征混合，这种设计既节省计算资源，又能有效增强特征提取能力，从而提高 YOLOv8 的检测精度和速度。
关于C2fCIB 的详细介绍可以看论文：https://arxiv.org/pdf/2405.14458
本文将讲解如何将C2fCIB 融合进yolov8
话不多说，上代码！
2， 将C2fCIB融合进yolov8
2.1 步骤一
找到如下的目录'ultralytics/nn/modules'，然后在这个目录下创建一个C2fCIB.py文件，文件名字可以根据你自己的习惯起，然后将C2fCIB的核心代码复制进去
import torch
import torch.nn as nn__all__ = ['C2fCIB']class Bottleneck(nn.Module):"""Standard bottleneck."""def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):"""Initializes a bottleneck module with given input/output channels, shortcut option, group, kernels, andexpansion."""super().__init__()c_ = int(c2 * e) # hidden channelsself.cv1 = Conv(c1, c_, k[0], 1)self.cv2 = Conv(c_, c2, k[1], 1, g=g)self.add = shortcut and c1 == c2def forward(self, x):"""'forward()' applies the YOLO FPN to input data."""return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))class C2f(nn.Module):"""Faster Implementation of CSP Bottleneck with 2 convolutions."""def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):"""Initialize CSP bottleneck layer with two convolutions with arguments ch_in, ch_out, number, shortcut, groups,expansion."""super().__init__()self.c = int(c2 * e) # hidden channelsself.cv1 = Conv(c1, 2 * self.c, 1, 1)self.cv2 = Conv((2 + n) * self.c, c2, 1) # optional act=FReLU(c2)self.m = nn.ModuleList(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n))def forward(self, x):"""Forward pass through C2f layer."""y = list(self.cv1(x).chunk(2, 1))y.extend(m(y[-1]) for m in self.m)return self.cv2(torch.cat(y, 1))def forward_split(self, x):"""Forward pass using split() instead of chunk()."""y = list(self.cv1(x).split((self.c, self.c), 1))y.extend(m(y[-1]) for m in self.m)return self.cv2(torch.cat(y, 1))def autopad(k, p=None, d=1): # kernel, padding, dilation"""Pad to 'same' shape outputs."""if d > 1:k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-sizeif p is None:p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-padreturn pclass Conv(nn.Module):"""Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""default_act = nn.SiLU() # default activationdef __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):"""Initialize Conv layer with given arguments including activation."""super().__init__()self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)self.bn = nn.BatchNorm2d(c2)self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()def forward(self, x):"""Apply convolution, batch normalization and activation to input tensor."""return self.act(self.bn(self.conv(x)))def forward_fuse(self, x):"""Perform transposed convolution of 2D data."""return self.act(self.conv(x))def fuse_conv_and_bn(conv, bn):"""Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/."""fusedconv = (nn.Conv2d(conv.in_channels,conv.out_channels,kernel_size=conv.kernel_size,stride=conv.stride,padding=conv.padding,dilation=conv.dilation,groups=conv.groups,bias=True,).requires_grad_(False).to(conv.weight.device))# Prepare filtersw_conv = conv.weight.clone().view(conv.out_channels, -1)w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))# Prepare spatial biasb_conv = torch.zeros(conv.weight.shape[0], device=conv.weight.device) if conv.bias is None else conv.biasb_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)return fusedconvclass RepVGGDW(torch.nn.Module):def __init__(self, ed) -> None:super().__init__()self.conv = Conv(ed, ed, 7, 1, 3, g=ed, act=False)self.conv1 = Conv(ed, ed, 3, 1, 1, g=ed, act=False)self.dim = edself.act = nn.SiLU()def forward(self, x):return self.act(self.conv(x) + self.conv1(x))def forward_fuse(self, x):return self.act(self.conv(x))@torch.no_grad()def fuse(self):conv = fuse_conv_and_bn(self.conv.conv, self.conv.bn)conv1 = fuse_conv_and_bn(self.conv1.conv, self.conv1.bn)conv_w = conv.weightconv_b = conv.biasconv1_w = conv1.weightconv1_b = conv1.biasconv1_w = torch.nn.functional.pad(conv1_w, [2, 2, 2, 2])final_conv_w = conv_w + conv1_wfinal_conv_b = conv_b + conv1_bconv.weight.data.copy_(final_conv_w)conv.bias.data.copy_(final_conv_b)self.conv = convdel self.conv1class CIB(nn.Module):"""Standard bottleneck."""def __init__(self, c1, c2, shortcut=True, e=0.5, lk=False):"""Initializes a bottleneck module with given input/output channels, shortcut option, group, kernels, andexpansion."""super().__init__()c_ = int(c2 * e) # hidden channelsself.cv1 = nn.Sequential(Conv(c1, c1, 3, g=c1),Conv(c1, 2 * c_, 1),Conv(2 * c_, 2 * c_, 3, g=2 * c_) if not lk else RepVGGDW(2 * c_),Conv(2 * c_, c2, 1),Conv(c2, c2, 3, g=c2),)self.add = shortcut and c1 == c2def forward(self, x):"""'forward()' applies the YOLO FPN to input data."""return x + self.cv1(x) if self.add else self.cv1(x)class C2fCIB(C2f):"""Faster Implementation of CSP Bottleneck with 2 convolutions."""def __init__(self, c1, c2, n=1, shortcut=False, lk=False, g=1, e=0.5):"""Initialize CSP bottleneck layer with two convolutions with arguments ch_in, ch_out, number, shortcut, groups,expansion."""super().__init__(c1, c2, n, shortcut, g, e)self.m = nn.ModuleList(CIB(self.c, self.c, shortcut, e=1.0, lk=lk) for _ in range(n))2.2 步骤二
在task.py导入我们的模块
from .modules.C2fCIB import C2fCIB2.3 步骤三
在task.py的parse_model方法里面注册我们的模块
注意需要在两个位置添加，如下图所示
到此注册成功,复制后面的yaml文件直接运行即可
yaml文件
# 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 GFLOPss: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPsm: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPsl: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOP# 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, C2fCIB, [128, True]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 6, C2fCIB, [256, True]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 6, C2fCIB, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 3, C2fCIB, [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, C2fCIB, [512]] # 12- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 3, C2fCIB, [256]] # 15 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]] # cat head P4- [-1, 3, C2fCIB, [512]] # 18 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]] # cat head P5- [-1, 3, C2fCIB, [1024, True, True]] # 21 (P5/32-large)- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)不知不觉已经看完了哦，动动小手留个点赞收藏吧--_--
