yolov5的C3全称

• 点击可找到C3模块然后查看全称：https://github.com/ultralytics/yolov5/blob/master/models/common.py
• 全称为：CSP Bottleneck with 3 convolutions

• C3模块代码

class C3(nn.Module):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):"""Initializes C3 module with options for channel count, bottleneck repetition, shortcut usage, groupconvolutions, and expansion."""super().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))def forward(self, x):"""Performs forward propagation using concatenated outputs from two convolutions and a Bottleneck sequence."""return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))

yolov8的C2f全称

• 点击可找到C2f模块然后查看全称：https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/modules/block.py

• 全称是：Faster Implementation of CSP Bottleneck with 2 convolutions
  

• C2f模块代码

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))