YOLOv11v10v8使用教程： YOLOv11入门到入土使用教程
YOLOv11改进汇总贴：YOLOv11及自研模型更新汇总
《SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy》
一、 模块介绍
论文链接：SCConv: Spatial and Channel Reconstruction Convolution...
代码链接（大佬复现）：https://github.com/cheng-haha/ScConv
论文速览：卷积神经网络 （CNN） 在各种计算机视觉任务中取得了卓越的性能，但这是以消耗大量计算资源为代价的，部分原因是卷积层提取了冗余特征。最近的工作要么压缩训练有素的大型模型，要么探索设计良好的轻量级模型。在本文中，我们尝试利用特征之间的空间和通道冗余进行 CNN 压缩，并提出了一种高效的卷积模块，称为 SCConv（空间和通道重建卷积），以减少冗余计算并促进代表性特征学习。所提出的 SCConv 由两个单元组成：空间重建单元 （SRU） 和通道重建单元 （CRU）。SRU 采用 separate-and-reconstruct 方法来抑制空间冗余，而 CRU 使用 split-transform-andfuse 策略来减少通道冗余。此外，SCConv 是一个即插即用的架构单元，可用于直接替换各种卷积神经网络中的标准卷积。实验结果表明，SCConv 嵌入式模型能够通过减少冗余特征来实现更好的性能，从而显著降低复杂性和计算成本。
总结：轻量化模块，好用。
二、 加入到YOLO中
2.1 创建脚本文件
首先在ultralytics->nn路径下创建blocks.py脚本，用于存放模块代码。
2.2 复制代码
复制代码粘到刚刚创建的blocks.py脚本中，如下图所示：
import torch
import torch.nn as nn
import torch.nn.functional as F
class 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_num
self.group_num = group_num
self.weight = nn.Parameter(torch.randn(c_num, 1, 1))
self.bias = nn.Parameter(torch.zeros(c_num, 1, 1))
self.eps = eps
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, self.group_num, -1)
mean = x.mean(dim=2, keepdim=True)
std = x.std(dim=2, keepdim=True)
x = (x - mean) / (std + self.eps)
x = x.view(N, C, H, W)
return x * self.weight + self.bias
class SRU(nn.Module):
def __init__(self,
oup_channels: int,
group_num: int = 16,
gate_treshold: float = 0.5,
torch_gn: bool = True
):
super().__init__()
self.gn = nn.GroupNorm(num_channels=oup_channels, num_groups=group_num) if torch_gn else GroupBatchnorm2d(
c_num=oup_channels, group_num=group_num)
self.gate_treshold = gate_treshold
self.sigomid = nn.Sigmoid()
def forward(self, x):
gn_x = self.gn(x)
w_gamma = self.gn.weight / sum(self.gn.weight)
w_gamma = w_gamma.view(1, -1, 1, 1)
reweigts = self.sigomid(gn_x * w_gamma)
# Gate
w1 = torch.where(reweigts > self.gate_treshold, torch.ones_like(reweigts), reweigts) # 大于门限值的设为1，否则保留原值
w2 = torch.where(reweigts > self.gate_treshold, torch.zeros_like(reweigts), reweigts) # 大于门限值的设为0，否则保留原值
x_1 = w1 * x
x_2 = w2 * x
y = self.reconstruct(x_1, x_2)
return y
def reconstruct(self, x_1, x_2):
x_11, x_12 = torch.split(x_1, x_1.size(1) // 2, dim=1)
x_21, x_22 = torch.split(x_2, x_2.size(1) // 2, dim=1)
return torch.cat([x_11 + x_22, x_12 + x_21], dim=1)
class CRU(nn.Module):
'''
alpha: 0<alpha<1
'''
def __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_channel
self.squeeze1 = nn.Conv2d(up_channel, up_channel // squeeze_radio, kernel_size=1, bias=False)
self.squeeze2 = nn.Conv2d(low_channel, low_channel // squeeze_radio, kernel_size=1, bias=False)
# up
self.GWC = nn.Conv2d(up_channel // squeeze_radio, op_channel, kernel_size=group_kernel_size, stride=1,
padding=group_kernel_size // 2, groups=group_size)
self.PWC1 = nn.Conv2d(up_channel // squeeze_radio, op_channel, kernel_size=1, bias=False)
# low
self.PWC2 = nn.Conv2d(low_channel // squeeze_radio, op_channel - low_channel // squeeze_radio, kernel_size=1,
bias=False)
self.advavg = nn.AdaptiveAvgPool2d(1)
def forward(self, x):
# Split
up, low = torch.split(x, [self.up_channel, self.low_channel], dim=1)
up, low = self.squeeze1(up), self.squeeze2(low)
# Transform
Y1 = self.GWC(up) + self.PWC1(up)
Y2 = torch.cat([self.PWC2(low), low], dim=1)
# Fuse
out = torch.cat([Y1, Y2], dim=1)
out = F.softmax(self.advavg(out), dim=1) * out
out1, out2 = torch.split(out, out.size(1) // 2, dim=1)
return out1 + out2
class 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_num=group_num,
gate_treshold=gate_treshold)
self.CRU = CRU(op_channel,
alpha=alpha,
squeeze_radio=squeeze_radio,
group_size=group_size,
group_kernel_size=group_kernel_size)
def forward(self, x):
x = self.SRU(x)
x = self.CRU(x)
return x
2.3 更改task.py文件
打开ultralytics->nn->modules->task.py，在脚本空白处导入函数。
from ultralytics.nn.blocks import *
之后找到模型解析函数parse_model（约在tasks.py脚本中940行左右位置，可能因代码版本不同变动），在该函数的最后一个else分支上面增加相关解析代码。
elif m is ScConv:
c2 = ch[f]
args = [ch[f], ]
2.4 更改yaml文件
yam文件解读：YOLO系列 “.yaml“文件解读_yolo yaml文件-****博客
打开更改ultralytics/cfg/models/11路径下的YOLOv11.yaml文件，替换原有模块。（放在该位置仅能插入该模块，具体效果未知。博主精力有限，仅完成与其他模块二次创新融合的测试，结构图见文末，代码见群文件更新。）
# 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 GFLOPs
s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
x: [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, ScConv, []]
- [-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, [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)
2.5 修改train.py文件
创建Train脚本用于训练。
from ultralytics.models import YOLO
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
if __name__ == '__main__':
model = YOLO(model='ultralytics/cfg/models/11/yolo11.yaml')
# model.load('yolov8n.pt')
model.train(data='./data.yaml', epochs=2, batch=1, device='0', imgsz=640, workers=2, cache=False,
amp=True, mosaic=False, project='runs/train', name='exp')
在train.py脚本中填入修改好的yaml路径，运行即可训练，数据集创建教程见下方链接。
YOLOv11入门到入土使用教程(含结构图)_yolov11使用教程-****博客
三、相关改进思路（2024/11/16日群文件）
根据ScConv模块特性，可替换C2f、C3模块中的BottleNeck部分，代码见群文件，结构如图。
⭐另外，融合上百种深度学习改进模块的YOLO项目仅79.9（含百种改进的v9），RTDETR79.9,含高性能自研模型，更易发论文，代码每周更新，欢迎点击下方小卡片加我了解。⭐
