1，本文介绍
本文给大家带来的是利用yolov9的sppelen模块来改进yolov8的sppf，关于sppelen模块大家可以取查看yolov9的论文
本文将讲解如何将SPPELEN融合进yolov8
话不多说，上代码！
２，将SPPELEN融合进YOLＯｖ８
2.1 步骤一
首先找到如下的目录'ultralytics/nn'，然后在这个目录下创建一个'Addmodules'文件夹，然后在这个目录下创建一个SPPELEN.py文件，文件名字可以根据你自己的习惯起，然后将SPPELEN的核心代码复制进去。
import torch
import torch.nn as nn
__all__ = ['SPPELAN']
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-size
if p is None:
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
return p
class Conv(nn.Module):
# Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
default_act = nn.SiLU() # default activation
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
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):
return self.act(self.bn(self.conv(x)))
def forward_fuse(self, x):
return self.act(self.conv(x))
class SP(nn.Module):
def __init__(self, k=3, s=1):
super(SP, self).__init__()
self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2)
def forward(self, x):
return self.m(x)
class SPPELAN(nn.Module):
# spp-elan
def __init__(self, c1, c2, c3): # ch_in, ch_out, number, shortcut, groups, expansion
super().__init__()
self.c = c3
self.cv1 = Conv(c1, c3, 1, 1)
self.cv2 = SP(5)
self.cv3 = SP(5)
self.cv4 = SP(5)
self.cv5 = Conv(4*c3, c2, 1, 1)
def forward(self, x):
y = [self.cv1(x)]
y.extend(m(y[-1]) for m in [self.cv2, self.cv3, self.cv4])
return self.cv5(torch.cat(y, 1))
第二步我们在该目录（Addmodules)下创建一个新的py文件名字为'init.py'，然后在其内部添加如下代码。
最终结果如下图标注所示
2.2 步骤二
在task.py中导入标注框内代码
2.4 步骤四
在parse_model方法如下位置添加如下代码
到此注册成功,复制后面的yaml文件直接运行即可
yaml文件如下
# 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 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
# 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, C2f, [128, True]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPELAN, [1024, 256]] # 9
# YOLOv8.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 12
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
# 关于SPPELEN添加的位置还可以放在颈部,针对不同数据集位置不同,效果不同
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