爆改YOLOv8| 利用ResNet18、34、50、101替换yolo主干网络

1，本文介绍

ResNet（深度残差网络）通过引入“快捷连接”来解决深层神经网络训练中的梯度消失问题。这些快捷连接允许网络的输入直接跳过中间的层，直接传递到后面的层，从而使得网络能够专注于学习输入与输出之间的残差（即差异），而非直接学习复杂的函数映射。这种设计方式使得网络在需要时可以简单地实现恒等映射，简化了训练过程，缓解了深层网络训练中的困难。因此，ResNet 能够有效地训练非常深的网络结构，并在多个视觉识别任务上取得显著的性能提升。

关于ResNet的详细介绍可以看论文：https://arxiv.org/pdf/1512.03385.pdf

本文将讲解如何将ResNet融合进yolov8

话不多说，上代码！

2，将ResNet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ResNet.py文件，文件名字可以根据你自己的习惯起，然后将ResNet的核心代码复制进去。

from collections import OrderedDict
import torch.nn as nn
import torch.nn.functional as F
 
class ConvNormLayer(nn.Module):
    def __init__(self,
                 ch_in,
                 ch_out,
                 filter_size,
                 stride,
                 groups=1,
                 act=None):
        super(ConvNormLayer, self).__init__()
        self.act = act
        self.conv = nn.Conv2d(
            in_channels=ch_in,
            out_channels=ch_out,
            kernel_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups)
 
        self.norm = nn.BatchNorm2d(ch_out)
 
 
    def forward(self, inputs):
 
        out = self.conv(inputs)
        out = self.norm(out)
        if self.act:
            out = getattr(F, self.act)(out)
        return out
 
 
class SELayer(nn.Module):
    def __init__(self, ch, reduction_ratio=16):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(ch, ch // reduction_ratio, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(ch // reduction_ratio, ch, bias=False),
            nn.Sigmoid()
        )
 
    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y.expand_as(x)
 
 
 
class BasicBlock(nn.Module):
    expansion = 1
    def __init__(self,
                 ch_in,
                 ch_out,
                 stride,
                 shortcut,
                 act='relu',
                 variant='b',
                 att=False):
        super(BasicBlock, self).__init__()
        self.shortcut = shortcut
        if not shortcut:
            if variant == 'd' and stride == 2:
                self.short = nn.Sequential()
                self.short.add_sublayer(
                    'pool',
                    nn.AvgPool2d(
                        kernel_size=2, stride=2, padding=0, ceil_mode=True))
                self.short.add_sublayer(
                    'conv',
                    ConvNormLayer(
                        ch_in=ch_in,
                        ch_out=ch_out,
                        filter_size=1,
                        stride=1))
            else:
                self.short = ConvNormLayer(
                    ch_in=ch_in,
                    ch_out=ch_out,
                    filter_size=1,
                    stride=stride)
 
        self.branch2a = ConvNormLayer(
            ch_in=ch_in,
            ch_out=ch_out,
            filter_size=3,
            stride=stride,
            act='relu')
 
        self.branch2b = ConvNormLayer(
            ch_in=ch_out,
            ch_out=ch_out,
            filter_size=3,
            stride=1,
            act=None)
 
        self.att = att
        if self.att:
            self.se = SELayer(ch_out)
 
    def forward(self, inputs):
        out = self.branch2a(inputs)
        out = self.branch2b(out)
 
        if self.att:
            out = self.se(out)
 
        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)
 
        out = out + short
        out = F.relu(out)
 
        return out
 
 
class BottleNeck(nn.Module):
    expansion = 4
 
    def __init__(self, ch_in, ch_out, stride, shortcut, act='relu', variant='d', att=False):
        super().__init__()
 
        if variant == 'a':
            stride1, stride2 = stride, 1
        else:
            stride1, stride2 = 1, stride
 
        width = ch_out
 
        self.branch2a = ConvNormLayer(ch_in, width, 1, stride1, act=act)
        self.branch2b = ConvNormLayer(width, width, 3, stride2, act=act)
        self.branch2c = ConvNormLayer(width, ch_out * self.expansion, 1, 1)
 
        self.shortcut = shortcut
        if not shortcut:
            if variant == 'd' and stride == 2:
                self.short = nn.Sequential(OrderedDict([
                    ('pool', nn.AvgPool2d(2, 2, 0, ceil_mode=True)),
                    ('conv', ConvNormLayer(ch_in, ch_out * self.expansion, 1, 1))
                ]))
            else:
                self.short = ConvNormLayer(ch_in, ch_out * self.expansion, 1, stride)
 
        self.att = att
        if self.att:
            self.se = SELayer(ch_out)
 
    def forward(self, x):
        out = self.branch2a(x)
        out = self.branch2b(out)
        out = self.branch2c(out)
 
        if self.att:
            out = self.se(out)
 
        if self.shortcut:
            short = x
        else:
            short = self.short(x)
 
        out = out + short
        out = F.relu(out)
 
        return out
 
 
 
class Blocks(nn.Module):
    def __init__(self,
                 ch_in,
                 ch_out,
                 count,
                 block,
                 stage_num,
                 att=False,
                 variant='b'):
        super(Blocks, self).__init__()
        self.blocks = nn.ModuleList()
        block = globals()[block]
        for i in range(count):
            self.blocks.append(
                block(
                    ch_in,
                    ch_out,
                    stride=2 if i == 0 and stage_num != 2 else 1,
                    shortcut=False if i == 0 else True,
                    variant=variant,
                    att=att)
            )
            if i == 0:
                ch_in = ch_out * block.expansion
 
    def forward(self, inputs):
        block_out = inputs
        for block in self.blocks:
            block_out = block(block_out)
        return block_out

2.2 步骤二

在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

2.3 步骤三

在task.py进行导入

2.4 步骤四

在task.py进行注册，即在parse_model添加代码

注意-共需要在三个位置修改

然后如下图所示，注释掉黄色框内代码，添加红色框内代码

到此注册成功,复制后面的yaml文件直接运行即可

yaml文件1-ResNet18

# 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, ConvNormLayer, [32, 3, 2, 1, 'relu']] # 0-P1
  - [-1, 1, ConvNormLayer, [32, 3, 1, 1, 'relu']] # 1
  - [-1, 1, ConvNormLayer, [64, 3, 1, 1, 'relu']] # 2
  - [-1, 1, nn.MaxPool2d, [3, 2, 1]] # 3-P2
 
  - [-1, 2, Blocks, [64,  BasicBlock, 2, False]] # 4
  - [-1, 2, Blocks, [128, BasicBlock, 3, False]] # 5-P3
  - [-1, 2, Blocks, [256, BasicBlock, 4, False]] # 6-P4
  - [-1, 2, Blocks, [512, BasicBlock, 5, False]] # 7-P5
  - [-1, 1, SPPF, [1024, 5]]  # 8
 
# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [512]]  # 11
 
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 5], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f, [256]]  # 14 (P3/8-small)
 
  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 11], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 17 (P4/16-medium)
 
  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 8], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 20 (P5/32-large)
 
  - [[14, 17, 20], 1, Detect, [nc]]  # Detect(P3, P4, P5)

yaml文件2-ResNet34


# 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, ConvNormLayer, [32, 3, 2, 1, 'relu']] # 0-P1
  - [-1, 1, ConvNormLayer, [32, 3, 1, 1, 'relu']] # 1
  - [-1, 1, ConvNormLayer, [64, 3, 1, 1, 'relu']] # 2
  - [-1, 1, nn.MaxPool2d, [3, 2, 1]] # 3-P2
 
  - [-1, 3, Blocks, [64,  BasicBlock, 2, False]] # 4
  - [-1, 4, Blocks, [128, BasicBlock, 3, False]] # 5-P3
  - [-1, 6, Blocks, [256, BasicBlock, 4, False]] # 6-P4
  - [-1, 3, Blocks, [512, BasicBlock, 5, False]] # 7-P5
  - [-1, 1, SPPF, [1024, 5]]  # 8
 
# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [512]]  # 11
 
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 5], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f, [256]]  # 14 (P3/8-small)
 
  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 11], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 17 (P4/16-medium)
 
  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 8], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 20 (P5/32-large)
 
  - [[14, 17, 20], 1, Detect, [nc]]  # Detect(P3, P4, P5)

yaml文件3-ResNet50

# 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, ConvNormLayer, [32, 3, 2, 1, 'relu']] # 0-P1
  - [-1, 1, ConvNormLayer, [32, 3, 1, 1, 'relu']] # 1
  - [-1, 1, ConvNormLayer, [64, 3, 1, 1, 'relu']] # 2
  - [-1, 1, nn.MaxPool2d, [3, 2, 1]] # 3-P2
 
  - [-1, 3, Blocks, [64,  BasicBlock, 2, False]] # 4
  - [-1, 4, Blocks, [128, BasicBlock, 3, False]] # 5-P3
  - [-1, 6, Blocks, [256, BasicBlock, 4, False]] # 6-P4
  - [-1, 3, Blocks, [512, BasicBlock, 5, False]] # 7-P5
  - [-1, 1, SPPF, [1024, 5]]  # 8
 
# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [512]]  # 11
 
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 5], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f, [256]]  # 14 (P3/8-small)
 
  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 11], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 17 (P4/16-medium)
 
  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 8], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 20 (P5/32-large)
 
  - [[14, 17, 20], 1, Detect, [nc]]  # Detect(P3, P4, P5)

yaml文件4-ResNet101


# 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, ConvNormLayer, [32, 3, 2, 1, 'relu']] # 0-P1
  - [-1, 1, ConvNormLayer, [32, 3, 1, 1, 'relu']] # 1
  - [-1, 1, ConvNormLayer, [64, 3, 1, 1, 'relu']] # 2
  - [-1, 1, nn.MaxPool2d, [3, 2, 1]] # 3-P2
 
  - [-1, 3, Blocks, [64,  BasicBlock, 2, False]] # 4
  - [-1, 4, Blocks, [128, BasicBlock, 3, False]] # 5-P3
  - [-1, 23, Blocks, [256, BasicBlock, 4, False]] # 6-P4
  - [-1, 3, Blocks, [512, BasicBlock, 5, False]] # 7-P5
  - [-1, 1, SPPF, [1024, 5]]  # 8
 
# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [512]]  # 11
 
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 5], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f, [256]]  # 14 (P3/8-small)
 
  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 11], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 17 (P4/16-medium)
 
  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 8], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 20 (P5/32-large)
 
  - [[14, 17, 20], 1, Detect, [nc]]  # Detect(P3, P4, P5)

不知不觉已经看完了哦，动动小手留个点赞吧--_--

标签： YOLO yolov8 目标检测

本文转载自: https://blog.csdn.net/weixin_43986124/article/details/141551149
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爆改YOLOv8| 利用ResNet18、34、50、101替换yolo主干网络

1，本文介绍

2，将ResNet融合进yolov8

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