yolov8n替换主干网络用什么网络好

根据提供的引用内容，YOLOv8可以使用ShuffleNetv2作为替换主干网络的选择。以下是替换主干网络的步骤：

1. 在ultralytics/models/v8文件夹下新建一个yolov8-ShuffleNetv2.yaml文件。
2. 将ShuffleNetv2的代码添加到ultralytics/nn/modules/block.py文件末尾。
3. 将CBRM和Shuffle_Block这两个类的名称添加到ultralytics/nn/tasks.py中。
4. 修改yolov8-ShuffleNetv2.yaml文件，使用CBRM和Shuffle_Block构建ShuffleNetv2主干网络。
5. 开始训练。

通过以上步骤，你可以将ShuffleNetv2作为YOLOv8的替换主干网络。这样做可以提高YOLOv8的性能和效率。

相关问题

将yolov5的主干网络替换成resnet50，给出代码演示并解释

首先，需要安装`yolov5`和`torchvision`：

pip install yolov5 torchvision

在`yolov5/models/yolo.py`文件中，可以看到原始的主干网络为`CSPDarknet`，我们需要将其替换成`ResNet`。具体实现可以参考以下代码：

import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.resnet import resnet50

class YOLOv5(nn.Module):
    def __init__(self, num_classes, width_mult=1.0, anchors=(), 
                 ch=(), training=False):
        super(YOLOv5, self).__init__()
        self.training = training
        self.num_classes = num_classes
        self.anchors = anchors
        self.stride = None
        self.n_anchors = None
        self.nl = len(anchors) // 3
        self.grid = [torch.zeros(1)] * self.nl
        self.anchor_grid = torch.tensor(anchors).float().view(self.nl, -1, 2) / self.stride

        # backbone
        base_net = resnet50(pretrained=True)
        self.backbone = nn.Sequential(
            base_net.conv1,
            base_net.bn1,
            base_net.relu,
            base_net.maxpool,
            base_net.layer1,
            base_net.layer2,
            base_net.layer3,
            base_net.layer4
        )
        c3, c4, c5 = 1024, 2048, 2048
        self.heads = nn.ModuleList([nn.Sequential(
            Conv(c, c * 2, 3),
            SPP(c * 2, c, k=(5, 9, 13)),
            Conv(c * 4, c * 2, 3),
            Conv(c * 2, c * 4, 1),
            nn.Conv2d(c * 4, anchor_per_scale * (num_classes + 5), 1))(c, s) for (c, s) in zip(ch, strides)])

    def forward(self, x):
        x = self.backbone(x)
        return x

在`__init__`函数中，我们使用`resnet50`作为主干网络，并将其前四层作为特征提取器。在`forward`函数中，我们仅使用了`backbone`函数的输出，作为后续的检测头的输入。

注意，此处仅给出了主干网络的替换部分代码，完整的`YOLOv5`实现可以在`yolov5/models/yolo.py`文件中找到。

将yolov5s的主干网络替换成resnet50，给出代码演示并详细解释

YoloV5是一个非常流行的目标检测框架，而ResNet50是一个非常流行的卷积神经网络模型。在这里，我们将介绍如何将YoloV5的主干网络替换成ResNet50。

首先，我们需要下载YoloV5的源代码，并安装其依赖项。然后，我们需要将源代码中的主干网络文件替换成ResNet50。具体来说，我们需要修改yolov5/models/yolo.py文件中的代码。以下是修改后的代码示例：

import torch.nn as nn
import torch.nn.functional as F
from models.common import Conv, DWConv

class ResNet50Backbone(nn.Module):
    def __init__(self, in_channels=3, stem_out_channels=64, features_out_channels=(256, 512, 1024, 2048)):
        super().__init__()
        self.stem = nn.Sequential(
            Conv(in_channels, stem_out_channels, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(stem_out_channels),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
        self.layer1 = nn.Sequential(
            nn.Conv2d(stem_out_channels, 256, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 1024, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(1024),
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(1024, 512, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 2048, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(2048),
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(2048, 1024, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True),
            nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True),
            nn.Conv2d(1024, 4096, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(4096),
        )
        self.layer4 = nn.Sequential(
            nn.Conv2d(4096, 2048, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(2048),
            nn.ReLU(inplace=True),
            nn.Conv2d(2048, 2048, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(2048),
            nn.ReLU(inplace=True),
            nn.Conv2d(2048, 8192, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(8192),
        )
        self.features_out_channels = features_out_channels

    def forward(self, x):
        x = self.stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        return x

class YOLOv5(nn.Module):
    def __init__(self, num_classes=80, width_multiple=1.0, depth_multiple=1.0, in_channels=3):
        super().__init__()
        self.backbone = ResNet50Backbone(in_channels=in_channels)
        self.num_classes = num_classes
        self.width_multiple = width_multiple
        self.depth_multiple = depth_multiple
        self.out_channels = [int(64 * width_multiple), 128, 256, 512, 1024]
        self.out_idx = [3, 4, 5]
        self.n_anchors = 3
        self.n_classes = num_classes
        self.n_features = len(self.out_idx)
        self.feat = nn.ModuleList()
        self.scales = nn.ModuleList()
        for i in range(self.n_features):
            self.feat.append(nn.Conv2d(self.out_channels[self.out_idx[i]], self.n_anchors * (self.n_classes + 5), 1))
            self.scales.append(nn.Parameter(torch.ones(1, self.n_anchors, 1, 1)))

    def forward(self, x):
        x = self.backbone(x)
        outputs = []
        for i in range(self.n_features):
            out = self.feat[i](x[self.out_idx[i]])
            out = out.permute(0, 2, 3, 1)
            outputs.append(out.contiguous().view(out.shape[0], -1, self.n_classes + 5))
        return outputs

在上面的代码中，我们定义了一个新的ResNet50Backbone类作为YoloV5的主干网络。我们还修改了YOLOv5类的构造函数以使用新的主干网络。我们还修改了YOLOv5类的前向函数以适应新的主干网络。

现在，我们可以使用新的YoloV5模型来进行目标检测。要使用新的模型，我们需要更新我们的训练和测试脚本以引用新的模型。