YOLOV5结构图怎么画
时间: 2023-09-14 15:15:08 浏览: 163
YOLOv5的结构图可以通过使用可视化工具来绘制,比如Netron。你可以通过打开Netron并导入YOLOv5的模型文件,然后它会自动显示出网络结构图。这样你就可以清晰地看到YOLOv5的输入端、Backbone、Neck和Prediction等部分的结构。另外,在引用和引用中提供了YOLOv5的网络结构图的可视化图片,你也可以直接点击查看这些图片来了解YOLOv5的结构。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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相关问题
yolov8结构图什么画
很抱歉,根据提供的引用内容,没有找到关于yolov8结构图的描述。引用提到了yolov8的代码已经放出,但是没有提到结构图。引用提到了关于splite和concat的问题,但是没有提到yolov8的结构图。引用提到了yolov8的anchor free对小目标的效果问题,但是同样没有提到结构图。如果您有更多的信息或者问题,请告诉我,我会尽力回答。
yolov5网络结构图怎么画
要画Yolov5网络结构图,可以使用一些工具,比如Graphviz和PyTorch的nn.Module。
以下是使用PyTorch的nn.Module画Yolov5网络结构图的示例代码:
```python
import torch
import torch.nn as nn
class Conv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=None, groups=1, bias=False):
super().__init__()
if padding is None:
padding = (kernel_size - 1) // 2
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, groups=groups, bias=bias)
self.bn = nn.BatchNorm2d(out_channels)
self.act = nn.ReLU()
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
return x
class CSPBlock(nn.Module):
def __init__(self, in_channels, out_channels, num_blocks, use_residual=True):
super().__init__()
self.use_residual = use_residual
mid_channels = out_channels // 2
self.conv1 = Conv(in_channels, mid_channels, 1)
self.conv2 = Conv(in_channels, mid_channels, 1)
self.conv3 = Conv(2 * mid_channels, out_channels, 1)
self.blocks = nn.Sequential(
*[ResidualBlock(mid_channels, mid_channels, use_residual) for _ in range(num_blocks)]
)
def forward(self, x):
x1 = self.conv1(x)
x2 = self.conv2(x)
x2 = self.blocks(x2)
x = torch.cat((x1, x2), dim=1)
x = self.conv3(x)
if self.use_residual:
x = x + x1
return x
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, use_residual=True):
super().__init__()
self.conv1 = Conv(in_channels, out_channels, 1)
self.conv2 = Conv(out_channels, in_channels, 3, padding=1)
self.use_residual = use_residual
def forward(self, x):
y = self.conv1(x)
y = self.conv2(y)
if self.use_residual:
y = y + x
return y
class Yolov5(nn.Module):
def __init__(self, num_classes=80):
super().__init__()
self.backbone = nn.Sequential(
Conv(3, 32, 3, stride=2),
CSPBlock(32, 64, num_blocks=1),
CSPBlock(64, 128, num_blocks=3),
CSPBlock(128, 256, num_blocks=15),
CSPBlock(256, 512, num_blocks=15),
CSPBlock(512, 1024, num_blocks=7),
)
self.neck = nn.Sequential(
Conv(1024, 512, 1),
nn.Upsample(scale_factor=2),
CSPBlock(1024, 512, num_blocks=3, use_residual=False),
Conv(512, 256, 1),
nn.Upsample(scale_factor=2),
CSPBlock(512, 256, num_blocks=3, use_residual=False),
Conv(256, 128, 1),
nn.Upsample(scale_factor=2),
CSPBlock(256, 128, num_blocks=3, use_residual=False),
)
self.head = nn.Sequential(
Conv(128, 256, 3, padding=1),
nn.Conv2d(256, 3 * (num_classes + 5), 1)
)
def forward(self, x):
x = self.backbone(x)
x = self.neck(x)
x = self.head(x)
return x
```
这里定义了Conv、CSPBlock、ResidualBlock和Yolov5四个类,分别表示卷积层、CSP块、残差块和整个Yolov5网络。
可以使用如下代码将网络结构图保存到文件中:
```python
from torchviz import make_dot
model = Yolov5()
x = torch.randn(1, 3, 416, 416)
y = model(x)
make_dot(y, params=dict(model.named_parameters())).render("yolov5", format="png")
```
这里使用了torchviz库的make_dot函数生成网络结构图,并将其保存为yolov5.png文件。
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