YOLO v5模型图怎么画

YOLOv5模型图的绘制可以使用一些绘图工具实现，比如Graphviz、drawio、Lucidchart等。以下是使用Graphviz绘制YOLOv5模型图的示例代码：

digraph YOLOv5 {
    graph [rankdir=TB, splines=polyline, nodesep=0.2, ranksep=1.2];
    node [shape=record, height=0.2, fontsize=10];
    
    input [label="Input\n[3 x 640 x 640]"];
    
    subgraph cluster_backbone {
        label="Backbone";
        rank=same;
        conv1 [label="Convolution\n[32 x 320 x 320]"];
        conv2 [label="Convolution\n[64 x 160 x 160]"];
        conv3 [label="Convolution\n[64 x 160 x 160]"];
        conv4 [label="Convolution\n[128 x 80 x 80]"];
        conv5 [label="Convolution\n[128 x 80 x 80]"];
        conv6 [label="Convolution\n[256 x 40 x 40]"];
        conv7 [label="Convolution\n[256 x 40 x 40]"];
        conv8 [label="Convolution\n[512 x 20 x 20]"];
        conv9 [label="Convolution\n[512 x 20 x 20]"];
        conv10 [label="Convolution\n[1024 x 10 x 10]"];
        conv11 [label="Convolution\n[1024 x 10 x 10]"];
    }
    
    subgraph cluster_neck {
        label="Neck";
        rank=same;
        conv12 [label="Convolution\n[512 x 20 x 20]"];
        upsample1 [label="Upsample\n[512 x 40 x 40]"];
        conv13 [label="Convolution\n[256 x 40 x 40]"];
        conv14 [label="Convolution\n[256 x 40 x 40]"];
        upsample2 [label="Upsample\n[256 x 80 x 80]"];
        conv15 [label="Convolution\n[128 x 80 x 80]"];
        conv16 [label="Convolution\n[128 x 80 x 80]"];
        upsample3 [label="Upsample\n[128 x 160 x 160]"];
        conv17 [label="Convolution\n[64 x 160 x 160]"];
        conv18 [label="Convolution\n[64 x 160 x 160]"];
    }
    
    subgraph cluster_head {
        label="Head";
        rank=same;
        conv19 [label="Convolution\n[128 x 80 x 80]"];
        conv20 [label="Convolution\n[128 x 80 x 80]"];
        conv21 [label="Convolution\n[3 x 80 x 80]"];
        conv22 [label="Convolution\n[256 x 40 x 40]"];
        conv23 [label="Convolution\n[256 x 40 x 40]"];
        conv24 [label="Convolution\n[3 x 40 x 40]"];
        conv25 [label="Convolution\n[512 x 20 x 20]"];
        conv26 [label="Convolution\n[512 x 20 x 20]"];
        conv27 [label="Convolution\n[3 x 20 x 20]"];
    }
    
    output1 [label="Output\n[3 x 80 x 80]"];
    output2 [label="Output\n[3 x 40 x 40]"];
    output3 [label="Output\n[3 x 20 x 20]"];
    
    input -> conv1;
    conv1 -> conv2;
    conv2 -> conv3;
    conv3 -> conv4;
    conv4 -> conv5;
    conv5 -> conv6;
    conv6 -> conv7;
    conv7 -> conv8;
    conv8 -> conv9;
    conv9 -> conv10;
    conv10 -> conv11;
    conv11 -> conv12;
    conv12 -> upsample1;
    upsample1 -> conv13;
    conv13 -> conv14;
    conv14 -> upsample2;
    upsample2 -> conv15;
    conv15 -> conv16;
    conv16 -> upsample3;
    upsample3 -> conv17;
    conv17 -> conv18;
    conv18 -> conv19;
    conv19 -> conv20;
    conv20 -> conv21;
    conv12 -> conv22;
    conv22 -> conv23;
    conv23 -> conv24;
    conv12 -> conv25;
    conv25 -> conv26;
    conv26 -> conv27;
    conv21 -> output1;
    conv24 -> output2;
    conv27 -> output3;
}

你可以根据上面的代码自行绘制YOLOv5模型图，并且根据需要进行修改和调整。