在YOLOv5中增加SENet模块的结构 给出添加结构后的模型结构图 而不是相应的代码
时间: 2024-05-08 13:21:15 浏览: 18
在 YOLOv5 中增加 SE 模块的结构可以通过在卷积层后添加 Squeeze-and-Excitation 模块来实现。下面是添加 SE 模块后的 YOLOv5 模型结构图:
```
+---------------------------------------------------------------------------------------------------+
| Input |
+---------------------------------------------------------------------------------------------------+
| |
| +-----------------------+ |
| | Conv2d (3, 32, 3x3, 1) | |
| +-----------------------+ |
| | |
| +------------------------+ |
| | Conv2d (32, 64, 3x3, 2) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | BottleneckCSP (64) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Squeeze-and-Excitation | |
| +------------------------+ |
| | |
| +------------------------+ |
| | BottleneckCSP (128) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Squeeze-and-Excitation | |
| +------------------------+ |
| | |
| +------------------------+ |
| | BottleneckCSP (256) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Squeeze-and-Excitation | |
| +------------------------+ |
| | |
| +------------------------+ |
| | BottleneckCSP (512) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Squeeze-and-Excitation | |
| +------------------------+ |
| | |
| +------------------------+ |
| | BottleneckCSP (1024) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Squeeze-and-Excitation | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Conv2d (1024, 512, 1x1) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Upsample (2x) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Concatenate (256+512) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | BottleneckCSP (512) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Squeeze-and-Excitation | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Conv2d (512, 256, 1x1) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Upsample (2x) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Concatenate (128+256) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | BottleneckCSP (256) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Squeeze-and-Excitation | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Conv2d (256, 256, 3x3) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | Conv2d (256, 3*(80+5), 1x1) | |
| +------------------------+ |
| | |
| +------------------------+ |
| | YOLO Layer | |
| +------------------------+ |
| |
+---------------------------------------------------------------------------------------------------+
```
其中,Squeeze-and-Excitation 模块可以通过以下公式来计算:
$$
\begin{aligned}
\text{SE}(x) &= \text{Excite}(\text{Squeeze}(x)\odot x) \\
\text{Squeeze}(x)_c &= \operatorname{GlobalAvgPool}(x_c) \\
\text{Excite}(z)_c &= \sigma(W_2\delta(W_1z_c)) \\
\end{aligned}
$$
其中,$\text{Squeeze}(x)$ 是将 $x$ 进行全局平均池化得到的特征图,$\text{Excite}(z)$ 是一个 MLP(多层感知机),用于计算通道权重,$\odot$ 表示逐元素相乘,$\sigma$ 表示 sigmoid 函数,$W_1$ 和 $W_2$ 是 MLP 的权重。
注意,上述模型结构图中仅包含一个 Squeeze-and-Excitation 模块,实际上可以在每个 BottleneckCSP 模块后都添加一个 Squeeze-and-Excitation 模块。