yolov5 PFN
时间: 2024-02-02 19:10:13 浏览: 27
根据提供的引用内容,yolov5 PFN指的是yolov5中的Neck部分的变化。在yolov5中,SPP(Spatial Pyramid Pooling)结构被替换为SPPF(Spatial Pyramid Pooling Fusion)结构。SPPF结构通过并行使用多个不同大小的MaxPool层,然后将它们进行进一步融合,以解决目标检测中的多尺度问题。
以下是yolov5中SPPF结构的示例代码:
```python
import torch
import torch.nn as nn
class SPPF(nn.Module):
def __init__(self, in_channels, out_channels):
super(SPPF, self).__init__()
self.pool1 = nn.MaxPool2d(kernel_size=1, stride=1)
self.pool2 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
self.pool3 = nn.MaxPool2d(kernel_size=5, stride=1, padding=2)
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
x1 = self.pool1(x)
x2 = self.pool2(x)
x3 = self.pool3(x)
out = torch.cat((x, x1, x2, x3), dim=1)
out = self.conv(out)
return out
# 使用SPPF结构
in_channels = 256
out_channels = 512
sppf = SPPF(in_channels, out_channels)
input_tensor = torch.randn(1, in_channels, 32, 32)
output_tensor = sppf(input_tensor)
print(output_tensor.shape) # 输出:torch.Size([1, 512, 32, 32])
```
通过使用SPPF结构,yolov5能够更好地处理目标检测中的多尺度问题。