yolov5加入densenet
时间: 2023-11-21 16:55:22 浏览: 56
根据我的了解,YOLOv5并没有直接加入DenseNet,但是有一些基于YOLOv5的改进版本使用了DenseNet来提高检测性能。例如,YOLOv5-DenseNet是一种基于YOLOv5的改进版本,它使用了DenseNet作为主干网络,以提高检测性能。这个改进版本的代码可以在https://github.com/WongKinYiu/yolov5-densev2中找到。
另外,DenseNet的思想也可以应用于其他目标检测算法中,例如Faster R-CNN和SSD等。通过在主干网络中加入Dense Block,可以提高特征的重用和传播,从而提高检测性能。
下面是一个使用DenseNet作为主干网络的YOLOv5-DenseNet的代码示例:
```python
import torch
import torch.nn as nn
from models.common import Conv, DWConv
class DenseLayer(nn.Module):
def __init__(self, in_channels, growth_rate):
super(DenseLayer, self).__init__()
self.conv1 = Conv(in_channels, 4 * growth_rate, kernel_size=1)
self.conv2 = DWConv(4 * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1)
def forward(self, x):
out = self.conv1(x)
out = self.conv2(out)
out = torch.cat([x, out], 1)
return out
class DenseBlock(nn.Module):
def __init__(self, in_channels, num_layers, growth_rate):
super(DenseBlock, self).__init__()
self.layers = nn.ModuleList([DenseLayer(in_channels + i * growth_rate, growth_rate) for i in range(num_layers)])
def forward(self, x):
for layer in self.layers:
x = layer(x)
return x
class YOLOv5_DenseNet(nn.Module):
def __init__(self, num_classes=80):
super(YOLOv5_DenseNet, self).__init__()
self.stem = nn.Sequential(
Conv(3, 32, kernel_size=3, stride=2),
DWConv(32, 64, kernel_size=3, stride=1, padding=1),
DenseBlock(64, 3, 16),
DWConv(64 + 3 * 16, 128, kernel_size=3, stride=2, padding=1),
DenseBlock(128, 6, 16),
DWConv(128 + 6 * 16, 256, kernel_size=3, stride=2, padding=1),
DenseBlock(256, 9, 16),
DWConv(256 + 9 * 16, 512, kernel_size=3, stride=2, padding=1),
DenseBlock(512, 9, 16),
DWConv(512 + 9 * 16, 1024, kernel_size=3, stride=2, padding=1),
Conv(1024, 1024, kernel_size=1),
Conv(1024, 512, kernel_size=1),
)
self.neck = nn.Sequential(
DWConv(512, 1024, kernel_size=3, stride=1, padding=1),
Conv(1024, 512, kernel_size=1),
DWConv(512, 1024, kernel_size=3, stride=1, padding=1),
Conv(1024, 512, kernel_size=1),
DWConv(512, 1024, kernel_size=3, stride=1, padding=1),
Conv(1024, 512, kernel_size=1),
)
self.head = nn.Sequential(
DWConv(512, 1024, kernel_size=3, stride=1, padding=1),
Conv(1024, num_classes, kernel_size=1),
)
def forward(self, x):
x = self.stem(x)
x = self.neck(x)
x = self.head(x)
return x
```