自己动手写yoloov5网络
时间: 2023-10-14 07:44:38 浏览: 56
YoloV5是一个流行的目标检测算法,它基于PyTorch实现。下面是一个简化版的YoloV5网络的代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
# 定义卷积块
def conv_block(in_channels, out_channels, kernel_size, stride, padding):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding),
nn.BatchNorm2d(out_channels),
nn.LeakyReLU(0.1, inplace=True)
)
# 定义YoloV5网络
class YoloV5(nn.Module):
def __init__(self, num_classes=80):
super(YoloV5, self).__init__()
self.num_classes = num_classes
self.backbone = nn.Sequential(
conv_block(3, 32, 3, 1, 1),
nn.MaxPool2d(2, 2),
conv_block(32, 64, 3, 1, 1),
nn.MaxPool2d(2, 2),
conv_block(64, 128, 3, 1, 1),
conv_block(128, 64, 1, 1, 0),
conv_block(64, 128, 3, 1, 1),
nn.MaxPool2d(2, 2),
conv_block(128, 256, 3, 1, 1),
conv_block(256, 128, 1, 1, 0),
conv_block(128, 256, 3, 1, 1),
nn.MaxPool2d(2, 2),
conv_block(256, 512, 3, 1, 1),
conv_block(512, 256, 1, 1, 0),
conv_block(256, 512, 3, 1, 1),
conv_block(512, 256, 1, 1, 0),
conv_block(256, 512, 3, 1, 1),
)
self.head = nn.Sequential(
conv_block(512, 1024, 3, 1, 1),
nn.Conv2d(1024, self.num_classes + 5, 1, 1, 0)
)
def forward(self, x):
x = self.backbone(x)
x = self.head(x)
x = F.adaptive_avg_pool2d(x, (1, 1))
x = torch.flatten(x, 1)
return x
# 创建YoloV5实例
model = YoloV5()
```
这只是一个简化版的YoloV5网络,具体实现可能与原版稍有不同。如果你想了解更多细节,建议参考YoloV5的官方代码实现。