yolov5的网络结构介绍
时间: 2023-11-22 11:50:58 浏览: 49
YOLOv5是一种目标检测算法,其网络结构主要由卷积层、池化层、上采样层、残差块等组成。YOLOv5官方代码中一共有4个版本,分别是Yolov5s、Yolov5m、Yolov5l、Yolov5x四个模型,这几个模型的结构基本是一样的,不同的是模型深度和模型宽度这两个参数。其中,YOLOv5s网络是YOLOv5系列中深度最小,特征图的宽度最小的网络。其他的三种都是在此基础上不断加深,不断加宽。
YOLOv5的网络结构主要分为两个部分:主干网络和检测头。主干网络采用CSPNet(Cross Stage Partial Network)结构,可以有效地减少参数数量和计算量。检测头主要包括三个部分:SPP(Spatial Pyramid Pooling)、PAN(Path Aggregation Network)和YOLOv5头部。其中,SPP用于提取不同尺度的特征,PAN用于融合不同层次的特征,YOLOv5头部用于预测目标的类别和位置。
下面是YOLOv5s的网络结构示意图:
```python
CSPNet(
(conv1): Conv(
(conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): Conv(
(conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
...
(conv5): Conv(
(conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(spp): SPP(
(conv1): Conv(
(conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): Conv(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
...
)
(pan): PAN(
(convs): ModuleList(
(0): Conv(
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
...
)
(upsamples): ModuleList(
(0): Upsample(scale_factor=2.0, mode=bilinear)
...
)
(convs_all): ModuleList(
(0): Conv(
(conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
...
)
)
(detect): Detect(
(m): ModuleList(
(0): Conv2d(1024, 255, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(512, 255, kernel_size=(1, 1), stride=(1, 1))
(2): Conv2d(256, 255, kernel_size=(1, 1), stride=(1, 1))
)
)
)
```