YOLOV5更换轻量级的backbone：mobilenetV2

2023年 4月 20日来源：新华社微博字号：默认大超大

简洁概要：

修改主干网络：

一：添加自己主干网络

二：在yolo.py中添加common中的两个函数

三：制作mobilenetv2的yaml配置文件

四：制作数据集VOC的yaml配置文件

五：启用训练

六：性能检测

简洁概要：

MobileNetV2主要采用了深度可分离卷积，在MobileNetv1的基础上引用了残差模块以及Relu6的激活函数，用1*n，n*1的思想代替了n*n的矩阵，计算量会更小。

修改主干网络：

一：添加自己主干网络

yolov5 6.1的版本中，在models/common中添加MobilenetV2作为backbone

class ConvBNReLU(nn.Sequential):  # 该函数主要做卷积 池化 ReLU6激活操作
    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
        padding = (kernel_size - 1) // 2  # 池化 = （步长-1）整除2
        super(ConvBNReLU, self).__init__(  # 调用ConvBNReLU父类添加模块
            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, bias=False, groups=groups),  # bias默认为False
            nn.BatchNorm2d(out_planes),
            nn.ReLU6(inplace=True))
class InvertedResidual(nn.Module):  # 该模块主要实现了倒残差模块
    def __init__(self, inp, oup, stride, expand_ratio):  # inp 输入 oup 输出 stride步长 exoand_ratio 按比例扩张
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]
        hidden_dim = int(round(inp * expand_ratio))  # 由于有到残差模块有1*1,3*3的卷积模块，所以可以靠expand_rarton来进行升维
        self.use_res_connect = self.stride == 1 and inp == oup  # 残差连接的判断条件：当步长=1且输入矩阵与输出矩阵的shape相同时进行
        layers = []
        if expand_ratio != 1:  # 如果expand_ratio不等于1，要做升维操作，对应图中的绿色模块
            # pw
            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))  # 这里添加的是1*1的卷积操作
        layers.extend([
            # dw
            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
            # 这里做3*3的卷积操作，步长可能是1也可能是2,groups=hidden_dim表示这里使用了分组卷积的操作，对应图上的蓝色模块
            # pw-linear
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),  # 对应图中的黄色模块
            nn.BatchNorm2d(oup),
        ])
        self.conv = nn.Sequential(*layers)  # 将layers列表中的元素解开依次传入nn.Sequential
    def forward(self, x):
        if self.use_res_connect:  # 如果使用了残差连接，就会进行一个x+的操作
            return x + self.conv(x)
        else:
            return self.conv(x)  # 否则不做操作

二：在yolo.py中添加common中的两个函数

if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
                 BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x,
                 ConvBNReLU, InvertedResidual):  # 添加 common中新加的两个模块 ConvBNReLU和InvertedResidual

三：制作mobilenetv2的yaml配置文件


# Parameters
nc: 1  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple
anchors:
  - [ 10,13, 16,30, 33,23 ]  # P3/8
  - [ 30,61, 62,45, 59,119 ]  # P4/16
  - [ 116,90, 156,198, 373,326 ]  # P5/32
# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [ [ -1, 1, Conv, [ 32, 3, 2 ] ],  # 0-P1/2 32x320x320
    [ -1, 1, InvertedResidual, [ 16, 1, 1 ] ],  # 1        16x320x320
    [ -1, 1, InvertedResidual, [ 24, 2, 6 ] ],  # 2-P2/4   24x160x160
    [ -1, 1, InvertedResidual, [ 24, 1, 6 ] ],  # 3-P2/4   24x160x160
    [ -1, 1, InvertedResidual, [ 32, 2, 6 ] ],  # 4-P3/8   32x80x80
    [ -1, 1, InvertedResidual, [ 32, 1, 6 ] ],  # 5-P3/8   32x80x80
    [ -1, 1, InvertedResidual, [ 32, 1, 6 ] ],  # 6-P3/8   32x80x80
    [ -1, 1, InvertedResidual, [ 64, 2, 6 ] ],  # 7-P4/16  64x40x40
    [ -1, 1, InvertedResidual, [ 64, 1, 6 ] ],  # 8-P4/16  64x40x40
    [ -1, 1, InvertedResidual, [ 64, 1, 6 ] ],  # 9-P4/16  64x40x40
    [ -1, 1, InvertedResidual, [ 64, 1, 6 ] ],  # 10-P4/16 64x40x40
    [ -1, 1, InvertedResidual, [ 96, 1, 6 ] ],  # 11       96X40X40
    [ -1, 1, InvertedResidual, [ 96, 1, 6 ] ],  # 12       96X40X40
    [ -1, 1, InvertedResidual, [ 96, 1, 6 ] ],  # 13       96X40X40
    [ -1, 1, InvertedResidual, [ 160, 2, 6 ] ], # 14-P5/32  160X20X20
    [ -1, 1, InvertedResidual, [ 160, 1, 6 ] ], # 15-P5/32  160X20X20
    [ -1, 1, InvertedResidual, [ 160, 1, 6 ] ], # 16-P5/32  160X20X20
    [ -1, 1, InvertedResidual, [ 320, 1, 6 ] ],  # 17       320X20X20
  ]
# YOLOv5 v6.0 head
head:
  [ [ -1, 1, Conv, [ 160, 1, 1 ] ],
    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 13 ], 1, Concat, [ 1 ] ],  # cat backbone P4
    [ -1, 1, C3, [ 160, False ] ],  # 21
    [ -1, 1, Conv, [ 80, 1, 1 ] ],
    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P3
    [ -1, 1, C3, [ 80, False ] ],  # 25 (P3/8-small)
    [ -1, 1, Conv, [ 80, 3, 2 ] ],
    [ [ -1, 22 ], 1, Concat, [ 1 ] ],  # cat head P4
    [ -1, 1, C3, [ 160, False ] ],  # 28 (P4/16-medium)
    [ -1, 1, Conv, [ 160, 3, 2 ] ],
    [ [ -1, 18 ], 1, Concat, [ 1 ] ],  # cat head P5
    [ -1, 1, C3, [ 320, False ] ],  # 31 (P5/32-large)
    [ [ 25, 28, 31 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5)
  ]

四：制作数据集VOC的yaml配置文件

# YOLOv5  by Ultralytics, GPL-3.0 license
# PASCAL VOC dataset '  # old img path
          lb_path = (lbs_path / f.name).with_suffix('.txt')  # new label path
          f.rename(imgs_path / f.name)  # move image
          convert_label(path, lb_path, year, id)  # convert labels to YOLO format