MobileNetv3继承了上两个版本的长处，引入了一些新颖的模块，并采用最新的神经网络架构搜索(Neural Architecture Search, NAS)技术来自动寻找目标数据集上的最优神经网络结构。

        首先，MobileNetv3在原先block的基础上引入了通道注意力机制SE模块，目的是学习通道之间的相关性，为比较重要的通道添加更大的权重，使其更受模型“重视”，提高模型的性能。

        其次，MobileNetv3对使用NAS技术学习出的最优网络结构做了进一步优化，改进和删除了许多所需计算时间较长的层，将第一个卷积层的卷积核个数由32降低为16，发现网络准确率几乎不变，另外还删除了几个卷积层，

        在commom.py中添加代码段：

class h_sigmoid(nn.Module):
    def __init__(self, inplace=True):
        super(h_sigmoid, self).__init__()
        self.relu = nn.ReLU6(inplace=inplace)

    def forward(self, x):
        return self.relu(x + 3) / 6


class h_swish(nn.Module):
    def __init__(self, inplace=True):
        super(h_swish, self).__init__()
        self.sigmoid = h_sigmoid(inplace=inplace)

    def forward(self, x):
        return x * self.sigmoid(x)


class SELayer(nn.Module):
    def __init__(self, channel, reduction=4):
        super(SELayer, self).__init__()
        # Squeeze
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        # Excitation(FC+ReLU+FC+Sigmoid)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel),
            h_sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x)
        y = y.view(b, c)
        y = self.fc(y).view(b, c, 1, 1) 
        return x * y


class conv_bn_hswish(nn.Module):
    """
    This equals to
    def conv_3x3_bn(inp, oup, stride):
        return nn.Sequential(
            nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
            nn.BatchNorm2d(oup),
            h_swish()
        )
    """

    def __init__(self, c1, c2, stride):
        super(conv_bn_hswish, self).__init__()
        self.conv = nn.Conv2d(c1, c2, 3, stride, 1, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = h_swish()

    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

    def fuseforward(self, x):
        return self.act(self.conv(x))


class MobileNet_Block(nn.Module):
    def __init__(self, inp, oup, hidden_dim, kernel_size, stride, use_se, use_hs):
        super(MobileNet_Block, self).__init__()
        assert stride in [1, 2]

        self.identity = stride == 1 and inp == oup

        if inp == hidden_dim:
            self.conv = nn.Sequential(
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                          bias=False),
                nn.BatchNorm2d(hidden_dim),
                h_swish() if use_hs else nn.ReLU(inplace=True),
                # Squeeze-and-Excite
                SELayer(hidden_dim) if use_se else nn.Sequential(),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )
        else:
            
            self.conv = nn.Sequential(
                # pw
                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                nn.BatchNorm2d(hidden_dim),
                h_swish() if use_hs else nn.ReLU(inplace=True),
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                          bias=False),
                nn.BatchNorm2d(hidden_dim),
                # Squeeze-and-Excite
                SELayer(hidden_dim) if use_se else nn.Sequential(),
                h_swish() if use_hs else nn.ReLU(inplace=True),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )

    def forward(self, x):
        y = self.conv(x)
        if self.identity:
            return x + y
        else:
            return y

        在yolo.py的parse_model()函数中，写入h_sigmoid, h_swish, SELayer, conv_bn_hswis h, MobileNet_Block。

        新建.yaml文件，添加下列代码：

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

# Parameters
nc: 20  # 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:
  # MobileNetV3-small 11层
  # [from, number, module, args]
  # MobileNet_Block: [out_ch, hidden_ch, kernel_size, stride, use_se, use_hs]
  # hidden_ch表示在Inverted residuals中的扩张通道数
  # use_se 表示是否使用 SELayer, use_hs 表示使用 h_swish 还是 ReLU
  [[-1, 1, conv_bn_hswish, [16, 2]],                 # 0-p1/2
   [-1, 1, MobileNet_Block, [16,  16, 3, 2, 1, 0]],  # 1-p2/4
   [-1, 1, MobileNet_Block, [24,  72, 3, 2, 0, 0]],  # 2-p3/8
   [-1, 1, MobileNet_Block, [24,  88, 3, 1, 0, 0]],  # 3-p3/8
   [-1, 1, MobileNet_Block, [40,  96, 5, 2, 1, 1]],  # 4-p4/16
   [-1, 1, MobileNet_Block, [40, 240, 5, 1, 1, 1]],  # 5-p4/16
   [-1, 1, MobileNet_Block, [40, 240, 5, 1, 1, 1]],  # 6-p4/16
   [-1, 1, MobileNet_Block, [48, 120, 5, 1, 1, 1]],  # 7-p4/16
   [-1, 1, MobileNet_Block, [48, 144, 5, 1, 1, 1]],  # 8-p4/16
   [-1, 1, MobileNet_Block, [96, 288, 5, 2, 1, 1]],  # 9-p5/32
   [-1, 1, MobileNet_Block, [96, 576, 5, 1, 1, 1]],  # 10-p5/32
   [-1, 1, MobileNet_Block, [96, 576, 5, 1, 1, 1]],  # 11-p5/32
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
   [-1, 1, C3, [256, False]],  # 15

   [-1, 1, Conv, [128, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 3], 1, Concat, [1]],  # cat backbone P3
   [-1, 1, C3, [128, False]],  # 19 (P3/8-small)

   [-1, 1, Conv, [128, 3, 2]],
   [[-1, 16], 1, Concat, [1]],  # cat head P4
   [-1, 1, C3, [256, False]],  # 22 (P4/16-medium)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 12], 1, Concat, [1]],  # cat head P5
   [-1, 1, C3, [512, False]],  # 25 (P5/32-large)

   [[19, 22, 25], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]