1、ymal文件修改

将models文件下yolov5s.py复制重命名如下图所示：

2、接着将如下代码替换，diamagnetic如下所示：

# YOLOv5 ? by Ultralytics, GPL-3.0 license# Parametersnc: 1  # number of classesdepth_multiple: 1.0  # model depth multiplewidth_multiple: 1.0  # layer channel multipleanchors:  - [10,13, 16,30, 33,23]  # P3/8  - [30,61, 62,45, 59,119]  # P4/16  - [116,90, 156,198, 373,326]  # P5/32   # Mobilenetv3-small backbone   # MobileNetV3_InvertedResidual [out_ch, hid_ch, k_s, stride, SE, HardSwish]backbone:  # [from, number, module, args]  [[-1, 1, Conv_BN_HSwish, [16, 2]],                              # 0-p1/2   [-1, 1, MobileNetV3_InvertedResidual, [16,  16, 3, 2, 1, 0]],  # 1-p2/4   [-1, 1, MobileNetV3_InvertedResidual, [24,  72, 3, 2, 0, 0]],  # 2-p3/8   [-1, 1, MobileNetV3_InvertedResidual, [24,  88, 3, 1, 0, 0]],  # 3   [-1, 1, MobileNetV3_InvertedResidual, [40,  96, 5, 2, 1, 1]],  # 4-p4/16   [-1, 1, MobileNetV3_InvertedResidual, [40, 240, 5, 1, 1, 1]],  # 5   [-1, 1, MobileNetV3_InvertedResidual, [40, 240, 5, 1, 1, 1]],  # 6   [-1, 1, MobileNetV3_InvertedResidual, [48, 120, 5, 1, 1, 1]],  # 7   [-1, 1, MobileNetV3_InvertedResidual, [48, 144, 5, 1, 1, 1]],  # 8   [-1, 1, MobileNetV3_InvertedResidual, [96, 288, 5, 2, 1, 1]],  # 9-p5/32   [-1, 1, MobileNetV3_InvertedResidual, [96, 576, 5, 1, 1, 1]],  # 10   [-1, 1, MobileNetV3_InvertedResidual, [96, 576, 5, 1, 1, 1]],  # 11  ]# YOLOv5 v6.0 headhead:  [[-1, 1, Conv, [96, 1, 1]],  # 12   [-1, 1, nn.Upsample, [None, 2, 'nearest']],   [[-1, 8], 1, Concat, [1]],  # cat backbone P4   [-1, 3, C3, [144, False]],  # 15   [-1, 1, Conv, [144, 1, 1]], # 16   [-1, 1, nn.Upsample, [None, 2, 'nearest']],   [[-1, 3], 1, Concat, [1]],  # cat backbone P3   [-1, 3, C3, [168, False]],  # 19 (P3/8-small)   [-1, 1, Conv, [168, 3, 2]],   [[-1, 16], 1, Concat, [1]], # cat head P4   [-1, 3, C3, [312, False]],  # 22 (P4/16-medium)   [-1, 1, Conv, [312, 3, 2]],   [[-1, 12], 1, Concat, [1]], # cat head P5   [-1, 3, C3, [408, False]],  # 25 (P5/32-large)   [[19, 22, 25], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)  ]

data文件也类似操作，如下图所示：

2、common.py文件修改

在common.py文件下方中加入如下代码：

# Mobilenetv3Smallclass SeBlock(nn.Module):    def __init__(self, in_channel, reduction=4):        super().__init__()        self.Squeeze = nn.AdaptiveAvgPool2d(1)        self.Excitation = nn.Sequential()        self.Excitation.add_module('FC1', nn.Conv2d(in_channel, in_channel // reduction, kernel_size=1))  # 1*1卷积与此效果相同        self.Excitation.add_module('ReLU', nn.ReLU())        self.Excitation.add_module('FC2', nn.Conv2d(in_channel // reduction, in_channel, kernel_size=1))        self.Excitation.add_module('Sigmoid', nn.Sigmoid())    def forward(self, x):        y = self.Squeeze(x)        ouput = self.Excitation(y)        return x * (ouput.expand_as(x))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 = nn.Hardswish()    def forward(self, x):        return self.act(self.bn(self.conv(x)))class MobileNetV3_InvertedResidual(nn.Module):    def __init__(self, inp, oup, hidden_dim, kernel_size, stride, use_se, use_hs):        super(MobileNetV3_InvertedResidual, 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),                nn.Hardswish() if use_hs else nn.ReLU(),                # Squeeze-and-Excite                SeBlock(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),                nn.Hardswish() if use_hs else nn.ReLU(),                # 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                SeBlock(hidden_dim) if use_se else nn.Sequential(),                nn.Hardswish() if use_hs else nn.ReLU(),                # 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

3、yolo.py文件修改

4、在yolo.py的parse_model函数中添加如下代码

Conv_BN_HSwish, MobileNetV3_InvertedResidual

4、train文件修改

在train文件进行如下路径修改，如下图所示：

接着对train.py运行训练，如下图所示：

上文如有错误，恳请各位大佬指正。