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：

# YOLOv5by 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 todef 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 == oupif inp == hidden_dim:self.conv = nn.Sequential(# dwnn.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-ExciteSeBlock(hidden_dim) if use_se else nn.Sequential(),# pw-linearnn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),nn.BatchNorm2d(oup),)else:self.conv = nn.Sequential(# pwnn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),nn.BatchNorm2d(hidden_dim),nn.Hardswish() if use_hs else nn.ReLU(),# dwnn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,bias=False),nn.BatchNorm2d(hidden_dim),# Squeeze-and-ExciteSeBlock(hidden_dim) if use_se else nn.Sequential(),nn.Hardswish() if use_hs else nn.ReLU(),# pw-linearnn.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 + yelse: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。