位置: IT常识 - 正文

YOLOV5更换轻量级的backbone：mobilenetV2(yolov5使用)

编辑：rootadmin

YOLOV5更换轻量级的backbone：mobilenetV2

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

推荐整理分享YOLOV5更换轻量级的backbone：mobilenetV2(yolov5使用)，希望有所帮助，仅作参考，欢迎阅读内容。

文章相关热门搜索词:yolov5 5.0,yolov5轻量化,yolov5商用,yolov5轻量化,yolov5轻量化,yolov5轻量化,yolov5更换backbone,yolov5轻量化,内容如对您有帮助，希望把文章链接给更多的朋友！

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# 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# YOLOv5 v6.0 backbonebackbone: # [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 headhead: [ [ -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五：启用训练

由于修改了网络所以不能加载预训练模型进行

预训练模型的作用：加快模型训练初期的超参数训练时间