Improvement 16 of yolov5: replace backbone network C3 with lightweight network pp-lcnet

 ​ front said ： As the current advanced deep learning target detection algorithm YOLOv5, A large number of trick, But there is still room for improvement , For the detection difficulties in specific application scenarios , There are different ways to improve . Subsequent articles , Focus on YOLOv5 How to improve is introduced in detail , The purpose is to provide their own meager help and reference for those who need innovation in scientific research or friends who need to achieve better results in engineering projects .

solve the problem ：YOLOv5 The backbone feature extraction network adopts C3 structure , Bring a large number of parameters , The detection speed is slow , Limited application , In some real application scenarios, such as mobile or embedded devices , Such a large and complex model is difficult to be applied . The first is that the model is too large , Facing the problem of insufficient memory , Second, these scenarios require low latency , In other words, the response speed should be fast , Imagine the pedestrian detection system of self driving cars. What terrible things will happen if the speed is slow? . therefore , Research small and efficient CNN Models are crucial in these scenarios , At least for now , Although the hardware will be faster and faster in the future . This paper attempts to replace the backbone feature extraction network with a lighter PP-LCNet The Internet , To realize the lightweight of the network model , Balance speed and accuracy .

YOLOv5 Improvement 12 ： Backbone network C3 Replace with lightweight network ShuffleNetV2_ AI algorithm engineer 0301 The blog of -CSDN Blog https://blog.csdn.net/m0_70388905/article/details/125612052YOLOv5 Improvement Xi ： Backbone network C3 Replace with lightweight network MobileNetV3_ AI algorithm engineer 0301 The blog of -CSDN Blog https://blog.csdn.net/m0_70388905/article/details/125593267

principle ：

arXiv:https://arxiv.org/pdf/2109.15099.pdf

code: https://github.com/PaddlePaddle

Baidu team aims at Intel-CPU High reasoning speed designed for end acceleration & High performance LCNet. This article is a combination of Baidu team Intel-CPU A lightweight and high-performance network designed for end-to-end reasoning PP-LCNet, The proposed scheme achieves better results in image classification task ShuffleNetV2、MobileNetV2、MobileNetV3 as well as GhostNet Better delay - Precision equalization .

Fang Law ：

Step 1 modify common.py, increase DepthSepConv modular .

class DepthSepConv(nn.Module):
    def __init__(self, inp, oup, dw_size, stride, use_se):
        super(DepthSepConv, self).__init__()
        self.stride = stride
        self.inp = inp
        self.oup = oup
        self.dw_size = dw_size
        self.dw_sp = nn.Sequential(
            nn.Conv2d(self.inp, self.inp, kernel_size=self.dw_size, stride=self.stride, padding=(dw_size - 1) // 2, groups=self.inp, bias=False),
            nn.BatchNorm2d(self.inp),

            SeBlock(self.inp, reduction=16) if use_se else nn.Sequential(),

            nn.Conv2d(self.inp, self.oup, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(self.oup),
            nn.Hardswish())

    def forward(self, x):
        y = self.dw_sp(x)
        return y

The second step ： take yolo.py Registration module in .

if m in [Conv,MobileNetV3_InvertedResidual,ShuffleNetV2_InvertedResidual,ghostc3,DepthSepConv

]:

The third step ： Make changes yaml file

backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [16, 3, 2, 1]],              # 0-P1/2  ch_out, kernel, stride, padding
   [-1, 1, DepthSepConv, [32, 3, 1, False]],            # 1 

   [-1, 1, DepthSepConv, [64, 3, 2, False]],            # 2-P2/4
   [-1, 1, DepthSepConv, [64, 3, 1, False]],            # 3

   [-1, 1, DepthSepConv, [128, 3, 2, False]],           # 4-P3/8
   [-1, 1, DepthSepConv, [128, 3, 1, False]],           # 5

   [-1, 1, DepthSepConv, [256, 3, 2, False]],           # 6-P4/16
   [-1, 5, DepthSepConv, [256, 5, 1, False]],           # 7

   [-1, 1, DepthSepConv, [512, 5, 2, True]],            # 8-P5/32
   [-1, 1, DepthSepConv, [512, 5, 1, True]],            # 9

   [-1, 1, SPPF, [1024, 5]], # 10
  ]

junction fruit ： I have done a lot of experiments on multiple data sets , For different data sets, the effect is different ,map Value down , But the size of the weight model decreases , The parameter quantity decreases .

Let me know ： The next content will continue to share the sharing of network lightweight methods . Interested friends can pay attention to me , If you have questions, you can leave a message or chat with me in private

PS： The replacement of backbone network is not only applicable to improvement YOLOv5, You can also improve others YOLO Network and target detection network , such as YOLOv4、v3 etc. .

Last , I hope I can powder each other , Be a friend , Learn and communicate together .

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