Yolov7改进--添加注意力机制

改进参考魔鬼导师：YOLOV7改进-添加注意力机制_哔哩哔哩_bilibili

视频教程：YOLOV7改进-添加注意力机制_哔哩哔哩_bilibili

GitHub改进项目地址：其中的cv_attentionGitHub - z1069614715/objectdetection_script: 一些关于目标检测的脚本的改进思路代码，详细请看readme.md

学习内容：

问：

涉及：

一些基本概念：通道数，卷积核大小，卷积步长：

一些基本概念_To-的博客-CSDN博客中进行解释

根据视频--添加注意力机制的步骤：

1.打开Yolov7/cfg/training/yolov7.yaml 模型配置文件

yolov7.yaml配置文件中存储这yolov7模型的一百多层卷积结构，通过代码注释和模型结构图搭配理解

# parameters
nc: 1  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple# anchors
anchors:- [12,16, 19,36, 40,28]  # P3/8- [36,75, 76,55, 72,146]  # P4/16- [142,110, 192,243, 459,401]  # P5/32# yolov7 backbone
backbone:# [from, number, module, args]     分别表示： 输入，重复次数，名称，参数数组[[-1, 1, Conv, [32, 3, 1]],  # 0 ##CBS  [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2 ##CBS      [-1, 1, Conv, [64, 3, 1]],  # ##CBS   实例：第一个-1表示以上一层的输出作为本层的输入，-1表示向前退一层，-2表示向前退两层，以此类推[-1, 1, Conv, [128, 3, 2]],  # 3-P2/4 ##CBS # 实例： 第二个1表示，该层操作一次[-1, 1, Conv, [64, 1, 1]],             # 实例： Conv表示该层操作的名称[-2, 1, Conv, [64, 1, 1]],   # [64, 1, 1]： 64表示输出特征图的通道数，来控制特征图的深度[-1, 1, Conv, [64, 3, 1]],   # [64, 3, 1]： 3表示卷积核大小，以3*3的卷积核对输入图像进行卷积，以提取图像特征[-1, 1, Conv, [64, 3, 1]],   # [64, 3, 1]： 1表示卷积步长，步长控制了每次卷积操作后特征图的大小变化[-1, 1, Conv, [64, 3, 1]],[-1, 1, Conv, [64, 3, 1]],[[-1, -3, -5, -6], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1]],  # 11  ##C7_1[-1, 1, MP, []],[-1, 1, Conv, [128, 1, 1]],[-3, 1, Conv, [128, 1, 1]],[-1, 1, Conv, [128, 3, 2]],[[-1, -3], 1, Concat, [1]],  # 16-P3/8  ##MP-C3[-1, 1, Conv, [128, 1, 1]],[-2, 1, Conv, [128, 1, 1]],[-1, 1, Conv, [128, 3, 1]],[-1, 1, Conv, [128, 3, 1]],[-1, 1, Conv, [128, 3, 1]],[-1, 1, Conv, [128, 3, 1]],[[-1, -3, -5, -6], 1, Concat, [1]],[-1, 1, Conv, [512, 1, 1]],  # 24  ##C7_1 待插入点[-1, 1, MP, []],[-1, 1, Conv, [256, 1, 1]],[-3, 1, Conv, [256, 1, 1]],[-1, 1, Conv, [256, 3, 2]],[[-1, -3], 1, Concat, [1]],  # 29-P4/16  ##MP-C3[-1, 1, Conv, [256, 1, 1]],[-2, 1, Conv, [256, 1, 1]],[-1, 1, Conv, [256, 3, 1]],[-1, 1, Conv, [256, 3, 1]],[-1, 1, Conv, [256, 3, 1]],[-1, 1, Conv, [256, 3, 1]],[[-1, -3, -5, -6], 1, Concat, [1]],[-1, 1, Conv, [1024, 1, 1]],  # 37  ##C7_1  待插入点[-1, 1, MP, []],[-1, 1, Conv, [512, 1, 1]],[-3, 1, Conv, [512, 1, 1]],[-1, 1, Conv, [512, 3, 2]],[[-1, -3], 1, Concat, [1]],  # 42-P5/32  ##MP-C3[-1, 1, Conv, [256, 1, 1]],[-2, 1, Conv, [256, 1, 1]],[-1, 1, Conv, [256, 3, 1]],[-1, 1, Conv, [256, 3, 1]],[-1, 1, Conv, [256, 3, 1]],[-1, 1, Conv, [256, 3, 1]],[[-1, -3, -5, -6], 1, Concat, [1]],[-1, 1, Conv, [1024, 1, 1]],  # 50  ##C7_1]# yolov7 head
head:[[-1, 1, SPPCSPC, [512]], # 51  ##SPPCSPC  待插入点[-1, 1, Conv, [256, 1, 1]],   ##CBS[-1, 1, nn.Upsample, [None, 2, 'nearest']],  ##上采样[37, 1, Conv, [256, 1, 1]], # route backbone P4  ##CBS[[-1, -2], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1]],[-2, 1, Conv, [256, 1, 1]],[-1, 1, Conv, [128, 3, 1]],[-1, 1, Conv, [128, 3, 1]],[-1, 1, Conv, [128, 3, 1]],[-1, 1, Conv, [128, 3, 1]],[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1]], # 63  ##C7_2[-1, 1, Conv, [128, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[24, 1, Conv, [128, 1, 1]], # route backbone P3[[-1, -2], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1]],[-2, 1, Conv, [128, 1, 1]],[-1, 1, Conv, [64, 3, 1]],[-1, 1, Conv, [64, 3, 1]],[-1, 1, Conv, [64, 3, 1]],[-1, 1, Conv, [64, 3, 1]],[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1]], # 75  ##C7_2[-1, 1, MP, []],[-1, 1, Conv, [128, 1, 1]],[-3, 1, Conv, [128, 1, 1]],[-1, 1, Conv, [128, 3, 2]],[[-1, -3, 63], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1]],[-2, 1, Conv, [256, 1, 1]],[-1, 1, Conv, [128, 3, 1]],[-1, 1, Conv, [128, 3, 1]],[-1, 1, Conv, [128, 3, 1]],[-1, 1, Conv, [128, 3, 1]],[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1]], # 88   ##C7_2[-1, 1, MP, []],[-1, 1, Conv, [256, 1, 1]],[-3, 1, Conv, [256, 1, 1]],[-1, 1, Conv, [256, 3, 2]],[[-1, -3, 51], 1, Concat, [1]],[-1, 1, Conv, [512, 1, 1]],[-2, 1, Conv, [512, 1, 1]],[-1, 1, Conv, [256, 3, 1]],[-1, 1, Conv, [256, 3, 1]],[-1, 1, Conv, [256, 3, 1]],[-1, 1, Conv, [256, 3, 1]],[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],[-1, 1, Conv, [512, 1, 1]], # 101  ##C7_2[75, 1, RepConv, [256, 3, 1]],[88, 1, RepConv, [512, 3, 1]],[101, 1, RepConv, [1024, 3, 1]],[[102,103,104], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)]

相应的yolov7网络模型结构图：