【YOLOv5/v7改进系列】引入特征融合网络—

一、导言

ASF-YOLO结合空间和尺度特征以实现精确且快速的细胞实例分割。在YOLO分割框架的基础上，通过引入尺度序列特征融合(SSFF)模块来增强网络的多尺度信息提取能力，并利用三重特征编码器(TFE)模块融合不同尺度的特征图以增加细节信息。此外，还引入了通道和位置注意力机制(CPAM)，整合SSFF和TFE模块，专注于有信息的通道和与小物体空间位置相关的特征，从而提升检测和分割性能。实验验证显示，ASF-YOLO模型在两个细胞数据集上取得了显著的分割精度和速度，包括在2018年数据科学碗数据集上的框mAP为0.91，掩码mAP为0.887，推理速度为47.3 FPS，优于当时最先进的方法。

优点：

创新的结构设计：ASF-YOLO创新性地融合了尺度序列特征，有效处理不同大小、方向和长宽比的细胞，特别是对于小目标的分割具有显著效果。
高效与准确并重：模型在保持高分割精度的同时，实现了较快的推理速度，这对于临床应用来说至关重要。
注意力机制的集成：通过CPAM，模型能够动态调整对关键通道和空间位置的聚焦，提高了对细胞实例的识别和分割能力。
性能超越现有方法：在多个细胞分割基准数据集上，ASF-YOLO的性能超越了包括CNN和其他改进型YOLO在内的多种先进方法。

二、准备工作

首先在YOLOv5/v7的models文件夹下新建文件asfyolo.py，导入如下代码

from models.common import *class Zoom_cat(nn.Module):def __init__(self):super().__init__()def forward(self, x):l, m, s = x[0], x[1], x[2]tgt_size = m.shape[2:]l = F.adaptive_max_pool2d(l, tgt_size) + F.adaptive_avg_pool2d(l, tgt_size)s = F.interpolate(s, m.shape[2:], mode='nearest')lms = torch.cat([l, m, s], dim=1)return lmsclass ScalSeq(nn.Module):def __init__(self, inc, channel):super(ScalSeq, self).__init__()self.conv0 = Conv(inc[0], channel, 1)self.conv1 = Conv(inc[1], channel, 1)self.conv2 = Conv(inc[2], channel, 1)self.conv3d = nn.Conv3d(channel, channel, kernel_size=(1, 1, 1))self.bn = nn.BatchNorm3d(channel)self.act = nn.LeakyReLU(0.1)self.pool_3d = nn.MaxPool3d(kernel_size=(3, 1, 1))def forward(self, x):p3, p4, p5 = x[0], x[1], x[2]p3 = self.conv0(p3)p4_2 = self.conv1(p4)p4_2 = F.interpolate(p4_2, p3.size()[2:], mode='nearest')p5_2 = self.conv2(p5)p5_2 = F.interpolate(p5_2, p3.size()[2:], mode='nearest')p3_3d = torch.unsqueeze(p3, -3)p4_3d = torch.unsqueeze(p4_2, -3)p5_3d = torch.unsqueeze(p5_2, -3)combine = torch.cat([p3_3d, p4_3d, p5_3d], dim=2)conv_3d = self.conv3d(combine)bn = self.bn(conv_3d)act = self.act(bn)x = self.pool_3d(act)x = torch.squeeze(x, 2)return xclass Add(nn.Module):# Concatenate a list of tensors along dimensiondef __init__(self):super().__init__()def forward(self, x):input1, input2 = x[0], x[1]x = input1 + input2return xclass channel_att(nn.Module):def __init__(self, channel, b=1, gamma=2):super(channel_att, self).__init__()kernel_size = int(abs((math.log(channel, 2) + b) / gamma))kernel_size = kernel_size if kernel_size % 2 else kernel_size + 1self.avg_pool = nn.AdaptiveAvgPool2d(1)self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(kernel_size - 1) // 2, bias=False)self.sigmoid = nn.Sigmoid()def forward(self, x):y = self.avg_pool(x)y = y.squeeze(-1)y = y.transpose(-1, -2)y = self.conv(y).transpose(-1, -2).unsqueeze(-1)y = self.sigmoid(y)return x * y.expand_as(x)class local_att(nn.Module):def __init__(self, channel, reduction=16):super(local_att, self).__init__()self.conv_1x1 = nn.Conv2d(in_channels=channel, out_channels=channel // reduction, kernel_size=1, stride=1,bias=False)self.relu = nn.ReLU()self.bn = nn.BatchNorm2d(channel // reduction)self.F_h = nn.Conv2d(in_channels=channel // reduction, out_channels=channel, kernel_size=1, stride=1,bias=False)self.F_w = nn.Conv2d(in_channels=channel // reduction, out_channels=channel, kernel_size=1, stride=1,bias=False)self.sigmoid_h = nn.Sigmoid()self.sigmoid_w = nn.Sigmoid()def forward(self, x):_, _, h, w = x.size()x_h = torch.mean(x, dim=3, keepdim=True).permute(0, 1, 3, 2)x_w = torch.mean(x, dim=2, keepdim=True)x_cat_conv_relu = self.relu(self.bn(self.conv_1x1(torch.cat((x_h, x_w), 3))))x_cat_conv_split_h, x_cat_conv_split_w = x_cat_conv_relu.split([h, w], 3)s_h = self.sigmoid_h(self.F_h(x_cat_conv_split_h.permute(0, 1, 3, 2)))s_w = self.sigmoid_w(self.F_w(x_cat_conv_split_w))out = x * s_h.expand_as(x) * s_w.expand_as(x)return outclass attention_model(nn.Module):# Concatenate a list of tensors along dimensiondef __init__(self, ch=256):super().__init__()self.channel_att = channel_att(ch)self.local_att = local_att(ch)def forward(self, x):input1, input2 = x[0], x[1]input1 = self.channel_att(input1)x = input1 + input2x = self.local_att(x)return x

其次在在YOLOv5/v7项目文件下的models/yolo.py中在文件首部添加代码

from models.asfyolo import *

并搜索def parse_model(d, ch)

定位到如下行添加以下代码

        elif m is Zoom_cat:c2 = sum(ch[x] for x in f)elif m is Add:c2 = ch[f[-1]]elif m is attention_model:c2 = ch[f[-1]]args = [c2]elif m is ScalSeq:c1 = [ch[x] for x in f]c2 = make_divisible(args[0] * gw, 8)args = [c1, c2]

三、YOLOv7-tiny改进工作

完成二后，在YOLOv7项目文件下的models文件夹下创建新的文件yolov7-tiny-asfyolo.yaml，导入如下代码。

# parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple# anchors
anchors:- [10,13, 16,30, 33,23]  # P3/8- [30,61, 62,45, 59,119]  # P4/16- [116,90, 156,198, 373,326]  # P5/32# yolov7-tiny backbone
backbone:# [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True[[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]],  # 0-P1/2[-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]],  # 1-P2/4[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 7[-1, 1, MP, []],  # 8-P3/8[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 14[-1, 1, MP, []],  # 15-P4/16[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 21[-1, 1, MP, []],  # 22-P5/32[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 28]# yolov7-tiny head
head:[[-1, 1, v7tiny_SPP, [256]], # 29[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[14, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, 21, -2], 1, Zoom_cat, []],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 38[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[7, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 40[[-1, 14, -2], 1, Zoom_cat, []],[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 47[-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]],[[-1, 38], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 55[-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]],[[-1, 29], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 63[[14, 21, 28], 1, ScalSeq, [64]],[[47, -1], 1, attention_model, []], #65[65, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[63, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[55, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[66, 67, 68], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)]

from  n    params  module                                  arguments                     0                -1  1       928  models.common.Conv                      [3, 32, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]2                -1  1      2112  models.common.Conv                      [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]3                -2  1      2112  models.common.Conv                      [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]4                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]5                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]6  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           7                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]8                -1  1         0  models.common.MP                        []                            9                -1  1      4224  models.common.Conv                      [64, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]10                -2  1      4224  models.common.Conv                      [64, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]11                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]12                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]13  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]15                -1  1         0  models.common.MP                        []                            16                -1  1     16640  models.common.Conv                      [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]17                -2  1     16640  models.common.Conv                      [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]18                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]19                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]20  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           21                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]22                -1  1         0  models.common.MP                        []                            23                -1  1     66048  models.common.Conv                      [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]24                -2  1     66048  models.common.Conv                      [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]25                -1  1    590336  models.common.Conv                      [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]26                -1  1    590336  models.common.Conv                      [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]27  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           28                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]29                -1  1    657408  models.common.v7tiny_SPP                [512, 256]                    30                -1  1     66048  models.common.Conv                      [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]31                14  1     33280  models.common.Conv                      [128, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]32      [-1, 21, -2]  1         0  models.asfyolo.Zoom_cat                 []                            33                -1  1     49280  models.common.Conv                      [768, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]34                -2  1     49280  models.common.Conv                      [768, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]35                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]36                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]37  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           38                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]39                -1  1     16640  models.common.Conv                      [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]40                 7  1      8448  models.common.Conv                      [64, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]41      [-1, 14, -2]  1         0  models.asfyolo.Zoom_cat                 []                            42                -1  1     12352  models.common.Conv                      [384, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]43                -2  1     12352  models.common.Conv                      [384, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]44                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]45                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]46  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           47                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]48                -1  1     73984  models.common.Conv                      [64, 128, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]49          [-1, 38]  1         0  models.common.Concat                    [1]                           50                -1  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]51                -2  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]52                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]53                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]54  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           55                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]56                -1  1    295424  models.common.Conv                      [128, 256, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]57          [-1, 29]  1         0  models.common.Concat                    [1]                           58                -1  1     65792  models.common.Conv                      [512, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]59                -2  1     65792  models.common.Conv                      [512, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]60                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]61                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]62  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           63                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]64      [14, 21, 28]  1     62016  models.asfyolo.ScalSeq                  [[128, 256, 512], 64]         65          [47, -1]  1       779  models.asfyolo.attention_model          [64]                          66                65  1     73984  models.common.Conv                      [64, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]67                63  1    590336  models.common.Conv                      [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]68                55  1    590848  models.common.Conv                      [128, 512, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]69      [66, 67, 68]  1     17132  models.yolo.IDetect                     [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]Model Summary: 288 layers, 5906519 parameters, 5906519 gradients, 15.2 GFLOPS

运行后若打印出如上文本代表改进成功。

四、YOLOv5s改进工作

完成二后，在YOLOv5项目文件下的models文件夹下创建新的文件yolov5s-asfyolo.yaml，导入如下代码。

# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:- [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 backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2[-1, 1, Conv, [128, 3, 2]],  # 1-P2/4[-1, 3, C3, [128]],[-1, 1, Conv, [256, 3, 2]],  # 3-P3/8[-1, 6, C3, [256]],[-1, 1, Conv, [512, 3, 2]],  # 5-P4/16[-1, 9, C3, [512]],[-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32[-1, 3, C3, [1024]],[-1, 1, SPPF, [1024, 5]],  # 9]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 1, 1]], #10[4, 1, Conv, [512, 1, 1]], #11[[-1, 6, -2], 1, Zoom_cat, []],  # 12 cat backbone P4[-1, 3, C3, [512, False]],  # 13[-1, 1, Conv, [256, 1, 1]], #14[2, 1, Conv, [256, 1, 1]], #15[[-1, 4, -2], 1, Zoom_cat, []],  #16  cat backbone P3[-1, 3, C3, [256, False]],  # 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]], #18[[-1, 14], 1, Concat, [1]],  #19 cat head P4[-1, 3, C3, [512, False]],  # 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]], #21[[-1, 10], 1, Concat, [1]],  #22 cat head P5[-1, 3, C3, [1024, False]],  # 23 (P5/32-large)[[4, 6, 8], 1, ScalSeq, [256]],[[17, -1], 1, attention_model, []], #25[[25, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

from  n    params  module                                  arguments                     0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                2                -1  1     18816  models.common.C3                        [64, 64, 1]                   3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               4                -1  2    115712  models.common.C3                        [128, 128, 2]                 5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              6                -1  3    625152  models.common.C3                        [256, 256, 3]                 7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              11                 4  1     33280  models.common.Conv                      [128, 256, 1, 1]              12       [-1, 6, -2]  1         0  models.asfyolo.Zoom_cat                 []                            13                -1  1    427520  models.common.C3                        [768, 256, 1, False]          14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              15                 2  1      8448  models.common.Conv                      [64, 128, 1, 1]               16       [-1, 4, -2]  1         0  models.asfyolo.Zoom_cat                 []                            17                -1  1    107264  models.common.C3                        [384, 128, 1, False]          18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              19          [-1, 14]  1         0  models.common.Concat                    [1]                           20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              22          [-1, 10]  1         0  models.common.Concat                    [1]                           23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          24         [4, 6, 8]  1    132224  models.asfyolo.ScalSeq                  [[128, 256, 512], 128]        25          [17, -1]  1      3093  models.asfyolo.attention_model          [128]                         26      [25, 20, 23]  1     16182  models.yolo.Detect                      [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]Model Summary: 306 layers, 7281291 parameters, 7281291 gradients, 18.3 GFLOPs

运行后若打印出如上文本代表改进成功。

五、YOLOv5n改进工作

完成二后，在YOLOv5项目文件下的models文件夹下创建新的文件yolov5n-asfyolo.yaml，导入如下代码。

# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.25  # layer channel multiple
anchors:- [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 backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2[-1, 1, Conv, [128, 3, 2]],  # 1-P2/4[-1, 3, C3, [128]],[-1, 1, Conv, [256, 3, 2]],  # 3-P3/8[-1, 6, C3, [256]],[-1, 1, Conv, [512, 3, 2]],  # 5-P4/16[-1, 9, C3, [512]],[-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32[-1, 3, C3, [1024]],[-1, 1, SPPF, [1024, 5]],  # 9]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 1, 1]], #10[4, 1, Conv, [512, 1, 1]], #11[[-1, 6, -2], 1, Zoom_cat, []],  # 12 cat backbone P4[-1, 3, C3, [512, False]],  # 13[-1, 1, Conv, [256, 1, 1]], #14[2, 1, Conv, [256, 1, 1]], #15[[-1, 4, -2], 1, Zoom_cat, []],  #16  cat backbone P3[-1, 3, C3, [256, False]],  # 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]], #18[[-1, 14], 1, Concat, [1]],  #19 cat head P4[-1, 3, C3, [512, False]],  # 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]], #21[[-1, 10], 1, Concat, [1]],  #22 cat head P5[-1, 3, C3, [1024, False]],  # 23 (P5/32-large)[[4, 6, 8], 1, ScalSeq, [256]],[[17, -1], 1, attention_model, []], #25[[25, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

from  n    params  module                                  arguments                     0                -1  1      1760  models.common.Conv                      [3, 16, 6, 2, 2]              1                -1  1      4672  models.common.Conv                      [16, 32, 3, 2]                2                -1  1      4800  models.common.C3                        [32, 32, 1]                   3                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                4                -1  2     29184  models.common.C3                        [64, 64, 2]                   5                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               6                -1  3    156928  models.common.C3                        [128, 128, 3]                 7                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              8                -1  1    296448  models.common.C3                        [256, 256, 1]                 9                -1  1    164608  models.common.SPPF                      [256, 256, 5]                 10                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              11                 4  1      8448  models.common.Conv                      [64, 128, 1, 1]               12       [-1, 6, -2]  1         0  models.asfyolo.Zoom_cat                 []                            13                -1  1    107264  models.common.C3                        [384, 128, 1, False]          14                -1  1      8320  models.common.Conv                      [128, 64, 1, 1]               15                 2  1      2176  models.common.Conv                      [32, 64, 1, 1]                16       [-1, 4, -2]  1         0  models.asfyolo.Zoom_cat                 []                            17                -1  1     27008  models.common.C3                        [192, 64, 1, False]           18                -1  1     36992  models.common.Conv                      [64, 64, 3, 2]                19          [-1, 14]  1         0  models.common.Concat                    [1]                           20                -1  1     74496  models.common.C3                        [128, 128, 1, False]          21                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              22          [-1, 10]  1         0  models.common.Concat                    [1]                           23                -1  1    296448  models.common.C3                        [256, 256, 1, False]          24         [4, 6, 8]  1     33344  models.asfyolo.ScalSeq                  [[64, 128, 256], 64]          25          [17, -1]  1       779  models.asfyolo.attention_model          [64]                          26      [25, 20, 23]  1      8118  models.yolo.Detect                      [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [64, 128, 256]]Model Summary: 306 layers, 1830497 parameters, 1830497 gradients, 4.8 GFLOPs

运行后打印如上代码说明改进成功。

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