【YOLOv5/v7改进系列】替换Neck为Gold-Yolo特征融合网络

一、导言

Gold-YOLO是一种高效的物体检测模型，它通过一种新的机制——Gather-and-Distribute（GD）机制来增强多尺度特征融合的能力，从而在保证实时性能的同时提高了检测精度。下面是对Gold-YOLO的主要特点和创新点的概述：

关键特点
  1. Gather-and-Distribute (GD) 机制：这是Gold-YOLO的核心贡献之一。GD机制通过卷积和自注意力操作来实现对多尺度特征的有效融合，从而提升模型在不同大小物体检测上的性能。
  2. 平衡速度与精度：Gold-YOLO在保证实时性能的同时，也达到了较高的检测精度，特别是在不同模型规模下的平衡。
  3. 预训练方法：Gold-YOLO首次在YOLO系列中实现了MAE-style预训练方法，允许模型从无监督学习中受益，进一步提高了模型的性能。
创新点

  1. GD机制的设计：GD机制由三个主要模块组成：特征对齐模块（FAM）、信息融合模块（IFM）和信息注入模块（Inject）。FAM负责收集和对齐来自不同层级的特征，IFM则融合这些对齐后的特征以生成全局信息。最后，通过简单的注意力操作，Inject模块将这些全局信息分配到各个层级，进而增强各个分支的检测能力。

    • 低级Gather-and-Distribute分支（Low-GD）：专门针对小目标和中等目标，通过融合高层级的特征来保留高分辨率信息。
    • 高级Gather-and-Distribute分支（High-GD）：主要关注大目标的检测，通过融合低层级的特征来获得更大的感受野。
    • 轻量级相邻层融合模块（LAF）：增强两个分支之间的信息交流，进一步提升模型性能。

  2. 高效的信息融合：通过上述的GD机制，Gold-YOLO能够有效解决传统FPN和PANet中存在的信息融合问题，实现了更好的多尺度特征融合，从而在检测不同大小的目标时表现出色。

  3. MAE-style预训练：Gold-YOLO在主干网络上采用了掩码自编码（MAE）式的预训练方法，这有助于模型在有监督训练前就具备良好的特征表示能力，进而加速收敛并提高最终性能。

性能评估

Gold-YOLO在多个方面都展示了出色的性能，包括但不限于：

  • 速度与精度的平衡：Gold-YOLO的不同版本（如Gold-YOLO-N、Gold-YOLO-S、Gold-YOLO-M等）均能在保持或超越竞争对手的速度的同时，提供更高的平均精度（AP）。
  • 跨模型规模的一致性：无论是在小型、中型还是大型模型版本中，Gold-YOLO都能够保持一致的高性能，证明了其机制的有效性和通用性。
  • 跨任务扩展性：除了物体检测任务外，GD机制还被应用于实例分割和语义分割任务中，同样取得了显著的性能提升。
实验结果
  • 物体检测：在COCO数据集上，Gold-YOLO的不同版本均优于或与同类模型相当，例如YOLov6-3.0、YOLOX和PPYOLOE等，在速度和精度上都有所提升。
  • 实例分割：在Mask R-CNN上应用GD机制后，在COCO实例分割数据集上也获得了显著的性能提升。
  • 语义分割：在PointRend模型上替换为GD机制后，在Cityscapes数据集上的语义分割任务中同样显示出了更好的性能。

综上所述，Gold-YOLO通过其创新的GD机制和其他优化措施，在保持高效运行的同时显著提高了物体检测的准确性，并且证明了其机制的广泛适用性。

二、准备工作

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

from models.common import *import torch.nn.functional as F# https://arxiv.org/pdf/2309.11331v4def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups=1, bias=False):'''Basic cell for rep-style block, including conv and bn'''result = nn.Sequential()result.add_module('conv', nn.Conv2d(in_channels=in_channels, out_channels=out_channels,kernel_size=kernel_size, stride=stride, padding=padding, groups=groups,bias=bias))result.add_module('bn', nn.BatchNorm2d(num_features=out_channels))return resultclass RepVGGBlock(nn.Module):'''RepVGGBlock is a basic rep-style block, including training and deploy statusThis code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py'''def __init__(self, in_channels, out_channels, kernel_size=3,stride=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False):super(RepVGGBlock, self).__init__()""" Initialization of the class.Args:in_channels (int): Number of channels in the input imageout_channels (int): Number of channels produced by the convolutionkernel_size (int or tuple): Size of the convolving kernelstride (int or tuple, optional): Stride of the convolution. Default: 1padding (int or tuple, optional): Zero-padding added to both sides ofthe input. Default: 1dilation (int or tuple, optional): Spacing between kernel elements. Default: 1groups (int, optional): Number of blocked connections from inputchannels to output channels. Default: 1padding_mode (string, optional): Default: 'zeros'deploy: Whether to be deploy status or training status. Default: Falseuse_se: Whether to use se. Default: False"""self.deploy = deployself.groups = groupsself.in_channels = in_channelsself.out_channels = out_channelsassert kernel_size == 3assert padding == 1padding_11 = padding - kernel_size // 2self.nonlinearity = nn.ReLU()if use_se:raise NotImplementedError("se block not supported yet")else:self.se = nn.Identity()if deploy:self.rbr_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,stride=stride,padding=padding, dilation=dilation, groups=groups, bias=True,padding_mode=padding_mode)else:self.rbr_identity = nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else Noneself.rbr_dense = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,stride=stride, padding=padding, groups=groups)self.rbr_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,padding=padding_11, groups=groups)def forward(self, inputs):'''Forward process'''if hasattr(self, 'rbr_reparam'):return self.nonlinearity(self.se(self.rbr_reparam(inputs)))if self.rbr_identity is None:id_out = 0else:id_out = self.rbr_identity(inputs)return self.nonlinearity(self.se(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out))def get_equivalent_kernel_bias(self):kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasiddef _pad_1x1_to_3x3_tensor(self, kernel1x1):if kernel1x1 is None:return 0else:return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])def _fuse_bn_tensor(self, branch):if branch is None:return 0, 0if isinstance(branch, nn.Sequential):kernel = branch.conv.weightrunning_mean = branch.bn.running_meanrunning_var = branch.bn.running_vargamma = branch.bn.weightbeta = branch.bn.biaseps = branch.bn.epselse:assert isinstance(branch, nn.BatchNorm2d)if not hasattr(self, 'id_tensor'):input_dim = self.in_channels // self.groupskernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)for i in range(self.in_channels):kernel_value[i, i % input_dim, 1, 1] = 1self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)kernel = self.id_tensorrunning_mean = branch.running_meanrunning_var = branch.running_vargamma = branch.weightbeta = branch.biaseps = branch.epsstd = (running_var + eps).sqrt()t = (gamma / std).reshape(-1, 1, 1, 1)return kernel * t, beta - running_mean * gamma / stddef switch_to_deploy(self):if hasattr(self, 'rbr_reparam'):returnkernel, bias = self.get_equivalent_kernel_bias()self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.conv.in_channels,out_channels=self.rbr_dense.conv.out_channels,kernel_size=self.rbr_dense.conv.kernel_size, stride=self.rbr_dense.conv.stride,padding=self.rbr_dense.conv.padding, dilation=self.rbr_dense.conv.dilation,groups=self.rbr_dense.conv.groups, bias=True)self.rbr_reparam.weight.data = kernelself.rbr_reparam.bias.data = biasfor para in self.parameters():para.detach_()self.__delattr__('rbr_dense')self.__delattr__('rbr_1x1')if hasattr(self, 'rbr_identity'):self.__delattr__('rbr_identity')if hasattr(self, 'id_tensor'):self.__delattr__('id_tensor')self.deploy = Truedef onnx_AdaptiveAvgPool2d(x, output_size):stride_size = np.floor(np.array(x.shape[-2:]) / output_size).astype(np.int32)kernel_size = np.array(x.shape[-2:]) - (output_size - 1) * stride_sizeavg = nn.AvgPool2d(kernel_size=list(kernel_size), stride=list(stride_size))x = avg(x)return xdef get_avg_pool():if torch.onnx.is_in_onnx_export():avg_pool = onnx_AdaptiveAvgPool2delse:avg_pool = nn.functional.adaptive_avg_pool2dreturn avg_poolclass SimFusion_3in(nn.Module):def __init__(self, in_channel_list, out_channels):super().__init__()self.cv1 = Conv(in_channel_list[0], out_channels, act=nn.ReLU()) if in_channel_list[0] != out_channels else nn.Identity()self.cv2 = Conv(in_channel_list[1], out_channels, act=nn.ReLU()) if in_channel_list[1] != out_channels else nn.Identity()self.cv3 = Conv(in_channel_list[2], out_channels, act=nn.ReLU()) if in_channel_list[2] != out_channels else nn.Identity()self.cv_fuse = Conv(out_channels * 3, out_channels, act=nn.ReLU())self.downsample = nn.functional.adaptive_avg_pool2ddef forward(self, x):N, C, H, W = x[1].shapeoutput_size = (H, W)if torch.onnx.is_in_onnx_export():self.downsample = onnx_AdaptiveAvgPool2doutput_size = np.array([H, W])x0 = self.cv1(self.downsample(x[0], output_size))x1 = self.cv2(x[1])x2 = self.cv3(F.interpolate(x[2], size=(H, W), mode='bilinear', align_corners=False))return self.cv_fuse(torch.cat((x0, x1, x2), dim=1))class SimFusion_4in(nn.Module):def __init__(self):super().__init__()self.avg_pool = nn.functional.adaptive_avg_pool2ddef forward(self, x):x_l, x_m, x_s, x_n = xB, C, H, W = x_s.shapeoutput_size = np.array([H, W])if torch.onnx.is_in_onnx_export():self.avg_pool = onnx_AdaptiveAvgPool2dx_l = self.avg_pool(x_l, output_size)x_m = self.avg_pool(x_m, output_size)x_n = F.interpolate(x_n, size=(H, W), mode='bilinear', align_corners=False)out = torch.cat([x_l, x_m, x_s, x_n], 1)return outclass IFM(nn.Module):def __init__(self, inc, ouc, embed_dim_p=96, fuse_block_num=3) -> None:super().__init__()self.conv = nn.Sequential(Conv(inc, embed_dim_p),*[RepVGGBlock(embed_dim_p, embed_dim_p) for _ in range(fuse_block_num)],Conv(embed_dim_p, sum(ouc)))def forward(self, x):return self.conv(x)class h_sigmoid(nn.Module):def __init__(self, inplace=True):super(h_sigmoid, self).__init__()self.relu = nn.ReLU6(inplace=inplace)def forward(self, x):return self.relu(x + 3) / 6class InjectionMultiSum_Auto_pool(nn.Module):def __init__(self,inp: int,oup: int,global_inp: list,flag: int) -> None:super().__init__()self.global_inp = global_inpself.flag = flagself.local_embedding = Conv(inp, oup, 1, act=False)self.global_embedding = Conv(global_inp[self.flag], oup, 1, act=False)self.global_act = Conv(global_inp[self.flag], oup, 1, act=False)self.act = h_sigmoid()def forward(self, x):'''x_g: global featuresx_l: local features'''x_l, x_g = xB, C, H, W = x_l.shapeg_B, g_C, g_H, g_W = x_g.shapeuse_pool = H < g_Hgloabl_info = x_g.split(self.global_inp, dim=1)[self.flag]local_feat = self.local_embedding(x_l)global_act = self.global_act(gloabl_info)global_feat = self.global_embedding(gloabl_info)if use_pool:avg_pool = get_avg_pool()output_size = np.array([H, W])sig_act = avg_pool(global_act, output_size)global_feat = avg_pool(global_feat, output_size)else:sig_act = F.interpolate(self.act(global_act), size=(H, W), mode='bilinear', align_corners=False)global_feat = F.interpolate(global_feat, size=(H, W), mode='bilinear', align_corners=False)out = local_feat * sig_act + global_featreturn outdef get_shape(tensor):shape = tensor.shapeif torch.onnx.is_in_onnx_export():shape = [i.cpu().numpy() for i in shape]return shapeclass PyramidPoolAgg(nn.Module):def __init__(self, inc, ouc, stride, pool_mode='torch'):super().__init__()self.stride = strideif pool_mode == 'torch':self.pool = nn.functional.adaptive_avg_pool2delif pool_mode == 'onnx':self.pool = onnx_AdaptiveAvgPool2dself.conv = Conv(inc, ouc)def forward(self, inputs):B, C, H, W = get_shape(inputs[-1])H = (H - 1) // self.stride + 1W = (W - 1) // self.stride + 1output_size = np.array([H, W])if not hasattr(self, 'pool'):self.pool = nn.functional.adaptive_avg_pool2dif torch.onnx.is_in_onnx_export():self.pool = onnx_AdaptiveAvgPool2dout = [self.pool(inp, output_size) for inp in inputs]return self.conv(torch.cat(out, dim=1))def drop_path(x, drop_prob: float = 0., training: bool = False):"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted forchanging the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use'survival rate' as the argument."""if drop_prob == 0. or not training:return xkeep_prob = 1 - drop_probshape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNetsrandom_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)random_tensor.floor_()  # binarizeoutput = x.div(keep_prob) * random_tensorreturn outputclass Mlp(nn.Module):def __init__(self, in_features, hidden_features=None, out_features=None, drop=0.):super().__init__()out_features = out_features or in_featureshidden_features = hidden_features or in_featuresself.fc1 = Conv(in_features, hidden_features, act=False)self.dwconv = nn.Conv2d(hidden_features, hidden_features, 3, 1, 1, bias=True, groups=hidden_features)self.act = nn.ReLU6()self.fc2 = Conv(hidden_features, out_features, act=False)self.drop = nn.Dropout(drop)def forward(self, x):x = self.fc1(x)x = self.dwconv(x)x = self.act(x)x = self.drop(x)x = self.fc2(x)x = self.drop(x)return xclass DropPath(nn.Module):"""Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""def __init__(self, drop_prob=None):super(DropPath, self).__init__()self.drop_prob = drop_probdef forward(self, x):return drop_path(x, self.drop_prob, self.training)class Attention(torch.nn.Module):def __init__(self, dim, key_dim, num_heads, attn_ratio=4):super().__init__()self.num_heads = num_headsself.scale = key_dim ** -0.5self.key_dim = key_dimself.nh_kd = nh_kd = key_dim * num_heads  # num_head key_dimself.d = int(attn_ratio * key_dim)self.dh = int(attn_ratio * key_dim) * num_headsself.attn_ratio = attn_ratioself.to_q = Conv(dim, nh_kd, 1, act=False)self.to_k = Conv(dim, nh_kd, 1, act=False)self.to_v = Conv(dim, self.dh, 1, act=False)self.proj = torch.nn.Sequential(nn.ReLU6(), Conv(self.dh, dim, act=False))def forward(self, x):  # x (B,N,C)B, C, H, W = get_shape(x)qq = self.to_q(x).reshape(B, self.num_heads, self.key_dim, H * W).permute(0, 1, 3, 2)kk = self.to_k(x).reshape(B, self.num_heads, self.key_dim, H * W)vv = self.to_v(x).reshape(B, self.num_heads, self.d, H * W).permute(0, 1, 3, 2)attn = torch.matmul(qq, kk)attn = attn.softmax(dim=-1)  # dim = kxx = torch.matmul(attn, vv)xx = xx.permute(0, 1, 3, 2).reshape(B, self.dh, H, W)xx = self.proj(xx)return xxclass top_Block(nn.Module):def __init__(self, dim, key_dim, num_heads, mlp_ratio=4., attn_ratio=2., drop=0.,drop_path=0.):super().__init__()self.dim = dimself.num_heads = num_headsself.mlp_ratio = mlp_ratioself.attn = Attention(dim, key_dim=key_dim, num_heads=num_heads, attn_ratio=attn_ratio)# NOTE: drop path for stochastic depth, we shall see if this is better than dropout hereself.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()mlp_hidden_dim = int(dim * mlp_ratio)self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, drop=drop)def forward(self, x1):x1 = x1 + self.drop_path(self.attn(x1))x1 = x1 + self.drop_path(self.mlp(x1))return x1class TopBasicLayer(nn.Module):def __init__(self, embedding_dim, ouc_list, block_num=2, key_dim=8, num_heads=4,mlp_ratio=4., attn_ratio=2., drop=0., attn_drop=0., drop_path=0.):super().__init__()self.block_num = block_numself.transformer_blocks = nn.ModuleList()for i in range(self.block_num):self.transformer_blocks.append(top_Block(embedding_dim, key_dim=key_dim, num_heads=num_heads,mlp_ratio=mlp_ratio, attn_ratio=attn_ratio,drop=drop, drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path))self.conv = nn.Conv2d(embedding_dim, sum(ouc_list), 1)def forward(self, x):# token * Nfor i in range(self.block_num):x = self.transformer_blocks[i](x)return self.conv(x)class AdvPoolFusion(nn.Module):def forward(self, x):x1, x2 = xif torch.onnx.is_in_onnx_export():self.pool = onnx_AdaptiveAvgPool2delse:self.pool = nn.functional.adaptive_avg_pool2dN, C, H, W = x2.shapeoutput_size = np.array([H, W])x1 = self.pool(x1, output_size)return torch.cat([x1, x2], 1)

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.goldyolo import *

并搜索def parse_model(d, ch)

定位到如下行添加以下代码

        elif m is SimFusion_4in: # goldyoloc2 = sum(ch[x] for x in f)elif m is SimFusion_3in:c2 = args[0]if c2 != no:  # if not outputc2 = make_divisible(c2 * gw, 8)args = [[ch[f_] for f_ in f], c2]elif m is IFM:c1 = ch[f]c2 = sum(args[0])args = [c1, *args]elif m is InjectionMultiSum_Auto_pool:c1 = ch[f[0]]c2 = args[0]args = [c1, *args]elif m is PyramidPoolAgg:c2 = args[0]args = [sum([ch[f_] for f_ in f]), *args]elif m is AdvPoolFusion:c2 = sum(ch[x] for x in f)elif m is TopBasicLayer:c2 = sum(args[1]) # goldyolo

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-goldyolo.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, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, SP, [5]],[-2, 1, SP, [9]],[-3, 1, SP, [13]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -7], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 37[[7, 14, 21, 37], 1, SimFusion_4in, []], # 38[-1, 1, IFM, [[64, 32]]], # 39[37, 1, Conv, [256, 1, 1]], # 40[[14, 21, -1], 1, SimFusion_3in, [256]], # 41[[-1, 39], 1, InjectionMultiSum_Auto_pool, [256, [64, 32], 0]], # 42[-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)]],  # 48[21, 1, Conv, [128, 1, 1]], # 49[[7, 21, -1], 1, SimFusion_3in, [128]], # 50[[-1, 39], 1, InjectionMultiSum_Auto_pool, [128, [64, 32], 1]], # 51[-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)]],  # 57[[57, 48, 37], 1, PyramidPoolAgg, [352, 2]], # 58[-1, 1, TopBasicLayer, [352, [64, 128]]], # 59[[57, 49], 1, AdvPoolFusion, []], # 60[[-1, 59], 1, InjectionMultiSum_Auto_pool, [128, [64, 128], 0]], # 61[-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)]],  # 67[[-1, 40], 1, AdvPoolFusion, []], # 68[[-1, 59], 1, InjectionMultiSum_Auto_pool, [256, [64, 128], 1]], # 69[-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)]],  # 75[57, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[67, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[75, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[76,77,78], 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    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]30                -2  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]31                -1  1         0  models.common.SP                        [5]                           32                -2  1         0  models.common.SP                        [9]                           33                -3  1         0  models.common.SP                        [13]                          34  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           35                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]36          [-1, -7]  1         0  models.common.Concat                    [1]                           37                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]38   [7, 14, 21, 37]  1         0  models.goldyolo.SimFusion_4in           []                            39                -1  1    355392  models.goldyolo.IFM                     [704, [64, 32]]               40                37  1     66048  models.common.Conv                      [256, 256, 1, 1]              41      [14, 21, -1]  1    230400  models.goldyolo.SimFusion_3in           [[128, 256, 256], 256]        42          [-1, 39]  1     99840  models.goldyolo.InjectionMultiSum_Auto_pool[256, 256, [64, 32], 0]       43                -1  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]44                -2  1     16512  models.common.Conv                      [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]45                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]46                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]47  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           48                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]49                21  1     33024  models.common.Conv                      [256, 128, 1, 1]              50       [7, 21, -1]  1     90880  models.goldyolo.SimFusion_3in           [[64, 256, 128], 128]         51          [-1, 39]  1     25344  models.goldyolo.InjectionMultiSum_Auto_pool[128, 128, [64, 32], 1]       52                -1  1      4160  models.common.Conv                      [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]53                -2  1      4160  models.common.Conv                      [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]54                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]55                -1  1      9280  models.common.Conv                      [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]56  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           57                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]58      [57, 48, 37]  1    158400  models.goldyolo.PyramidPoolAgg          [448, 352, 2]                 59                -1  1   2222528  models.goldyolo.TopBasicLayer           [352, [64, 128]]              60          [57, 49]  1         0  models.goldyolo.AdvPoolFusion           []                            61          [-1, 59]  1     41728  models.goldyolo.InjectionMultiSum_Auto_pool[192, 128, [64, 128], 0]      62                -1  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]63                -2  1      8320  models.common.Conv                      [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]64                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]65                -1  1     36992  models.common.Conv                      [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]66  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           67                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]68          [-1, 40]  1         0  models.goldyolo.AdvPoolFusion           []                            69          [-1, 59]  1    165376  models.goldyolo.InjectionMultiSum_Auto_pool[384, 256, [64, 128], 1]      70                -1  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]71                -2  1     33024  models.common.Conv                      [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]72                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]73                -1  1    147712  models.common.Conv                      [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]74  [-1, -2, -3, -4]  1         0  models.common.Concat                    [1]                           75                -1  1    131584  models.common.Conv                      [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]76                57  1     73984  models.common.Conv                      [64, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]77                67  1    295424  models.common.Conv                      [128, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]78                75  1   1180672  models.common.Conv                      [256, 512, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]79      [76, 77, 78]  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: 443 layers, 8969932 parameters, 8969932 gradients, 16.1 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-goldyolo.yaml，导入如下代码。

# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license# 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:[[[2, 4, 6, 9], 1, SimFusion_4in, []], # 10[-1, 1, IFM, [[64, 32]]], # 11[9, 1, Conv, [512, 1, 1]], # 12[[4, 6, -1], 1, SimFusion_3in, [512]], # 13[[-1, 11], 1, InjectionMultiSum_Auto_pool, [512, [64, 32], 0]], # 14[-1, 3, C3, [512, False]], # 15[6, 1, Conv, [256, 1, 1]], # 16[[2, 4, -1], 1, SimFusion_3in, [256]], # 17[[-1, 11], 1, InjectionMultiSum_Auto_pool, [256, [64, 32], 1]], # 18[-1, 3, C3, [256, False]], # 19[[19, 15, 9], 1, PyramidPoolAgg, [352, 2]], # 20[-1, 1, TopBasicLayer, [352, [64, 128]]], # 21[[19, 16], 1, AdvPoolFusion, []], # 22[[-1, 21], 1, InjectionMultiSum_Auto_pool, [256, [64, 128], 0]], # 23[-1, 3, C3, [256, False]], # 24[[-1, 12], 1, AdvPoolFusion, []], # 25[[-1, 21], 1, InjectionMultiSum_Auto_pool, [512, [64, 128], 1]], # 26[-1, 3, C3, [512, False]], # 27[[19, 24, 27], 1, Detect, [nc, anchors]] # 28]
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      [2, 4, 6, 9]  1         0  models.common.SimFusion_4in             []                            11                -1  1    379968  models.common.IFM                       [960, [64, 32]]               12                 9  1    131584  models.common.Conv                      [512, 256, 1, 1]              13        [4, 6, -1]  1    230400  models.common.SimFusion_3in             [[128, 256, 256], 256]        14          [-1, 11]  1    199680  models.common.InjectionMultiSum_Auto_pool[256, 512, [64, 32], 0]       15                -1  1    361984  models.common.C3                        [512, 256, 1, False]          16                 6  1     33024  models.common.Conv                      [256, 128, 1, 1]              17        [2, 4, -1]  1     57856  models.common.SimFusion_3in             [[64, 128, 128], 128]         18          [-1, 11]  1     50688  models.common.InjectionMultiSum_Auto_pool[128, 256, [64, 32], 1]       19                -1  1     90880  models.common.C3                        [256, 128, 1, False]          20       [19, 15, 9]  1    316096  models.common.PyramidPoolAgg            [896, 352, 2]                 21                -1  1   2222528  models.common.TopBasicLayer             [352, [64, 128]]              22          [19, 16]  1         0  models.common.AdvPoolFusion             []                            23          [-1, 21]  1     99840  models.common.InjectionMultiSum_Auto_pool[256, 256, [64, 128], 0]      24                -1  1     90880  models.common.C3                        [256, 128, 1, False]          25          [-1, 12]  1         0  models.common.AdvPoolFusion             []                            26          [-1, 21]  1    330752  models.common.InjectionMultiSum_Auto_pool[384, 512, [64, 128], 1]      27                -1  1    361984  models.common.C3                        [512, 256, 1, False]          28      [19, 24, 27]  1      9270  models.yolo.Detect                      [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 128, 256]]Model Summary: 455 layers, 9138870 parameters, 9138870 gradients, 20.1 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-goldyolo.yaml，导入如下代码。

# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license# 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:[[[2, 4, 6, 9], 1, SimFusion_4in, []], # 10[-1, 1, IFM, [[64, 32]]], # 11[9, 1, Conv, [512, 1, 1]], # 12[[4, 6, -1], 1, SimFusion_3in, [512]], # 13[[-1, 11], 1, InjectionMultiSum_Auto_pool, [512, [64, 32], 0]], # 14[-1, 3, C3, [512, False]], # 15[6, 1, Conv, [256, 1, 1]], # 16[[2, 4, -1], 1, SimFusion_3in, [256]], # 17[[-1, 11], 1, InjectionMultiSum_Auto_pool, [256, [64, 32], 1]], # 18[-1, 3, C3, [256, False]], # 19[[19, 15, 9], 1, PyramidPoolAgg, [352, 2]], # 20[-1, 1, TopBasicLayer, [352, [64, 128]]], # 21[[19, 16], 1, AdvPoolFusion, []], # 22[[-1, 21], 1, InjectionMultiSum_Auto_pool, [256, [64, 128], 0]], # 23[-1, 3, C3, [256, False]], # 24[[-1, 12], 1, AdvPoolFusion, []], # 25[[-1, 21], 1, InjectionMultiSum_Auto_pool, [512, [64, 128], 1]], # 26[-1, 3, C3, [512, False]], # 27[[19, 24, 27], 1, Detect, [nc, anchors]] # 28]
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      [2, 4, 6, 9]  1         0  models.common.SimFusion_4in             []                            11                -1  1    333888  models.common.IFM                       [480, [64, 32]]               12                 9  1     33024  models.common.Conv                      [256, 128, 1, 1]              13        [4, 6, -1]  1     57856  models.common.SimFusion_3in             [[64, 128, 128], 128]         14          [-1, 11]  1    134144  models.common.InjectionMultiSum_Auto_pool[128, 512, [64, 32], 0]       15                -1  1    123648  models.common.C3                        [512, 128, 1, False]          16                 6  1      8320  models.common.Conv                      [128, 64, 1, 1]               17        [2, 4, -1]  1     14592  models.common.SimFusion_3in             [[32, 64, 64], 64]            18          [-1, 11]  1     34304  models.common.InjectionMultiSum_Auto_pool[64, 256, [64, 32], 1]        19                -1  1     31104  models.common.C3                        [256, 64, 1, False]           20       [19, 15, 9]  1    158400  models.common.PyramidPoolAgg            [448, 352, 2]                 21                -1  1   2222528  models.common.TopBasicLayer             [352, [64, 128]]              22          [19, 16]  1         0  models.common.AdvPoolFusion             []                            23          [-1, 21]  1     67072  models.common.InjectionMultiSum_Auto_pool[128, 256, [64, 128], 0]      24                -1  1     31104  models.common.C3                        [256, 64, 1, False]           25          [-1, 12]  1         0  models.common.AdvPoolFusion             []                            26          [-1, 21]  1    232448  models.common.InjectionMultiSum_Auto_pool[192, 512, [64, 128], 1]      27                -1  1    123648  models.common.C3                        [512, 128, 1, False]          28      [19, 24, 27]  1      4662  models.yolo.Detect                      [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [64, 64, 128]]Model Summary: 455 layers, 4657110 parameters, 4657110 gradients, 8.3 GFLOPs

运行后打印如上代码说明改进成功。

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