• 💡统一使用 YOLOv5 代码框架，结合不同模块来构建不同的YOLO目标检测模型。
• 🌟本项目包含大量的改进方式,降低改进难度,改进点包含【Backbone特征主干】、【Neck特征融合】、【Head检测头】、【注意力机制】、【IoU损失函数】、【NMS】、【Loss计算方式】、【自注意力机制】、【数据增强部分】、【标签分配策略】、【激活函数】等各个部分。

本篇是《增加一个Swin检测头结构🚀》的代码演示

最新创新点改进博客推荐

（🔥 博客内 附有多种模型改进方式，均适用于YOLOv5系列 以及 YOLOv7系列 改进！！！）

• 💡🎈☁️：改进YOLOv7系列：首发最新结合多种X-Transformer结构新增小目标检测层，让YOLO目标检测任务中的小目标无处遁形

• 💡🎈☁️：改进YOLOv7系列：结合Adaptively Spatial Feature Fusion自适应空间特征融合结构，提高特征尺度不变性

• 💡🎈☁️：改进YOLOv5系列：首发结合最新Extended efficient Layer Aggregation Networks结构，高效的聚合网络设计，提升性能

• 💡🎈☁️：改进YOLOv7系列：首发结合最新CSPNeXt主干结构(适用YOLOv7)，高性能，低延时的单阶段目标检测器主干，通过COCO数据集验证高效涨点

• 💡🎈☁️：改进YOLOv7系列：最新结合DO-DConv卷积、Slim范式提高性能涨点，打造高性能检测器

• 💡🎈☁️：改进YOLOv7系列：结合最新即插即用的动态卷积ODConv

• 💡🎈☁️：改进YOLOv7系列：首发结合最新Transformer视觉模型MOAT结构：交替移动卷积和注意力带来强大的Transformer视觉模型，超强的提升

• 💡🎈☁️：改进YOLOv7系列：首发结合最新Centralized Feature Pyramid集中特征金字塔，通过COCO数据集验证强势涨点

• 💡🎈☁️：改进YOLOv7系列：首发结合 RepLKNet 构建 最新 RepLKDeXt 结构｜CVPR2022 超大卷积核, 越大越暴力,大到31x31, 涨点高效

• 💡🎈☁️：改进YOLOv5系列：4.YOLOv5_最新MobileOne结构换Backbone修改，超轻量型架构，移动端仅需1ms推理！苹果最新移动端高效主干网络

• 💡🎈☁️：改进YOLOv7系列：最新HorNet结合YOLOv7应用！ | 新增 HorBc结构，多种搭配，即插即用 | Backbone主干、递归门控卷积的高效高阶空间交互

文章目录

• • 最新创新点改进博客推荐
  • YOLOv5网络
  • • 1.YOLOv5s标准网络配置
    • 2.增加Swin Transformer小目标检测头配置
  • 3.核心代码
  • • 4.运行

YOLOv5网络

1.YOLOv5s标准网络配置

# YOLOv5 🚀 by Ultralytics, GPL-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:[[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]],  # cat backbone P4[-1, 3, C3, [512, False]],  # 13[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]],  # cat backbone P3[-1, 3, C3, [256, False]],  # 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 14], 1, Concat, [1]],  # cat head P4[-1, 3, C3, [512, False]],  # 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]],  # cat head P5[-1, 3, C3, [1024, False]],  # 23 (P5/32-large)[[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

2.增加Swin Transformer小目标检测头配置

增加yolov5s6_swin.yaml文件

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:- [19,27,  44,40,  38,94]  # P3/8- [96,68,  86,152,  180,137]  # P4/16- [140,301,  303,264,  238,542]  # P5/32- [436,615,  739,380,  925,792]  # P6/64# 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, [768, 3, 2]],  # 7-P5/32[-1, 3, C3, [768]],[-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64[-1, 3, C3, [1024]],[-1, 1, SPPF, [1024, 5]],  # 11]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [768, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 8], 1, Concat, [1]],  # cat backbone P5[-1, 3, C3, [768, False]],  # 15[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]],  # cat backbone P4[-1, 3, C3, [512, False]],  # 19[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]],  # cat backbone P3[-1, 3, C3, [256, False]],  # 23 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 20], 1, Concat, [1]],  # cat head P4[-1, 3, C3, [512, False]],  # 26 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 16], 1, Concat, [1]],  # cat head P5[-1, 3, C3, [768, False]],  # 29 (P5/32-large)[-1, 1, Conv, [768, 3, 2]],[[-1, 12], 1, Concat, [1]],  # cat head P6[-1, 3, C3STR, [512]],  # 32 (P6/64-xlarge)[[23, 26, 29, 32], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5, P6)]

3.核心代码

参考本博主的这篇：👉改进YOLOv5系列：3.YOLOv5结合Swin Transformer结构，ICCV 2021最佳论文 使用 Shifted Windows 的分层视觉转换器
里面的common.py配置部分

class SwinTransformerBlock(nn.Module):def __init__(self, c1, c2, num_heads, num_layers, window_size=8):super().__init__()self.conv = Noneif c1 != c2:self.conv = Conv(c1, c2)# remove input_resolutionself.blocks = nn.Sequential(*[SwinTransformerLayer(dim=c2, num_heads=num_heads, window_size=window_size,shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)])def forward(self, x):if self.conv is not None:x = self.conv(x)x = self.blocks(x)return x
class WindowAttention(nn.Module):def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):super().__init__()self.dim = dimself.window_size = window_size  # Wh, Wwself.num_heads = num_headshead_dim = dim // num_headsself.scale = qk_scale or head_dim ** -0.5# define a parameter table of relative position biasself.relative_position_bias_table = nn.Parameter(torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH# get pair-wise relative position index for each token inside the windowcoords_h = torch.arange(self.window_size[0])coords_w = torch.arange(self.window_size[1])coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Wwcoords_flatten = torch.flatten(coords, 1)  # 2, Wh*Wwrelative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Wwrelative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0relative_coords[:, :, 1] += self.window_size[1] - 1relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Wwself.register_buffer("relative_position_index", relative_position_index)self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)self.attn_drop = nn.Dropout(attn_drop)self.proj = nn.Linear(dim, dim)self.proj_drop = nn.Dropout(proj_drop)nn.init.normal_(self.relative_position_bias_table, std=.02)self.softmax = nn.Softmax(dim=-1)def forward(self, x, mask=None):B_, N, C = x.shapeqkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)q = q * self.scaleattn = (q @ k.transpose(-2, -1))relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nHrelative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Wwattn = attn + relative_position_bias.unsqueeze(0)if mask is not None:nW = mask.shape[0]attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)attn = attn.view(-1, self.num_heads, N, N)attn = self.softmax(attn)else:attn = self.softmax(attn)attn = self.attn_drop(attn)# print(attn.dtype, v.dtype)try:x = (attn @ v).transpose(1, 2).reshape(B_, N, C)except:#print(attn.dtype, v.dtype)x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C)x = self.proj(x)x = self.proj_drop(x)return xclass Mlp(nn.Module):def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.):super().__init__()out_features = out_features or in_featureshidden_features = hidden_features or in_featuresself.fc1 = nn.Linear(in_features, hidden_features)self.act = act_layer()self.fc2 = nn.Linear(hidden_features, out_features)self.drop = nn.Dropout(drop)def forward(self, x):x = self.fc1(x)x = self.act(x)x = self.drop(x)x = self.fc2(x)x = self.drop(x)return xclass SwinTransformerLayer(nn.Module):def __init__(self, dim, num_heads, window_size=8, shift_size=0,mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,act_layer=nn.SiLU, norm_layer=nn.LayerNorm):super().__init__()self.dim = dimself.num_heads = num_headsself.window_size = window_sizeself.shift_size = shift_sizeself.mlp_ratio = mlp_ratio# if min(self.input_resolution) <= self.window_size:#     # if window size is larger than input resolution, we don't partition windows#     self.shift_size = 0#     self.window_size = min(self.input_resolution)assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"self.norm1 = norm_layer(dim)self.attn = WindowAttention(dim, window_size=(self.window_size, self.window_size), num_heads=num_heads,qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()self.norm2 = norm_layer(dim)mlp_hidden_dim = int(dim * mlp_ratio)self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)def create_mask(self, H, W):# calculate attention mask for SW-MSAimg_mask = torch.zeros((1, H, W, 1))  # 1 H W 1h_slices = (slice(0, -self.window_size),slice(-self.window_size, -self.shift_size),slice(-self.shift_size, None))w_slices = (slice(0, -self.window_size),slice(-self.window_size, -self.shift_size),slice(-self.shift_size, None))cnt = 0for h in h_slices:for w in w_slices:img_mask[:, h, w, :] = cntcnt += 1mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1mask_windows = mask_windows.view(-1, self.window_size * self.window_size)attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))return attn_maskdef forward(self, x):# reshape x[b c h w] to x[b l c]_, _, H_, W_ = x.shapePadding = Falseif min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0:Padding = True# print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.')pad_r = (self.window_size - W_ % self.window_size) % self.window_sizepad_b = (self.window_size - H_ % self.window_size) % self.window_sizex = F.pad(x, (0, pad_r, 0, pad_b))# print('2', x.shape)B, C, H, W = x.shapeL = H * Wx = x.permute(0, 2, 3, 1).contiguous().view(B, L, C)  # b, L, c# create mask from init to forwardif self.shift_size > 0:attn_mask = self.create_mask(H, W).to(x.device)else:attn_mask = Noneshortcut = xx = self.norm1(x)x = x.view(B, H, W, C)# cyclic shiftif self.shift_size > 0:shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))else:shifted_x = x# partition windowsx_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, Cx_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C# W-MSA/SW-MSAattn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C# merge windowsattn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C# reverse cyclic shiftif self.shift_size > 0:x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))else:x = shifted_xx = x.view(B, H * W, C)# FFNx = shortcut + self.drop_path(x)x = x + self.drop_path(self.mlp(self.norm2(x)))x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W)  # b c h wif Padding:x = x[:, :, :H_, :W_]  # reverse paddingreturn xclass C3STR(C3):# C3 module with SwinTransformerBlock()def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):super().__init__(c1, c2, c2, n, shortcut, g, e)c_ = int(c2 * e)num_heads = c_ // 32self.m = SwinTransformerBlock(c_, c_, num_heads, n)

4.运行

python train.py --cfg yolov5s6_swin.yaml