YoloV8改进策略：InternImage与YoloV8深度融合，动态卷积DCNv3大显神威

Posted 2023-04-05 AI浩

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文章目录

摘要
改进方法
结果对比

摘要

他来了！他来了！他带着氩弧焊的光芒过来了！作为CV的大模型，InternImage的光芒太强了。

2023年3月14日:

改进YOLOv5系列：增加Swin-Transformer小目标检测头

💡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

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 = None
        if c1 != c2:
            self.conv = Conv(c1, c2)

        # remove input_resolution
        self.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 = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        # define a parameter table of relative position bias
        self.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 window
        coords_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, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.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.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv以上是关于YoloV8改进策略：InternImage与YoloV8深度融合，动态卷积DCNv3大显神威的主要内容，如果未能解决你的问题，请参考以下文章 
 YOLOv8原理解析：重新定义实时目标检测的速度和精度
 YOLOV8最强操作教程.
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 CSDN独家原创《芒果YOLO改进高阶指南》适用YOLOv5YOLOv7YOLOv8等改进专栏，来自人工智能专家唐宇迪老师联袂推荐
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 YOLOv8模型学习笔记

YoloV8改进策略：InternImage与YoloV8深度融合，动态卷积DCNv3大显神威

文章目录

摘要

改进YOLOv5系列：增加Swin-Transformer小目标检测头

最新创新点改进博客推荐

文章目录

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