YOLOv8.On backbone（骨干网络）配置

Question

这篇文章“YOLOv8 极限突破：融合 Hybrid Attention Transformer (HAT) 实现小目标检测与模型实战部署的全方位指南-附源码”的的3.3yaml配置文件的YOLOv8.On backbone（骨干网络）是怎么配置的，文章乱码，下载链接过期了

Answer 1

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根据您描述的情况，这篇文章中的YOLOv8骨干网络配置应该是将原始的YOLOv8骨干网络替换为Hybrid Attention Transformer (HAT)结构。由于原文链接失效，我将基于HAT网络结构和YOLOv8的架构特点，为您提供一个完整的配置方案。

YOLOv8 + HAT 骨干网络配置

以下是完整的YAML配置文件：

# YOLOv8 with HAT backbone configuration
nc: 80  # number of classes
scales:
  # 模型尺度配置 (n, s, m, l, x)
  n: [0.33, 0.25, 1024]
  s: [0.33, 0.50, 1024]
  m: [0.67, 0.75, 1024]
  l: [1.00, 1.00, 1024]
  x: [1.00, 1.25, 1024]


# 骨干网络配置 (Backbone)
backbone:
  # [from, repeats, module, args]
  # HAT 骨干网络结构
  - [-1, 1, "Conv", [64, 3, 2]]  # 0-P1/2
  - [-1, 1, "Conv", [128, 3, 2]]  # 1-P2/4
  
  # HAT Block Stage 1
  - [-1, 1, "HATBlock", [128, 4, 8, 2]]  # 2
  - [-1, 2, "HATBlock", [128, 4, 8, 2]]  # 3-4
  
  # HAT Block Stage 2
  - [-1, 1, "Conv", [256, 3, 2]]  # 5-P3/8
  - [-1, 1, "HATBlock", [256, 4, 8, 2]]  # 6
  - [-1, 6, "HATBlock", [256, 4, 8, 2]]  # 7-12
  
  # HAT Block Stage 3
  - [-1, 1, "Conv", [512, 3, 2]]  # 13-P4/16
  - [-1, 1, "HATBlock", [512, 4, 8, 2]]  # 14
  - [-1, 6, "HATBlock", [512, 4, 8, 2]]  # 15-20
  
  # HAT Block Stage 4
  - [-1, 1, "Conv", [1024, 3, 2]]  # 21-P5/32
  - [-1, 1, "HATBlock", [1024, 4, 8, 2]]  # 22
  - [-1, 3, "HATBlock", [1024, 4, 8, 2]]  # 23-25


# 颈部网络 (Neck)
head:
  - [-1, 1, "SPPF", [1024, 5]]  # 26
  
  # 上采样路径
  - [-1, 1, "Conv", [512, 1, 1]]
  - [-1, 1, "nn.Upsample", [None, 2, "nearest"]]
  - [[-1, 20], 1, "Concat", [1]]  # 从骨干网络第20层concat
  - [-1, 3, "C2f", [512]]  # 30
  
  - [-1, 1, "Conv", [256, 1, 1]]
  - [-1, 1, "nn.Upsample", [None, 2, "nearest"]]
  - [[-1, 12], 1, "Concat", [1]]  # 从骨干网络第12层concat
  - [-1, 3, "C2f", [256]]  # 34 (P3/8)
  
  # 下采样路径
  - [-1, 1, "Conv", [256, 3, 2]]
  - [[-1, 30], 1, "Concat", [1]]  # 与第30层concat
  - [-1, 3, "C2f", [512]]  # 37 (P4/16)
  
  - [-1, 1, "Conv", [512, 3, 2]]
  - [[-1, 26], 1, "Concat", [1]]  # 与第26层concat
  - [-1, 3, "C2f", [1024]]  # 40 (P5/32)
  
  # 检测头
  - [[34, 37, 40], 1, "Detect", [nc]]  # Detect(P3, P4, P5)

HAT Block 实现代码

您还需要在模型中实现HATBlock模块：

import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import DropPath


class WindowAttention(nn.Module):
    """窗口注意力机制"""
    def __init__(self, dim, window_size, num_heads):
        super().__init__()
        self.dim = dim
        self.window_size = window_size
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5
        
        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim)
        
    def forward(self, x):
        B, H, W, C = x.shape
        x = x.view(B, H // self.window_size, self.window_size, 
                  W // self.window_size, self.window_size, C)
        x = x.permute(0, 1, 3, 2, 4, 5).contiguous()
        
        qkv = self.qkv(x).reshape(B, -1, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        
        x = (attn @ v).transpose(1, 2).reshape(B, H, W, C)
        x = self.proj(x)
        return x


class HATBlock(nn.Module):
    """Hybrid Attention Transformer Block"""
    def __init__(self, dim, num_heads, window_size, mlp_ratio=4., drop_path=0.):
        super().__init__()
        self.norm1 = nn.LayerNorm(dim)
        self.attn = WindowAttention(dim, window_size, num_heads)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = nn.LayerNorm(dim)
        
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = nn.Sequential(
            nn.Linear(dim, mlp_hidden_dim),
            nn.GELU(),
            nn.Linear(mlp_hidden_dim, dim)
        )
        
    def forward(self, x):
        # 简化实现，实际HAT有更复杂的注意力机制
        shortcut = x
        x = self.norm1(x)
        x = self.attn(x)
        x = shortcut + self.drop_path(x)
        
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x

使用说明

1. 配置文件使用：将上述YAML配置保存为 yolov8_hat.yaml
2. 模型创建：

from ultralytics import YOLO


# 创建模型
model = YOLO('yolov8_hat.yaml')
model.train(data='coco128.yaml', epochs=100, imgsz=640)

这个配置将原始的CSPDarknet骨干网络替换为HAT结构，特别适合小目标检测任务。HAT的混合注意力机制能够更好地捕捉全局和局部特征，提升检测性能。