将yolov9的好东西迁移到yolov11

### 将 YOLOv9 改进特性集成到 YOLOv11 #### 集成 EMA 模块 EMA (Exponential Moving Average) 模块作为高效且通用的注意力机制，能够显著提高模型性能。为了将此模块引入YOLOv11，在网络架构中加入EMA层是一个有效的方法。具体来说，可以在特征提取阶段的关键位置插入EMA模块，从而增强对重要特征的关注度并抑制不必要信息的影响[^2]。 ```python class EMALayer(nn.Module): def __init__(self, channels): super(EMALayer, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(channels, channels // 8), nn.ReLU(inplace=True), nn.Linear(channels // 8, channels), nn.Sigmoid() ) def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x).view(b, c) y = self.fc(y).view(b, c, 1, 1) return x * y.expand_as(x) def integrate_ema_to_yolov11(model): for name, module in model.named_modules(): if isinstance(module, nn.Conv2d): # 假设在每个卷积层之后添加EMA ema_layer = EMALayer(module.out_channels) setattr(model, name + '_ema', ema_layer) ``` #### 利用 PreAct ResNet 设计思路优化残差连接 考虑到PreAct ResNet的设计理念有助于构建更深更稳定的网络结构，尽管其存在某些局限性，但在适当调整下仍可为YOLO系列带来正面影响。特别是当面对复杂场景检测任务时，合理运用预激活策略可能改善梯度传播效果，进而促进训练过程中的稳定性与效率提升[^3]。 ```python import torch.nn as nn class PreActBlock(nn.Module): expansion = 1 def __init__(self, in_planes, planes, stride=1): super(PreActBlock, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes) self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) def forward(self, x): out = F.relu(self.bn1(x)) shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x out = self.conv1(out) out += shortcut return out def apply_preactivation_to_residual_connections(model): for m in model.modules(): if isinstance(m, Bottleneck): # 对于瓶颈型残差单元应用预激活模式 preact_block = PreActBlock(m.inplanes, m.planes, m.stride) m.add_module('preactivation', preact_block) ``` #### 结合最新研究进展实现混合聚合网络(Mixed Aggregation Network) 借鉴Hyper-YOLO 和 StarNet CVPR2024 提出的技术方案，采用Mixed Aggregation Network 及StarBlock来进一步强化多尺度特征融合能力。这些技术不仅提升了目标定位精度，还增强了小物体识别的效果，使得整体检测性能得到质的飞跃[^1]。 ```python from yolox.models import YOLOPAFPN class EnhancedAggregationNetwork(YOLOPAFPN): def __init__(self, depth=1.0, width=1.0, act="silu"): super().__init__(depth, width, act) # 实现具体的混合聚合逻辑... def forward(self, inputs): outputs = [] for i, feat in enumerate(inputs[::-1]): lateral_conv = getattr(self, f'lateral_conv{i}') fpn_out = lateral_conv(feat) if i != 0: upsample = F.interpolate(fpn_out, scale_factor=2., mode='nearest') fused_feature = torch.cat([upsample, prev_fpn], dim=1) starblock = getattr(self, f'starblock{i}', None) if starblock is not None: fused_feature = starblock(fused_feature) prev_fpn = fused_feature outputs.insert(0, fused_feature) return tuple(outputs) ```