Deeper Bottleneck(瓶颈) Architectures

Deeper Bottleneck(瓶颈) Architectures

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  看50-layer那一栏,在进入到DBA层之前的网络比较简单,分别是:①卷积层"7×7, 64, stride 2"、②BN层、③ReLU层、④池化层"3×3 max pool, stride 2",最终的输出结果是一个大小为 [batch_size, height, width, kernels] 矩阵。
  再看conv2_x层(即第一个bottleneck),该结构总共3×3=9层,可是原作毕竟篇幅有限,网络实现的细节不是很清楚,于是我就参考了Ryan Dahl的tensorflow-resnet程序源码,按照Ryan Dahl实现的ResNet,画出了DBA内部网络的具体实现,这个DBA是全网络中第一个DBA的前三层,输入的image大小为[batch_size,56,56,64],输出大小为[batch_size,56,56,256],如下图是DBA的结构(Bottleneck V1 ):
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### YOLO Bottleneck Module Implementation and Usage in Version 11 In the Ultralytics YOLOv11 model, the Bottleneck module plays a crucial role as part of the backbone architecture designed to enhance feature extraction efficiency while maintaining computational simplicity[^1]. The Bottleneck structure is particularly important because it allows for deeper networks without significantly increasing parameter count or computation cost. The standard Bottleneck design typically consists of two convolutional layers with batch normalization followed by an activation function like ReLU (Rectified Linear Unit). However, in YOLOv11, this concept has been extended through several innovations that improve performance further: #### Standard Bottleneck Structure A typical Bottleneck layer within YOLOv11 can be represented using Python code similar to what follows: ```python import torch.nn as nn class Bottleneck(nn.Module): def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, k=1) self.cv2 = Conv(c_, c2, k=3, p=1, g=g) self.add = shortcut and c1 == c2 def forward(self, x): return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) ``` This implementation includes residual connections when input and output channel numbers match (`c1==c2`), which helps mitigate vanishing gradient problems during training deep neural networks. #### Enhanced Features via ODConv Integration To boost accuracy even more efficiently, some implementations integrate Omni-Dimensional Dynamic Convolutions (ODConvs)[^3], allowing dynamic adjustments based on spatial positions across different scales. This results not only in better generalization but also reduced memory footprint compared to traditional convolutions. For integrating such advanced functionalities into existing bottlenecks, one might modify the above class definition accordingly: ```python from odconv import ODConv2d class ODBottleneck(Bottleneck): def __init__(self, c1, c2, **kwargs): super().__init__(c1=c1, c2=c2, **kwargs) delattr(self,'cv1') setattr(self,'cv1',ODConv2d(in_channels=c1,out_channels=int(c2*e))) def forward(self,x): out=self.cv2(self.cv1(x)) return x+out if hasattr(self,"add")and self.add else out ``` Here `ODConv2d` replaces conventional pointwise convolutions inside bottleneck blocks, enhancing representational power at lower costs. #### Contextual Guidance Enhancements Another significant enhancement involves incorporating contextual guidance mechanisms directly into these modules[^4]. By combining local receptive fields from multiple dilated kernels alongside global information captured over larger regions, models gain improved understanding of object contexts within scenes—beneficial especially for complex tasks requiring precise localization capabilities. Such modifications often involve altering how features are aggregated after passing them through individual components of each block: ```python def fuse_context_and_local_features(local_feat,context_guided_block_output,alpha=0.5): fused=(alpha*local_feat)+(1-alpha)*context_guided_block_output return fused ``` These adaptations ensure richer representations suitable for diverse applications ranging from simple bounding box predictions up to detailed instance segmentations.
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