带读YOLOv13，HyperACE ｜ FullPAD到底是什么

实验验证

在VOC数据集且不使用预训练权重的前提下，YOLOv13效果较YOLOv12及YOLOv11均有所差距：在带来较大推理开销的同时，检测精度均有所下降（mAP50-95精度相似，但mAP50精度有不同程度的下降）。

测试结果(epoch：100； imagese: 640; batch: 32; 数据集：VOC)：

Model	mAP50-95	mAP50	run time (h)	params (M)	interence time (ms)
YOLOv8n	0.549	0.760	1.051	3.01	0.2+0.3(postprocess)
YOLOv11n	0.553	0.757	1.142	2.59	0.2+0.3(postprocess)
YOLOv12n	0.553	0.762	1.965	2.51	0.4+0.2(postprocess)
YOLOv13n	0.553	0.751	2.263	2.45	0.5+0.2(postprocess)

带读YOLOv13

YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception 主要提出了以下改进：

1. HyperACE：基于超图的自适应关系增强 ｜ Hypergraph-based Adaptive Correlation Enhancement

   • 通过C3AH模块学习全局高阶感知信息，其主要借助自适应超图计算实现，仅具有线性复杂性。

   • 通过DS-C3k模块学习局部低阶感知信息。

2. FullPAD：全流程“聚合-分发”策略 ｜ Full-Pipeline Aggregation-and-Distribution Paradigm

   • 从主干网络中收集多尺度特征图，并将其传送至HyperACE，然后通过不同的FullPAD策略将增强后的特征重新分发到整个流程（Neck）的各个位置。

   • 实现了细粒度的信息流动与表示协同，显著提升了梯度传播效率并增强了检测性能。

   • NeurIPS2023 Gold-YOLO也提出了一种“聚合-分发”策略，通过收集并对齐不同层的特征信息完成聚合，然后通过简单的注意力机制将聚合信息注入到每个Level中。

HyperACE

1. 对Backbone部分的B3、B5分别进行下采样、上采样操作，然后将前两者与B4进行拼接，最后使用1x1卷积进行通道调整。

class FuseModule(nn.Module):
    """
    A module to fuse multi-scale features for the HyperACE block.

    This module takes a list of three feature maps from different scales, aligns them to a common
    spatial resolution by downsampling the first and upsampling the third, and then concatenates
    and fuses them with a convolution layer.

    Attributes:
        c_in (int): The number of channels of the input feature maps.
        channel_adjust (bool): Whether to adjust the channel count of the concatenated features.

    Methods:
        forward: Fuses a list of three multi-scale feature maps.

    Examples:
        >>> import torch
        >>> model = FuseModule(c_in=64, channel_adjust=False)
        >>> # Input is a list of features from different backbone stages
        >>> x_list = [torch.randn(2, 64, 64, 64), torch.randn(2, 64, 32, 32), torch.randn(2, 64, 16, 16)]
        >>> output = model(x_list)
        >>> print(output.shape)
        torch.Size([2, 64, 32, 32])
    """
    def __init__(self, c_in, channel_adjust):
        super(FuseModule, self).__init__()
        self.downsample = nn.AvgPool2d(kernel_size=2)
        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
        if channel_adjust:
            self.conv_out = Conv(4 * c_in, c_in, 1)
        else:
            self.conv_out = Conv(3 * c_in, c_in, 1)

    def forward(self, x):
        x1_ds = self.downsample(x[0])
        x3_up = self.upsample(x[2])
        x_cat = torch.cat([x1_ds, x[1], x3_up], dim=1)
        out = self.conv_out(x_cat)
        return out

2. 通过C3AH模块学习全局高阶感知信息：1）AdaHyperedgeGen生成超边矩阵；2）AdaHGConv根据超边生成超图；3）AdaHGComputation根据超图捕捉高级感知信息

   

   2.1 AdaHyperedgeGen 生成一个自适应超边参与矩阵，通过节点特征的全局上下文（‘mean’，‘max’和‘both’）动态生成超边原型，并计算每个节点与每个超边之间的连续性参与矩阵。（以下代码包含解读）

   class AdaHyperedgeGen(nn.Module):
   """
   Generates an adaptive hyperedge participation matrix from a set of vertex features.
   """
   def __init__(self, node_dim, num_hyperedges, num_heads=4, dropout=0.1, context="both"):
       super().__init__()
       self.num_heads = num_heads
       self.num_hyperedges = num_hyperedges
       self.head_dim = no