2. 基于Yolov8的Polarized Self-Attention实现 2.1 加入 modules.py中 核心代码: ### PolarizedSelfAttention ### start by AI&CV ### from torch import nn from torch.nn import init class PolarizedSelfAttention(nn.Module): def __init__(self, channel=512): super().__init__() self.ch_...
增强(Enhancement):采用细粒度回归输出分布的非线性函数。 yolov10 引入 classPolarizedSelfAttention(nn.Module):def__init__(self, channel=512):super().__init__()# 通道注意力权重计算层self.ch_wv = nn.Conv2d(channel, channel //2, kernel_size=(1,1))self.ch_wq = nn.Conv2d(channel,1, ke...
增强(Enhancement):采用细粒度回归输出分布的非线性函数。 yolov10 引入 classPolarizedSelfAttention(nn.Module):def__init__(self,channel=512):super().__init__()# 通道注意力权重计算层self.ch_wv=nn.Conv2d(channel,channel//2,kernel_size=(1,1))self.ch_wq=nn.Conv2d(channel,1,kernel_size=(1...
Initially, YOLOv8 is employed as the foundational model. Subsequently, a lightweight, non-deep feature extraction module, PotentNet, is designed and integrated into the backbone network. In the neck network, a feature fusion module incorporating polarized self-attention, C2f_ParallelPolarized, has ...
yolov8 引入 classPolarizedSelfAttention(nn.Module):def__init__(self, channel=512):super().__init__()# 通道注意力权重计算层self.ch_wv = nn.Conv2d(channel, channel //2, kernel_size=(1,1))self.ch_wq = nn.Conv2d(channel,1, kernel_size=(1,1))# 通道和空间注意力的softmaxself.soft...
修改对应的配置文件,并且将plugins的参数“type”设置为“ParallelPolarizedSelfAttention”,可参考 修改后的PolarizedSelfAttention.py import torch from torch import nn from mmyolo.registry import MODELS @MODELS.register_module() class ParallelPolarizedSelfAttention(nn.Module): def __init__(self, in_channels...