YOLO融合SaliencyMamba中的CrossModelAtt模块

YOLOv11v10v8使用教程：  YOLOv11入门到入土使用教程

YOLOv11改进汇总贴：YOLOv11及自研模型更新汇总 

《SalM²: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention》

一、 模块介绍

        论文链接：https://ojs.aaai.org/index.php/AAAI/article/view/32157

        代码链接：https://github.com/zhao-chunyu/SaliencyMamba

论文速览：

        驾驶场景下的驾驶员注意力识别是交通场景感知技术中的热门方向。它旨在了解人类驾驶员的注意力，以关注驾驶场景中的特定目标/物体。然而，交通场景不仅包含大量的视觉信息，还包含与驾驶任务相关的语义信息。现有方法缺乏对驾驶场景中存在的实际语义信息的关注。此外，交通场景是一个复杂且动态的过程，需要持续关注与当前驾驶任务相关的物体。现有模型受其基础框架的影响，往往具有大量参数和复杂的结构。因此，该文提出了一种基于最新Mamba框架的实时显著性Mamba网络。如图 1 所示，我们的模型使用了极少的参数（0.08M，其他模型仅为 0.09~11.16%），同时保持了 SOTA 性能或实现了 SOTA 模型 98% 以上的性能。

总结：本文更新其中的CrossModelAtt模块代码及使用方法​

⭐⭐本文二创模块仅更新于付费群中，往期免费教程可看下方链接⭐⭐

YOLOv11及自研模型更新汇总（含免费教程）文章浏览阅读366次，点赞3次，收藏4次。群文件2024/11/08日更新。,群文件2024/11/08日更新。_yolo11部署自己的数据集https://xy2668825911.blog.csdn.net/article/details/143633356https://xy2668825911.blog.csdn.net/article/details/143633356

二、二创融合模块

2.1 相关代码

# https://blog.csdn.net/StopAndGoyyy?spm=1011.2124.3001.5343
# SalM²: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention
# https://github.com/zhao-chunyu/SaliencyMamba
# https://ojs.aaai.org/index.php/AAAI/article/view/32157
class CrossModelAtt(nn.Module):
def __init__(self,):
super().__init__()
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=-1)

def forward(self, x):
img_feat, text_feat = x
B, C, H, W = img_feat.shape
q = img_feat.view(B, C, -1)
k = text_feat.view(B, C, -1).permute(0, 2, 1)
attention_map = torch.bmm(q, k) # [B, C, C]
attention_map = self.softmax(attention_map)
v = text_feat.view(B, C, -1)
attention_info = torch.bmm(attention_map, v)
attention_info = attention_info.view(B, C, H, W)
output = self.gamma * attention_info + img_feat

return output

2.2更改yaml文件 (以自研模型为例)

yam文件解读：YOLO系列 “.yaml“文件解读_yolo yaml文件-CSDN博客

       打开更改ultralytics/cfg/models/11路径下的YOLOv11.yaml文件，替换原有模块。

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# ⭐⭐Powered by https://blog.csdn.net/StopAndGoyyy, 技术指导QQ:2668825911⭐⭐

# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 377 layers, 2,249,525 parameters, 2,249,509 gradients, 8.7 GFLOPs/258 layers, 2,219,405 parameters, 0 gradients, 8.5 GFLOPs
s: [0.50, 0.50, 1024] # summary: 377 layers, 8,082,389 parameters, 8,082,373 gradients, 29.8 GFLOPs/258 layers, 7,972,885 parameters, 0 gradients, 29.2 GFLOPs
m: [0.50, 1.00, 512] # summary: 377 layers, 20,370,221 parameters, 20,370,205 gradients, 103.0 GFLOPs/258 layers, 20,153,773 parameters, 0 gradients, 101.2 GFLOPs
l: [1.00, 1.00, 512] # summary: 521 layers, 23,648,717 parameters, 23,648,701 gradients, 124.5 GFLOPs/330 layers, 23,226,989 parameters, 0 gradients, 121.2 GFLOPs
x: [1.00, 1.50, 512] # summary: 521 layers, 53,125,237 parameters, 53,125,221 gradients, 278.9 GFLOPs/330 layers, 52,191,589 parameters, 0 gradients, 272.1 GFLOPs

# n: [0.33, 0.25, 1024]
# s: [0.50, 0.50, 1024]
# m: [0.67, 0.75, 768]
# l: [1.00, 1.00, 512]
# x: [1.00, 1.25, 512]
# YOLO11n backbone
backbone:
# [from, repeats, module, args]
– [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
– [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
– [-1, 2, RCRep2A, [128, False, 0.25]]
– [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
– [-1, 4, RCRep2A, [256, False, 0.25]]
– [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
– [-1, 4, RCRep2A, [512, True]]
– [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
– [-1, 2, RCRep2A, [1024, True]]
– [-1, 1, CrossModelAtt, []] # 9

# YOLO11n head
head:
– [[3, 5, 7], 1, align_3In, [256, 1]] # 10
– [[4, 6, 9], 1, align_3In, [256, 1]] # 11

– [[-1, -2], 1, Concat, [1]] #12 cat

– [-1, 1, RepVGGBlocks, []] #13

– [-1, 1, nn.Upsample, [None, 2, "nearest"]] #14
– [[-1, 4], 1, Concat, [1]] #15 cat

– [-1, 1, Conv, [256, 3]] # 16
– [13, 1, Conv, [512, 3]] #17
– [13, 1, Conv, [1024, 3, 2]] #18

– [[16, 17, 18], 1, Detect, [nc]] # Detect(P3, P4, P5)

# ⭐⭐Powered by https://blog.csdn.net/StopAndGoyyy, 技术指导QQ:2668825911⭐⭐

 2.3 修改train.py文件

       创建Train脚本用于训练。

from ultralytics.models import YOLO
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'

if __name__ == '__main__':
model = YOLO(model='ultralytics/cfg/models/xy_YOLO/xy_yolov1.yaml')
# model = YOLO(model='ultralytics/cfg/models/11/yolo11l.yaml')
model.train(data='./datasets/data.yaml', epochs=1, batch=1, device='0', imgsz=320, workers=1, cache=False,
amp=True, mosaic=False, project='run/train', name='exp',)

         在train.py脚本中填入修改好的yaml路径，运行即可训练，数据集创建教程见下方链接。

YOLOv11入门到入土使用教程(含结构图)_yolov11使用教程-CSDN博客

​