RTDETR融合SaliencyMamba中的CrossModelAtt模块

​

RT-DETR使用教程： RT-DETR使用教程

RT-DETR改进汇总贴：RT-DETR更新汇总贴

《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⭐⭐

RT-DETR更新汇总贴（含免费教程）文章浏览阅读264次。RT-DETR使用教程：缝合教程： RT-DETR中的yaml文件详解：labelimg使用教程：_rt-deterhttps://xy2668825911.blog.csdn.net/article/details/143696113https://xy2668825911.blog.csdn.net/article/details/143696113

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 （以自研模型加入为例）

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/rt-detr路径下的rtdetr-l.yaml文件，替换原有模块。

​​

# Ultralytics YOLO 🚀, AGPL-3.0 license
# RT-DETR-l object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr
# ⭐⭐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=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
# [depth, width, max_channels]
l: [1.00, 1.00, 512]
# n: [ 0.33, 0.25, 1024 ]
# s: [ 0.33, 0.50, 1024 ]
# m: [ 0.67, 0.75, 768 ]
# l: [ 1.00, 1.00, 512 ]
# x: [ 1.00, 1.25, 512 ]
# ⭐⭐Powered by https://blog.csdn.net/StopAndGoyyy, 技术指导QQ:2668825911⭐⭐

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, CCRI, [128, 5, True, False]]
– [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
– [-1, 4, CCRI, [256, 3, True, True]]
– [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
– [-1, 4, CCRI, [512, 3, True, True]]
– [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
– [-1, 2, CCRI, [1024, 3, True, False]]

head:
– [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9 input_proj.2
– [-1, 1, CrossModelAtt, []]
– [-1, 1, Conv, [256, 1, 1]] # 11, Y5, lateral_convs.0

– [-1, 1, nn.Upsample, [None, 2, "nearest"]]
– [6, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 13 input_proj.1
– [[-2, -1], 1, Concat, [1]]
– [-1, 2, RepC4, [256]] # 15, fpn_blocks.0
– [-1, 1, Conv, [256, 1, 1]] # 16, Y4, lateral_convs.1

– [-1, 1, nn.Upsample, [None, 2, "nearest"]]
– [4, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 18 input_proj.0
– [[-2, -1], 1, Concat, [1]] # cat backbone P4
– [-1, 2, RepC4, [256]] # X3 (20), fpn_blocks.1

– [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0
– [[-1, 16], 1, Concat, [1]] # cat Y4
– [-1, 2, RepC4, [256]] # F4 (23), pan_blocks.0

– [-1, 1, Conv, [256, 3, 2]] # 24, downsample_convs.1
– [[-1, 11], 1, Concat, [1]] # cat Y5
– [-1, 2, RepC4, [256]] # F5 (26), pan_blocks.1

– [[20, 23, 26], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
# ⭐⭐Powered by https://blog.csdn.net/StopAndGoyyy, 技术指导QQ:2668825911⭐⭐

 2.2 修改train.py文件

       创建Train_RT脚本用于训练。

from ultralytics.models import RTDETR
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'

if __name__ == '__main__':
model = RTDETR(model='ultralytics/cfg/models/rt-detr/rtdetr-l.yaml')
# model.load('yolov8n.pt')
model.train(data='./data.yaml', epochs=2, batch=1, device='0', imgsz=640, workers=2, cache=False,
amp=True, mosaic=False, project='runs/train', name='exp')

​​

         在train.py脚本中填入修改好的yaml路径，运行即可训。​​