{"id":47227,"date":"2025-07-30T07:07:15","date_gmt":"2025-07-29T23:07:15","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/47227.html"},"modified":"2025-07-30T07:07:15","modified_gmt":"2025-07-29T23:07:15","slug":"yolo%e8%9e%8d%e5%90%88saliencymamba%e4%b8%ad%e7%9a%84crossmodelatt%e6%a8%a1%e5%9d%97","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/47227.html","title":{"rendered":"YOLO\u878d\u5408SaliencyMamba\u4e2d\u7684CrossModelAtt\u6a21\u5757"},"content":{"rendered":"

\"\"<\/p>\n

\n

YOLOv11v10v8\u4f7f\u7528\u6559\u7a0b：\u00a0\u00a0YOLOv11\u5165\u95e8\u5230\u5165\u571f\u4f7f\u7528\u6559\u7a0b<\/h4>\n

YOLOv11\u6539\u8fdb\u6c47\u603b\u8d34：YOLOv11\u53ca\u81ea\u7814\u6a21\u578b\u66f4\u65b0\u6c47\u603b\u00a0<\/h4>\n
\n

\u300aSalM\u00b2: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention\u300b<\/h3>\n

\u4e00\u3001 \u6a21\u5757\u4ecb\u7ecd<\/h2>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u8bba\u6587\u94fe\u63a5：https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/32157<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u4ee3\u7801\u94fe\u63a5：https:\/\/github.com\/zhao-chunyu\/SaliencyMamba<\/p>\n

\u8bba\u6587\u901f\u89c8：<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u9a7e\u9a76\u573a\u666f\u4e0b\u7684\u9a7e\u9a76\u5458\u6ce8\u610f\u529b\u8bc6\u522b\u662f\u4ea4\u901a\u573a\u666f\u611f\u77e5\u6280\u672f\u4e2d\u7684\u70ed\u95e8\u65b9\u5411\u3002\u5b83\u65e8\u5728\u4e86\u89e3\u4eba\u7c7b\u9a7e\u9a76\u5458\u7684\u6ce8\u610f\u529b，\u4ee5\u5173\u6ce8\u9a7e\u9a76\u573a\u666f\u4e2d\u7684\u7279\u5b9a\u76ee\u6807\/\u7269\u4f53\u3002\u7136\u800c，\u4ea4\u901a\u573a\u666f\u4e0d\u4ec5\u5305\u542b\u5927\u91cf\u7684\u89c6\u89c9\u4fe1\u606f，\u8fd8\u5305\u542b\u4e0e\u9a7e\u9a76\u4efb\u52a1\u76f8\u5173\u7684\u8bed\u4e49\u4fe1\u606f\u3002\u73b0\u6709\u65b9\u6cd5\u7f3a\u4e4f\u5bf9\u9a7e\u9a76\u573a\u666f\u4e2d\u5b58\u5728\u7684\u5b9e\u9645\u8bed\u4e49\u4fe1\u606f\u7684\u5173\u6ce8\u3002\u6b64\u5916，\u4ea4\u901a\u573a\u666f\u662f\u4e00\u4e2a\u590d\u6742\u4e14\u52a8\u6001\u7684\u8fc7\u7a0b，\u9700\u8981\u6301\u7eed\u5173\u6ce8\u4e0e\u5f53\u524d\u9a7e\u9a76\u4efb\u52a1\u76f8\u5173\u7684\u7269\u4f53\u3002\u73b0\u6709\u6a21\u578b\u53d7\u5176\u57fa\u7840\u6846\u67b6\u7684\u5f71\u54cd，\u5f80\u5f80\u5177\u6709\u5927\u91cf\u53c2\u6570\u548c\u590d\u6742\u7684\u7ed3\u6784\u3002\u56e0\u6b64，\u8be5\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u6700\u65b0Mamba\u6846\u67b6\u7684\u5b9e\u65f6\u663e\u8457\u6027Mamba\u7f51\u7edc\u3002\u5982\u56fe 1 \u6240\u793a，\u6211\u4eec\u7684\u6a21\u578b\u4f7f\u7528\u4e86\u6781\u5c11\u7684\u53c2\u6570（0.08M，\u5176\u4ed6\u6a21\u578b\u4ec5\u4e3a 0.09~11.16%），\u540c\u65f6\u4fdd\u6301\u4e86 SOTA \u6027\u80fd\u6216\u5b9e\u73b0\u4e86 SOTA \u6a21\u578b 98% \u4ee5\u4e0a\u7684\u6027\u80fd\u3002<\/p>\n

\u603b\u7ed3：\u672c\u6587\u66f4\u65b0\u5176\u4e2d\u7684CrossModelAtt\u6a21\u5757\u4ee3\u7801\u53ca\u4f7f\u7528\u65b9\u6cd5\u200b<\/p>\n

\n

\u2b50\u2b50\u672c\u6587\u4e8c\u521b\u6a21\u5757\u4ec5\u66f4\u65b0\u4e8e\u4ed8\u8d39\u7fa4\u4e2d，\u5f80\u671f\u514d\u8d39\u6559\u7a0b\u53ef\u770b\u4e0b\u65b9\u94fe\u63a5\u2b50\u2b50<\/p>\n

YOLOv11\u53ca\u81ea\u7814\u6a21\u578b\u66f4\u65b0\u6c47\u603b（\u542b\u514d\u8d39\u6559\u7a0b）\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb366\u6b21，\u70b9\u8d5e3\u6b21，\u6536\u85cf4\u6b21\u3002\u7fa4\u6587\u4ef62024\/11\/08\u65e5\u66f4\u65b0\u3002,\u7fa4\u6587\u4ef62024\/11\/08\u65e5\u66f4\u65b0\u3002_yolo11\u90e8\u7f72\u81ea\u5df1\u7684\u6570\u636e\u96c6https:\/\/xy2668825911.blog.csdn.net\/article\/details\/143633356<\/span>https:\/\/xy2668825911.blog.csdn.net\/article\/details\/143633356<\/span><\/span><\/p>\n

\u4e8c\u3001\u4e8c\u521b\u878d\u5408\u6a21\u5757<\/h2>\n

2.1 \u76f8\u5173\u4ee3\u7801<\/h3>\n

# https:\/\/blog.csdn.net\/StopAndGoyyy?spm=1011.2124.3001.5343
\n# SalM\u00b2: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention
\n# https:\/\/github.com\/zhao-chunyu\/SaliencyMamba
\n# https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/32157
\nclass CrossModelAtt(nn.Module):
\n def __init__(self,):
\n super().__init__()
\n self.gamma = nn.Parameter(torch.zeros(1))
\n self.softmax = nn.Softmax(dim=-1)<\/p>\n

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

return output <\/p>\n

2.2\u66f4\u6539yaml\u6587\u4ef6 (\u4ee5\u81ea\u7814\u6a21\u578b\u4e3a\u4f8b)<\/h3>\n

yam\u6587\u4ef6\u89e3\u8bfb：YOLO\u7cfb\u5217 \u201c.yaml\u201c\u6587\u4ef6\u89e3\u8bfb_yolo yaml\u6587\u4ef6-CSDN\u535a\u5ba2<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0\u6253\u5f00\u66f4\u6539ultralytics\/cfg\/models\/11\u8def\u5f84\u4e0b\u7684YOLOv11.yaml\u6587\u4ef6，\u66ff\u6362\u539f\u6709\u6a21\u5757\u3002<\/p>\n

\"\"<\/p>\n

# Ultralytics YOLO 🚀, AGPL-3.0 license
\n# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https:\/\/docs.ultralytics.com\/tasks\/detect
\n# \u2b50\u2b50Powered by https:\/\/blog.csdn.net\/StopAndGoyyy, \u6280\u672f\u6307\u5bfcQQ:2668825911\u2b50\u2b50<\/p>\n

# Parameters
\nnc: 80 # number of classes
\nscales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
\n # [depth, width, max_channels]
\n 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
\n 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
\n 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
\n 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
\n 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<\/p>\n

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

# YOLO11n head
\nhead:
\n – [[3, 5, 7], 1, align_3In, [256, 1]] # 10
\n – [[4, 6, 9], 1, align_3In, [256, 1]] # 11<\/p>\n

– [[-1, -2], 1, Concat, [1]] #12 cat<\/p>\n

– [-1, 1, RepVGGBlocks, []] #13<\/p>\n

– [-1, 1, nn.Upsample, [None, 2, "nearest"]] #14
\n – [[-1, 4], 1, Concat, [1]] #15 cat<\/p>\n

– [-1, 1, Conv, [256, 3]] # 16
\n – [13, 1, Conv, [512, 3]] #17
\n – [13, 1, Conv, [1024, 3, 2]] #18<\/p>\n

– [[16, 17, 18], 1, Detect, [nc]] # Detect(P3, P4, P5)<\/p>\n

# \u2b50\u2b50Powered by https:\/\/blog.csdn.net\/StopAndGoyyy, \u6280\u672f\u6307\u5bfcQQ:2668825911\u2b50\u2b50<\/p>\n

\n

\u00a02.3 \u4fee\u6539train.py\u6587\u4ef6<\/h3>\n

\u00a0 \u00a0 \u00a0 \u00a0\u521b\u5efaTrain\u811a\u672c\u7528\u4e8e\u8bad\u7ec3\u3002<\/p>\n

from ultralytics.models import YOLO
\nimport os
\nos.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'<\/p>\n

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

\"\"<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u5728train.py\u811a\u672c\u4e2d\u586b\u5165\u4fee\u6539\u597d\u7684yaml\u8def\u5f84，\u8fd0\u884c\u5373\u53ef\u8bad\u7ec3，\u6570\u636e\u96c6\u521b\u5efa\u6559\u7a0b\u89c1\u4e0b\u65b9\u94fe\u63a5\u3002<\/p>\n

YOLOv11\u5165\u95e8\u5230\u5165\u571f\u4f7f\u7528\u6559\u7a0b(\u542b\u7ed3\u6784\u56fe)_yolov11\u4f7f\u7528\u6559\u7a0b-CSDN\u535a\u5ba2<\/p>\n

\u200b<\/p>\n","protected":false},"excerpt":{"rendered":"

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