{"id":47232,"date":"2025-07-30T07:07:20","date_gmt":"2025-07-29T23:07:20","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/47232.html"},"modified":"2025-07-30T07:07:20","modified_gmt":"2025-07-29T23:07:20","slug":"rtdetr%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\/47232.html","title":{"rendered":"RTDETR\u878d\u5408SaliencyMamba\u4e2d\u7684CrossModelAtt\u6a21\u5757"},"content":{"rendered":"

\"\"\u200b<\/p>\n

\n

RT-DETR\u4f7f\u7528\u6559\u7a0b：\u00a0RT-DETR\u4f7f\u7528\u6559\u7a0b<\/h4>\n

RT-DETR\u6539\u8fdb\u6c47\u603b\u8d34：RT-DETR\u66f4\u65b0\u6c47\u603b\u8d34<\/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\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

RT-DETR\u66f4\u65b0\u6c47\u603b\u8d34（\u542b\u514d\u8d39\u6559\u7a0b）\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb264\u6b21\u3002RT-DETR\u4f7f\u7528\u6559\u7a0b：\u7f1d\u5408\u6559\u7a0b： RT-DETR\u4e2d\u7684yaml\u6587\u4ef6\u8be6\u89e3：labelimg\u4f7f\u7528\u6559\u7a0b：_rt-deterhttps:\/\/xy2668825911.blog.csdn.net\/article\/details\/143696113<\/span>https:\/\/xy2668825911.blog.csdn.net\/article\/details\/143696113<\/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\u52a0\u5165\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\/rt-detr\u8def\u5f84\u4e0b\u7684rtdetr-l.yaml\u6587\u4ef6，\u66ff\u6362\u539f\u6709\u6a21\u5757\u3002<\/p>\n

\"\"\u200b\u200b<\/p>\n

# Ultralytics YOLO 🚀, AGPL-3.0 license
\n# RT-DETR-l object detection model with P3-P5 outputs. For details see https:\/\/docs.ultralytics.com\/models\/rtdetr
\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=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
\n # [depth, width, max_channels]
\n l: [1.00, 1.00, 512]
\n# n: [ 0.33, 0.25, 1024 ]
\n# s: [ 0.33, 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# \u2b50\u2b50Powered by https:\/\/blog.csdn.net\/StopAndGoyyy, \u6280\u672f\u6307\u5bfcQQ:2668825911\u2b50\u2b50<\/p>\n

backbone:
\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, CCRI, [128, 5, True, False]]
\n – [-1, 1, Conv, [256, 3, 2]] # 3-P3\/8
\n – [-1, 4, CCRI, [256, 3, True, True]]
\n – [-1, 1, Conv, [512, 3, 2]] # 5-P4\/16
\n – [-1, 4, CCRI, [512, 3, True, True]]
\n – [-1, 1, Conv, [1024, 3, 2]] # 7-P5\/32
\n – [-1, 2, CCRI, [1024, 3, True, False]]<\/p>\n

head:
\n – [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9 input_proj.2
\n – [-1, 1, CrossModelAtt, []]
\n – [-1, 1, Conv, [256, 1, 1]] # 11, Y5, lateral_convs.0<\/p>\n

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

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

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

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

– [[20, 23, 26], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
\n# \u2b50\u2b50Powered by https:\/\/blog.csdn.net\/StopAndGoyyy, \u6280\u672f\u6307\u5bfcQQ:2668825911\u2b50\u2b50<\/p>\n

\n

\u00a02.2\u00a0\u4fee\u6539train.py\u6587\u4ef6<\/h3>\n

\u00a0 \u00a0 \u00a0 \u00a0\u521b\u5efaTrain_RT\u811a\u672c\u7528\u4e8e\u8bad\u7ec3\u3002<\/p>\n

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

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

\"\"\u200b\u200b<\/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\u3002\u200b\u200b<\/p>\n

\n","protected":false},"excerpt":{"rendered":"

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