Day48 - Backbone中C3/C2f后添加一层SEAttention在FLIR上的表现

yolov5n/s/m和yolov8n/s仅在backbone的C3/C2f模块后添加一层SE,在FLIR上训练200epochs

• Yolov5n-se

  Parameters: 2.666M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.587	0.438	0.483	0.29
  person	0.831	0.608	0.741	0.415
  bike	0.495	0.447	0.415	0.258
  car	0.836	0.744	0.83	0.585
  motor	0.749	0.582	0.653	0.35
  bus	0.796	0.419	0.578	0.396
  truck	0.222	0.299	0.146	0.0889
  light	0.717	0.392	0.481	0.219
  other vehicle	0.0461	0.0159	0.0212	0.00766

• Yolov5s-se

  Parameters: 9.197M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.65	0.497	0.537	0.331
  person	0.858	0.654	0.784	0.463
  bike	0.567	0.512	0.517	0.318
  car	0.874	0.774	0.865	0.624
  motor	0.86	0.582	0.672	0.362
  bus	0.784	0.527	0.654	0.466
  truck	0.354	0.413	0.209	0.138
  light	0.79	0.464	0.566	0.269
  other vehicle	0.11	0.0476	0.0278	0.00848

• Yolov5m-se

  Parameters: 25.209M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.67	0.521	0.574	0.364
  person	0.893	0.666	0.811	0.497
  bike	0.644	0.565	0.55	0.355
  car	0.9	0.781	0.882	0.65
  motor	0.757	0.655	0.711	0.394
  bus	0.826	0.478	0.664	0.485
  truck	0.371	0.478	0.291	0.194
  light	0.825	0.499	0.623	0.304
  other vehicle	0.142	0.0476	0.0617	0.0322

• Yolov8n-se

  Parameters: 3.168M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.619	0.449	0.488	0.292
  person	0.83	0.61	0.744	0.42
  bike	0.479	0.435	0.399	0.24
  car	0.848	0.752	0.843	0.598
  motor	0.83	0.582	0.632	0.319
  bus	0.835	0.441	0.62	0.432
  truck	0.256	0.321	0.169	0.101
  light	0.715	0.389	0.467	0.211
  other vehicle	0.158	0.0635	0.0297	0.0154

• Yolov8s-se

  Parameters: 11.210M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.624	0.505	0.544	0.338
  person	0.846	0.675	0.792	0.471
  bike	0.543	0.541	0.514	0.328
  car	0.87	0.785	0.87	0.635
  motor	0.736	0.6	0.678	0.385
  bus	0.786	0.513	0.667	0.465
  truck	0.268	0.348	0.202	0.136
  light	0.767	0.479	0.56	0.26
  other vehicle	0.175	0.0952	0.0683	0.0237