Day38 - Neck中C3/C2f后添加一层CBAM在FLIR上的表现

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

• Yolov5n-cbam

  Parameters: 2.758M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.571	0.472	0.488	0.288
  person	0.792	0.642	0.741	0.412
  bike	0.471	0.441	0.412	0.256
  car	0.8	0.763	0.832	0.585
  motor	0.676	0.618	0.653	0.313
  bus	0.783	0.484	0.61	0.402
  truck	0.234	0.37	0.174	0.116
  light	0.664	0.393	0.455	0.207
  other vehicle	0.151	0.065	0.0295	0.0106

• Yolov5s-cbam

  Parameters: 9.564M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.651	0.489	0.541	0.333
  person	0.874	0.634	0.783	0.463
  bike	0.562	0.494	0.475	0.29
  car	0.886	0.766	0.864	0.624
  motor	0.827	0.607	0.715	0.359
  bus	0.781	0.48	0.636	0.461
  truck	0.315	0.37	0.232	0.164
  light	0.796	0.478	0.573	0.275
  other vehicle	0.168	0.0794	0.0502	0.0269

• Yolov5m-cbam

  Parameters: 26.035M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.696	0.5	0.569	0.362
  person	0.899	0.647	0.806	0.5
  bike	0.699	0.535	0.553	0.362
  car	0.906	0.78	0.882	0.652
  motor	0.828	0.6	0.694	0.401
  bus	0.852	0.48	0.647	0.47
  truck	0.361	0.413	0.297	0.195
  light	0.844	0.483	0.622	0.298
  other vehicle	0.179	0.0635	0.0516	0.0218

• Yolov8n-cbam

  Parameters: 3.260M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.584	0.444	0.486	0.294
  person	0.818	0.625	0.748	0.427
  bike	0.434	0.459	0.421	0.259
  car	0.845	0.756	0.842	0.596
  motor	0.759	0.545	0.616	0.313
  bus	0.783	0.419	0.59	0.421
  truck	0.207	0.326	0.142	0.089
  light	0.771	0.403	0.513	0.237
  other vehicle	0.0543	0.0159	0.0199	0.00801

• Yolov8s-cbam

  Parameters: 11.577M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.646	0.513	0.558	0.346
  person	0.852	0.665	0.786	0.472
  bike	0.577	0.553	0.543	0.353
  car	0.868	0.79	0.87	0.631
  motor	0.787	0.618	0.71	0.38
  bus	0.8	0.537	0.668	0.476
  truck	0.344	0.413	0.259	0.162
  light	0.766	0.462	0.55	0.259
  other vehicle	0.176	0.0677	0.0773	0.0293