Day49 - Backbone中C3/C2f后添加一层CA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CA,在FLIR上训练200epochs

• Yolov5n-ca

  Parameters: 2.667M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.628	0.443	0.494	0.292
  person	0.836	0.607	0.742	0.411
  bike	0.537	0.418	0.41	0.245
  car	0.853	0.732	0.833	0.587
  motor	0.804	0.521	0.623	0.302
  bus	0.801	0.475	0.619	0.417
  truck	0.33	0.391	0.218	0.141
  light	0.748	0.37	0.479	0.224
  other vehicle	0.116	0.0317	0.0291	0.0122

• Yolov5s-ca

  Parameters: 9.191M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.628	0.513	0.557	0.338
  person	0.836	0.678	0.785	0.465
  bike	0.587	0.512	0.521	0.321
  car	0.859	0.788	0.865	0.627
  motor	0.748	0.6	0.724	0.38
  bus	0.746	0.508	0.655	0.439
  truck	0.39	0.478	0.286	0.184
  light	0.777	0.509	0.583	0.277
  other vehicle	0.0765	0.0317	0.0361	0.0122

• Yolov5m-ca

  Parameters: 25.191M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.694	0.527	0.575	0.363
  person	0.884	0.659	0.808	0.49
  bike	0.621	0.559	0.561	0.35
  car	0.901	0.788	0.881	0.648
  motor	0.811	0.582	0.674	0.388
  bus	0.807	0.531	0.672	0.481
  truck	0.398	0.457	0.288	0.198
  light	0.842	0.502	0.616	0.298
  other vehicle	0.292	0.143	0.0975	0.0472

• Yolov8n-ca

  Parameters: 3.170M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.583	0.481	0.505	0.3
  person	0.786	0.665	0.749	0.422
  bike	0.471	0.441	0.435	0.263
  car	0.811	0.772	0.841	0.595
  motor	0.767	0.636	0.671	0.332
  bus	0.777	0.488	0.615	0.427
  truck	0.232	0.37	0.213	0.131
  light	0.67	0.43	0.48	0.22
  other vehicle	0.152	0.0483	0.0382	0.0116

• Yolov8s-ca

  Parameters: 11.204M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.654	0.515	0.557	0.345
  person	0.852	0.665	0.788	0.472
  bike	0.642	0.518	0.528	0.32
  car	0.869	0.791	0.87	0.631
  motor	0.86	0.636	0.723	0.394
  bus	0.731	0.531	0.671	0.472
  truck	0.382	0.444	0.255	0.173
  light	0.767	0.478	0.568	0.271
  other vehicle	0.131	0.0601	0.0512	0.0233