Day43 - Backbone中C3/C2f后添加一层GAM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GAM,在FLIR上训练200epochs

• Yolov5n-gam

  Parameters: 3.234M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.599	0.473	0.492	0.296
  person	0.805	0.624	0.738	0.413
  bike	0.433	0.494	0.427	0.26
  car	0.803	0.766	0.838	0.592
  motor	0.804	0.545	0.62	0.316
  bus	0.717	0.492	0.607	0.443
  truck	0.29	0.37	0.176	0.111
  light	0.703	0.416	0.482	0.219
  other vehicle	0.236	0.0794	0.0463	0.0154

• Yolov5s-gam

  Parameters: 11.465M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.635	0.521	0.546	0.341
  person	0.837	0.686	0.796	0.47
  bike	0.593	0.512	0.527	0.343
  car	0.861	0.795	0.871	0.635
  motor	0.767	0.564	0.664	0.362
  bus	0.763	0.497	0.65	0.466
  truck	0.309	0.543	0.228	0.155
  light	0.768	0.476	0.574	0.274
  other vehicle	0.182	0.0952	0.0608	0.0263

• Yolov5m-gam

  Parameters: 30.308M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.685	0.516	0.587	0.372
  person	0.897	0.654	0.813	0.502
  bike	0.63	0.529	0.563	0.361
  car	0.905	0.778	0.884	0.655
  motor	0.786	0.673	0.781	0.438
  bus	0.828	0.48	0.662	0.481
  truck	0.393	0.478	0.302	0.203
  light	0.837	0.48	0.609	0.298
  other vehicle	0.205	0.0574	0.0809	0.0348

• Yolov8n-gam

  Parameters: 3.736M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.564	0.468	0.494	0.3
  person	0.79	0.646	0.744	0.416
  bike	0.481	0.465	0.445	0.268
  car	0.808	0.773	0.842	0.596
  motor	0.674	0.582	0.635	0.361
  bus	0.722	0.478	0.6	0.423
  truck	0.209	0.326	0.145	0.0917
  light	0.675	0.413	0.47	0.211
  other vehicle	0.15	0.0635	0.0694	0.033

• Yolov8s-gam

  Parameters: 13.478M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.635	0.516	0.544	0.339
  person	0.848	0.684	0.797	0.473
  bike	0.575	0.553	0.515	0.32
  car	0.851	0.8	0.869	0.632
  motor	0.757	0.6	0.683	0.378
  bus	0.771	0.525	0.642	0.462
  truck	0.28	0.391	0.207	0.134
  light	0.752	0.496	0.572	0.273
  other vehicle	0.244	0.0794	0.0661	0.0381