Day37 - Backbone中C3/C2f后添加一层CBAM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CBAM,在FLIR上训练200epochs

• Yolov5n-cbam

  Parameters: 2.742M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.578	0.463	0.494	0.297
  person	0.804	0.632	0.737	0.411
  bike	0.529	0.429	0.433	0.261
  car	0.822	0.763	0.837	0.589
  motor	0.696	0.564	0.632	0.336
  bus	0.769	0.508	0.634	0.427
  truck	0.243	0.378	0.162	0.116
  light	0.715	0.418	0.497	0.227
  other vehicle	0.0483	0.0159	0.0183	0.00953

• Yolov5s-cbam

  Parameters: 9.502M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.652	0.503	0.556	0.342
  person	0.862	0.649	0.787	0.465
  bike	0.566	0.512	0.518	0.327
  car	0.878	0.779	0.868	0.629
  motor	0.887	0.573	0.732	0.384
  bus	0.809	0.52	0.667	0.465
  truck	0.363	0.478	0.241	0.171
  light	0.772	0.481	0.574	0.272
  other vehicle	0.0789	0.0317	0.0633	0.0255

• Yolov5m-cbam

  Parameters: 25.896M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.688	0.525	0.591	0.372
  person	0.878	0.67	0.81	0.5
  bike	0.653	0.518	0.548	0.356
  car	0.897	0.79	0.882	0.653
  motor	0.797	0.655	0.776	0.408
  bus	0.838	0.514	0.687	0.5
  truck	0.433	0.481	0.33	0.22
  light	0.846	0.493	0.631	0.303
  other vehicle	0.16	0.0794	0.0627	0.0346

• Yolov8n-cbam

  Parameters: 3.245M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.597	0.462	0.499	0.303
  person	0.808	0.626	0.743	0.421
  bike	0.569	0.447	0.454	0.269
  car	0.831	0.752	0.837	0.591
  motor	0.844	0.564	0.642	0.354
  bus	0.732	0.52	0.625	0.437
  truck	0.267	0.37	0.182	0.114
  light	0.694	0.399	0.468	0.221
  other vehicle	0.0349	0.0159	0.039	0.0152

• Yolov8s-cbam

  Parameters: 19.245M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.678	0.496	0.573	0.36
  person	0.891	0.63	0.791	0.472
  bike	0.607	0.488	0.544	0.329
  car	0.907	0.751	0.865	0.628
  motor	0.757	0.636	0.731	0.407
  bus	0.816	0.522	0.673	0.496
  truck	0.435	0.435	0.338	0.243
  light	0.811	0.443	0.563	0.269
  other vehicle	0.201	0.0635	0.0745	0.0395