Day31 - P2改进在FLIR上的表现

yolov5n/s/m和yolov8n/s仅添加p2检测头,在FLIR上训练200epochs

• Yolov5n-p2

  Parameters: 2.838M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.613	0.467	0.511	0.306
  person	0.838	0.662	0.794	0.459
  bike	0.525	0.441	0.435	0.256
  car	0.857	0.773	0.865	0.618
  motor	0.801	0.545	0.604	0.303
  bus	0.748	0.469	0.629	0.43
  truck	0.237	0.304	0.14	0.0806
  light	0.758	0.492	0.578	0.279
  other vehicle	0.135	0.0476	0.0401	0.0238

• Yolov5s-p2

  Parameters: 8.816M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.637	0.525	0.572	0.358
  person	0.873	0.697	0.836	0.507
  bike	0.612	0.506	0.506	0.32
  car	0.876	0.809	0.891	0.654
  motor	0.624	0.604	0.658	0.348
  bus	0.853	0.503	0.692	0.487
  truck	0.261	0.457	0.262	0.183
  light	0.823	0.544	0.659	0.329
  other vehicle	0.174	0.0794	0.0713	0.0327

• Yolov5m-p2

  Parameters: 23.863M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.683	0.539	0.596	0.384
  person	0.901	0.694	0.852	0.53
  bike	0.628	0.566	0.551	0.36
  car	0.907	0.805	0.901	0.669
  motor	0.765	0.651	0.713	0.406
  bus	0.784	0.52	0.679	0.493
  truck	0.334	0.413	0.299	0.218
  light	0.836	0.566	0.696	0.35
  other vehicle	0.306	0.0952	0.0773	0.0482

• Yolov8n-p2

  Parameters: 3.411M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.592	0.475	0.528	0.32
  person	0.809	0.693	0.805	0.471
  bike	0.486	0.467	0.455	0.279
  car	0.841	0.796	0.872	0.627
  motor	0.753	0.545	0.67	0.331
  bus	0.748	0.447	0.625	0.449
  truck	0.19	0.326	0.156	0.101
  light	0.732	0.479	0.573	0.275
  other vehicle	0.179	0.0476	0.0672	0.0254

• Yolov8s-p2

  Parameters: 11.110M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.658	0.517	0.574	0.357
  person	0.873	0.7	0.838	0.512
  bike	0.512	0.529	0.508	0.332
  car	0.87	0.812	0.891	0.656
  motor	0.849	0.582	0.702	0.369
  bus	0.802	0.508	0.68	0.483
  truck	0.334	0.348	0.235	0.142
  light	0.783	0.532	0.626	0.311
  other vehicle	0.24	0.127	0.109	0.0546