Day42 - Neck中C3/C2f后添加一层GAM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GAM,在FLIR上训练200epochs

• Yolov5n-gam

  Parameters: 3.336M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.566	0.465	0.487	0.286
  person	0.807	0.62	0.732	0.406
  bike	0.462	0.435	0.428	0.245
  car	0.816	0.76	0.833	0.581
  motor	0.6	0.6	0.616	0.305
  bus	0.781	0.477	0.621	0.422
  truck	0.233	0.391	0.158	0.1
  light	0.717	0.384	0.465	0.213
  other vehicle	0.117	0.0524	0.0429	0.0163

• Yolov5s-gam

  Parameters: 11.873M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.626	0.496	0.534	0.333
  person	0.852	0.662	0.785	0.464
  bike	0.628	0.518	0.506	0.319
  car	0.862	0.785	0.863	0.625
  motor	0.68	0.6	0.651	0.367
  bus	0.798	0.486	0.642	0.452
  truck	0.339	0.435	0.234	0.149
  light	0.778	0.449	0.555	0.272
  other vehicle	0.0731	0.0317	0.04	0.0137

• Yolov5m-gam

  Parameters: 31.226M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.698	0.523	0.58	0.367
  person	0.883	0.668	0.806	0.489
  bike	0.648	0.531	0.545	0.356
  car	0.906	0.782	0.878	0.644
  motor	0.754	0.582	0.665	0.352
  bus	0.872	0.52	0.674	0.487
  truck	0.41	0.522	0.388	0.278
  light	0.839	0.485	0.605	0.292
  other vehicle	0.274	0.0952	0.0824	0.0403

• Yolov8n-gam

  Parameters: 3.838M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.621	0.461	0.501	0.3
  person	0.842	0.613	0.749	0.422
  bike	0.463	0.488	0.464	0.276
  car	0.852	0.753	0.838	0.595
  motor	0.847	0.564	0.619	0.313
  bus	0.805	0.439	0.601	0.412
  truck	0.286	0.435	0.222	0.15
  light	0.744	0.353	0.467	0.215
  other vehicle	0.125	0.0476	0.0476	0.0185

• Yolov8s-gam

  Parameters: 13.886M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.647	0.498	0.545	0.338
  person	0.86	0.653	0.785	0.467
  bike	0.515	0.512	0.492	0.323
  car	0.883	0.779	0.866	0.63
  motor	0.816	0.566	0.692	0.382
  bus	0.798	0.536	0.663	0.461
  truck	0.361	0.435	0.242	0.153
  light	0.819	0.458	0.577	0.273
  other vehicle	0.123	0.0476	0.0418	0.0186