Day54 - CBAM+NWD改进在FLIR上的表现

yolov5s/m和yolov8s在backbone的C3/C2f模块后添加一层CBAM,使用NWD损失函数,在FLIR上训练200epochs

• Yolov5s-cbam-nwd

  Parameters: 9.502M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.681	0.515	0.565	0.343
  person	0.857	0.661	0.787	0.459
  bike	0.593	0.512	0.507	0.314
  car	0.879	0.775	0.859	0.62
  motor	0.817	0.648	0.753	0.388
  bus	0.783	0.531	0.678	0.477
  truck	0.441	0.391	0.277	0.189
  light	0.781	0.472	0.587	0.275
  other vehicle	0.3	0.127	0.0739	0.0256

• Yolov5m-cbam-nwd

  Parameters: 25.896M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.631	0.55	0.577	0.363
  person	0.843	0.716	0.81	0.49
  bike	0.573	0.571	0.55	0.361
  car	0.857	0.813	0.878	0.647
  motor	0.709	0.618	0.733	0.399
  bus	0.748	0.587	0.679	0.493
  truck	0.354	0.435	0.273	0.182
  light	0.796	0.547	0.629	0.303
  other vehicle	0.171	0.111	0.0652	0.0316

• Yolov8s-cbam-nwd

  Parameters: 11.516M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.676	0.507	0.558	0.34
  person	0.865	0.676	0.8	0.47
  bike	0.651	0.524	0.538	0.338
  car	0.89	0.779	0.867	0.63
  motor	0.824	0.596	0.669	0.356
  bus	0.807	0.542	0.683	0.471
  truck	0.405	0.391	0.255	0.158
  light	0.79	0.483	0.575	0.264
  other vehicle	0.175	0.0635	0.0772	0.0359