Day50 - Backbone中C3/C2f后添加一层ECA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ECA,在FLIR上训练200epochs

• Yolov5n-eca

  Parameters: 2.655M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.556	0.449	0.474	0.283
  person	0.793	0.642	0.733	0.405
  bike	0.456	0.418	0.407	0.256
  car	0.825	0.748	0.83	0.581
  motor	0.67	0.564	0.576	0.291
  bus	0.75	0.525	0.608	0.417
  truck	0.237	0.283	0.145	0.0893
  light	0.714	0.41	0.473	0.216
  other vehicle	0	0	0.0191	0.00739

• Yolov5s-eca

  Parameters: 9.153M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.602	0.531	0.547	0.335
  person	0.808	0.692	0.777	0.455
  bike	0.501	0.547	0.513	0.322
  car	0.826	0.796	0.858	0.618
  motor	0.74	0.655	0.7	0.358
  bus	0.74	0.54	0.657	0.457
  truck	0.333	0.457	0.269	0.191
  light	0.731	0.483	0.557	0.26
  other vehicle	0.139	0.0794	0.0467	0.018

• Yolov5m-eca

  Parameters: 25.111M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.621	0.557	0.574	0.364
  person	0.808	0.737	0.811	0.5
  bike	0.597	0.541	0.526	0.34
  car	0.831	0.835	0.884	0.654
  motor	0.642	0.618	0.732	0.407
  bus	0.795	0.564	0.687	0.505
  truck	0.306	0.457	0.253	0.169
  light	0.768	0.548	0.621	0.305
  other vehicle	0.224	0.159	0.0774	0.0344

• Yolov8n-eca

  Parameters: 3.157M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.593	0.457	0.494	0.297
  person	0.827	0.621	0.746	0.422
  bike	0.491	0.447	0.443	0.268
  car	0.85	0.749	0.838	0.592
  motor	0.62	0.582	0.607	0.307
  bus	0.852	0.508	0.633	0.448
  truck	0.208	0.263	0.141	0.0937
  light	0.745	0.418	0.515	0.23
  other vehicle	0.152	0.0635	0.0322	0.0128

• Yolov8s-eca

  Parameters: 11.167M

  Class	Precision	Recall	mAP50	mAP50-95
  all	0.661	0.508	0.56	0.342
  person	0.846	0.685	0.79	0.471
  bike	0.615	0.553	0.535	0.332
  car	0.853	0.801	0.869	0.634
  motor	0.721	0.564	0.697	0.374
  bus	0.774	0.537	0.689	0.479
  truck	0.388	0.348	0.228	0.13
  light	0.775	0.482	0.574	0.273
  other vehicle	0.318	0.0952	0.0977	0.0435