le-yolo/runs/detect/train38/results.csv

1	epoch	time	train/box_loss	train/cls_loss	train/dfl_loss	metrics/precision(B)	metrics/recall(B)	metrics/mAP50(B)	metrics/mAP50-95(B)	val/box_loss	val/cls_loss	val/dfl_loss	lr/pg0	lr/pg1	lr/pg2
2	1	7.63419	0.30828	0.28183	0.78764	0.86722	0.69493	0.79783	0.60216	1.87359	0.91892	2.94203	4e-05	4e-05	4e-05
3	2	14.645	0.35212	0.30821	0.80886	0.86631	0.68952	0.80227	0.60808	1.86119	0.91462	2.95005	9.901e-05	9.901e-05	9.901e-05
4	3	21.7566	0.26862	0.2501	0.75986	0.88308	0.68085	0.80748	0.61797	1.85552	0.89891	2.98183	0.000156832	0.000156832	0.000156832
5	4	29.1495	0.28496	0.2599	0.79828	0.96558	0.65957	0.81509	0.62687	1.85645	0.88115	3.00633	0.000213466	0.000213466	0.000213466
6	5	36.5832	0.33966	0.28165	0.80119	0.96147	0.65957	0.81595	0.62653	1.86387	0.88326	3.01386	0.000268912	0.000268912	0.000268912
7	6	44.4226	0.33829	0.28887	0.80318	0.96183	0.65957	0.81014	0.62147	1.86848	0.86437	3.05306	0.00032317	0.00032317	0.00032317
8	7	52.4005	0.31478	0.28083	0.81064	0.96355	0.65957	0.78394	0.60928	1.86892	0.87634	3.05709	0.00037624	0.00037624	0.00037624
9	8	60.477	0.34567	0.29483	0.80056	0.96464	0.65957	0.77884	0.60987	1.86904	0.89065	3.05554	0.000428122	0.000428122	0.000428122
10	9	67.7826	0.33369	0.31412	0.7704	0.96851	0.65445	0.77243	0.61168	1.87493	0.93488	3.07352	0.000478816	0.000478816	0.000478816
11	10	75.0285	0.49483	0.32269	0.87559	0.93359	0.65957	0.7765	0.60857	1.86432	0.99859	3.02147	0.000528322	0.000528322	0.000528322
12	11	82.2757	0.39982	0.29848	0.79798	0.91385	0.6772	0.78622	0.60723	1.86969	1.09871	2.9569	0.00057664	0.00057664	0.00057664
13	12	89.8078	0.42748	0.30629	0.80657	0.97466	0.65957	0.78702	0.60116	1.86946	1.17368	2.89691	0.00062377	0.00062377	0.00062377
14	13	97.6266	0.38214	0.32367	0.82461	0.96398	0.65957	0.79028	0.59399	1.86126	1.28949	2.79979	0.000669712	0.000669712	0.000669712