pupilometer/Training/runs/segment/train/results.csv

1	epoch	time	train/box_loss	train/seg_loss	train/cls_loss	train/dfl_loss	metrics/precision(B)	metrics/recall(B)	metrics/mAP50(B)	metrics/mAP50-95(B)	metrics/precision(M)	metrics/recall(M)	metrics/mAP50(M)	metrics/mAP50-95(M)	val/box_loss	val/seg_loss	val/cls_loss	val/dfl_loss	lr/pg0	lr/pg1	lr/pg2
2	1	77.0773	1.63447	4.89567	2.42805	1.64779	0.31775	0.02154	0.02563	0.01532	0.00344	0.00935	0.00208	0.00085	1.92736	4.62883	2.36944	1.57179	0.000661728	0.000661728	0.000661728
3	2	129.259	1.72409	4.65218	1.97457	1.58365	0.22944	0.03963	0.03888	0.02299	0.06295	0.00984	0.00401	0.00193	1.66369	4.21896	1.88039	1.51004	0.00119688	0.00119688	0.00119688
4	3	180.012	1.69114	4.53047	1.87858	1.53174	0.2613	0.03487	0.03708	0.02161	0.05776	0.00817	0.00796	0.0044	1.92901	4.29243	2.05413	1.58552	0.00160004	0.00160004	0.00160004
5	4	228.986	1.68305	4.58002	1.82888	1.53668	0.2472	0.04244	0.04145	0.02409	0.05309	0.00943	0.00382	0.00162	1.67113	4.19368	1.81316	1.39984	0.001406	0.001406	0.001406
6	5	277.303	1.65018	4.5201	1.78299	1.48356	0.26621	0.04076	0.04198	0.02543	0.09759	0.01582	0.01235	0.00741	1.57192	4.11322	1.67254	1.3557	0.001208	0.001208	0.001208
7	6	326.182	1.61036	4.45228	1.70764	1.4527	0.28505	0.04273	0.04463	0.02729	0.07887	0.01238	0.00595	0.00243	1.56286	3.99092	1.61271	1.30411	0.00101	0.00101	0.00101
8	7	373.892	1.54803	4.3777	1.64538	1.3981	0.31593	0.03985	0.04408	0.02765	0.08359	0.01078	0.01015	0.00601	1.5071	3.9971	1.50944	1.27598	0.000812	0.000812	0.000812
9	8	422.978	1.5309	4.37591	1.60905	1.3875	0.29736	0.04199	0.04521	0.02769	0.10808	0.0156	0.01238	0.00721	1.52317	3.9642	1.53943	1.28964	0.000614	0.000614	0.000614
10	9	471.104	1.50778	4.33331	1.59454	1.38351	0.31702	0.041	0.04475	0.02811	0.10697	0.01455	0.00835	0.00324	1.47336	3.89118	1.58276	1.24859	0.000416	0.000416	0.000416
11	10	518.71	1.47276	4.29465	1.55818	1.35967	0.37034	0.0397	0.046	0.02938	0.11371	0.01261	0.01191	0.0075	1.42259	3.86621	1.45772	1.20884	0.000218	0.000218	0.000218