pupilometer/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	68.9306	1.67439	5.21272	2.37725	1.71937	0.06185	0.03901	0.00782	0.00438	0.04498	0.01941	0.00337	0.00138	1.96535	16.1944	13.8668	2.15778	0.000661728	0.000661728	0.000661728
3	2	119.439	1.84587	4.81908	2.18805	1.82192	0.13735	0.03762	0.02265	0.0096	0.06858	0.02129	0.01181	0.00382	2.34528	5.02317	2.85563	2.81438	0.00119688	0.00119688	0.00119688
4	3	169.627	1.77272	4.75853	2.02206	1.78531	0.20465	0.03916	0.01835	0.00966	0.11835	0.02313	0.007	0.00206	2.17918	4.64507	4.80196	2.61955	0.00160004	0.00160004	0.00160004
5	4	218.654	1.76383	4.6796	1.92828	1.73722	0.1904	0.04511	0.02454	0.01366	0.14415	0.03062	0.0108	0.00357	1.79783	4.56636	3.31712	1.91757	0.001406	0.001406	0.001406
6	5	267.557	1.63559	4.62415	1.83526	1.66224	0.25751	0.05137	0.04275	0.02818	0.14092	0.02844	0.01437	0.00792	1.60667	4.38472	1.71263	1.56219	0.001208	0.001208	0.001208
7	6	316.743	1.5618	4.64254	1.7288	1.59133	0.28573	0.05076	0.04463	0.02853	0.18688	0.0332	0.02238	0.00996	1.5398	4.20444	1.7239	1.54621	0.00101	0.00101	0.00101
8	7	366.328	1.50222	4.52026	1.66843	1.5132	0.27684	0.05167	0.04549	0.03091	0.15635	0.03107	0.01394	0.00431	1.51971	4.19928	1.59874	1.4326	0.000812	0.000812	0.000812
9	8	414.245	1.47892	4.53097	1.64474	1.46401	0.32706	0.04973	0.0459	0.03116	0.22665	0.03335	0.02357	0.00884	1.54521	4.16963	1.64349	1.41198	0.000614	0.000614	0.000614
10	9	463.204	1.41705	4.44071	1.60995	1.44785	0.24827	0.05454	0.04666	0.03176	0.18271	0.03975	0.02353	0.00818	1.44664	4.10913	1.62938	1.3764	0.000416	0.000416	0.000416
11	10	511.111	1.35419	4.42608	1.53199	1.40832	0.31153	0.05435	0.04746	0.03192	0.2144	0.03861	0.02481	0.00999	1.50732	4.1011	1.61017	1.39669	0.000218	0.000218	0.000218