NAO_Roboter_Erkennung/yolo_training/NAO_detector/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	43.7328	1.25814	1.72863	1.17659	0.74518	0.84077	0.84291	0.48474	1.24266	1.73231	1.41437	0.000661654	0.000661654	0.000661654
3	2	85.9308	1.31019	1.25131	1.21989	0.71446	0.65	0.71046	0.40902	1.50998	1.53101	1.53065	0.00130202	0.00130202	0.00130202
4	3	128.01	1.33351	1.0706	1.24283	0.82519	0.675	0.75726	0.47482	1.28739	1.66125	1.38419	0.00191599	0.00191599	0.00191599
5	4	170.005	1.29976	0.99576	1.22534	0.85278	0.9125	0.95516	0.6481	1.14824	1.2724	1.25304	0.0018812	0.0018812	0.0018812
6	5	212.093	1.26398	0.91562	1.19885	0.90834	0.875	0.93973	0.63297	1.16508	0.85274	1.26465	0.0018416	0.0018416	0.0018416
7	6	254.328	1.21589	0.82583	1.18872	0.8238	0.87662	0.90482	0.59631	1.16718	0.94529	1.27788	0.001802	0.001802	0.001802
8	7	296.649	1.21417	0.82002	1.17498	0.90981	0.875	0.945	0.59768	1.24293	0.82515	1.34108	0.0017624	0.0017624	0.0017624
9	8	338.847	1.18677	0.79578	1.1575	0.97318	0.90724	0.95959	0.68662	1.07251	0.72583	1.18407	0.0017228	0.0017228	0.0017228
10	9	381.136	1.17178	0.76381	1.16491	0.95158	0.8375	0.91832	0.65263	1.03967	1.1827	1.21166	0.0016832	0.0016832	0.0016832
11	10	423.336	1.18489	0.74399	1.16868	0.94528	0.9625	0.96416	0.70055	1.06039	0.69439	1.20436	0.0016436	0.0016436	0.0016436
12	11	465.686	1.15965	0.73219	1.14847	0.87476	0.96038	0.94967	0.69634	1.02899	0.68159	1.14522	0.001604	0.001604	0.001604
13	12	508.067	1.15236	0.71709	1.14044	0.95194	0.9125	0.97032	0.74034	0.94112	0.59602	1.11346	0.0015644	0.0015644	0.0015644
14	13	550.483	1.13722	0.70397	1.14222	0.92314	0.90078	0.95561	0.69837	0.93819	0.72891	1.12948	0.0015248	0.0015248	0.0015248
15	14	592.651	1.12905	0.69081	1.14225	0.92667	0.9375	0.96263	0.70679	0.99774	0.59732	1.18065	0.0014852	0.0014852	0.0014852
16	15	635.008	1.12174	0.67997	1.12656	0.91732	0.875	0.94963	0.71413	0.95528	0.57018	1.13455	0.0014456	0.0014456	0.0014456
17	16	677.376	1.11231	0.65944	1.12721	0.93497	0.89861	0.96771	0.7444	0.93139	0.54339	1.11307	0.001406	0.001406	0.001406
18	17	719.657	1.07777	0.67041	1.09882	0.93645	0.92108	0.97476	0.75162	0.94171	0.51412	1.10385	0.0013664	0.0013664	0.0013664
19	18	761.969	1.07516	0.63592	1.10574	0.96448	0.9375	0.97748	0.75255	0.9098	0.48929	1.10691	0.0013268	0.0013268	0.0013268
20	19	804.231	1.0694	0.61849	1.10328	0.96186	0.925	0.9737	0.76578	0.86653	0.49392	1.08609	0.0012872	0.0012872	0.0012872
21	20	846.579	1.04875	0.61657	1.10205	0.96185	0.95	0.97948	0.76197	0.87411	0.51301	1.10399	0.0012476	0.0012476	0.0012476
22	21	888.791	1.0645	0.61355	1.1024	0.91306	0.9375	0.97366	0.73882	0.87144	0.50829	1.0843	0.001208	0.001208	0.001208
23	22	931.259	1.06394	0.60756	1.10152	0.92649	0.94529	0.98047	0.73572	0.89228	0.53384	1.07704	0.0011684	0.0011684	0.0011684
24	23	973.702	1.05014	0.60532	1.10591	0.94753	0.9375	0.97509	0.74799	0.89053	0.48995	1.08055	0.0011288	0.0011288	0.0011288
25	24	1015.97	1.03435	0.58299	1.07959	0.94908	0.93203	0.97254	0.76005	0.89917	0.47759	1.0904	0.0010892	0.0010892	0.0010892