NAO_Roboter_Erkennung/yolo_training/NAO_detector9/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	24.5837	1.38337	1.59497	1.13299	0.77381	0.63415	0.79174	0.51402	1.07247	1.59311	1.10924	0.000661064	0.000661064	0.000661064
3	2	44.1935	1.37648	1.1011	1.12629	0.47912	0.56098	0.48856	0.29811	1.65133	2.146	1.48548	0.00130144	0.00130144	0.00130144
4	3	63.5209	1.37521	1.00512	1.14473	0.75871	0.76707	0.85137	0.51276	1.45374	1.14832	1.33055	0.00191542	0.00191542	0.00191542
5	4	83.0181	1.39248	0.97729	1.1521	0.72427	0.90244	0.83962	0.55562	1.1063	1.1655	1.1848	0.0018812	0.0018812	0.0018812
6	5	102.099	1.34909	0.9365	1.1294	0.80313	0.82927	0.87456	0.59494	1.10444	0.98597	1.10162	0.0018416	0.0018416	0.0018416
7	6	120.495	1.30565	0.85466	1.10696	0.78937	0.90244	0.88324	0.6495	1.01957	0.99015	1.0765	0.001802	0.001802	0.001802
8	7	139.265	1.29615	0.83925	1.10297	0.81199	0.84285	0.91349	0.69203	0.9179	0.87651	1.0519	0.0017624	0.0017624	0.0017624
9	8	158.55	1.23532	0.78344	1.08741	0.83294	0.8513	0.90222	0.70301	0.93553	0.8022	1.00482	0.0017228	0.0017228	0.0017228
10	9	177.315	1.24022	0.77411	1.07758	0.89615	0.80488	0.92216	0.66249	0.98643	0.82604	1.02006	0.0016832	0.0016832	0.0016832
11	10	195.778	1.22761	0.76174	1.09093	0.77696	0.9347	0.94549	0.74916	0.89332	0.75543	1.02896	0.0016436	0.0016436	0.0016436
12	11	214.175	1.20899	0.74909	1.08335	0.80922	0.87805	0.91758	0.68197	0.89423	0.88144	1.04086	0.001604	0.001604	0.001604
13	12	232.891	1.20618	0.74583	1.07317	0.9041	0.90244	0.94701	0.72502	0.86928	0.74318	0.9874	0.0015644	0.0015644	0.0015644
14	13	251.636	1.18134	0.71034	1.05533	0.8864	0.95162	0.9707	0.75873	0.84101	0.64098	0.99018	0.0015248	0.0015248	0.0015248
15	14	270.239	1.17983	0.70683	1.06267	0.79901	0.90244	0.92739	0.74632	0.74994	0.70678	0.96296	0.0014852	0.0014852	0.0014852
16	15	288.895	1.17521	0.69821	1.06029	0.82494	0.87805	0.90711	0.68548	0.81982	0.72279	0.95689	0.0014456	0.0014456	0.0014456
17	16	307.27	1.15779	0.68632	1.04943	0.92729	0.95122	0.96588	0.76468	0.82413	0.6346	0.99141	0.001406	0.001406	0.001406
18	17	325.407	1.16109	0.6758	1.04592	0.92107	0.95122	0.96739	0.7732	0.7881	0.62989	0.9065	0.0013664	0.0013664	0.0013664
19	18	343.35	1.12661	0.66474	1.04166	0.97356	0.97561	0.98827	0.81068	0.69881	0.55234	0.88231	0.0013268	0.0013268	0.0013268
20	19	361.278	1.12698	0.64788	1.03084	0.94956	0.92683	0.96542	0.7717	0.7068	0.63099	0.92191	0.0012872	0.0012872	0.0012872
21	20	379.226	1.13069	0.65455	1.03885	0.90112	0.97561	0.97418	0.78791	0.70749	0.60267	0.89277	0.0012476	0.0012476	0.0012476
22	21	397.629	1.11647	0.63365	1.0299	0.92627	0.97561	0.9702	0.81019	0.67045	0.62768	0.8665	0.001208	0.001208	0.001208
23	22	416.662	1.09492	0.62911	1.02721	0.95114	0.94962	0.98024	0.81772	0.69665	0.538	0.87337	0.0011684	0.0011684	0.0011684
24	23	435.458	1.11042	0.63233	1.02942	0.91751	0.95122	0.9419	0.80744	0.6747	0.58275	0.86386	0.0011288	0.0011288	0.0011288
25	24	453.303	1.09507	0.61695	1.01568	0.97617	0.99934	0.98929	0.84059	0.60503	0.48919	0.84754	0.0010892	0.0010892	0.0010892
26	25	472.178	1.0891	0.61445	1.01644	0.92973	1	0.97934	0.8638	0.62973	0.48138	0.85107	0.0010496	0.0010496	0.0010496
27	26	491.604	1.08137	0.60952	1.01945	0.93175	0.99902	0.97946	0.83194	0.63044	0.51215	0.87336	0.00101	0.00101	0.00101
28	27	510.376	1.07061	0.59402	1.01434	0.9511	0.94891	0.96358	0.83484	0.60126	0.50725	0.86608	0.0009704	0.0009704	0.0009704
29	28	528.926	1.07173	0.58656	1.00666	0.85579	0.97561	0.95489	0.8313	0.61699	0.53136	0.8611	0.0009308	0.0009308	0.0009308
30	29	547.212	1.0585	0.59106	1.01413	0.90673	1	0.96285	0.80755	0.70054	0.54551	0.89977	0.0008912	0.0008912	0.0008912
31	30	565.478	1.04424	0.57113	1.00423	0.88856	0.97245	0.9692	0.85572	0.61174	0.4804	0.83299	0.0008516	0.0008516	0.0008516