NAO_Roboter_Erkennung/yolo_training/NAO_detector3/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	29.4662	2.15409	2.10792	1.24391	0.70243	0.4878	0.62258	0.37583	1.54244	1.4288	1.21992	0.000661064	0.000661064	0.000661064
3	2	51.925	1.91187	1.19581	1.12279	0.49108	0.63415	0.60834	0.32513	1.60866	1.27692	1.43341	0.00130144	0.00130144	0.00130144
4	3	72.4804	1.83625	1.09237	1.10677	0.59025	0.65854	0.64126	0.31251	1.64287	1.30798	1.50193	0.00191542	0.00191542	0.00191542
5	4	98.9677	1.77744	1.09722	1.09541	0.78965	0.5122	0.60462	0.39948	1.50564	1.37216	1.36987	0.0018812	0.0018812	0.0018812
6	5	118.106	1.72451	1.00674	1.0756	0.69027	0.58537	0.65348	0.36967	1.56627	1.19791	1.38099	0.0018416	0.0018416	0.0018416
7	6	136.699	1.6816	0.96545	1.06602	0.5608	0.73171	0.62227	0.31853	1.65704	1.24745	1.38968	0.001802	0.001802	0.001802
8	7	155.502	1.66449	0.95214	1.05808	0.80001	0.7561	0.86342	0.53905	1.33301	0.97867	1.1847	0.0017624	0.0017624	0.0017624
9	8	174.612	1.61801	0.92736	1.03716	0.7216	0.78049	0.77649	0.54305	1.18629	0.92563	1.14214	0.0017228	0.0017228	0.0017228
10	9	193.332	1.54485	0.88521	1.02098	0.7252	0.77253	0.77037	0.46198	1.47832	1.13508	1.3266	0.0016832	0.0016832	0.0016832
11	10	213.278	1.52407	0.88361	1.02719	0.68497	0.7561	0.82585	0.47667	1.48575	0.98921	1.26247	0.0016436	0.0016436	0.0016436
12	11	234.356	1.49379	0.85295	1.01718	0.79063	0.58537	0.74858	0.47275	1.34454	1.0461	1.17443	0.001604	0.001604	0.001604
13	12	254.546	1.49671	0.86267	1.01529	0.9186	0.82577	0.89399	0.51789	1.40833	0.89727	1.26746	0.0015644	0.0015644	0.0015644