NAO_Roboter_Erkennung/yolo_training/NAO_detector2/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	24.9125	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	44.9091	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	64.026	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	84.1255	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	106.065	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	128.173	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	149.813	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	175.593	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	200.309	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