product-recognition-demo/recognization/runs/detect/train/results.csv

1	epoch	time	train/box_loss	train/cls_loss	train/l1_loss	metrics/precision(B)	metrics/recall(B)	metrics/mAP50(B)	metrics/mAP50-95(B)	val/box_loss	val/cls_loss	val/l1_loss	lr/pg0	lr/pg1	lr/pg2
2	1	1565.97	1.87842	1.31062	0.00473	0.77594	0.66625	0.7326	0.37541	1.82395	1.09779	0.00496	0.00333008	0.00333008	0.0700293
3	2	3114.04	1.84885	1.03949	0.00464	0.82083	0.71551	0.78275	0.40906	1.71736	0.96438	0.00463	0.00650198	0.00650198	0.0398679
4	3	4713.02	1.79522	0.94506	0.0044	0.83292	0.73762	0.80286	0.44416	1.73388	0.90931	0.00467	0.00905169	0.00905169	0.00908424
5	4	6295.49	1.74914	0.88703	0.00428	0.85104	0.7522	0.82324	0.46497	1.64389	0.80414	0.00435	0.00795954	0.00795954	0.00795954
6	5	7858.73	1.7071	0.84335	0.00412	0.85919	0.7726	0.84039	0.48024	1.65108	0.78838	0.00438	0.00657963	0.00657963	0.00657963
7	6	9442.04	1.68114	0.80819	0.004	0.87317	0.78189	0.8509	0.49609	1.59614	0.74834	0.00414	0.00505	0.00505	0.00505
8	7	11003.4	1.64466	0.77151	0.00386	0.87628	0.79337	0.85823	0.50336	1.5728	0.70957	0.0041	0.00352037	0.00352037	0.00352037
9	8	12622.9	1.63356	0.75494	0.00384	0.88156	0.79801	0.86493	0.50986	1.56183	0.69742	0.00404	0.00214046	0.00214046	0.00214046
10	9	14222.6	1.60481	0.73602	0.00376	0.88322	0.8031	0.86886	0.51655	1.53246	0.69223	0.00395	0.00104537	0.00104537	0.00104537
11	10	15812.1	1.59291	0.72805	0.0037	0.88426	0.80407	0.8692	0.518	1.51821	0.68363	0.00389	0.00034227	0.00034227	0.00034227