projekt_widzenie_komputerowe/runs/train/results_8/results.csv

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2023-01-31 18:36:44 +01:00
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
0, 0.069347, 0.02614, 0.031257, 0.48989, 0.27777, 0.28012, 0.15386, 0.053376, 0.019235, 0.023568, 0.070085, 0.0033239, 0.0033239
1, 0.048776, 0.020244, 0.017335, 0.36561, 0.4144, 0.38856, 0.21573, 0.047874, 0.018281, 0.015492, 0.039425, 0.0059982, 0.0059982
2, 0.043484, 0.018109, 0.011119, 0.45173, 0.49741, 0.43037, 0.22956, 0.043897, 0.017971, 0.014825, 0.0081064, 0.0080125, 0.0080125
3, 0.03828, 0.017498, 0.0088776, 0.61153, 0.57353, 0.57693, 0.32726, 0.040996, 0.017888, 0.01196, 0.00703, 0.00703, 0.00703
4, 0.036056, 0.016835, 0.0078008, 0.77317, 0.57956, 0.63012, 0.37014, 0.03875, 0.016991, 0.0095734, 0.00703, 0.00703, 0.00703
5, 0.033192, 0.015954, 0.0065002, 0.7303, 0.56386, 0.60915, 0.37545, 0.036429, 0.018573, 0.011016, 0.00604, 0.00604, 0.00604
6, 0.030798, 0.015143, 0.0053448, 0.72521, 0.64764, 0.67945, 0.4289, 0.035101, 0.016675, 0.0092714, 0.00505, 0.00505, 0.00505
7, 0.028716, 0.014587, 0.0044241, 0.74238, 0.60477, 0.66168, 0.39363, 0.035546, 0.016748, 0.0093834, 0.00406, 0.00406, 0.00406
8, 0.02654, 0.013744, 0.0036463, 0.75025, 0.67852, 0.71584, 0.45037, 0.032078, 0.015876, 0.0081273, 0.00307, 0.00307, 0.00307
9, 0.024503, 0.012948, 0.0029252, 0.80428, 0.66697, 0.72371, 0.47146, 0.03154, 0.015771, 0.0077915, 0.00208, 0.00208, 0.00208