projekt_widzenie_komputerowe/runs/train/results_3/results.csv

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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, 0.084574, 0.020132, 0.035698, 0.48087, 0.22452, 0.22325, 0.10901, 0.060934, 0.01588, 0.025968, 0.070085, 0.0033239, 0.0033239
1, 0.056059, 0.018437, 0.024198, 0.54066, 0.26521, 0.3123, 0.17168, 0.052825, 0.014783, 0.021491, 0.039425, 0.0059982, 0.0059982
2, 0.050859, 0.016925, 0.019594, 0.67609, 0.30748, 0.34502, 0.16825, 0.051766, 0.014737, 0.018702, 0.0081064, 0.0080125, 0.0080125
3, 0.046762, 0.016753, 0.015399, 0.50219, 0.4334, 0.43979, 0.22872, 0.047696, 0.014186, 0.01638, 0.00703, 0.00703, 0.00703
4, 0.04474, 0.016423, 0.013035, 0.54237, 0.45423, 0.47537, 0.24834, 0.046939, 0.014413, 0.013866, 0.00703, 0.00703, 0.00703
5, 0.041912, 0.015774, 0.011253, 0.4832, 0.51201, 0.45422, 0.24426, 0.045174, 0.014024, 0.015313, 0.00604, 0.00604, 0.00604
6, 0.039236, 0.01527, 0.0096607, 0.57824, 0.50901, 0.50279, 0.28417, 0.042745, 0.013987, 0.012881, 0.00505, 0.00505, 0.00505
7, 0.037107, 0.014786, 0.0081398, 0.66669, 0.52358, 0.56147, 0.32199, 0.041162, 0.013688, 0.011917, 0.00406, 0.00406, 0.00406
8, 0.034843, 0.014237, 0.007172, 0.67894, 0.55268, 0.60193, 0.34292, 0.039691, 0.012839, 0.0096556, 0.00307, 0.00307, 0.00307
9, 0.032906, 0.013823, 0.0060825, 0.66232, 0.55073, 0.60221, 0.34825, 0.03849, 0.012679, 0.009747, 0.00208, 0.00208, 0.00208