projekt_widzenie_komputerowe/runs/train/results_1/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, 0.13469, 0.0060624, 0.038626, 0.30075, 0.038743, 0.02025, 0.0051783, 0.13018, 0.0033932, 0.033715, 0.070085, 0.0033239, 0.0033239
1, 0.1126, 0.0077347, 0.031893, 0.65128, 0.14117, 0.096937, 0.033664, 0.10578, 0.0040751, 0.028294, 0.039425, 0.0059982, 0.0059982
2, 0.10186, 0.0077215, 0.028553, 0.42974, 0.15813, 0.12459, 0.047166, 0.1012, 0.0043691, 0.034101, 0.0081064, 0.0080125, 0.0080125
3, 0.09818, 0.0078559, 0.028182, 0.44465, 0.17819, 0.1568, 0.063103, 0.097293, 0.0040085, 0.027273, 0.00703, 0.00703, 0.00703
4, 0.09584, 0.0078941, 0.026812, 0.70401, 0.1895, 0.18107, 0.071967, 0.093913, 0.0041139, 0.024438, 0.00703, 0.00703, 0.00703
5, 0.092692, 0.0079571, 0.025986, 0.257, 0.18774, 0.1733, 0.071203, 0.091866, 0.0041356, 0.025365, 0.00604, 0.00604, 0.00604
6, 0.090693, 0.0079465, 0.024737, 0.30127, 0.19902, 0.19957, 0.089435, 0.08979, 0.0041636, 0.024563, 0.00505, 0.00505, 0.00505
7, 0.088785, 0.008105, 0.023571, 0.31896, 0.19599, 0.21638, 0.094838, 0.08762, 0.0042317, 0.022281, 0.00406, 0.00406, 0.00406
8, 0.086847, 0.0079977, 0.022821, 0.29883, 0.21423, 0.22019, 0.097198, 0.085885, 0.0042165, 0.02358, 0.00307, 0.00307, 0.00307
9, 0.085288, 0.0080271, 0.022086, 0.3179, 0.20329, 0.23418, 0.1055, 0.084576, 0.004295, 0.021792, 0.00208, 0.00208, 0.00208