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, 0.074498, 0.020847, 0.032067, 0.75428, 0.22348, 0.25689, 0.13497, 0.055433, 0.015301, 0.023689, 0.070085, 0.0033239, 0.0033239 1, 0.051145, 0.017354, 0.019695, 0.4091, 0.39594, 0.38807, 0.21163, 0.048864, 0.014006, 0.017083, 0.039425, 0.0059982, 0.0059982 2, 0.046066, 0.01567, 0.013473, 0.69122, 0.37805, 0.40579, 0.21571, 0.047747, 0.014243, 0.016765, 0.0081064, 0.0080125, 0.0080125 3, 0.041101, 0.015213, 0.011109, 0.64602, 0.50087, 0.52603, 0.2835, 0.044736, 0.013588, 0.013264, 0.00703, 0.00703, 0.00703 4, 0.038934, 0.014793, 0.009597, 0.7379, 0.52402, 0.56409, 0.33132, 0.042318, 0.013249, 0.011311, 0.00703, 0.00703, 0.00703 5, 0.035945, 0.013984, 0.0079636, 0.63181, 0.54416, 0.54514, 0.31192, 0.039803, 0.012871, 0.010107, 0.00604, 0.00604, 0.00604 6, 0.033565, 0.013347, 0.0069709, 0.68802, 0.55658, 0.6004, 0.34142, 0.037006, 0.012789, 0.0093024, 0.00505, 0.00505, 0.00505 7, 0.031161, 0.012741, 0.0055128, 0.75912, 0.5722, 0.64561, 0.38051, 0.037048, 0.012647, 0.0089898, 0.00406, 0.00406, 0.00406 8, 0.028813, 0.012143, 0.0049633, 0.71432, 0.62543, 0.65067, 0.39142, 0.034818, 0.012236, 0.0083539, 0.00307, 0.00307, 0.00307 9, 0.026723, 0.011531, 0.0040393, 0.7773, 0.64868, 0.70057, 0.43678, 0.033089, 0.011777, 0.0080895, 0.00208, 0.00208, 0.00208