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, 0.078542, 0.02568, 0.035032, 0.74281, 0.23605, 0.23602, 0.11138, 0.058415, 0.019894, 0.026096, 0.070085, 0.0033239, 0.0033239 1, 0.052498, 0.021771, 0.022678, 0.61487, 0.35, 0.33481, 0.16656, 0.052383, 0.019588, 0.020902, 0.039425, 0.0059982, 0.0059982 2, 0.047611, 0.020013, 0.016105, 0.38711, 0.36097, 0.3337, 0.17089, 0.048994, 0.019399, 0.017263, 0.0081064, 0.0080125, 0.0080125 3, 0.043559, 0.019648, 0.012751, 0.64691, 0.43444, 0.42968, 0.22844, 0.045498, 0.019466, 0.014561, 0.00703, 0.00703, 0.00703 4, 0.041165, 0.018984, 0.010539, 0.58156, 0.49039, 0.49236, 0.26249, 0.044204, 0.018673, 0.013861, 0.00703, 0.00703, 0.00703 5, 0.038333, 0.018162, 0.0088667, 0.57762, 0.45581, 0.46899, 0.27385, 0.04282, 0.01874, 0.016473, 0.00604, 0.00604, 0.00604 6, 0.035928, 0.017525, 0.007596, 0.64519, 0.55322, 0.5943, 0.32928, 0.040422, 0.018116, 0.010035, 0.00505, 0.00505, 0.00505 7, 0.033772, 0.016956, 0.0062505, 0.70005, 0.59095, 0.63776, 0.36488, 0.037801, 0.01726, 0.009911, 0.00406, 0.00406, 0.00406 8, 0.031587, 0.01603, 0.0052052, 0.70292, 0.60754, 0.6441, 0.37798, 0.036394, 0.017252, 0.0098161, 0.00307, 0.00307, 0.00307 9, 0.029743, 0.015454, 0.0044488, 0.76678, 0.60668, 0.67996, 0.40603, 0.034748, 0.016629, 0.008751, 0.00208, 0.00208, 0.00208