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, 0.096813, 0.037328, 0.041379, 0.0040891, 0.80352, 0.02009, 0.0058749, 0.057555, 0.017495, 0.029821, 0.0811, 0.0021, 0.0021 1, 0.072579, 0.04069, 0.02882, 0.12845, 0.24419, 0.15756, 0.05033, 0.048461, 0.019931, 0.010532, 0.060874, 0.0038743, 0.0038743 2, 0.067962, 0.032544, 0.014419, 0.18928, 0.39416, 0.22056, 0.076052, 0.058436, 0.013271, 0.0040205, 0.040213, 0.005213, 0.005213 3, 0.066434, 0.028655, 0.0080511, 0.34442, 0.50343, 0.38057, 0.15462, 0.046054, 0.0092229, 0.0023441, 0.019116, 0.0061161, 0.0061161 4, 0.062911, 0.024652, 0.0056447, 0.41559, 0.57603, 0.44972, 0.21637, 0.044851, 0.0083119, 0.003041, 0.00604, 0.00604, 0.00604 5, 0.058346, 0.02502, 0.0053083, 0.37331, 0.58438, 0.50334, 0.25572, 0.059538, 0.0072045, 0.0026608, 0.00604, 0.00604, 0.00604 6, 0.052821, 0.02206, 0.0047106, 0.69074, 0.61106, 0.66258, 0.25812, 0.037017, 0.0061149, 0.002599, 0.00505, 0.00505, 0.00505 7, 0.045153, 0.021208, 0.0037388, 0.77055, 0.76227, 0.74814, 0.39531, 0.027794, 0.0057149, 0.0020172, 0.00406, 0.00406, 0.00406 8, 0.041362, 0.02252, 0.0032779, 0.74324, 0.6537, 0.71637, 0.43989, 0.031088, 0.0058168, 0.0015123, 0.00307, 0.00307, 0.00307 9, 0.037679, 0.019484, 0.0024481, 0.75642, 0.71139, 0.71802, 0.48023, 0.02487, 0.0056382, 0.0013283, 0.00208, 0.00208, 0.00208