epoch, train/loss, val/loss, metrics/accuracy_top1, metrics/accuracy_top5, lr/0 0, 0.76833, 0.88951, 0.81386, 1, 0.00098763 1, 0.56961, 0.76173, 0.8272, 1, 0.00097525 2, 0.52156, 0.75181, 0.84346, 1, 0.00096288 3, 0.50394, 0.73263, 0.85304, 1, 0.0009505 4, 0.49153, 0.75253, 0.85167, 1, 0.00093813 5, 0.48479, 0.71112, 0.85783, 1, 0.00092575 6, 0.47884, 0.78273, 0.8296, 1, 0.00091337 7, 0.47331, 0.71544, 0.85697, 1, 0.000901 8, 0.46737, 0.76133, 0.82173, 1, 0.00088862 9, 0.46363, 0.71349, 0.86125, 1, 0.00087625 10, 0.46036, 0.74129, 0.84927, 1, 0.00086388 11, 0.46043, 0.70623, 0.85321, 1, 0.0008515 12, 0.45487, 0.73228, 0.84175, 1, 0.00083913 13, 0.45247, 0.75296, 0.84192, 1, 0.00082675 14, 0.4525, 0.73755, 0.83011, 1, 0.00081437 15, 0.44834, 0.68267, 0.87169, 1, 0.000802 16, 0.44707, 0.72935, 0.83165, 1, 0.00078963 17, 0.44355, 0.73995, 0.83285, 1, 0.00077725 18, 0.44291, 0.7031, 0.85817, 1, 0.00076488 19, 0.44263, 0.70539, 0.85663, 1, 0.0007525 20, 0.44351, 0.69666, 0.85543, 1, 0.00074013 21, 0.43801, 0.68528, 0.8645, 1, 0.00072775 22, 0.44098, 0.73154, 0.84808, 1, 0.00071537 23, 0.43726, 0.7072, 0.8556, 1, 0.000703 24, 0.43937, 0.71911, 0.84842, 1, 0.00069063 25, 0.43528, 0.6947, 0.85081, 1, 0.00067825 26, 0.43546, 0.71886, 0.85252, 1, 0.00066588 27, 0.43499, 0.69865, 0.85406, 1, 0.0006535 28, 0.43395, 0.68224, 0.86313, 1, 0.00064112 29, 0.43236, 0.68289, 0.86262, 1, 0.00062875 30, 0.43158, 0.73109, 0.84893, 1, 0.00061638 31, 0.43218, 0.69988, 0.86159, 1, 0.000604 32, 0.43354, 0.70973, 0.85577, 1, 0.00059163 33, 0.42806, 0.71899, 0.84842, 1, 0.00057925 34, 0.42888, 0.68793, 0.86518, 1, 0.00056687 35, 0.43093, 0.67981, 0.86792, 1, 0.0005545 36, 0.42497, 0.67168, 0.87032, 1, 0.00054212 37, 0.42323, 0.72155, 0.85064, 1, 0.00052975 38, 0.42387, 0.72693, 0.8426, 1, 0.00051737 39, 0.42333, 0.68789, 0.85885, 1, 0.000505 40, 0.42672, 0.68061, 0.86484, 1, 0.00049263 41, 0.42602, 0.71904, 0.8426, 1, 0.00048025 42, 0.42367, 0.67061, 0.86672, 1, 0.00046788 43, 0.42397, 0.69298, 0.86245, 1, 0.0004555 44, 0.42405, 0.69577, 0.85868, 1, 0.00044312 45, 0.42383, 0.70744, 0.84927, 1, 0.00043075 46, 0.42419, 0.71168, 0.85543, 1, 0.00041838 47, 0.42105, 0.70456, 0.84996, 1, 0.000406 48, 0.42149, 0.71426, 0.85064, 1, 0.00039362 49, 0.4207, 0.72387, 0.84979, 1, 0.00038125 50, 0.41919, 0.68517, 0.86501, 1, 0.00036888 51, 0.41588, 0.68697, 0.858, 1, 0.0003565 52, 0.41676, 0.67847, 0.87066, 1, 0.00034413 53, 0.41357, 0.68348, 0.8698, 1, 0.00033175 54, 0.41561, 0.6747, 0.87117, 1, 0.00031938 55, 0.41378, 0.68828, 0.85577, 1, 0.000307 56, 0.41204, 0.68438, 0.85954, 1, 0.00029462 57, 0.41022, 0.69516, 0.86587, 1, 0.00028225 58, 0.41145, 0.68544, 0.86313, 1, 0.00026987 59, 0.41068, 0.70406, 0.85595, 1, 0.0002575 60, 0.41089, 0.70139, 0.85577, 1, 0.00024513 61, 0.40866, 0.69262, 0.85954, 1, 0.00023275 62, 0.40811, 0.699, 0.85749, 1, 0.00022038 63, 0.40757, 0.67834, 0.86536, 1, 0.000208 64, 0.40896, 0.69648, 0.85201, 1, 0.00019562 65, 0.40856, 0.71111, 0.85047, 1, 0.00018325 66, 0.40829, 0.68339, 0.86724, 1, 0.00017087 67, 0.40592, 0.70005, 0.85184, 1, 0.0001585 68, 0.4078, 0.71784, 0.84551, 1, 0.00014612 69, 0.40634, 0.69866, 0.85406, 1, 0.00013375 70, 0.4051, 0.69982, 0.85201, 1, 0.00012138 71, 0.40516, 0.68997, 0.85834, 1, 0.000109 72, 0.40603, 0.69182, 0.85629, 1, 9.6625e-05 73, 0.40428, 0.68093, 0.86262, 1, 8.425e-05 74, 0.40516, 0.68302, 0.85868, 1, 7.1875e-05 75, 0.40422, 0.6931, 0.85612, 1, 5.95e-05 76, 0.40299, 0.68952, 0.86005, 1, 4.7125e-05 77, 0.40328, 0.68088, 0.85766, 1, 3.475e-05 78, 0.40284, 0.67786, 0.86159, 1, 2.2375e-05 79, 0.40196, 0.68056, 0.86159, 1, 1e-05