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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