diff --git "a/face_recognition1/face_feature/log.log" "b/face_recognition1/face_feature/log.log"
deleted file mode 100644--- "a/face_recognition1/face_feature/log.log"
+++ /dev/null
@@ -1,8614 +0,0 @@
-20220630-16:38:42 Setting up a new session...
-20220630-16:38:42 Visdom successfully connected to server
-20220630-16:38:42 Train Epoch: 1/18 ...
-20220630-16:40:49 Iters: 000100/[01], loss: 20.0625, train_accuracy: 0.0000, time: 1.26 s/iter, learning rate: 0.05
-20220630-16:42:56 Iters: 000200/[01], loss: 19.2577, train_accuracy: 0.0000, time: 1.27 s/iter, learning rate: 0.05
-20220630-16:45:05 Iters: 000300/[01], loss: 19.5273, train_accuracy: 0.0000, time: 1.29 s/iter, learning rate: 0.05
-20220630-16:47:11 Iters: 000400/[01], loss: 20.2498, train_accuracy: 0.0000, time: 1.27 s/iter, learning rate: 0.05
-20220630-16:49:17 Iters: 000500/[01], loss: 20.0103, train_accuracy: 0.0000, time: 1.25 s/iter, learning rate: 0.05
-20220630-16:51:23 Iters: 000600/[01], loss: 20.6090, train_accuracy: 0.0000, time: 1.26 s/iter, learning rate: 0.05
-20220630-16:53:27 Iters: 000700/[01], loss: 20.1846, train_accuracy: 0.0078, time: 1.25 s/iter, learning rate: 0.05
-20220630-16:55:32 Iters: 000800/[01], loss: 20.5324, train_accuracy: 0.0000, time: 1.25 s/iter, learning rate: 0.05
-20220630-16:57:37 Iters: 000900/[01], loss: 20.7694, train_accuracy: 0.0000, time: 1.25 s/iter, learning rate: 0.05
-20220630-16:59:44 Iters: 001000/[01], loss: 19.8633, train_accuracy: 0.0078, time: 1.27 s/iter, learning rate: 0.05
-20220630-17:01:47 Iters: 001100/[01], loss: 19.4772, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220630-17:03:51 Iters: 001200/[01], loss: 20.0557, train_accuracy: 0.0000, time: 1.24 s/iter, learning rate: 0.05
-20220630-17:05:59 Iters: 001300/[01], loss: 19.5078, train_accuracy: 0.0000, time: 1.27 s/iter, learning rate: 0.05
-20220630-17:08:05 Iters: 001400/[01], loss: 19.1199, train_accuracy: 0.0000, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:10:11 Iters: 001500/[01], loss: 18.8668, train_accuracy: 0.0000, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:12:17 Iters: 001600/[01], loss: 17.4952, train_accuracy: 0.0000, time: 1.27 s/iter, learning rate: 0.05
-20220630-17:14:23 Iters: 001700/[01], loss: 18.5381, train_accuracy: 0.0000, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:16:29 Iters: 001800/[01], loss: 17.8031, train_accuracy: 0.0078, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:18:35 Iters: 001900/[01], loss: 17.3867, train_accuracy: 0.0078, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:20:41 Iters: 002000/[01], loss: 16.7205, train_accuracy: 0.0000, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:22:48 Iters: 002100/[01], loss: 16.1001, train_accuracy: 0.0078, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:24:54 Iters: 002200/[01], loss: 16.4960, train_accuracy: 0.0000, time: 1.27 s/iter, learning rate: 0.05
-20220630-17:26:59 Iters: 002300/[01], loss: 16.0385, train_accuracy: 0.0000, time: 1.25 s/iter, learning rate: 0.05
-20220630-17:29:05 Iters: 002400/[01], loss: 16.1072, train_accuracy: 0.0000, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:31:08 Iters: 002500/[01], loss: 16.1451, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220630-17:33:13 Iters: 002600/[01], loss: 15.9080, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220630-17:35:17 Iters: 002700/[01], loss: 15.9791, train_accuracy: 0.0000, time: 1.25 s/iter, learning rate: 0.05
-20220630-17:37:20 Iters: 002800/[01], loss: 15.5529, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220630-17:39:27 Iters: 002900/[01], loss: 13.3363, train_accuracy: 0.0156, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:41:31 Iters: 003000/[01], loss: 13.8127, train_accuracy: 0.0000, time: 1.25 s/iter, learning rate: 0.05
-20220630-17:43:37 Iters: 003100/[01], loss: 13.8907, train_accuracy: 0.0078, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:45:44 Iters: 003200/[01], loss: 14.3198, train_accuracy: 0.0156, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:47:50 Iters: 003300/[01], loss: 13.3309, train_accuracy: 0.0312, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:49:56 Iters: 003400/[01], loss: 13.3648, train_accuracy: 0.0156, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:52:02 Iters: 003500/[01], loss: 13.2147, train_accuracy: 0.0312, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:54:08 Iters: 003600/[01], loss: 14.2585, train_accuracy: 0.0000, time: 1.26 s/iter, learning rate: 0.05
-20220630-17:56:15 Iters: 003700/[01], loss: 13.3423, train_accuracy: 0.0234, time: 1.27 s/iter, learning rate: 0.05
-20220630-17:58:21 Iters: 003800/[01], loss: 13.5289, train_accuracy: 0.0312, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:00:27 Iters: 003900/[01], loss: 12.6478, train_accuracy: 0.0234, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:02:33 Iters: 004000/[01], loss: 12.4929, train_accuracy: 0.0156, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:04:39 Iters: 004100/[01], loss: 12.5916, train_accuracy: 0.0078, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:06:45 Iters: 004200/[01], loss: 12.7267, train_accuracy: 0.0234, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:08:50 Iters: 004300/[01], loss: 13.3752, train_accuracy: 0.0078, time: 1.25 s/iter, learning rate: 0.05
-20220630-18:10:56 Iters: 004400/[01], loss: 11.8711, train_accuracy: 0.0156, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:13:02 Iters: 004500/[01], loss: 12.3482, train_accuracy: 0.0078, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:15:08 Iters: 004600/[01], loss: 13.1396, train_accuracy: 0.0078, time: 1.27 s/iter, learning rate: 0.05
-20220630-18:17:14 Iters: 004700/[01], loss: 12.7013, train_accuracy: 0.0234, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:19:20 Iters: 004800/[01], loss: 12.2291, train_accuracy: 0.0078, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:21:25 Iters: 004900/[01], loss: 12.5359, train_accuracy: 0.0312, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:23:32 Iters: 005000/[01], loss: 12.5428, train_accuracy: 0.0078, time: 1.27 s/iter, learning rate: 0.05
-20220630-18:25:38 Iters: 005100/[01], loss: 13.0179, train_accuracy: 0.0312, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:27:42 Iters: 005200/[01], loss: 12.0935, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-18:29:49 Iters: 005300/[01], loss: 11.5881, train_accuracy: 0.0234, time: 1.27 s/iter, learning rate: 0.05
-20220630-18:31:53 Iters: 005400/[01], loss: 12.4582, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-18:34:00 Iters: 005500/[01], loss: 11.6767, train_accuracy: 0.0469, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:36:05 Iters: 005600/[01], loss: 12.7326, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220630-18:38:10 Iters: 005700/[01], loss: 12.9674, train_accuracy: 0.0312, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:40:16 Iters: 005800/[01], loss: 11.7937, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-18:42:20 Iters: 005900/[01], loss: 12.8238, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220630-18:44:26 Iters: 006000/[01], loss: 11.8075, train_accuracy: 0.0391, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:46:32 Iters: 006100/[01], loss: 12.5232, train_accuracy: 0.0234, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:48:38 Iters: 006200/[01], loss: 11.9104, train_accuracy: 0.0156, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:50:44 Iters: 006300/[01], loss: 11.8216, train_accuracy: 0.0156, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:52:50 Iters: 006400/[01], loss: 11.9315, train_accuracy: 0.0234, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:54:54 Iters: 006500/[01], loss: 11.9345, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220630-18:56:59 Iters: 006600/[01], loss: 12.2135, train_accuracy: 0.0312, time: 1.26 s/iter, learning rate: 0.05
-20220630-18:59:06 Iters: 006700/[01], loss: 11.7133, train_accuracy: 0.0156, time: 1.27 s/iter, learning rate: 0.05
-20220630-19:01:12 Iters: 006800/[01], loss: 11.9283, train_accuracy: 0.0156, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:03:17 Iters: 006900/[01], loss: 11.7598, train_accuracy: 0.0078, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:05:23 Iters: 007000/[01], loss: 11.8255, train_accuracy: 0.0312, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:07:28 Iters: 007100/[01], loss: 11.9760, train_accuracy: 0.0000, time: 1.25 s/iter, learning rate: 0.05
-20220630-19:09:34 Iters: 007200/[01], loss: 12.3674, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-19:11:39 Iters: 007300/[01], loss: 11.9335, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-19:13:43 Iters: 007400/[01], loss: 11.2701, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220630-19:15:49 Iters: 007500/[01], loss: 11.3916, train_accuracy: 0.0391, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:17:53 Iters: 007600/[01], loss: 11.9061, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220630-19:19:58 Iters: 007700/[01], loss: 11.1933, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220630-19:22:04 Iters: 007800/[01], loss: 11.1660, train_accuracy: 0.0469, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:24:10 Iters: 007900/[01], loss: 11.8091, train_accuracy: 0.0547, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:26:15 Iters: 008000/[01], loss: 11.6959, train_accuracy: 0.0625, time: 1.25 s/iter, learning rate: 0.05
-20220630-19:28:21 Iters: 008100/[01], loss: 11.1003, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-19:30:27 Iters: 008200/[01], loss: 12.0242, train_accuracy: 0.0234, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:32:32 Iters: 008300/[01], loss: 11.3920, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-19:34:38 Iters: 008400/[01], loss: 12.1156, train_accuracy: 0.0078, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:36:44 Iters: 008500/[01], loss: 12.4508, train_accuracy: 0.0078, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:38:49 Iters: 008600/[01], loss: 11.8956, train_accuracy: 0.0391, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:40:55 Iters: 008700/[01], loss: 12.1465, train_accuracy: 0.0000, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:42:59 Iters: 008800/[01], loss: 11.9722, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-19:45:06 Iters: 008900/[01], loss: 11.7257, train_accuracy: 0.0156, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:47:10 Iters: 009000/[01], loss: 11.5601, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-19:49:15 Iters: 009100/[01], loss: 11.8287, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220630-19:51:20 Iters: 009200/[01], loss: 13.1837, train_accuracy: 0.0078, time: 1.25 s/iter, learning rate: 0.05
-20220630-19:53:26 Iters: 009300/[01], loss: 12.1132, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220630-19:55:31 Iters: 009400/[01], loss: 11.3624, train_accuracy: 0.0312, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:57:37 Iters: 009500/[01], loss: 12.4750, train_accuracy: 0.0156, time: 1.26 s/iter, learning rate: 0.05
-20220630-19:59:43 Iters: 009600/[01], loss: 11.3134, train_accuracy: 0.0469, time: 1.26 s/iter, learning rate: 0.05
-20220630-20:01:48 Iters: 009700/[01], loss: 12.4367, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:03:53 Iters: 009800/[01], loss: 11.4048, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220630-20:05:59 Iters: 009900/[01], loss: 11.2492, train_accuracy: 0.0469, time: 1.26 s/iter, learning rate: 0.05
-20220630-20:08:04 Iters: 010000/[01], loss: 11.2687, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:08:04 Saving checkpoint: 10000
-20220630-20:09:20 LFW Ave Accuracy: 98.7165
-20220630-20:10:35 AgeDB-30 Ave Accuracy: 93.0167
-20220630-20:12:02 CFP-FP Ave Accuracy: 86.9000
-20220630-20:12:02 Current Best Accuracy: LFW: 98.7165 in iters: 10000, AgeDB-30: 93.0167 in iters: 10000 and CFP-FP: 86.9000 in iters: 10000
-20220630-20:14:07 Iters: 010100/[01], loss: 11.1374, train_accuracy: 0.0547, time: 3.63 s/iter, learning rate: 0.05
-20220630-20:16:12 Iters: 010200/[01], loss: 11.0682, train_accuracy: 0.0156, time: 1.26 s/iter, learning rate: 0.05
-20220630-20:18:17 Iters: 010300/[01], loss: 11.2882, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:20:23 Iters: 010400/[01], loss: 12.6716, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:22:28 Iters: 010500/[01], loss: 11.6022, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:24:33 Iters: 010600/[01], loss: 10.2224, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:26:39 Iters: 010700/[01], loss: 11.9506, train_accuracy: 0.0078, time: 1.26 s/iter, learning rate: 0.05
-20220630-20:28:44 Iters: 010800/[01], loss: 11.7347, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:30:49 Iters: 010900/[01], loss: 11.4881, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:32:54 Iters: 011000/[01], loss: 11.6399, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:35:00 Iters: 011100/[01], loss: 11.7817, train_accuracy: 0.0312, time: 1.26 s/iter, learning rate: 0.05
-20220630-20:37:04 Iters: 011200/[01], loss: 11.7797, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220630-20:39:09 Iters: 011300/[01], loss: 12.1715, train_accuracy: 0.0234, time: 1.26 s/iter, learning rate: 0.05
-20220630-20:41:14 Iters: 011400/[01], loss: 11.9413, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:43:19 Iters: 011500/[01], loss: 12.0871, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220630-20:45:24 Iters: 011600/[01], loss: 12.0167, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:47:29 Iters: 011700/[01], loss: 12.6158, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:49:32 Iters: 011800/[01], loss: 10.8940, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-20:51:37 Iters: 011900/[01], loss: 12.2561, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:53:41 Iters: 012000/[01], loss: 12.1594, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:55:46 Iters: 012100/[01], loss: 12.0740, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:57:51 Iters: 012200/[01], loss: 11.3215, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-20:59:54 Iters: 012300/[01], loss: 11.4316, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220630-21:02:00 Iters: 012400/[01], loss: 11.9833, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:04:04 Iters: 012500/[01], loss: 12.5649, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-21:06:08 Iters: 012600/[01], loss: 12.1056, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220630-21:08:13 Iters: 012700/[01], loss: 12.4893, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:10:18 Iters: 012800/[01], loss: 11.5958, train_accuracy: 0.0078, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:12:23 Iters: 012900/[01], loss: 11.2983, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:14:28 Iters: 013000/[01], loss: 11.9740, train_accuracy: 0.0000, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:16:34 Iters: 013100/[01], loss: 11.8278, train_accuracy: 0.0078, time: 1.26 s/iter, learning rate: 0.05
-20220630-21:18:38 Iters: 013200/[01], loss: 12.1489, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220630-21:20:43 Iters: 013300/[01], loss: 11.2506, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:22:47 Iters: 013400/[01], loss: 11.9923, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220630-21:24:50 Iters: 013500/[01], loss: 11.7996, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220630-21:26:55 Iters: 013600/[01], loss: 11.7998, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:28:59 Iters: 013700/[01], loss: 12.8082, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220630-21:31:04 Iters: 013800/[01], loss: 10.8490, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220630-21:33:09 Iters: 013900/[01], loss: 12.6718, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:35:13 Iters: 014000/[01], loss: 12.1663, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220630-21:37:17 Iters: 014100/[01], loss: 11.1713, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220630-21:39:22 Iters: 014200/[01], loss: 10.7771, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220630-21:41:26 Iters: 014300/[01], loss: 11.3951, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:43:31 Iters: 014400/[01], loss: 11.5433, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:45:36 Iters: 014500/[01], loss: 11.6415, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220630-21:47:41 Iters: 014600/[01], loss: 12.2698, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:49:46 Iters: 014700/[01], loss: 11.7846, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:51:50 Iters: 014800/[01], loss: 11.8545, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-21:53:55 Iters: 014900/[01], loss: 12.2453, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:55:59 Iters: 015000/[01], loss: 12.2990, train_accuracy: 0.0000, time: 1.25 s/iter, learning rate: 0.05
-20220630-21:58:04 Iters: 015100/[01], loss: 12.3534, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:00:08 Iters: 015200/[01], loss: 11.7781, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:02:13 Iters: 015300/[01], loss: 12.0465, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:04:18 Iters: 015400/[01], loss: 12.2721, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:06:22 Iters: 015500/[01], loss: 10.7219, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:08:25 Iters: 015600/[01], loss: 11.3623, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220630-22:10:31 Iters: 015700/[01], loss: 10.7974, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:12:34 Iters: 015800/[01], loss: 11.6881, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:14:39 Iters: 015900/[01], loss: 12.1142, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:16:44 Iters: 016000/[01], loss: 11.1876, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:18:48 Iters: 016100/[01], loss: 11.0110, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:20:53 Iters: 016200/[01], loss: 12.3639, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:22:57 Iters: 016300/[01], loss: 11.0680, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:25:02 Iters: 016400/[01], loss: 11.6412, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:27:07 Iters: 016500/[01], loss: 11.1368, train_accuracy: 0.0547, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:29:12 Iters: 016600/[01], loss: 11.5297, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:31:16 Iters: 016700/[01], loss: 12.7750, train_accuracy: 0.0078, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:33:21 Iters: 016800/[01], loss: 11.4092, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:35:25 Iters: 016900/[01], loss: 12.1012, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:37:31 Iters: 017000/[01], loss: 11.6980, train_accuracy: 0.0312, time: 1.26 s/iter, learning rate: 0.05
-20220630-22:39:35 Iters: 017100/[01], loss: 11.4866, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:41:39 Iters: 017200/[01], loss: 11.2509, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:43:45 Iters: 017300/[01], loss: 11.7655, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:45:49 Iters: 017400/[01], loss: 12.3935, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:47:54 Iters: 017500/[01], loss: 11.3054, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:49:59 Iters: 017600/[01], loss: 12.3600, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:52:02 Iters: 017700/[01], loss: 12.1139, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220630-22:54:08 Iters: 017800/[01], loss: 11.8162, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:56:12 Iters: 017900/[01], loss: 11.0721, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-22:58:17 Iters: 018000/[01], loss: 10.6487, train_accuracy: 0.0703, time: 1.25 s/iter, learning rate: 0.05
-20220630-23:00:21 Iters: 018100/[01], loss: 11.7918, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:02:25 Iters: 018200/[01], loss: 11.0600, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:04:31 Iters: 018300/[01], loss: 11.6251, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220630-23:06:35 Iters: 018400/[01], loss: 11.9639, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:08:40 Iters: 018500/[01], loss: 11.0882, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-23:10:44 Iters: 018600/[01], loss: 11.6982, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-23:12:48 Iters: 018700/[01], loss: 12.2908, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:14:53 Iters: 018800/[01], loss: 11.1374, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220630-23:16:58 Iters: 018900/[01], loss: 11.3076, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220630-23:19:02 Iters: 019000/[01], loss: 11.8681, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-23:21:06 Iters: 019100/[01], loss: 11.0559, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:23:09 Iters: 019200/[01], loss: 11.7909, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220630-23:25:15 Iters: 019300/[01], loss: 11.0974, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220630-23:27:18 Iters: 019400/[01], loss: 11.0624, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:29:23 Iters: 019500/[01], loss: 10.7418, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220630-23:31:27 Iters: 019600/[01], loss: 11.1713, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:33:31 Iters: 019700/[01], loss: 11.3269, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:35:36 Iters: 019800/[01], loss: 11.5654, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220630-23:37:40 Iters: 019900/[01], loss: 12.0226, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:39:44 Iters: 020000/[01], loss: 11.0354, train_accuracy: 0.0781, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:39:44 Saving checkpoint: 20000
-20220630-23:41:00 LFW Ave Accuracy: 99.0999
-20220630-23:42:15 AgeDB-30 Ave Accuracy: 93.4667
-20220630-23:43:41 CFP-FP Ave Accuracy: 87.1286
-20220630-23:43:41 Current Best Accuracy: LFW: 99.0999 in iters: 20000, AgeDB-30: 93.4667 in iters: 20000 and CFP-FP: 87.1286 in iters: 20000
-20220630-23:45:45 Iters: 020100/[01], loss: 11.5514, train_accuracy: 0.0469, time: 3.61 s/iter, learning rate: 0.05
-20220630-23:47:50 Iters: 020200/[01], loss: 11.5414, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220630-23:49:53 Iters: 020300/[01], loss: 10.8997, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220630-23:51:57 Iters: 020400/[01], loss: 10.9668, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:54:01 Iters: 020500/[01], loss: 12.0623, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:56:05 Iters: 020600/[01], loss: 11.0889, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220630-23:58:09 Iters: 020700/[01], loss: 12.2413, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:00:13 Iters: 020800/[01], loss: 11.5123, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:02:17 Iters: 020900/[01], loss: 11.6141, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:04:22 Iters: 021000/[01], loss: 11.0537, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220701-00:06:25 Iters: 021100/[01], loss: 12.3202, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:08:30 Iters: 021200/[01], loss: 11.1309, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:10:34 Iters: 021300/[01], loss: 11.8202, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:12:39 Iters: 021400/[01], loss: 11.0946, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:14:42 Iters: 021500/[01], loss: 11.6951, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:16:47 Iters: 021600/[01], loss: 11.8300, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:18:51 Iters: 021700/[01], loss: 11.1428, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:20:55 Iters: 021800/[01], loss: 11.9226, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:22:59 Iters: 021900/[01], loss: 11.4334, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:25:04 Iters: 022000/[01], loss: 11.2505, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220701-00:27:08 Iters: 022100/[01], loss: 11.5022, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:29:12 Iters: 022200/[01], loss: 11.4580, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:31:15 Iters: 022300/[01], loss: 10.9963, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:33:20 Iters: 022400/[01], loss: 12.5162, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:35:24 Iters: 022500/[01], loss: 11.8478, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:37:28 Iters: 022600/[01], loss: 10.8928, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:39:32 Iters: 022700/[01], loss: 11.7991, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:41:37 Iters: 022800/[01], loss: 11.4535, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220701-00:43:42 Iters: 022900/[01], loss: 11.4141, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220701-00:45:45 Iters: 023000/[01], loss: 11.9053, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:47:50 Iters: 023100/[01], loss: 11.4387, train_accuracy: 0.0781, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:49:53 Iters: 023200/[01], loss: 11.5925, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-00:51:57 Iters: 023300/[01], loss: 10.9169, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:54:02 Iters: 023400/[01], loss: 11.0074, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220701-00:56:06 Iters: 023500/[01], loss: 11.6325, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-00:58:10 Iters: 023600/[01], loss: 11.3721, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:00:14 Iters: 023700/[01], loss: 11.4161, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-01:02:17 Iters: 023800/[01], loss: 11.4110, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-01:04:20 Iters: 023900/[01], loss: 11.1508, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-01:06:24 Iters: 024000/[01], loss: 11.5563, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:08:28 Iters: 024100/[01], loss: 11.6082, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:10:33 Iters: 024200/[01], loss: 11.6187, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220701-01:12:38 Iters: 024300/[01], loss: 11.3500, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:14:42 Iters: 024400/[01], loss: 11.6109, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:16:46 Iters: 024500/[01], loss: 11.7305, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:18:49 Iters: 024600/[01], loss: 11.6310, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-01:20:53 Iters: 024700/[01], loss: 11.2771, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:22:58 Iters: 024800/[01], loss: 11.5567, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220701-01:25:03 Iters: 024900/[01], loss: 11.3558, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220701-01:27:07 Iters: 025000/[01], loss: 11.2803, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:29:12 Iters: 025100/[01], loss: 10.7225, train_accuracy: 0.0547, time: 1.25 s/iter, learning rate: 0.05
-20220701-01:31:17 Iters: 025200/[01], loss: 12.2035, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220701-01:33:21 Iters: 025300/[01], loss: 11.3065, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:35:25 Iters: 025400/[01], loss: 12.0750, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:37:29 Iters: 025500/[01], loss: 11.8207, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:39:33 Iters: 025600/[01], loss: 11.7863, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:41:37 Iters: 025700/[01], loss: 11.8052, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:43:42 Iters: 025800/[01], loss: 11.6967, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220701-01:45:46 Iters: 025900/[01], loss: 11.0112, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:47:50 Iters: 026000/[01], loss: 11.3416, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:49:53 Iters: 026100/[01], loss: 10.8171, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-01:51:57 Iters: 026200/[01], loss: 11.8251, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:54:00 Iters: 026300/[01], loss: 11.8820, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:56:04 Iters: 026400/[01], loss: 10.9085, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-01:58:08 Iters: 026500/[01], loss: 11.5130, train_accuracy: 0.0000, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:00:12 Iters: 026600/[01], loss: 11.3758, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:02:17 Iters: 026700/[01], loss: 12.2802, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220701-02:04:21 Iters: 026800/[01], loss: 12.3203, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:06:25 Iters: 026900/[01], loss: 10.6449, train_accuracy: 0.0859, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:08:29 Iters: 027000/[01], loss: 12.0619, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:10:33 Iters: 027100/[01], loss: 11.4207, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:12:38 Iters: 027200/[01], loss: 13.1299, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220701-02:14:43 Iters: 027300/[01], loss: 10.8236, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220701-02:16:46 Iters: 027400/[01], loss: 11.8329, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:18:51 Iters: 027500/[01], loss: 10.4999, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:20:55 Iters: 027600/[01], loss: 11.5405, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220701-02:22:59 Iters: 027700/[01], loss: 10.9165, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:25:03 Iters: 027800/[01], loss: 11.8060, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:27:06 Iters: 027900/[01], loss: 12.3301, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:29:11 Iters: 028000/[01], loss: 11.5623, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:31:15 Iters: 028100/[01], loss: 11.5636, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:33:18 Iters: 028200/[01], loss: 11.4913, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-02:35:23 Iters: 028300/[01], loss: 11.1452, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220701-02:37:28 Iters: 028400/[01], loss: 10.8106, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:39:32 Iters: 028500/[01], loss: 11.5544, train_accuracy: 0.0625, time: 1.25 s/iter, learning rate: 0.05
-20220701-02:41:36 Iters: 028600/[01], loss: 10.8337, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:43:40 Iters: 028700/[01], loss: 11.3281, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:45:44 Iters: 028800/[01], loss: 11.6489, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:47:48 Iters: 028900/[01], loss: 10.9198, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:49:52 Iters: 029000/[01], loss: 11.2658, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-02:51:57 Iters: 029100/[01], loss: 12.0812, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220701-02:54:02 Iters: 029200/[01], loss: 11.4445, train_accuracy: 0.0547, time: 1.25 s/iter, learning rate: 0.05
-20220701-02:56:07 Iters: 029300/[01], loss: 11.4030, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220701-02:58:11 Iters: 029400/[01], loss: 11.1770, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:00:16 Iters: 029500/[01], loss: 12.2362, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220701-03:02:19 Iters: 029600/[01], loss: 11.6468, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:04:24 Iters: 029700/[01], loss: 11.4783, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:06:28 Iters: 029800/[01], loss: 10.6760, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:08:32 Iters: 029900/[01], loss: 11.2065, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220701-03:10:37 Iters: 030000/[01], loss: 11.3240, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220701-03:10:37 Saving checkpoint: 30000
-20220701-03:11:57 LFW Ave Accuracy: 99.0164
-20220701-03:13:12 AgeDB-30 Ave Accuracy: 92.6500
-20220701-03:14:38 CFP-FP Ave Accuracy: 88.3000
-20220701-03:14:38 Current Best Accuracy: LFW: 99.0999 in iters: 20000, AgeDB-30: 93.4667 in iters: 20000 and CFP-FP: 88.3000 in iters: 30000
-20220701-03:16:42 Iters: 030100/[01], loss: 11.6138, train_accuracy: 0.0469, time: 3.65 s/iter, learning rate: 0.05
-20220701-03:18:46 Iters: 030200/[01], loss: 11.6062, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:20:50 Iters: 030300/[01], loss: 11.6169, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:22:54 Iters: 030400/[01], loss: 11.6023, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220701-03:25:00 Iters: 030500/[01], loss: 11.2769, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220701-03:27:03 Iters: 030600/[01], loss: 11.5895, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-03:29:07 Iters: 030700/[01], loss: 12.0915, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220701-03:31:11 Iters: 030800/[01], loss: 11.0746, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:33:15 Iters: 030900/[01], loss: 11.8210, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:35:18 Iters: 031000/[01], loss: 11.7391, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-03:37:22 Iters: 031100/[01], loss: 11.4027, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:39:26 Iters: 031200/[01], loss: 11.0473, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:41:30 Iters: 031300/[01], loss: 11.8716, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:43:34 Iters: 031400/[01], loss: 11.0186, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:45:38 Iters: 031500/[01], loss: 11.5562, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220701-03:47:43 Iters: 031600/[01], loss: 12.2051, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:49:46 Iters: 031700/[01], loss: 11.8673, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:51:50 Iters: 031800/[01], loss: 11.8609, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-03:53:54 Iters: 031900/[01], loss: 10.9192, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:55:58 Iters: 032000/[01], loss: 11.1613, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-03:58:02 Iters: 032100/[01], loss: 11.9953, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220701-04:00:06 Iters: 032200/[01], loss: 10.9818, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-04:02:11 Iters: 032300/[01], loss: 11.6221, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220701-04:04:15 Iters: 032400/[01], loss: 11.7292, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:06:19 Iters: 032500/[01], loss: 11.6653, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:08:23 Iters: 032600/[01], loss: 11.3497, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:10:27 Iters: 032700/[01], loss: 10.8980, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:12:31 Iters: 032800/[01], loss: 11.6778, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:14:34 Iters: 032900/[01], loss: 11.0522, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-04:16:38 Iters: 033000/[01], loss: 12.1940, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:18:42 Iters: 033100/[01], loss: 12.1898, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:20:47 Iters: 033200/[01], loss: 11.9182, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220701-04:22:50 Iters: 033300/[01], loss: 11.4907, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:24:54 Iters: 033400/[01], loss: 10.7558, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:26:57 Iters: 033500/[01], loss: 11.2069, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-04:29:02 Iters: 033600/[01], loss: 11.1885, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:31:05 Iters: 033700/[01], loss: 10.9452, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-04:33:08 Iters: 033800/[01], loss: 11.0576, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-04:35:12 Iters: 033900/[01], loss: 12.2320, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:37:16 Iters: 034000/[01], loss: 11.3396, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:39:20 Iters: 034100/[01], loss: 11.4776, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:41:24 Iters: 034200/[01], loss: 12.0859, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:43:27 Iters: 034300/[01], loss: 11.3382, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:45:31 Iters: 034400/[01], loss: 12.0628, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:47:35 Iters: 034500/[01], loss: 11.5695, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:49:40 Iters: 034600/[01], loss: 11.8723, train_accuracy: 0.0000, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:51:44 Iters: 034700/[01], loss: 11.1827, train_accuracy: 0.0078, time: 1.25 s/iter, learning rate: 0.05
-20220701-04:53:49 Iters: 034800/[01], loss: 11.1093, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:55:52 Iters: 034900/[01], loss: 10.9200, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-04:57:56 Iters: 035000/[01], loss: 10.4321, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-05:00:00 Iters: 035100/[01], loss: 11.8893, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:02:04 Iters: 035200/[01], loss: 11.2568, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220701-05:04:09 Iters: 035300/[01], loss: 10.4308, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:06:13 Iters: 035400/[01], loss: 10.9350, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:08:17 Iters: 035500/[01], loss: 11.4569, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:10:21 Iters: 035600/[01], loss: 11.2916, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:12:26 Iters: 035700/[01], loss: 11.2180, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:14:29 Iters: 035800/[01], loss: 11.9831, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:16:34 Iters: 035900/[01], loss: 11.0056, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220701-05:18:38 Iters: 036000/[01], loss: 10.7409, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:20:42 Iters: 036100/[01], loss: 11.7702, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:22:45 Iters: 036200/[01], loss: 12.1758, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:24:49 Iters: 036300/[01], loss: 10.9207, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-05:26:53 Iters: 036400/[01], loss: 11.2893, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:28:56 Iters: 036500/[01], loss: 11.6541, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:31:01 Iters: 036600/[01], loss: 10.3341, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220701-05:33:06 Iters: 036700/[01], loss: 11.8912, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:35:10 Iters: 036800/[01], loss: 11.6473, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:37:14 Iters: 036900/[01], loss: 11.3900, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:39:18 Iters: 037000/[01], loss: 12.0032, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:41:22 Iters: 037100/[01], loss: 11.5540, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:43:25 Iters: 037200/[01], loss: 11.7384, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:45:30 Iters: 037300/[01], loss: 11.3316, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:47:33 Iters: 037400/[01], loss: 11.1057, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:49:38 Iters: 037500/[01], loss: 11.9859, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:51:42 Iters: 037600/[01], loss: 10.7516, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:53:45 Iters: 037700/[01], loss: 10.9528, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-05:55:50 Iters: 037800/[01], loss: 11.8421, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220701-05:57:54 Iters: 037900/[01], loss: 11.8987, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-05:59:58 Iters: 038000/[01], loss: 10.8755, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:02:02 Iters: 038100/[01], loss: 10.9241, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:04:06 Iters: 038200/[01], loss: 11.6272, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:06:10 Iters: 038300/[01], loss: 11.2140, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:08:13 Iters: 038400/[01], loss: 11.5150, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-06:10:17 Iters: 038500/[01], loss: 11.7515, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:12:22 Iters: 038600/[01], loss: 11.9528, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220701-06:14:26 Iters: 038700/[01], loss: 12.2517, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:16:30 Iters: 038800/[01], loss: 11.5458, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:18:34 Iters: 038900/[01], loss: 10.9502, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:20:38 Iters: 039000/[01], loss: 11.1686, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-06:22:41 Iters: 039100/[01], loss: 11.1516, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-06:24:44 Iters: 039200/[01], loss: 11.4633, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:26:49 Iters: 039300/[01], loss: 11.4060, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:28:53 Iters: 039400/[01], loss: 10.8091, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:30:57 Iters: 039500/[01], loss: 11.4708, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:33:02 Iters: 039600/[01], loss: 11.1045, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:35:05 Iters: 039700/[01], loss: 11.4680, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-06:37:08 Iters: 039800/[01], loss: 10.8580, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-06:39:13 Iters: 039900/[01], loss: 11.8548, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:41:17 Iters: 040000/[01], loss: 11.0208, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220701-06:41:17 Saving checkpoint: 40000
-20220701-06:42:34 LFW Ave Accuracy: 98.8499
-20220701-06:43:50 AgeDB-30 Ave Accuracy: 93.3333
-20220701-06:45:17 CFP-FP Ave Accuracy: 88.2143
-20220701-06:45:17 Current Best Accuracy: LFW: 99.0999 in iters: 20000, AgeDB-30: 93.4667 in iters: 20000 and CFP-FP: 88.3000 in iters: 30000
-20220701-06:47:21 Iters: 040100/[01], loss: 10.6595, train_accuracy: 0.0312, time: 3.63 s/iter, learning rate: 0.05
-20220701-06:49:24 Iters: 040200/[01], loss: 11.7789, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:51:29 Iters: 040300/[01], loss: 12.0041, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:53:32 Iters: 040400/[01], loss: 11.2319, train_accuracy: 0.0859, time: 1.23 s/iter, learning rate: 0.05
-20220701-06:55:36 Iters: 040500/[01], loss: 11.2636, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:57:40 Iters: 040600/[01], loss: 11.5869, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-06:59:44 Iters: 040700/[01], loss: 11.7784, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:01:48 Iters: 040800/[01], loss: 11.2890, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:03:51 Iters: 040900/[01], loss: 12.1085, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:05:55 Iters: 041000/[01], loss: 11.3644, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:07:59 Iters: 041100/[01], loss: 10.6851, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:10:04 Iters: 041200/[01], loss: 11.7198, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220701-07:12:07 Iters: 041300/[01], loss: 11.2463, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:14:12 Iters: 041400/[01], loss: 12.1332, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220701-07:16:16 Iters: 041500/[01], loss: 10.6909, train_accuracy: 0.0781, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:18:21 Iters: 041600/[01], loss: 11.4365, train_accuracy: 0.0312, time: 1.25 s/iter, learning rate: 0.05
-20220701-07:20:25 Iters: 041700/[01], loss: 11.0170, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-07:22:29 Iters: 041800/[01], loss: 11.9191, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:24:34 Iters: 041900/[01], loss: 11.9884, train_accuracy: 0.0078, time: 1.25 s/iter, learning rate: 0.05
-20220701-07:26:39 Iters: 042000/[01], loss: 11.2622, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220701-07:28:44 Iters: 042100/[01], loss: 11.0805, train_accuracy: 0.0156, time: 1.26 s/iter, learning rate: 0.05
-20220701-07:30:48 Iters: 042200/[01], loss: 12.2300, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:32:53 Iters: 042300/[01], loss: 11.2316, train_accuracy: 0.0547, time: 1.25 s/iter, learning rate: 0.05
-20220701-07:34:57 Iters: 042400/[01], loss: 10.8216, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:37:01 Iters: 042500/[01], loss: 11.2542, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:39:05 Iters: 042600/[01], loss: 10.7266, train_accuracy: 0.0781, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:41:09 Iters: 042700/[01], loss: 11.1635, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:43:14 Iters: 042800/[01], loss: 11.2245, train_accuracy: 0.0469, time: 1.25 s/iter, learning rate: 0.05
-20220701-07:45:18 Iters: 042900/[01], loss: 11.1957, train_accuracy: 0.0547, time: 1.25 s/iter, learning rate: 0.05
-20220701-07:47:23 Iters: 043000/[01], loss: 11.9149, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:49:27 Iters: 043100/[01], loss: 11.4861, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:51:30 Iters: 043200/[01], loss: 10.4382, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-07:53:34 Iters: 043300/[01], loss: 11.6306, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:55:37 Iters: 043400/[01], loss: 11.3022, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:57:41 Iters: 043500/[01], loss: 11.1832, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-07:59:46 Iters: 043600/[01], loss: 11.3687, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220701-08:01:50 Iters: 043700/[01], loss: 11.2268, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-08:03:54 Iters: 043800/[01], loss: 12.1766, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-08:05:59 Iters: 043900/[01], loss: 11.4827, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220701-08:08:03 Iters: 044000/[01], loss: 11.3685, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220701-08:10:07 Iters: 044100/[01], loss: 11.6365, train_accuracy: 0.0234, time: 1.25 s/iter, learning rate: 0.05
-20220701-08:12:11 Iters: 044200/[01], loss: 10.5997, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220701-08:14:15 Iters: 044300/[01], loss: 10.4330, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-08:16:18 Iters: 044400/[01], loss: 11.3721, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-08:18:22 Iters: 044500/[01], loss: 11.8784, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220701-08:20:27 Iters: 044600/[01], loss: 10.9594, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220701-08:22:32 Iters: 044700/[01], loss: 10.9820, train_accuracy: 0.0391, time: 1.25 s/iter, learning rate: 0.05
-20220701-08:24:36 Iters: 044800/[01], loss: 11.6877, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220701-08:26:40 Iters: 044900/[01], loss: 11.7713, train_accuracy: 0.0078, time: 1.25 s/iter, learning rate: 0.05
-20220701-08:28:45 Iters: 045000/[01], loss: 11.3359, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220701-08:30:48 Iters: 045100/[01], loss: 11.4180, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-08:32:53 Iters: 045200/[01], loss: 11.3603, train_accuracy: 0.0156, time: 1.25 s/iter, learning rate: 0.05
-20220701-08:34:57 Iters: 045300/[01], loss: 11.3593, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220701-08:37:02 Iters: 045400/[01], loss: 11.0523, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220701-08:38:53 Train Epoch: 2/18 ...
-20220701-08:39:06 Iters: 045500/[02], loss: 11.3777, train_accuracy: 0.0547, time: 0.13 s/iter, learning rate: 0.05
-20220701-08:41:09 Iters: 045600/[02], loss: 10.8130, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-08:43:12 Iters: 045700/[02], loss: 11.3874, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-08:45:15 Iters: 045800/[02], loss: 10.9940, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-08:47:18 Iters: 045900/[02], loss: 11.7009, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-08:49:22 Iters: 046000/[02], loss: 10.9632, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-08:51:25 Iters: 046100/[02], loss: 11.3342, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-08:53:28 Iters: 046200/[02], loss: 11.6632, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-08:55:31 Iters: 046300/[02], loss: 11.2327, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-08:57:34 Iters: 046400/[02], loss: 10.7878, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-08:59:38 Iters: 046500/[02], loss: 11.2125, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:01:41 Iters: 046600/[02], loss: 10.5475, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:03:44 Iters: 046700/[02], loss: 11.3504, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:05:47 Iters: 046800/[02], loss: 11.1472, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:07:50 Iters: 046900/[02], loss: 11.0970, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:09:53 Iters: 047000/[02], loss: 10.6553, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:11:56 Iters: 047100/[02], loss: 11.0271, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:13:59 Iters: 047200/[02], loss: 11.8924, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:16:02 Iters: 047300/[02], loss: 10.3753, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:18:06 Iters: 047400/[02], loss: 12.0026, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:20:09 Iters: 047500/[02], loss: 11.3175, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:22:12 Iters: 047600/[02], loss: 11.8772, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:24:15 Iters: 047700/[02], loss: 11.5293, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:26:18 Iters: 047800/[02], loss: 10.8375, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:28:21 Iters: 047900/[02], loss: 11.0596, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:30:25 Iters: 048000/[02], loss: 11.3227, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:32:28 Iters: 048100/[02], loss: 11.9806, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:34:31 Iters: 048200/[02], loss: 12.4077, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:36:34 Iters: 048300/[02], loss: 10.6686, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:38:37 Iters: 048400/[02], loss: 11.9728, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:40:41 Iters: 048500/[02], loss: 11.2181, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:42:44 Iters: 048600/[02], loss: 11.1506, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:44:47 Iters: 048700/[02], loss: 10.7332, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:46:50 Iters: 048800/[02], loss: 11.1280, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:48:54 Iters: 048900/[02], loss: 11.2126, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:50:57 Iters: 049000/[02], loss: 10.9195, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:53:00 Iters: 049100/[02], loss: 11.5358, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:55:03 Iters: 049200/[02], loss: 10.9355, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:57:07 Iters: 049300/[02], loss: 11.3713, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-09:59:10 Iters: 049400/[02], loss: 11.2712, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:01:13 Iters: 049500/[02], loss: 11.8483, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:03:17 Iters: 049600/[02], loss: 10.6599, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:05:20 Iters: 049700/[02], loss: 11.0727, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:07:23 Iters: 049800/[02], loss: 10.5531, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:09:26 Iters: 049900/[02], loss: 11.3240, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:11:30 Iters: 050000/[02], loss: 11.5065, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:11:30 Saving checkpoint: 50000
-20220701-10:12:46 LFW Ave Accuracy: 99.0832
-20220701-10:14:01 AgeDB-30 Ave Accuracy: 93.8500
-20220701-10:15:28 CFP-FP Ave Accuracy: 87.3571
-20220701-10:15:28 Current Best Accuracy: LFW: 99.0999 in iters: 20000, AgeDB-30: 93.8500 in iters: 50000 and CFP-FP: 88.3000 in iters: 30000
-20220701-10:17:30 Iters: 050100/[02], loss: 11.4706, train_accuracy: 0.0469, time: 3.61 s/iter, learning rate: 0.05
-20220701-10:19:34 Iters: 050200/[02], loss: 11.0847, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:21:37 Iters: 050300/[02], loss: 10.3842, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:23:40 Iters: 050400/[02], loss: 10.2440, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:25:43 Iters: 050500/[02], loss: 11.3645, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:27:47 Iters: 050600/[02], loss: 11.0963, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:29:50 Iters: 050700/[02], loss: 12.0142, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:31:53 Iters: 050800/[02], loss: 11.2891, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:33:56 Iters: 050900/[02], loss: 11.2048, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:35:59 Iters: 051000/[02], loss: 11.1233, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:38:02 Iters: 051100/[02], loss: 10.9940, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:40:05 Iters: 051200/[02], loss: 10.9394, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:42:09 Iters: 051300/[02], loss: 11.7138, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:44:12 Iters: 051400/[02], loss: 11.8562, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:46:15 Iters: 051500/[02], loss: 10.9915, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:48:18 Iters: 051600/[02], loss: 12.0126, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:50:21 Iters: 051700/[02], loss: 12.1562, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:52:25 Iters: 051800/[02], loss: 11.7749, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:54:28 Iters: 051900/[02], loss: 11.6039, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:56:31 Iters: 052000/[02], loss: 11.3388, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-10:58:34 Iters: 052100/[02], loss: 11.7277, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:00:37 Iters: 052200/[02], loss: 11.0198, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:02:41 Iters: 052300/[02], loss: 11.5014, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:04:44 Iters: 052400/[02], loss: 11.5661, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:06:47 Iters: 052500/[02], loss: 11.7062, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:08:50 Iters: 052600/[02], loss: 10.8239, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:10:53 Iters: 052700/[02], loss: 10.4416, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:12:56 Iters: 052800/[02], loss: 11.1741, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:15:00 Iters: 052900/[02], loss: 11.1235, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:17:03 Iters: 053000/[02], loss: 11.2268, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:19:06 Iters: 053100/[02], loss: 10.9320, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:21:09 Iters: 053200/[02], loss: 11.2962, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:23:13 Iters: 053300/[02], loss: 11.4998, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:25:16 Iters: 053400/[02], loss: 11.3197, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:27:19 Iters: 053500/[02], loss: 10.9350, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:29:22 Iters: 053600/[02], loss: 10.6309, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:31:25 Iters: 053700/[02], loss: 10.4785, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:33:29 Iters: 053800/[02], loss: 12.0230, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:35:32 Iters: 053900/[02], loss: 11.9488, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:37:35 Iters: 054000/[02], loss: 11.8138, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:39:38 Iters: 054100/[02], loss: 10.2534, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:41:42 Iters: 054200/[02], loss: 11.5225, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:43:45 Iters: 054300/[02], loss: 10.9699, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:45:48 Iters: 054400/[02], loss: 11.1050, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:47:51 Iters: 054500/[02], loss: 11.1209, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:49:54 Iters: 054600/[02], loss: 10.9397, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:51:58 Iters: 054700/[02], loss: 11.1241, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:54:01 Iters: 054800/[02], loss: 11.2471, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:56:04 Iters: 054900/[02], loss: 11.5014, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-11:58:07 Iters: 055000/[02], loss: 11.2419, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:00:10 Iters: 055100/[02], loss: 11.2045, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:02:13 Iters: 055200/[02], loss: 11.6060, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:04:16 Iters: 055300/[02], loss: 12.0218, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:06:19 Iters: 055400/[02], loss: 12.6624, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:08:23 Iters: 055500/[02], loss: 12.4267, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:10:26 Iters: 055600/[02], loss: 11.1433, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:12:29 Iters: 055700/[02], loss: 11.4008, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:14:32 Iters: 055800/[02], loss: 10.7816, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:16:35 Iters: 055900/[02], loss: 11.2613, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:18:39 Iters: 056000/[02], loss: 11.7647, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:20:42 Iters: 056100/[02], loss: 11.7802, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:22:45 Iters: 056200/[02], loss: 11.7118, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:24:48 Iters: 056300/[02], loss: 11.9924, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:26:52 Iters: 056400/[02], loss: 10.9051, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:28:55 Iters: 056500/[02], loss: 11.2141, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:30:58 Iters: 056600/[02], loss: 11.8505, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:33:01 Iters: 056700/[02], loss: 10.5919, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:35:05 Iters: 056800/[02], loss: 11.4143, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:37:08 Iters: 056900/[02], loss: 10.8397, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:39:11 Iters: 057000/[02], loss: 11.4805, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:41:14 Iters: 057100/[02], loss: 11.2353, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:43:18 Iters: 057200/[02], loss: 11.6418, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:45:21 Iters: 057300/[02], loss: 11.9256, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:47:24 Iters: 057400/[02], loss: 11.5614, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:49:27 Iters: 057500/[02], loss: 11.4809, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:51:30 Iters: 057600/[02], loss: 10.6213, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:53:34 Iters: 057700/[02], loss: 10.7778, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:55:37 Iters: 057800/[02], loss: 10.9082, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:57:40 Iters: 057900/[02], loss: 11.8766, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-12:59:43 Iters: 058000/[02], loss: 11.4525, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:01:46 Iters: 058100/[02], loss: 11.1749, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:03:50 Iters: 058200/[02], loss: 11.1178, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:05:53 Iters: 058300/[02], loss: 10.9077, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:07:56 Iters: 058400/[02], loss: 10.8782, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:09:59 Iters: 058500/[02], loss: 11.5324, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:12:02 Iters: 058600/[02], loss: 11.3961, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:14:06 Iters: 058700/[02], loss: 11.1905, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:16:09 Iters: 058800/[02], loss: 10.6920, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:18:12 Iters: 058900/[02], loss: 12.5790, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:20:15 Iters: 059000/[02], loss: 11.3147, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:22:19 Iters: 059100/[02], loss: 11.3903, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:24:22 Iters: 059200/[02], loss: 10.8218, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:26:25 Iters: 059300/[02], loss: 11.7005, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:28:28 Iters: 059400/[02], loss: 11.1932, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:30:31 Iters: 059500/[02], loss: 11.0754, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:32:35 Iters: 059600/[02], loss: 11.9513, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:34:38 Iters: 059700/[02], loss: 11.3771, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:36:41 Iters: 059800/[02], loss: 10.9005, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:38:44 Iters: 059900/[02], loss: 11.1600, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:40:47 Iters: 060000/[02], loss: 11.2613, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:40:47 Saving checkpoint: 60000
-20220701-13:42:06 LFW Ave Accuracy: 99.0665
-20220701-13:43:23 AgeDB-30 Ave Accuracy: 93.4167
-20220701-13:44:52 CFP-FP Ave Accuracy: 87.0714
-20220701-13:44:52 Current Best Accuracy: LFW: 99.0999 in iters: 20000, AgeDB-30: 93.8500 in iters: 50000 and CFP-FP: 88.3000 in iters: 30000
-20220701-13:46:55 Iters: 060100/[02], loss: 11.6218, train_accuracy: 0.0469, time: 3.67 s/iter, learning rate: 0.05
-20220701-13:48:58 Iters: 060200/[02], loss: 10.8459, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:51:01 Iters: 060300/[02], loss: 12.0334, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:53:04 Iters: 060400/[02], loss: 11.8395, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:55:07 Iters: 060500/[02], loss: 10.4572, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:57:11 Iters: 060600/[02], loss: 11.9049, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-13:59:14 Iters: 060700/[02], loss: 10.5665, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:01:17 Iters: 060800/[02], loss: 12.0553, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:03:20 Iters: 060900/[02], loss: 11.9882, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:05:23 Iters: 061000/[02], loss: 11.4557, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:07:26 Iters: 061100/[02], loss: 11.1476, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:09:30 Iters: 061200/[02], loss: 10.9017, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:11:33 Iters: 061300/[02], loss: 11.2218, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:13:37 Iters: 061400/[02], loss: 11.5096, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:15:40 Iters: 061500/[02], loss: 10.8786, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:17:43 Iters: 061600/[02], loss: 10.5790, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:19:46 Iters: 061700/[02], loss: 11.1113, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:21:49 Iters: 061800/[02], loss: 10.3030, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:23:52 Iters: 061900/[02], loss: 10.7226, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:25:56 Iters: 062000/[02], loss: 11.3425, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:27:59 Iters: 062100/[02], loss: 11.4352, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:30:02 Iters: 062200/[02], loss: 11.3326, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:32:05 Iters: 062300/[02], loss: 12.1565, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:34:08 Iters: 062400/[02], loss: 11.0912, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:36:11 Iters: 062500/[02], loss: 10.8905, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:38:14 Iters: 062600/[02], loss: 11.4491, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:40:17 Iters: 062700/[02], loss: 11.4861, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:42:21 Iters: 062800/[02], loss: 11.8881, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:44:24 Iters: 062900/[02], loss: 11.5941, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:46:27 Iters: 063000/[02], loss: 10.9631, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:48:30 Iters: 063100/[02], loss: 11.7712, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:50:33 Iters: 063200/[02], loss: 11.3094, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:52:36 Iters: 063300/[02], loss: 11.2082, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:54:40 Iters: 063400/[02], loss: 11.6057, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:56:43 Iters: 063500/[02], loss: 11.3491, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-14:58:46 Iters: 063600/[02], loss: 10.8315, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:00:49 Iters: 063700/[02], loss: 12.4770, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:02:53 Iters: 063800/[02], loss: 11.0696, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:04:56 Iters: 063900/[02], loss: 11.3321, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:06:59 Iters: 064000/[02], loss: 11.6057, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:09:02 Iters: 064100/[02], loss: 10.7432, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:11:05 Iters: 064200/[02], loss: 11.4880, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:13:09 Iters: 064300/[02], loss: 11.2105, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:15:12 Iters: 064400/[02], loss: 12.0593, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:17:15 Iters: 064500/[02], loss: 12.2498, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:19:18 Iters: 064600/[02], loss: 10.7997, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:21:22 Iters: 064700/[02], loss: 12.2014, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:23:25 Iters: 064800/[02], loss: 11.2451, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:25:28 Iters: 064900/[02], loss: 10.6202, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:27:31 Iters: 065000/[02], loss: 10.5707, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:29:35 Iters: 065100/[02], loss: 11.1211, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:31:38 Iters: 065200/[02], loss: 11.2001, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:33:41 Iters: 065300/[02], loss: 10.5994, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:35:44 Iters: 065400/[02], loss: 11.7002, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:37:47 Iters: 065500/[02], loss: 11.3764, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:39:51 Iters: 065600/[02], loss: 10.3652, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:41:54 Iters: 065700/[02], loss: 11.1825, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:43:57 Iters: 065800/[02], loss: 11.1303, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:46:00 Iters: 065900/[02], loss: 12.0685, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:48:04 Iters: 066000/[02], loss: 10.7738, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:50:07 Iters: 066100/[02], loss: 11.2373, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:52:10 Iters: 066200/[02], loss: 11.6033, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:54:13 Iters: 066300/[02], loss: 12.1365, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:56:16 Iters: 066400/[02], loss: 11.0229, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-15:58:19 Iters: 066500/[02], loss: 11.0899, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:00:23 Iters: 066600/[02], loss: 11.7759, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:02:26 Iters: 066700/[02], loss: 11.1959, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:04:29 Iters: 066800/[02], loss: 10.8188, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:06:32 Iters: 066900/[02], loss: 11.8492, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:08:36 Iters: 067000/[02], loss: 11.3015, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:10:39 Iters: 067100/[02], loss: 11.7709, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:12:42 Iters: 067200/[02], loss: 10.5634, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:14:45 Iters: 067300/[02], loss: 10.8215, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:16:48 Iters: 067400/[02], loss: 10.2530, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:18:52 Iters: 067500/[02], loss: 11.0018, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:20:55 Iters: 067600/[02], loss: 10.6240, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:22:58 Iters: 067700/[02], loss: 11.2951, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:25:01 Iters: 067800/[02], loss: 10.4171, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:27:04 Iters: 067900/[02], loss: 11.4265, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:29:07 Iters: 068000/[02], loss: 11.4521, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:31:10 Iters: 068100/[02], loss: 11.8178, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:33:13 Iters: 068200/[02], loss: 10.4364, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:35:16 Iters: 068300/[02], loss: 10.0415, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:37:20 Iters: 068400/[02], loss: 10.8054, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:39:23 Iters: 068500/[02], loss: 11.1383, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:41:26 Iters: 068600/[02], loss: 11.8221, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:43:29 Iters: 068700/[02], loss: 11.6900, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:45:32 Iters: 068800/[02], loss: 11.1314, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:47:35 Iters: 068900/[02], loss: 11.5156, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:49:38 Iters: 069000/[02], loss: 10.7400, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:51:41 Iters: 069100/[02], loss: 11.2625, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:53:45 Iters: 069200/[02], loss: 10.8511, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:55:48 Iters: 069300/[02], loss: 12.1566, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:57:51 Iters: 069400/[02], loss: 10.7608, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-16:59:54 Iters: 069500/[02], loss: 11.6498, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:01:57 Iters: 069600/[02], loss: 10.9578, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:04:00 Iters: 069700/[02], loss: 12.3631, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:06:03 Iters: 069800/[02], loss: 11.3112, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:08:07 Iters: 069900/[02], loss: 11.6831, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:10:10 Iters: 070000/[02], loss: 11.3448, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:10:10 Saving checkpoint: 70000
-20220701-17:11:26 LFW Ave Accuracy: 98.7999
-20220701-17:12:41 AgeDB-30 Ave Accuracy: 93.5500
-20220701-17:14:08 CFP-FP Ave Accuracy: 87.0143
-20220701-17:14:08 Current Best Accuracy: LFW: 99.0999 in iters: 20000, AgeDB-30: 93.8500 in iters: 50000 and CFP-FP: 88.3000 in iters: 30000
-20220701-17:16:11 Iters: 070100/[02], loss: 12.3252, train_accuracy: 0.0234, time: 3.61 s/iter, learning rate: 0.05
-20220701-17:18:14 Iters: 070200/[02], loss: 11.3671, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:20:17 Iters: 070300/[02], loss: 10.9270, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:22:21 Iters: 070400/[02], loss: 10.6114, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:24:24 Iters: 070500/[02], loss: 11.5936, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:26:27 Iters: 070600/[02], loss: 10.8611, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:28:30 Iters: 070700/[02], loss: 12.1698, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:30:33 Iters: 070800/[02], loss: 12.2463, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:32:36 Iters: 070900/[02], loss: 10.6855, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:34:40 Iters: 071000/[02], loss: 11.3705, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:36:43 Iters: 071100/[02], loss: 11.6324, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:38:46 Iters: 071200/[02], loss: 10.9262, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:40:49 Iters: 071300/[02], loss: 11.4971, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:42:52 Iters: 071400/[02], loss: 11.7900, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:44:55 Iters: 071500/[02], loss: 11.2069, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:46:59 Iters: 071600/[02], loss: 11.0826, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:49:02 Iters: 071700/[02], loss: 12.0381, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:51:05 Iters: 071800/[02], loss: 10.9181, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:53:08 Iters: 071900/[02], loss: 11.0194, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:55:11 Iters: 072000/[02], loss: 11.4099, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:57:14 Iters: 072100/[02], loss: 11.5255, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-17:59:17 Iters: 072200/[02], loss: 12.1669, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:01:20 Iters: 072300/[02], loss: 11.9424, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:03:24 Iters: 072400/[02], loss: 11.5015, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:05:27 Iters: 072500/[02], loss: 11.6687, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:07:30 Iters: 072600/[02], loss: 10.6107, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:09:33 Iters: 072700/[02], loss: 11.7978, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:11:36 Iters: 072800/[02], loss: 11.4909, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:13:39 Iters: 072900/[02], loss: 10.7908, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:15:43 Iters: 073000/[02], loss: 10.6959, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:17:46 Iters: 073100/[02], loss: 10.7505, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:19:49 Iters: 073200/[02], loss: 11.8440, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:21:52 Iters: 073300/[02], loss: 11.4555, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:23:55 Iters: 073400/[02], loss: 10.1807, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:25:58 Iters: 073500/[02], loss: 10.4330, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:28:01 Iters: 073600/[02], loss: 11.1879, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:30:05 Iters: 073700/[02], loss: 11.3853, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:32:08 Iters: 073800/[02], loss: 11.3291, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:34:11 Iters: 073900/[02], loss: 11.5705, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:36:14 Iters: 074000/[02], loss: 11.0735, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:38:17 Iters: 074100/[02], loss: 11.0281, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:40:20 Iters: 074200/[02], loss: 10.6831, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:42:23 Iters: 074300/[02], loss: 11.8844, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:44:26 Iters: 074400/[02], loss: 10.8144, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:46:29 Iters: 074500/[02], loss: 11.4114, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:48:32 Iters: 074600/[02], loss: 11.2183, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:50:35 Iters: 074700/[02], loss: 11.9152, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:52:39 Iters: 074800/[02], loss: 11.5130, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:54:42 Iters: 074900/[02], loss: 11.4228, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:56:45 Iters: 075000/[02], loss: 10.9852, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-18:58:48 Iters: 075100/[02], loss: 11.3479, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:00:51 Iters: 075200/[02], loss: 11.0608, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:02:54 Iters: 075300/[02], loss: 11.7775, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:04:57 Iters: 075400/[02], loss: 11.3870, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:07:00 Iters: 075500/[02], loss: 11.5651, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:09:03 Iters: 075600/[02], loss: 10.7751, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:11:06 Iters: 075700/[02], loss: 11.7845, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:13:10 Iters: 075800/[02], loss: 11.1999, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:15:13 Iters: 075900/[02], loss: 10.8030, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:17:16 Iters: 076000/[02], loss: 11.2561, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:19:19 Iters: 076100/[02], loss: 10.6658, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:21:22 Iters: 076200/[02], loss: 10.9004, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:23:25 Iters: 076300/[02], loss: 12.3554, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:25:28 Iters: 076400/[02], loss: 11.2170, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:27:31 Iters: 076500/[02], loss: 11.1363, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:29:34 Iters: 076600/[02], loss: 11.2896, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:31:38 Iters: 076700/[02], loss: 11.4222, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:33:41 Iters: 076800/[02], loss: 10.9845, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:35:44 Iters: 076900/[02], loss: 11.2339, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:37:47 Iters: 077000/[02], loss: 11.7973, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:39:50 Iters: 077100/[02], loss: 11.6574, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:41:53 Iters: 077200/[02], loss: 11.7823, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:43:57 Iters: 077300/[02], loss: 10.8007, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:46:00 Iters: 077400/[02], loss: 11.3099, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:48:03 Iters: 077500/[02], loss: 10.8044, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:50:06 Iters: 077600/[02], loss: 11.4902, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:52:09 Iters: 077700/[02], loss: 11.6147, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:54:12 Iters: 077800/[02], loss: 10.7196, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:56:15 Iters: 077900/[02], loss: 11.4317, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-19:58:18 Iters: 078000/[02], loss: 11.2198, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:00:22 Iters: 078100/[02], loss: 11.0653, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:02:25 Iters: 078200/[02], loss: 11.3422, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:04:28 Iters: 078300/[02], loss: 11.3783, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:06:31 Iters: 078400/[02], loss: 11.3628, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:08:34 Iters: 078500/[02], loss: 11.4813, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:10:37 Iters: 078600/[02], loss: 11.3951, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:12:41 Iters: 078700/[02], loss: 12.2157, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:14:44 Iters: 078800/[02], loss: 10.7734, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:16:47 Iters: 078900/[02], loss: 11.0579, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:18:50 Iters: 079000/[02], loss: 11.6215, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:20:53 Iters: 079100/[02], loss: 11.5993, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:22:56 Iters: 079200/[02], loss: 11.6234, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:25:00 Iters: 079300/[02], loss: 11.5999, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:27:03 Iters: 079400/[02], loss: 11.3447, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:29:06 Iters: 079500/[02], loss: 11.7329, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:31:09 Iters: 079600/[02], loss: 11.3550, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:33:12 Iters: 079700/[02], loss: 10.6570, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:35:16 Iters: 079800/[02], loss: 11.0054, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:37:19 Iters: 079900/[02], loss: 11.1905, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:39:22 Iters: 080000/[02], loss: 11.6163, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:39:22 Saving checkpoint: 80000
-20220701-20:40:38 LFW Ave Accuracy: 99.0832
-20220701-20:41:53 AgeDB-30 Ave Accuracy: 92.9000
-20220701-20:43:19 CFP-FP Ave Accuracy: 87.1714
-20220701-20:43:19 Current Best Accuracy: LFW: 99.0999 in iters: 20000, AgeDB-30: 93.8500 in iters: 50000 and CFP-FP: 88.3000 in iters: 30000
-20220701-20:45:22 Iters: 080100/[02], loss: 11.1080, train_accuracy: 0.0312, time: 3.60 s/iter, learning rate: 0.05
-20220701-20:47:25 Iters: 080200/[02], loss: 11.4404, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:49:28 Iters: 080300/[02], loss: 11.2513, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:51:31 Iters: 080400/[02], loss: 11.8009, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:53:34 Iters: 080500/[02], loss: 11.4461, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:55:37 Iters: 080600/[02], loss: 11.2796, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:57:41 Iters: 080700/[02], loss: 11.4793, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-20:59:44 Iters: 080800/[02], loss: 10.6255, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:01:47 Iters: 080900/[02], loss: 10.7846, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:03:50 Iters: 081000/[02], loss: 11.2862, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:05:53 Iters: 081100/[02], loss: 10.9128, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:07:56 Iters: 081200/[02], loss: 11.1183, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:09:59 Iters: 081300/[02], loss: 11.8509, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:12:03 Iters: 081400/[02], loss: 11.7142, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:14:06 Iters: 081500/[02], loss: 11.6409, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:16:09 Iters: 081600/[02], loss: 10.6759, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:18:12 Iters: 081700/[02], loss: 10.7438, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:20:15 Iters: 081800/[02], loss: 11.7473, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:22:18 Iters: 081900/[02], loss: 11.4023, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:24:21 Iters: 082000/[02], loss: 12.1101, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:26:24 Iters: 082100/[02], loss: 11.6078, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:28:27 Iters: 082200/[02], loss: 11.2177, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:30:30 Iters: 082300/[02], loss: 12.1487, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:32:33 Iters: 082400/[02], loss: 11.2891, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:34:36 Iters: 082500/[02], loss: 12.3982, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:36:39 Iters: 082600/[02], loss: 11.1080, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:38:42 Iters: 082700/[02], loss: 10.8696, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:40:45 Iters: 082800/[02], loss: 12.3388, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:42:48 Iters: 082900/[02], loss: 11.1150, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:44:52 Iters: 083000/[02], loss: 10.5534, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:46:55 Iters: 083100/[02], loss: 12.6892, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:48:58 Iters: 083200/[02], loss: 11.4105, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:51:01 Iters: 083300/[02], loss: 11.3229, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:53:04 Iters: 083400/[02], loss: 11.5713, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:55:07 Iters: 083500/[02], loss: 10.9549, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:57:10 Iters: 083600/[02], loss: 10.6126, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-21:59:13 Iters: 083700/[02], loss: 10.7677, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:01:16 Iters: 083800/[02], loss: 10.7455, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:03:19 Iters: 083900/[02], loss: 10.9432, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:05:22 Iters: 084000/[02], loss: 10.8579, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:07:25 Iters: 084100/[02], loss: 12.0410, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:09:28 Iters: 084200/[02], loss: 10.8398, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:11:31 Iters: 084300/[02], loss: 10.6232, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:13:34 Iters: 084400/[02], loss: 11.3088, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:15:37 Iters: 084500/[02], loss: 10.6504, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:17:40 Iters: 084600/[02], loss: 10.6812, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:19:43 Iters: 084700/[02], loss: 10.2860, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:21:46 Iters: 084800/[02], loss: 11.3267, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:23:49 Iters: 084900/[02], loss: 10.2832, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:25:52 Iters: 085000/[02], loss: 11.0796, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:27:55 Iters: 085100/[02], loss: 11.0919, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:29:58 Iters: 085200/[02], loss: 10.5042, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:32:01 Iters: 085300/[02], loss: 10.9777, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:34:04 Iters: 085400/[02], loss: 10.9723, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:36:07 Iters: 085500/[02], loss: 11.7295, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:38:10 Iters: 085600/[02], loss: 11.9288, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:40:13 Iters: 085700/[02], loss: 11.5515, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:42:16 Iters: 085800/[02], loss: 11.0371, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:44:19 Iters: 085900/[02], loss: 11.2287, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:46:22 Iters: 086000/[02], loss: 11.5523, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:48:25 Iters: 086100/[02], loss: 10.9386, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:50:28 Iters: 086200/[02], loss: 11.5405, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:52:31 Iters: 086300/[02], loss: 11.7447, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:54:34 Iters: 086400/[02], loss: 12.0745, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:56:37 Iters: 086500/[02], loss: 11.5202, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-22:58:41 Iters: 086600/[02], loss: 10.6674, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:00:44 Iters: 086700/[02], loss: 11.3150, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:02:47 Iters: 086800/[02], loss: 10.9531, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:04:50 Iters: 086900/[02], loss: 11.7971, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:06:53 Iters: 087000/[02], loss: 11.5118, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:08:56 Iters: 087100/[02], loss: 11.8129, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:10:59 Iters: 087200/[02], loss: 10.7608, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:13:02 Iters: 087300/[02], loss: 11.4557, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:15:05 Iters: 087400/[02], loss: 11.2769, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:17:08 Iters: 087500/[02], loss: 10.9840, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:19:10 Iters: 087600/[02], loss: 11.8196, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:21:13 Iters: 087700/[02], loss: 11.2232, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:23:16 Iters: 087800/[02], loss: 11.3344, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:25:19 Iters: 087900/[02], loss: 12.1373, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:27:22 Iters: 088000/[02], loss: 11.9316, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:29:25 Iters: 088100/[02], loss: 11.2079, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:31:28 Iters: 088200/[02], loss: 11.4781, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:33:31 Iters: 088300/[02], loss: 11.6840, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:35:34 Iters: 088400/[02], loss: 11.2718, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:37:37 Iters: 088500/[02], loss: 11.7125, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:39:40 Iters: 088600/[02], loss: 11.7535, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:41:43 Iters: 088700/[02], loss: 11.7334, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:43:46 Iters: 088800/[02], loss: 11.3865, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:45:49 Iters: 088900/[02], loss: 10.3675, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:47:52 Iters: 089000/[02], loss: 11.3599, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:49:55 Iters: 089100/[02], loss: 10.9871, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:51:58 Iters: 089200/[02], loss: 11.1143, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:54:01 Iters: 089300/[02], loss: 11.1085, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:56:04 Iters: 089400/[02], loss: 11.1963, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220701-23:58:07 Iters: 089500/[02], loss: 11.1799, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:00:10 Iters: 089600/[02], loss: 11.4256, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:02:13 Iters: 089700/[02], loss: 12.2037, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:04:16 Iters: 089800/[02], loss: 11.1605, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:06:19 Iters: 089900/[02], loss: 11.3663, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:08:22 Iters: 090000/[02], loss: 11.3065, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:08:22 Saving checkpoint: 90000
-20220702-00:09:41 LFW Ave Accuracy: 99.0832
-20220702-00:11:05 AgeDB-30 Ave Accuracy: 93.7000
-20220702-00:12:39 CFP-FP Ave Accuracy: 87.1286
-20220702-00:12:39 Current Best Accuracy: LFW: 99.0999 in iters: 20000, AgeDB-30: 93.8500 in iters: 50000 and CFP-FP: 88.3000 in iters: 30000
-20220702-00:14:41 Iters: 090100/[02], loss: 10.9673, train_accuracy: 0.0312, time: 3.79 s/iter, learning rate: 0.05
-20220702-00:16:44 Iters: 090200/[02], loss: 11.4925, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:18:47 Iters: 090300/[02], loss: 10.8348, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:20:50 Iters: 090400/[02], loss: 12.0060, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:22:53 Iters: 090500/[02], loss: 10.8498, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:24:56 Iters: 090600/[02], loss: 11.0933, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:26:59 Iters: 090700/[02], loss: 11.7785, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:29:02 Iters: 090800/[02], loss: 12.3455, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:31:05 Iters: 090900/[02], loss: 10.4424, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:32:43 Train Epoch: 3/18 ...
-20220702-00:33:12 Iters: 091000/[03], loss: 10.0875, train_accuracy: 0.0469, time: 0.29 s/iter, learning rate: 0.05
-20220702-00:35:15 Iters: 091100/[03], loss: 11.0766, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:37:18 Iters: 091200/[03], loss: 11.1149, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:39:21 Iters: 091300/[03], loss: 11.2012, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:41:23 Iters: 091400/[03], loss: 11.2715, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:43:26 Iters: 091500/[03], loss: 11.0696, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:45:29 Iters: 091600/[03], loss: 12.2892, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:47:32 Iters: 091700/[03], loss: 11.8391, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:49:35 Iters: 091800/[03], loss: 10.8769, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:51:38 Iters: 091900/[03], loss: 11.1967, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:53:41 Iters: 092000/[03], loss: 10.5004, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:55:44 Iters: 092100/[03], loss: 11.7290, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:57:47 Iters: 092200/[03], loss: 12.1559, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-00:59:50 Iters: 092300/[03], loss: 11.2420, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:01:53 Iters: 092400/[03], loss: 11.0708, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:03:56 Iters: 092500/[03], loss: 11.9411, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:05:59 Iters: 092600/[03], loss: 11.0583, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:08:02 Iters: 092700/[03], loss: 10.5011, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:10:05 Iters: 092800/[03], loss: 11.8803, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:12:08 Iters: 092900/[03], loss: 11.2903, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:14:11 Iters: 093000/[03], loss: 11.2765, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:16:14 Iters: 093100/[03], loss: 11.2228, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:18:17 Iters: 093200/[03], loss: 12.2428, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:20:20 Iters: 093300/[03], loss: 12.1895, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:22:23 Iters: 093400/[03], loss: 10.7447, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:24:26 Iters: 093500/[03], loss: 11.1851, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:26:28 Iters: 093600/[03], loss: 11.6275, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:28:31 Iters: 093700/[03], loss: 11.6245, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:30:34 Iters: 093800/[03], loss: 11.2330, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:32:37 Iters: 093900/[03], loss: 11.8331, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:34:40 Iters: 094000/[03], loss: 10.5911, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:36:43 Iters: 094100/[03], loss: 12.5796, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:38:46 Iters: 094200/[03], loss: 11.6530, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:40:48 Iters: 094300/[03], loss: 11.0569, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:42:51 Iters: 094400/[03], loss: 10.7548, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:44:54 Iters: 094500/[03], loss: 11.1141, train_accuracy: 0.0859, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:46:57 Iters: 094600/[03], loss: 11.4804, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:49:00 Iters: 094700/[03], loss: 11.6698, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:51:03 Iters: 094800/[03], loss: 11.4871, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:53:06 Iters: 094900/[03], loss: 11.6567, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:55:08 Iters: 095000/[03], loss: 11.2741, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:57:11 Iters: 095100/[03], loss: 11.7700, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-01:59:14 Iters: 095200/[03], loss: 10.4033, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:01:17 Iters: 095300/[03], loss: 11.2736, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:03:20 Iters: 095400/[03], loss: 11.6109, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:05:23 Iters: 095500/[03], loss: 11.0302, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:07:26 Iters: 095600/[03], loss: 11.0903, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:09:29 Iters: 095700/[03], loss: 10.3622, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:11:32 Iters: 095800/[03], loss: 10.6696, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:13:35 Iters: 095900/[03], loss: 10.5715, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:15:38 Iters: 096000/[03], loss: 11.5804, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:17:40 Iters: 096100/[03], loss: 11.4335, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:19:43 Iters: 096200/[03], loss: 10.4772, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:21:46 Iters: 096300/[03], loss: 11.4153, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:23:49 Iters: 096400/[03], loss: 10.7786, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:25:52 Iters: 096500/[03], loss: 11.2538, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:27:55 Iters: 096600/[03], loss: 10.4855, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:29:58 Iters: 096700/[03], loss: 11.2645, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:32:00 Iters: 096800/[03], loss: 11.6664, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:34:03 Iters: 096900/[03], loss: 12.5294, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:36:06 Iters: 097000/[03], loss: 11.1766, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:38:09 Iters: 097100/[03], loss: 11.1031, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:40:12 Iters: 097200/[03], loss: 11.2285, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:42:14 Iters: 097300/[03], loss: 11.3800, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:44:17 Iters: 097400/[03], loss: 10.8819, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:46:20 Iters: 097500/[03], loss: 11.3744, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:48:23 Iters: 097600/[03], loss: 10.5838, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:50:26 Iters: 097700/[03], loss: 11.4536, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:52:29 Iters: 097800/[03], loss: 11.5720, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:54:31 Iters: 097900/[03], loss: 11.3411, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:56:34 Iters: 098000/[03], loss: 10.2354, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-02:58:37 Iters: 098100/[03], loss: 10.7118, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:00:40 Iters: 098200/[03], loss: 11.4450, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:02:43 Iters: 098300/[03], loss: 12.1546, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:04:46 Iters: 098400/[03], loss: 11.6284, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:06:48 Iters: 098500/[03], loss: 11.6014, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:08:51 Iters: 098600/[03], loss: 10.6553, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:10:54 Iters: 098700/[03], loss: 11.3168, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:12:57 Iters: 098800/[03], loss: 12.4457, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:15:00 Iters: 098900/[03], loss: 10.5490, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:17:03 Iters: 099000/[03], loss: 12.0830, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:19:06 Iters: 099100/[03], loss: 11.3578, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:21:09 Iters: 099200/[03], loss: 11.5124, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:23:11 Iters: 099300/[03], loss: 10.8782, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:25:14 Iters: 099400/[03], loss: 11.0237, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:27:17 Iters: 099500/[03], loss: 11.5509, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:29:20 Iters: 099600/[03], loss: 11.0774, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:31:23 Iters: 099700/[03], loss: 11.9533, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:33:26 Iters: 099800/[03], loss: 11.1980, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:35:28 Iters: 099900/[03], loss: 11.4264, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:37:31 Iters: 100000/[03], loss: 10.8192, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:37:31 Saving checkpoint: 100000
-20220702-03:38:49 LFW Ave Accuracy: 99.1333
-20220702-03:40:06 AgeDB-30 Ave Accuracy: 92.9000
-20220702-03:41:35 CFP-FP Ave Accuracy: 87.9000
-20220702-03:41:35 Current Best Accuracy: LFW: 99.1333 in iters: 100000, AgeDB-30: 93.8500 in iters: 50000 and CFP-FP: 88.3000 in iters: 30000
-20220702-03:43:38 Iters: 100100/[03], loss: 11.2570, train_accuracy: 0.0391, time: 3.67 s/iter, learning rate: 0.05
-20220702-03:45:41 Iters: 100200/[03], loss: 11.4610, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:47:44 Iters: 100300/[03], loss: 11.7988, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:49:46 Iters: 100400/[03], loss: 11.4968, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:51:49 Iters: 100500/[03], loss: 11.1872, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:53:52 Iters: 100600/[03], loss: 10.6812, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:55:55 Iters: 100700/[03], loss: 11.4256, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-03:57:58 Iters: 100800/[03], loss: 10.8933, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:00:01 Iters: 100900/[03], loss: 10.8627, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:02:04 Iters: 101000/[03], loss: 11.0615, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:04:07 Iters: 101100/[03], loss: 11.1744, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:06:09 Iters: 101200/[03], loss: 11.4632, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:08:12 Iters: 101300/[03], loss: 11.4029, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:10:15 Iters: 101400/[03], loss: 11.3231, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:12:18 Iters: 101500/[03], loss: 11.9176, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:14:21 Iters: 101600/[03], loss: 10.6512, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:16:24 Iters: 101700/[03], loss: 10.4673, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:18:27 Iters: 101800/[03], loss: 11.2115, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:20:30 Iters: 101900/[03], loss: 11.5661, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:22:33 Iters: 102000/[03], loss: 11.2327, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:24:35 Iters: 102100/[03], loss: 12.0389, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:26:38 Iters: 102200/[03], loss: 10.9901, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:28:41 Iters: 102300/[03], loss: 12.0733, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:30:44 Iters: 102400/[03], loss: 11.0046, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:32:47 Iters: 102500/[03], loss: 11.7213, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:34:51 Iters: 102600/[03], loss: 11.4381, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:36:54 Iters: 102700/[03], loss: 11.3340, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:38:57 Iters: 102800/[03], loss: 10.5126, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:41:00 Iters: 102900/[03], loss: 11.2076, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:43:03 Iters: 103000/[03], loss: 10.8428, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:45:06 Iters: 103100/[03], loss: 11.4656, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:47:09 Iters: 103200/[03], loss: 10.3774, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:49:12 Iters: 103300/[03], loss: 11.1950, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:51:15 Iters: 103400/[03], loss: 11.3565, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:53:18 Iters: 103500/[03], loss: 12.2540, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:55:22 Iters: 103600/[03], loss: 11.0237, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:57:25 Iters: 103700/[03], loss: 11.5775, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-04:59:28 Iters: 103800/[03], loss: 10.6322, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:01:31 Iters: 103900/[03], loss: 11.2496, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:03:34 Iters: 104000/[03], loss: 11.0190, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:05:37 Iters: 104100/[03], loss: 12.2623, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:07:40 Iters: 104200/[03], loss: 11.2154, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:09:43 Iters: 104300/[03], loss: 11.0183, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:11:46 Iters: 104400/[03], loss: 11.4217, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:13:49 Iters: 104500/[03], loss: 11.4030, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:15:52 Iters: 104600/[03], loss: 10.6223, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:17:55 Iters: 104700/[03], loss: 11.2028, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:19:58 Iters: 104800/[03], loss: 10.5397, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:22:01 Iters: 104900/[03], loss: 11.9618, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:24:04 Iters: 105000/[03], loss: 11.7027, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:26:07 Iters: 105100/[03], loss: 11.0864, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:28:10 Iters: 105200/[03], loss: 10.3178, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:30:13 Iters: 105300/[03], loss: 12.0078, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:32:16 Iters: 105400/[03], loss: 10.6631, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:34:19 Iters: 105500/[03], loss: 11.0193, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:36:22 Iters: 105600/[03], loss: 10.4901, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:38:25 Iters: 105700/[03], loss: 10.5201, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:40:28 Iters: 105800/[03], loss: 11.2843, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:42:31 Iters: 105900/[03], loss: 11.0705, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:44:34 Iters: 106000/[03], loss: 11.4069, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:46:37 Iters: 106100/[03], loss: 11.2373, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:48:40 Iters: 106200/[03], loss: 11.5745, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:50:43 Iters: 106300/[03], loss: 11.3055, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:52:46 Iters: 106400/[03], loss: 10.8524, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:54:49 Iters: 106500/[03], loss: 11.7339, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:56:52 Iters: 106600/[03], loss: 11.1861, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-05:58:55 Iters: 106700/[03], loss: 11.8001, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:00:58 Iters: 106800/[03], loss: 11.8919, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:03:01 Iters: 106900/[03], loss: 11.1096, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:05:05 Iters: 107000/[03], loss: 12.0678, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:07:08 Iters: 107100/[03], loss: 10.6569, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:09:11 Iters: 107200/[03], loss: 11.8299, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:11:14 Iters: 107300/[03], loss: 12.0252, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:13:17 Iters: 107400/[03], loss: 11.5277, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:15:20 Iters: 107500/[03], loss: 11.2905, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:17:23 Iters: 107600/[03], loss: 11.3033, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:19:26 Iters: 107700/[03], loss: 11.8931, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:21:29 Iters: 107800/[03], loss: 10.9256, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:23:32 Iters: 107900/[03], loss: 10.6364, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:25:35 Iters: 108000/[03], loss: 11.4360, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:27:38 Iters: 108100/[03], loss: 11.0911, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:29:41 Iters: 108200/[03], loss: 11.8286, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:31:44 Iters: 108300/[03], loss: 10.0340, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:33:47 Iters: 108400/[03], loss: 11.2730, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:35:50 Iters: 108500/[03], loss: 11.6090, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:37:53 Iters: 108600/[03], loss: 11.3039, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:39:56 Iters: 108700/[03], loss: 11.3380, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:41:59 Iters: 108800/[03], loss: 11.0802, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:44:02 Iters: 108900/[03], loss: 11.9104, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:46:05 Iters: 109000/[03], loss: 10.9655, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:48:08 Iters: 109100/[03], loss: 10.9756, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:50:11 Iters: 109200/[03], loss: 11.5812, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:52:14 Iters: 109300/[03], loss: 11.8247, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:54:17 Iters: 109400/[03], loss: 11.1706, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:56:20 Iters: 109500/[03], loss: 11.6034, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-06:58:23 Iters: 109600/[03], loss: 11.2093, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:00:26 Iters: 109700/[03], loss: 11.2574, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:02:29 Iters: 109800/[03], loss: 11.9114, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:04:33 Iters: 109900/[03], loss: 11.1106, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:06:36 Iters: 110000/[03], loss: 11.6430, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:06:36 Saving checkpoint: 110000
-20220702-07:07:53 LFW Ave Accuracy: 99.0832
-20220702-07:09:10 AgeDB-30 Ave Accuracy: 93.6500
-20220702-07:10:40 CFP-FP Ave Accuracy: 86.9429
-20220702-07:10:40 Current Best Accuracy: LFW: 99.1333 in iters: 100000, AgeDB-30: 93.8500 in iters: 50000 and CFP-FP: 88.3000 in iters: 30000
-20220702-07:12:43 Iters: 110100/[03], loss: 11.7653, train_accuracy: 0.0312, time: 3.67 s/iter, learning rate: 0.05
-20220702-07:14:46 Iters: 110200/[03], loss: 11.3802, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:16:49 Iters: 110300/[03], loss: 10.8620, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:18:52 Iters: 110400/[03], loss: 11.8255, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:20:55 Iters: 110500/[03], loss: 10.9426, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:22:58 Iters: 110600/[03], loss: 11.1083, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:25:01 Iters: 110700/[03], loss: 11.9451, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:27:04 Iters: 110800/[03], loss: 11.5891, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:29:07 Iters: 110900/[03], loss: 10.8365, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:31:11 Iters: 111000/[03], loss: 11.4347, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:33:14 Iters: 111100/[03], loss: 11.3385, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:35:17 Iters: 111200/[03], loss: 11.1491, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:37:20 Iters: 111300/[03], loss: 10.5571, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:39:23 Iters: 111400/[03], loss: 11.6663, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:41:26 Iters: 111500/[03], loss: 10.8956, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:43:29 Iters: 111600/[03], loss: 11.5593, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:45:32 Iters: 111700/[03], loss: 10.7948, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:47:35 Iters: 111800/[03], loss: 11.7606, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:49:38 Iters: 111900/[03], loss: 11.6230, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:51:41 Iters: 112000/[03], loss: 11.7014, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:53:44 Iters: 112100/[03], loss: 11.3224, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:55:47 Iters: 112200/[03], loss: 11.2686, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:57:50 Iters: 112300/[03], loss: 12.1378, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-07:59:53 Iters: 112400/[03], loss: 11.9168, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:01:56 Iters: 112500/[03], loss: 12.0494, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:03:59 Iters: 112600/[03], loss: 10.7953, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:06:02 Iters: 112700/[03], loss: 11.6886, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:08:05 Iters: 112800/[03], loss: 11.2497, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:10:08 Iters: 112900/[03], loss: 12.0952, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:12:11 Iters: 113000/[03], loss: 11.7579, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:14:14 Iters: 113100/[03], loss: 10.7802, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:16:17 Iters: 113200/[03], loss: 10.7219, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:18:20 Iters: 113300/[03], loss: 11.0936, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:20:23 Iters: 113400/[03], loss: 11.3744, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:22:26 Iters: 113500/[03], loss: 11.2801, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:24:30 Iters: 113600/[03], loss: 11.8733, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:26:33 Iters: 113700/[03], loss: 11.3076, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:28:36 Iters: 113800/[03], loss: 11.8112, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:30:39 Iters: 113900/[03], loss: 10.9219, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:32:42 Iters: 114000/[03], loss: 11.9759, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:34:45 Iters: 114100/[03], loss: 10.6551, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:36:48 Iters: 114200/[03], loss: 11.6623, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:38:51 Iters: 114300/[03], loss: 10.9778, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:40:54 Iters: 114400/[03], loss: 11.6703, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:42:57 Iters: 114500/[03], loss: 11.6790, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:45:00 Iters: 114600/[03], loss: 11.2677, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:47:03 Iters: 114700/[03], loss: 11.0286, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:49:06 Iters: 114800/[03], loss: 11.9417, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:51:09 Iters: 114900/[03], loss: 11.6132, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:53:13 Iters: 115000/[03], loss: 10.9356, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:55:16 Iters: 115100/[03], loss: 10.7405, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:57:19 Iters: 115200/[03], loss: 11.7804, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-08:59:23 Iters: 115300/[03], loss: 12.0996, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:01:27 Iters: 115400/[03], loss: 11.1838, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:03:30 Iters: 115500/[03], loss: 11.7289, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:05:34 Iters: 115600/[03], loss: 11.2962, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:07:38 Iters: 115700/[03], loss: 11.1436, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:09:41 Iters: 115800/[03], loss: 11.4762, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:11:45 Iters: 115900/[03], loss: 11.2111, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:13:49 Iters: 116000/[03], loss: 10.6940, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:15:52 Iters: 116100/[03], loss: 11.2543, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:17:56 Iters: 116200/[03], loss: 11.1598, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-09:19:59 Iters: 116300/[03], loss: 11.8859, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:22:03 Iters: 116400/[03], loss: 11.5739, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:24:06 Iters: 116500/[03], loss: 11.3491, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:26:10 Iters: 116600/[03], loss: 11.4106, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:28:14 Iters: 116700/[03], loss: 11.9487, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:30:18 Iters: 116800/[03], loss: 10.5873, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:32:22 Iters: 116900/[03], loss: 11.9578, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:34:25 Iters: 117000/[03], loss: 11.4530, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:36:29 Iters: 117100/[03], loss: 11.4672, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:38:33 Iters: 117200/[03], loss: 11.1519, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:40:37 Iters: 117300/[03], loss: 11.3725, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:42:40 Iters: 117400/[03], loss: 11.3249, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:44:44 Iters: 117500/[03], loss: 11.2039, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:46:48 Iters: 117600/[03], loss: 10.5234, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:48:52 Iters: 117700/[03], loss: 11.8611, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:50:55 Iters: 117800/[03], loss: 12.2735, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:52:59 Iters: 117900/[03], loss: 10.9307, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:55:03 Iters: 118000/[03], loss: 10.7395, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:57:06 Iters: 118100/[03], loss: 10.9440, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-09:59:10 Iters: 118200/[03], loss: 10.4507, train_accuracy: 0.0703, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:01:14 Iters: 118300/[03], loss: 10.9573, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:03:18 Iters: 118400/[03], loss: 11.8458, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:05:22 Iters: 118500/[03], loss: 11.6248, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:07:25 Iters: 118600/[03], loss: 11.5280, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:09:29 Iters: 118700/[03], loss: 11.0756, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:11:33 Iters: 118800/[03], loss: 10.9334, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:13:37 Iters: 118900/[03], loss: 11.1462, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:15:40 Iters: 119000/[03], loss: 11.8225, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:17:44 Iters: 119100/[03], loss: 12.0099, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:19:48 Iters: 119200/[03], loss: 11.5011, train_accuracy: 0.0703, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:21:52 Iters: 119300/[03], loss: 12.1691, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:23:55 Iters: 119400/[03], loss: 10.4668, train_accuracy: 0.0703, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:25:59 Iters: 119500/[03], loss: 11.0295, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:28:03 Iters: 119600/[03], loss: 11.4233, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:30:07 Iters: 119700/[03], loss: 11.6121, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:32:10 Iters: 119800/[03], loss: 11.1133, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:34:14 Iters: 119900/[03], loss: 10.9089, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:36:18 Iters: 120000/[03], loss: 12.3622, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:36:18 Saving checkpoint: 120000
-20220702-10:37:35 LFW Ave Accuracy: 99.2333
-20220702-10:38:50 AgeDB-30 Ave Accuracy: 93.1000
-20220702-10:40:17 CFP-FP Ave Accuracy: 88.3000
-20220702-10:40:17 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.8500 in iters: 50000 and CFP-FP: 88.3000 in iters: 120000
-20220702-10:42:20 Iters: 120100/[03], loss: 11.5480, train_accuracy: 0.0391, time: 3.62 s/iter, learning rate: 0.05
-20220702-10:44:24 Iters: 120200/[03], loss: 11.2926, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:46:27 Iters: 120300/[03], loss: 11.3658, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:48:31 Iters: 120400/[03], loss: 10.5148, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:50:35 Iters: 120500/[03], loss: 11.4977, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:52:38 Iters: 120600/[03], loss: 11.3240, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:54:42 Iters: 120700/[03], loss: 12.0107, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:56:46 Iters: 120800/[03], loss: 11.0145, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220702-10:58:50 Iters: 120900/[03], loss: 11.7255, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:00:53 Iters: 121000/[03], loss: 11.2509, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:02:57 Iters: 121100/[03], loss: 12.0811, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:05:01 Iters: 121200/[03], loss: 11.9388, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:07:04 Iters: 121300/[03], loss: 11.5740, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:09:08 Iters: 121400/[03], loss: 11.1105, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:11:11 Iters: 121500/[03], loss: 11.7581, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-11:13:15 Iters: 121600/[03], loss: 11.9974, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:15:18 Iters: 121700/[03], loss: 12.3966, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:17:22 Iters: 121800/[03], loss: 10.5557, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-11:19:25 Iters: 121900/[03], loss: 11.4338, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-11:21:29 Iters: 122000/[03], loss: 11.5735, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-11:23:32 Iters: 122100/[03], loss: 11.1255, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-11:25:35 Iters: 122200/[03], loss: 11.3760, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-11:27:39 Iters: 122300/[03], loss: 11.3381, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-11:29:42 Iters: 122400/[03], loss: 11.5830, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-11:31:46 Iters: 122500/[03], loss: 11.4235, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:33:49 Iters: 122600/[03], loss: 10.7045, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:35:53 Iters: 122700/[03], loss: 11.3251, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:37:57 Iters: 122800/[03], loss: 11.4380, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:40:01 Iters: 122900/[03], loss: 11.5199, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:42:04 Iters: 123000/[03], loss: 10.9198, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:44:08 Iters: 123100/[03], loss: 11.2054, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:46:12 Iters: 123200/[03], loss: 11.3857, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:48:16 Iters: 123300/[03], loss: 11.2791, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:50:19 Iters: 123400/[03], loss: 10.3308, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:52:23 Iters: 123500/[03], loss: 11.5220, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:54:27 Iters: 123600/[03], loss: 12.3050, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:56:31 Iters: 123700/[03], loss: 11.2485, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-11:58:34 Iters: 123800/[03], loss: 10.4566, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:00:38 Iters: 123900/[03], loss: 10.7641, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:02:42 Iters: 124000/[03], loss: 11.8546, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:04:46 Iters: 124100/[03], loss: 11.7214, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:06:49 Iters: 124200/[03], loss: 11.1326, train_accuracy: 0.0703, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:08:53 Iters: 124300/[03], loss: 10.8921, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:10:57 Iters: 124400/[03], loss: 11.4065, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:13:01 Iters: 124500/[03], loss: 11.1831, train_accuracy: 0.0781, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:15:04 Iters: 124600/[03], loss: 10.3985, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:17:08 Iters: 124700/[03], loss: 10.5808, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:19:12 Iters: 124800/[03], loss: 11.4490, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:21:16 Iters: 124900/[03], loss: 11.2390, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:23:19 Iters: 125000/[03], loss: 10.6411, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:25:23 Iters: 125100/[03], loss: 11.9072, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:27:27 Iters: 125200/[03], loss: 11.5257, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:29:31 Iters: 125300/[03], loss: 11.6423, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:31:34 Iters: 125400/[03], loss: 11.6895, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:33:38 Iters: 125500/[03], loss: 12.1533, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:35:42 Iters: 125600/[03], loss: 10.8494, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:37:46 Iters: 125700/[03], loss: 10.7577, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:39:49 Iters: 125800/[03], loss: 11.6522, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:41:53 Iters: 125900/[03], loss: 10.8224, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:43:57 Iters: 126000/[03], loss: 11.9173, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:46:01 Iters: 126100/[03], loss: 11.9537, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:48:04 Iters: 126200/[03], loss: 11.5271, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:50:08 Iters: 126300/[03], loss: 10.5772, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:52:12 Iters: 126400/[03], loss: 10.8306, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:54:16 Iters: 126500/[03], loss: 11.3193, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:56:19 Iters: 126600/[03], loss: 11.2117, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-12:58:23 Iters: 126700/[03], loss: 11.1504, train_accuracy: 0.0703, time: 1.24 s/iter, learning rate: 0.05
-20220702-13:00:27 Iters: 126800/[03], loss: 11.1023, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-13:02:31 Iters: 126900/[03], loss: 11.2834, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-13:04:34 Iters: 127000/[03], loss: 10.9226, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220702-13:06:38 Iters: 127100/[03], loss: 11.4992, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220702-13:08:42 Iters: 127200/[03], loss: 12.1559, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-13:10:45 Iters: 127300/[03], loss: 11.0380, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-13:12:49 Iters: 127400/[03], loss: 11.9161, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:14:52 Iters: 127500/[03], loss: 10.7660, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:16:55 Iters: 127600/[03], loss: 10.7787, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:18:59 Iters: 127700/[03], loss: 11.2025, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:21:02 Iters: 127800/[03], loss: 11.6998, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:23:05 Iters: 127900/[03], loss: 10.8655, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:25:08 Iters: 128000/[03], loss: 11.4906, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:27:12 Iters: 128100/[03], loss: 11.1444, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:29:15 Iters: 128200/[03], loss: 11.0672, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:31:18 Iters: 128300/[03], loss: 11.8201, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:33:21 Iters: 128400/[03], loss: 11.1551, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:35:24 Iters: 128500/[03], loss: 11.5695, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:37:28 Iters: 128600/[03], loss: 11.0980, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:39:31 Iters: 128700/[03], loss: 10.9764, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:41:34 Iters: 128800/[03], loss: 11.0576, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:43:37 Iters: 128900/[03], loss: 11.7871, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:45:40 Iters: 129000/[03], loss: 12.2465, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:47:43 Iters: 129100/[03], loss: 11.0129, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:49:47 Iters: 129200/[03], loss: 11.2250, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:51:49 Iters: 129300/[03], loss: 11.2002, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:53:53 Iters: 129400/[03], loss: 9.9889, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:55:56 Iters: 129500/[03], loss: 11.3680, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-13:57:59 Iters: 129600/[03], loss: 10.8778, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:00:02 Iters: 129700/[03], loss: 11.4850, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:02:05 Iters: 129800/[03], loss: 11.4726, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:04:08 Iters: 129900/[03], loss: 11.9710, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:06:11 Iters: 130000/[03], loss: 11.6744, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:06:11 Saving checkpoint: 130000
-20220702-14:07:28 LFW Ave Accuracy: 99.0499
-20220702-14:08:44 AgeDB-30 Ave Accuracy: 93.5000
-20220702-14:10:13 CFP-FP Ave Accuracy: 87.8000
-20220702-14:10:13 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.8500 in iters: 50000 and CFP-FP: 88.3000 in iters: 120000
-20220702-14:12:15 Iters: 130100/[03], loss: 11.4323, train_accuracy: 0.0391, time: 3.65 s/iter, learning rate: 0.05
-20220702-14:14:18 Iters: 130200/[03], loss: 11.0011, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:16:21 Iters: 130300/[03], loss: 11.1061, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:18:24 Iters: 130400/[03], loss: 10.6098, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:20:27 Iters: 130500/[03], loss: 11.3008, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:22:30 Iters: 130600/[03], loss: 11.3009, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:24:33 Iters: 130700/[03], loss: 10.9683, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:26:36 Iters: 130800/[03], loss: 11.9657, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:28:39 Iters: 130900/[03], loss: 10.6162, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:30:42 Iters: 131000/[03], loss: 10.8843, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:32:45 Iters: 131100/[03], loss: 11.5667, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:34:48 Iters: 131200/[03], loss: 11.5151, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:36:51 Iters: 131300/[03], loss: 10.7394, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:38:54 Iters: 131400/[03], loss: 11.1171, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:40:57 Iters: 131500/[03], loss: 11.1461, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:43:00 Iters: 131600/[03], loss: 10.5117, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:45:03 Iters: 131700/[03], loss: 11.7957, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:47:06 Iters: 131800/[03], loss: 11.8293, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:49:09 Iters: 131900/[03], loss: 11.1031, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:51:12 Iters: 132000/[03], loss: 11.0055, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:53:15 Iters: 132100/[03], loss: 12.5758, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:55:18 Iters: 132200/[03], loss: 10.7974, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:57:21 Iters: 132300/[03], loss: 12.0782, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-14:59:24 Iters: 132400/[03], loss: 11.3105, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:01:27 Iters: 132500/[03], loss: 11.1878, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:03:30 Iters: 132600/[03], loss: 12.6733, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:05:33 Iters: 132700/[03], loss: 11.5948, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:07:36 Iters: 132800/[03], loss: 10.8142, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:09:39 Iters: 132900/[03], loss: 11.7787, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:11:42 Iters: 133000/[03], loss: 11.8632, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:13:45 Iters: 133100/[03], loss: 11.7967, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:15:48 Iters: 133200/[03], loss: 11.5428, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:17:51 Iters: 133300/[03], loss: 10.8747, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:19:54 Iters: 133400/[03], loss: 11.6664, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:21:57 Iters: 133500/[03], loss: 11.6713, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:24:00 Iters: 133600/[03], loss: 11.2246, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:26:03 Iters: 133700/[03], loss: 10.0348, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:28:06 Iters: 133800/[03], loss: 10.8734, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:30:09 Iters: 133900/[03], loss: 12.6557, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:32:12 Iters: 134000/[03], loss: 11.5711, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:34:15 Iters: 134100/[03], loss: 11.2273, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:36:18 Iters: 134200/[03], loss: 11.0096, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:38:21 Iters: 134300/[03], loss: 11.0847, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:40:24 Iters: 134400/[03], loss: 11.7204, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:42:28 Iters: 134500/[03], loss: 11.2273, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:44:30 Iters: 134600/[03], loss: 11.7264, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:46:33 Iters: 134700/[03], loss: 11.2356, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:48:37 Iters: 134800/[03], loss: 11.8889, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:50:40 Iters: 134900/[03], loss: 11.2243, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:52:43 Iters: 135000/[03], loss: 11.2854, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:54:46 Iters: 135100/[03], loss: 10.1520, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:56:49 Iters: 135200/[03], loss: 11.5735, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-15:58:52 Iters: 135300/[03], loss: 11.0943, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:00:55 Iters: 135400/[03], loss: 11.6488, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:02:58 Iters: 135500/[03], loss: 11.3264, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:05:01 Iters: 135600/[03], loss: 12.1123, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:07:04 Iters: 135700/[03], loss: 11.3525, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:09:07 Iters: 135800/[03], loss: 11.3625, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:11:10 Iters: 135900/[03], loss: 11.7157, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:13:13 Iters: 136000/[03], loss: 11.5131, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:15:17 Iters: 136100/[03], loss: 10.7012, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:17:20 Iters: 136200/[03], loss: 12.0135, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:19:23 Iters: 136300/[03], loss: 10.7920, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:21:26 Iters: 136400/[03], loss: 11.6538, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:22:51 Train Epoch: 4/18 ...
-20220702-16:23:29 Iters: 136500/[04], loss: 10.5065, train_accuracy: 0.0625, time: 0.37 s/iter, learning rate: 0.05
-20220702-16:25:32 Iters: 136600/[04], loss: 11.2743, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:27:35 Iters: 136700/[04], loss: 11.0206, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:29:38 Iters: 136800/[04], loss: 10.4722, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:31:41 Iters: 136900/[04], loss: 10.8302, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:33:44 Iters: 137000/[04], loss: 10.7250, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:35:47 Iters: 137100/[04], loss: 11.5221, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:37:51 Iters: 137200/[04], loss: 12.0592, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:39:54 Iters: 137300/[04], loss: 11.3607, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:41:57 Iters: 137400/[04], loss: 10.7214, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:44:00 Iters: 137500/[04], loss: 11.5342, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:46:03 Iters: 137600/[04], loss: 12.0527, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:48:06 Iters: 137700/[04], loss: 10.4175, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:50:09 Iters: 137800/[04], loss: 12.2903, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:52:12 Iters: 137900/[04], loss: 11.0694, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:54:15 Iters: 138000/[04], loss: 10.9786, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:56:18 Iters: 138100/[04], loss: 11.8667, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-16:58:21 Iters: 138200/[04], loss: 11.6059, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:00:25 Iters: 138300/[04], loss: 10.6973, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:02:28 Iters: 138400/[04], loss: 10.6759, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:04:32 Iters: 138500/[04], loss: 11.5327, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-17:06:35 Iters: 138600/[04], loss: 11.0650, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-17:08:39 Iters: 138700/[04], loss: 11.4567, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-17:10:43 Iters: 138800/[04], loss: 10.4901, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-17:12:47 Iters: 138900/[04], loss: 11.2707, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-17:14:50 Iters: 139000/[04], loss: 11.5773, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220702-17:16:54 Iters: 139100/[04], loss: 12.1220, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220702-17:18:58 Iters: 139200/[04], loss: 10.7923, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220702-17:21:01 Iters: 139300/[04], loss: 10.8787, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:23:04 Iters: 139400/[04], loss: 11.5820, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:25:08 Iters: 139500/[04], loss: 11.5125, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:27:11 Iters: 139600/[04], loss: 11.1429, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:29:14 Iters: 139700/[04], loss: 11.4964, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:31:17 Iters: 139800/[04], loss: 10.8577, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:33:20 Iters: 139900/[04], loss: 11.5401, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:35:23 Iters: 140000/[04], loss: 10.7699, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:35:23 Saving checkpoint: 140000
-20220702-17:36:39 LFW Ave Accuracy: 98.9999
-20220702-17:37:54 AgeDB-30 Ave Accuracy: 93.9500
-20220702-17:39:21 CFP-FP Ave Accuracy: 87.1286
-20220702-17:39:21 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.3000 in iters: 120000
-20220702-17:41:24 Iters: 140100/[04], loss: 11.6589, train_accuracy: 0.0078, time: 3.61 s/iter, learning rate: 0.05
-20220702-17:43:28 Iters: 140200/[04], loss: 11.9645, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-17:45:31 Iters: 140300/[04], loss: 10.7242, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220702-17:47:34 Iters: 140400/[04], loss: 10.4897, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:49:37 Iters: 140500/[04], loss: 10.5114, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:51:41 Iters: 140600/[04], loss: 10.7605, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:53:44 Iters: 140700/[04], loss: 11.5973, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:55:47 Iters: 140800/[04], loss: 12.5674, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:57:50 Iters: 140900/[04], loss: 11.9579, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-17:59:53 Iters: 141000/[04], loss: 11.2625, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:01:56 Iters: 141100/[04], loss: 10.8190, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:03:59 Iters: 141200/[04], loss: 11.5222, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:06:02 Iters: 141300/[04], loss: 10.7851, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:08:05 Iters: 141400/[04], loss: 11.6944, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:10:08 Iters: 141500/[04], loss: 10.5718, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:12:11 Iters: 141600/[04], loss: 10.6040, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:14:14 Iters: 141700/[04], loss: 11.9435, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:16:17 Iters: 141800/[04], loss: 11.2316, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:18:20 Iters: 141900/[04], loss: 11.3810, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:20:23 Iters: 142000/[04], loss: 11.4291, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:22:26 Iters: 142100/[04], loss: 10.9345, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:24:29 Iters: 142200/[04], loss: 10.8025, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:26:32 Iters: 142300/[04], loss: 11.2173, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:28:35 Iters: 142400/[04], loss: 10.6233, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:30:38 Iters: 142500/[04], loss: 11.5390, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:32:41 Iters: 142600/[04], loss: 11.9552, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:34:45 Iters: 142700/[04], loss: 11.8594, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:36:48 Iters: 142800/[04], loss: 11.8120, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:38:51 Iters: 142900/[04], loss: 11.2392, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:40:54 Iters: 143000/[04], loss: 10.9087, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:42:57 Iters: 143100/[04], loss: 11.6193, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:45:00 Iters: 143200/[04], loss: 10.8855, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:47:03 Iters: 143300/[04], loss: 11.6196, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:49:06 Iters: 143400/[04], loss: 10.5414, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:51:09 Iters: 143500/[04], loss: 10.8455, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:53:12 Iters: 143600/[04], loss: 12.1704, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:55:15 Iters: 143700/[04], loss: 10.8320, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:57:18 Iters: 143800/[04], loss: 11.3351, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-18:59:21 Iters: 143900/[04], loss: 11.8932, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:01:24 Iters: 144000/[04], loss: 11.3004, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:03:27 Iters: 144100/[04], loss: 11.3632, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:05:30 Iters: 144200/[04], loss: 11.1385, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:07:33 Iters: 144300/[04], loss: 11.6640, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:09:36 Iters: 144400/[04], loss: 11.4521, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:11:39 Iters: 144500/[04], loss: 11.0499, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:13:42 Iters: 144600/[04], loss: 11.4937, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:15:45 Iters: 144700/[04], loss: 11.0760, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:17:48 Iters: 144800/[04], loss: 11.5750, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:19:51 Iters: 144900/[04], loss: 11.7381, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:21:54 Iters: 145000/[04], loss: 11.6016, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:23:57 Iters: 145100/[04], loss: 10.0352, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:26:00 Iters: 145200/[04], loss: 11.3545, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:28:03 Iters: 145300/[04], loss: 11.0235, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:30:06 Iters: 145400/[04], loss: 11.8599, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:32:09 Iters: 145500/[04], loss: 11.6544, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:34:12 Iters: 145600/[04], loss: 10.9427, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:36:15 Iters: 145700/[04], loss: 11.4017, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:38:18 Iters: 145800/[04], loss: 11.0677, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:40:21 Iters: 145900/[04], loss: 11.5001, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:42:24 Iters: 146000/[04], loss: 11.8079, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:44:27 Iters: 146100/[04], loss: 11.1241, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:46:30 Iters: 146200/[04], loss: 11.0587, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:48:33 Iters: 146300/[04], loss: 12.0473, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:50:36 Iters: 146400/[04], loss: 11.5345, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:52:39 Iters: 146500/[04], loss: 10.9246, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:54:42 Iters: 146600/[04], loss: 11.9991, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:56:45 Iters: 146700/[04], loss: 11.4034, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-19:58:48 Iters: 146800/[04], loss: 10.3768, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:00:51 Iters: 146900/[04], loss: 10.8434, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:02:54 Iters: 147000/[04], loss: 11.2195, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:04:56 Iters: 147100/[04], loss: 11.3777, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:06:59 Iters: 147200/[04], loss: 10.7921, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:09:02 Iters: 147300/[04], loss: 11.4148, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:11:05 Iters: 147400/[04], loss: 11.3398, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:13:08 Iters: 147500/[04], loss: 10.2848, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:15:11 Iters: 147600/[04], loss: 10.8306, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:17:14 Iters: 147700/[04], loss: 10.9907, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:19:17 Iters: 147800/[04], loss: 11.1985, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:21:20 Iters: 147900/[04], loss: 11.4080, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:23:23 Iters: 148000/[04], loss: 13.0027, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:25:26 Iters: 148100/[04], loss: 10.9940, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:27:29 Iters: 148200/[04], loss: 11.1180, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:29:33 Iters: 148300/[04], loss: 11.6524, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:31:36 Iters: 148400/[04], loss: 10.8103, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:33:39 Iters: 148500/[04], loss: 10.6521, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:35:42 Iters: 148600/[04], loss: 11.6980, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:37:45 Iters: 148700/[04], loss: 10.8612, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:39:48 Iters: 148800/[04], loss: 11.4524, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:41:51 Iters: 148900/[04], loss: 11.0737, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:43:54 Iters: 149000/[04], loss: 11.5063, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:45:57 Iters: 149100/[04], loss: 11.2804, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:48:00 Iters: 149200/[04], loss: 11.1492, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:50:02 Iters: 149300/[04], loss: 11.3080, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:52:05 Iters: 149400/[04], loss: 11.5800, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:54:08 Iters: 149500/[04], loss: 11.4100, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:56:11 Iters: 149600/[04], loss: 10.8515, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-20:58:14 Iters: 149700/[04], loss: 11.5474, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:00:17 Iters: 149800/[04], loss: 11.7310, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:02:20 Iters: 149900/[04], loss: 11.9377, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:04:23 Iters: 150000/[04], loss: 11.7504, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:04:23 Saving checkpoint: 150000
-20220702-21:05:40 LFW Ave Accuracy: 99.0832
-20220702-21:06:55 AgeDB-30 Ave Accuracy: 93.6167
-20220702-21:08:21 CFP-FP Ave Accuracy: 87.8571
-20220702-21:08:21 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.3000 in iters: 120000
-20220702-21:10:24 Iters: 150100/[04], loss: 11.2980, train_accuracy: 0.0391, time: 3.61 s/iter, learning rate: 0.05
-20220702-21:12:27 Iters: 150200/[04], loss: 10.7900, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:14:30 Iters: 150300/[04], loss: 10.9237, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:16:33 Iters: 150400/[04], loss: 10.9831, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:18:36 Iters: 150500/[04], loss: 11.0676, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:20:39 Iters: 150600/[04], loss: 11.2197, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:22:42 Iters: 150700/[04], loss: 11.2748, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:24:45 Iters: 150800/[04], loss: 11.7859, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:26:48 Iters: 150900/[04], loss: 10.8515, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:28:51 Iters: 151000/[04], loss: 11.2819, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:30:54 Iters: 151100/[04], loss: 11.6747, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:32:57 Iters: 151200/[04], loss: 11.4359, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:35:01 Iters: 151300/[04], loss: 10.4320, train_accuracy: 0.1016, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:37:04 Iters: 151400/[04], loss: 11.3195, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:39:07 Iters: 151500/[04], loss: 12.4645, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:41:10 Iters: 151600/[04], loss: 10.8549, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:43:13 Iters: 151700/[04], loss: 10.5132, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:45:16 Iters: 151800/[04], loss: 11.1872, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:47:19 Iters: 151900/[04], loss: 11.3409, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:49:22 Iters: 152000/[04], loss: 12.2447, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:51:25 Iters: 152100/[04], loss: 10.7299, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:53:28 Iters: 152200/[04], loss: 11.2778, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:55:31 Iters: 152300/[04], loss: 12.2457, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:57:34 Iters: 152400/[04], loss: 11.9112, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-21:59:38 Iters: 152500/[04], loss: 11.0486, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:01:41 Iters: 152600/[04], loss: 10.8415, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:03:43 Iters: 152700/[04], loss: 12.0082, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:05:46 Iters: 152800/[04], loss: 11.9595, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:07:49 Iters: 152900/[04], loss: 11.7819, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:09:52 Iters: 153000/[04], loss: 11.5101, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:11:55 Iters: 153100/[04], loss: 11.5210, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:13:58 Iters: 153200/[04], loss: 11.2321, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:16:01 Iters: 153300/[04], loss: 11.5106, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:18:05 Iters: 153400/[04], loss: 12.0349, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:20:08 Iters: 153500/[04], loss: 11.1622, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:22:11 Iters: 153600/[04], loss: 11.6572, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:24:14 Iters: 153700/[04], loss: 10.2737, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:26:16 Iters: 153800/[04], loss: 10.4736, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:28:19 Iters: 153900/[04], loss: 10.9480, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:30:22 Iters: 154000/[04], loss: 10.8610, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:32:25 Iters: 154100/[04], loss: 10.6684, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:34:28 Iters: 154200/[04], loss: 11.4221, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:36:31 Iters: 154300/[04], loss: 10.6678, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:38:34 Iters: 154400/[04], loss: 11.5805, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:40:37 Iters: 154500/[04], loss: 10.7436, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:42:39 Iters: 154600/[04], loss: 10.4327, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:44:42 Iters: 154700/[04], loss: 11.4051, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:46:45 Iters: 154800/[04], loss: 11.0675, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:48:48 Iters: 154900/[04], loss: 11.1456, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:50:51 Iters: 155000/[04], loss: 11.2481, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:52:54 Iters: 155100/[04], loss: 11.3906, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:54:56 Iters: 155200/[04], loss: 10.5666, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:56:59 Iters: 155300/[04], loss: 11.3618, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-22:59:02 Iters: 155400/[04], loss: 11.7348, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:01:05 Iters: 155500/[04], loss: 11.2996, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:03:08 Iters: 155600/[04], loss: 11.0121, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:05:11 Iters: 155700/[04], loss: 11.4915, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:07:14 Iters: 155800/[04], loss: 10.7824, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:09:18 Iters: 155900/[04], loss: 11.1956, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:11:21 Iters: 156000/[04], loss: 11.0144, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:13:24 Iters: 156100/[04], loss: 10.4280, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220702-23:15:28 Iters: 156200/[04], loss: 11.5528, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:17:31 Iters: 156300/[04], loss: 11.9970, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:19:34 Iters: 156400/[04], loss: 11.3050, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:21:37 Iters: 156500/[04], loss: 11.8814, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:23:41 Iters: 156600/[04], loss: 11.1375, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:25:44 Iters: 156700/[04], loss: 11.4557, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:27:47 Iters: 156800/[04], loss: 11.8844, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:29:50 Iters: 156900/[04], loss: 10.9881, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:31:54 Iters: 157000/[04], loss: 11.3536, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:33:57 Iters: 157100/[04], loss: 11.3484, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:36:00 Iters: 157200/[04], loss: 11.1874, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:38:04 Iters: 157300/[04], loss: 10.8777, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220702-23:40:07 Iters: 157400/[04], loss: 11.9263, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:42:10 Iters: 157500/[04], loss: 12.0226, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:44:14 Iters: 157600/[04], loss: 11.6676, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:46:17 Iters: 157700/[04], loss: 11.1248, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:48:20 Iters: 157800/[04], loss: 11.7507, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:50:23 Iters: 157900/[04], loss: 11.2548, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:52:26 Iters: 158000/[04], loss: 10.4368, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:54:30 Iters: 158100/[04], loss: 10.8228, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220702-23:56:33 Iters: 158200/[04], loss: 10.7929, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220702-23:58:37 Iters: 158300/[04], loss: 11.1219, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-00:00:40 Iters: 158400/[04], loss: 10.6885, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:02:43 Iters: 158500/[04], loss: 11.2960, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:04:46 Iters: 158600/[04], loss: 12.0813, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:06:49 Iters: 158700/[04], loss: 11.2466, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:08:53 Iters: 158800/[04], loss: 10.9056, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:10:56 Iters: 158900/[04], loss: 11.3172, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:12:59 Iters: 159000/[04], loss: 12.8257, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:15:03 Iters: 159100/[04], loss: 10.8481, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:17:06 Iters: 159200/[04], loss: 11.4906, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:19:09 Iters: 159300/[04], loss: 10.7940, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220703-00:21:13 Iters: 159400/[04], loss: 11.0202, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:23:16 Iters: 159500/[04], loss: 10.5230, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:25:19 Iters: 159600/[04], loss: 11.4270, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:27:23 Iters: 159700/[04], loss: 11.4403, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:29:26 Iters: 159800/[04], loss: 10.7266, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:31:29 Iters: 159900/[04], loss: 11.6495, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:33:32 Iters: 160000/[04], loss: 11.4739, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:33:32 Saving checkpoint: 160000
-20220703-00:34:50 LFW Ave Accuracy: 99.1332
-20220703-00:36:06 AgeDB-30 Ave Accuracy: 93.4167
-20220703-00:37:32 CFP-FP Ave Accuracy: 87.9571
-20220703-00:37:32 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.3000 in iters: 120000
-20220703-00:39:35 Iters: 160100/[04], loss: 10.6388, train_accuracy: 0.0391, time: 3.63 s/iter, learning rate: 0.05
-20220703-00:41:39 Iters: 160200/[04], loss: 11.6504, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:43:42 Iters: 160300/[04], loss: 10.7937, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:45:45 Iters: 160400/[04], loss: 11.3872, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:47:48 Iters: 160500/[04], loss: 11.5527, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:49:51 Iters: 160600/[04], loss: 10.7489, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:51:54 Iters: 160700/[04], loss: 10.8998, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:53:57 Iters: 160800/[04], loss: 11.6191, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:56:00 Iters: 160900/[04], loss: 11.5338, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-00:58:04 Iters: 161000/[04], loss: 10.3660, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:00:07 Iters: 161100/[04], loss: 11.5742, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:02:10 Iters: 161200/[04], loss: 11.1968, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:04:13 Iters: 161300/[04], loss: 11.1042, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:06:16 Iters: 161400/[04], loss: 11.5879, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:08:20 Iters: 161500/[04], loss: 11.4507, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:10:23 Iters: 161600/[04], loss: 11.2687, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-01:12:26 Iters: 161700/[04], loss: 11.6079, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:14:30 Iters: 161800/[04], loss: 11.2584, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:16:33 Iters: 161900/[04], loss: 11.5496, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:18:36 Iters: 162000/[04], loss: 11.2919, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-01:20:39 Iters: 162100/[04], loss: 11.4932, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:22:43 Iters: 162200/[04], loss: 11.2100, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:24:46 Iters: 162300/[04], loss: 12.1522, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:26:49 Iters: 162400/[04], loss: 10.7945, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:28:53 Iters: 162500/[04], loss: 10.8767, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:30:56 Iters: 162600/[04], loss: 10.7893, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:32:59 Iters: 162700/[04], loss: 10.7935, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:35:03 Iters: 162800/[04], loss: 11.5969, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:37:06 Iters: 162900/[04], loss: 11.9092, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:39:09 Iters: 163000/[04], loss: 12.4528, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-01:41:13 Iters: 163100/[04], loss: 11.5090, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:43:16 Iters: 163200/[04], loss: 10.5122, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:45:19 Iters: 163300/[04], loss: 11.8236, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:47:23 Iters: 163400/[04], loss: 12.0338, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-01:49:26 Iters: 163500/[04], loss: 12.0307, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:51:29 Iters: 163600/[04], loss: 11.4403, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:53:32 Iters: 163700/[04], loss: 11.0801, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:55:35 Iters: 163800/[04], loss: 10.7742, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:57:38 Iters: 163900/[04], loss: 11.8920, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-01:59:42 Iters: 164000/[04], loss: 11.2004, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:01:45 Iters: 164100/[04], loss: 11.5952, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:03:48 Iters: 164200/[04], loss: 11.8433, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:05:51 Iters: 164300/[04], loss: 11.4181, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:07:55 Iters: 164400/[04], loss: 10.5927, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:09:58 Iters: 164500/[04], loss: 11.4861, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:12:01 Iters: 164600/[04], loss: 10.6010, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:14:04 Iters: 164700/[04], loss: 11.7074, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:16:08 Iters: 164800/[04], loss: 11.1176, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:18:11 Iters: 164900/[04], loss: 11.4947, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:20:14 Iters: 165000/[04], loss: 11.2230, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:22:17 Iters: 165100/[04], loss: 10.6953, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:24:20 Iters: 165200/[04], loss: 11.2672, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:26:24 Iters: 165300/[04], loss: 10.6024, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:28:27 Iters: 165400/[04], loss: 12.1915, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:30:30 Iters: 165500/[04], loss: 11.6951, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220703-02:32:34 Iters: 165600/[04], loss: 11.7119, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:34:37 Iters: 165700/[04], loss: 10.3755, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:36:40 Iters: 165800/[04], loss: 11.4943, train_accuracy: 0.0703, time: 1.24 s/iter, learning rate: 0.05
-20220703-02:38:43 Iters: 165900/[04], loss: 10.4626, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:40:46 Iters: 166000/[04], loss: 11.6359, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:42:50 Iters: 166100/[04], loss: 11.5917, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:44:53 Iters: 166200/[04], loss: 11.4703, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:46:56 Iters: 166300/[04], loss: 10.5756, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:49:00 Iters: 166400/[04], loss: 11.8014, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:51:03 Iters: 166500/[04], loss: 12.1459, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:53:06 Iters: 166600/[04], loss: 11.2548, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:55:09 Iters: 166700/[04], loss: 11.3631, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:57:12 Iters: 166800/[04], loss: 11.0172, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-02:59:16 Iters: 166900/[04], loss: 12.1290, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:01:19 Iters: 167000/[04], loss: 11.8936, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:03:22 Iters: 167100/[04], loss: 11.6546, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:05:26 Iters: 167200/[04], loss: 11.4351, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220703-03:07:29 Iters: 167300/[04], loss: 11.9533, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:09:32 Iters: 167400/[04], loss: 10.9228, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:11:35 Iters: 167500/[04], loss: 10.8946, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:13:38 Iters: 167600/[04], loss: 10.9732, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:15:42 Iters: 167700/[04], loss: 11.7659, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:17:45 Iters: 167800/[04], loss: 11.0789, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:19:48 Iters: 167900/[04], loss: 11.5885, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:21:51 Iters: 168000/[04], loss: 10.7375, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:23:54 Iters: 168100/[04], loss: 11.6857, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:25:58 Iters: 168200/[04], loss: 11.3234, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:28:01 Iters: 168300/[04], loss: 11.0982, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:30:05 Iters: 168400/[04], loss: 11.5805, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:32:08 Iters: 168500/[04], loss: 10.5014, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:34:11 Iters: 168600/[04], loss: 11.6099, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:36:15 Iters: 168700/[04], loss: 11.6198, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:38:18 Iters: 168800/[04], loss: 10.7295, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:40:21 Iters: 168900/[04], loss: 11.2277, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:42:24 Iters: 169000/[04], loss: 10.8717, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:44:28 Iters: 169100/[04], loss: 11.0310, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-03:46:31 Iters: 169200/[04], loss: 11.2781, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:48:35 Iters: 169300/[04], loss: 11.0967, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-03:50:38 Iters: 169400/[04], loss: 11.6984, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:52:41 Iters: 169500/[04], loss: 11.2152, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:54:44 Iters: 169600/[04], loss: 11.2090, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:56:47 Iters: 169700/[04], loss: 11.4477, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-03:58:50 Iters: 169800/[04], loss: 11.3522, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:00:53 Iters: 169900/[04], loss: 11.8567, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:02:57 Iters: 170000/[04], loss: 11.3803, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:02:57 Saving checkpoint: 170000
-20220703-04:04:14 LFW Ave Accuracy: 98.8498
-20220703-04:05:31 AgeDB-30 Ave Accuracy: 93.7000
-20220703-04:07:02 CFP-FP Ave Accuracy: 87.5429
-20220703-04:07:02 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.3000 in iters: 120000
-20220703-04:09:05 Iters: 170100/[04], loss: 10.9676, train_accuracy: 0.0156, time: 3.68 s/iter, learning rate: 0.05
-20220703-04:11:08 Iters: 170200/[04], loss: 11.0585, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:13:11 Iters: 170300/[04], loss: 11.2576, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:15:14 Iters: 170400/[04], loss: 11.3021, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:17:17 Iters: 170500/[04], loss: 11.3388, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:19:21 Iters: 170600/[04], loss: 10.7267, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:21:24 Iters: 170700/[04], loss: 11.1490, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220703-04:23:27 Iters: 170800/[04], loss: 10.5229, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:25:31 Iters: 170900/[04], loss: 10.6656, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:27:34 Iters: 171000/[04], loss: 12.0756, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:29:37 Iters: 171100/[04], loss: 11.7639, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:31:40 Iters: 171200/[04], loss: 10.9506, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:33:44 Iters: 171300/[04], loss: 11.2137, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:35:47 Iters: 171400/[04], loss: 11.5136, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:37:50 Iters: 171500/[04], loss: 11.2281, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:39:54 Iters: 171600/[04], loss: 10.6277, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:41:57 Iters: 171700/[04], loss: 11.6778, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:44:00 Iters: 171800/[04], loss: 10.9989, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:46:03 Iters: 171900/[04], loss: 12.2266, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:48:06 Iters: 172000/[04], loss: 10.9043, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:50:10 Iters: 172100/[04], loss: 12.3333, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:52:13 Iters: 172200/[04], loss: 10.5526, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:54:16 Iters: 172300/[04], loss: 10.4559, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:56:19 Iters: 172400/[04], loss: 11.7750, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-04:58:22 Iters: 172500/[04], loss: 11.7363, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:00:25 Iters: 172600/[04], loss: 10.5294, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:02:28 Iters: 172700/[04], loss: 11.3421, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:04:32 Iters: 172800/[04], loss: 10.7646, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:06:35 Iters: 172900/[04], loss: 10.7779, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:08:38 Iters: 173000/[04], loss: 12.1677, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:10:41 Iters: 173100/[04], loss: 11.8744, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:12:45 Iters: 173200/[04], loss: 11.2200, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:14:48 Iters: 173300/[04], loss: 10.6752, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:16:51 Iters: 173400/[04], loss: 11.0221, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:18:54 Iters: 173500/[04], loss: 10.8674, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:20:58 Iters: 173600/[04], loss: 11.4896, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:23:01 Iters: 173700/[04], loss: 10.2997, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:25:04 Iters: 173800/[04], loss: 10.9052, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:27:07 Iters: 173900/[04], loss: 11.7840, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:29:11 Iters: 174000/[04], loss: 11.8271, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:31:14 Iters: 174100/[04], loss: 10.8944, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:33:17 Iters: 174200/[04], loss: 11.3712, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:35:21 Iters: 174300/[04], loss: 10.6664, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-05:37:24 Iters: 174400/[04], loss: 12.0462, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:39:28 Iters: 174500/[04], loss: 12.0607, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220703-05:41:31 Iters: 174600/[04], loss: 11.6459, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:43:34 Iters: 174700/[04], loss: 11.6692, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:45:37 Iters: 174800/[04], loss: 11.8145, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:47:41 Iters: 174900/[04], loss: 11.9825, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-05:49:44 Iters: 175000/[04], loss: 11.4366, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:51:48 Iters: 175100/[04], loss: 11.4934, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-05:53:51 Iters: 175200/[04], loss: 11.5713, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:55:54 Iters: 175300/[04], loss: 10.7301, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-05:57:58 Iters: 175400/[04], loss: 11.3917, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-06:00:01 Iters: 175500/[04], loss: 11.7297, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:02:04 Iters: 175600/[04], loss: 11.7364, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:04:08 Iters: 175700/[04], loss: 11.6172, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-06:06:11 Iters: 175800/[04], loss: 10.6644, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:08:14 Iters: 175900/[04], loss: 12.4654, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:10:17 Iters: 176000/[04], loss: 10.6014, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:12:20 Iters: 176100/[04], loss: 11.3025, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:14:24 Iters: 176200/[04], loss: 11.6132, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:16:27 Iters: 176300/[04], loss: 12.5052, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:18:30 Iters: 176400/[04], loss: 10.8751, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:20:33 Iters: 176500/[04], loss: 10.8867, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:22:36 Iters: 176600/[04], loss: 11.9145, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:24:40 Iters: 176700/[04], loss: 11.3786, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-06:26:43 Iters: 176800/[04], loss: 11.0239, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:28:46 Iters: 176900/[04], loss: 11.7247, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:30:49 Iters: 177000/[04], loss: 10.2924, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:32:53 Iters: 177100/[04], loss: 10.8103, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:34:56 Iters: 177200/[04], loss: 10.6932, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:36:59 Iters: 177300/[04], loss: 11.5990, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:39:02 Iters: 177400/[04], loss: 10.9876, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:41:05 Iters: 177500/[04], loss: 10.4154, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:43:09 Iters: 177600/[04], loss: 11.0254, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:45:12 Iters: 177700/[04], loss: 11.9169, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:47:15 Iters: 177800/[04], loss: 11.3258, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:49:19 Iters: 177900/[04], loss: 11.9184, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-06:51:22 Iters: 178000/[04], loss: 10.8822, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:53:25 Iters: 178100/[04], loss: 12.5114, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-06:55:28 Iters: 178200/[04], loss: 11.7025, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-06:57:32 Iters: 178300/[04], loss: 11.2129, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-06:59:35 Iters: 178400/[04], loss: 10.7484, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:01:39 Iters: 178500/[04], loss: 10.7435, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-07:03:42 Iters: 178600/[04], loss: 11.1171, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:05:45 Iters: 178700/[04], loss: 11.2655, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:07:49 Iters: 178800/[04], loss: 11.0791, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:09:52 Iters: 178900/[04], loss: 11.3001, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:11:55 Iters: 179000/[04], loss: 11.7666, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:13:58 Iters: 179100/[04], loss: 11.9073, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:16:02 Iters: 179200/[04], loss: 10.8817, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:18:05 Iters: 179300/[04], loss: 10.5308, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:20:08 Iters: 179400/[04], loss: 11.4320, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:22:11 Iters: 179500/[04], loss: 10.9888, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220703-07:24:15 Iters: 179600/[04], loss: 11.1719, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:26:18 Iters: 179700/[04], loss: 11.2721, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-07:28:21 Iters: 179800/[04], loss: 11.7787, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:30:25 Iters: 179900/[04], loss: 11.0932, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-07:32:28 Iters: 180000/[04], loss: 11.5095, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:32:28 Saving checkpoint: 180000
-20220703-07:33:45 LFW Ave Accuracy: 98.9999
-20220703-07:35:01 AgeDB-30 Ave Accuracy: 93.4833
-20220703-07:36:30 CFP-FP Ave Accuracy: 88.1000
-20220703-07:36:30 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.3000 in iters: 120000
-20220703-07:38:33 Iters: 180100/[04], loss: 12.2851, train_accuracy: 0.0234, time: 3.65 s/iter, learning rate: 0.05
-20220703-07:40:36 Iters: 180200/[04], loss: 11.3466, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:42:40 Iters: 180300/[04], loss: 11.3431, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:44:43 Iters: 180400/[04], loss: 11.4970, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:46:46 Iters: 180500/[04], loss: 11.2417, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:48:49 Iters: 180600/[04], loss: 11.5930, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:50:53 Iters: 180700/[04], loss: 13.0737, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:52:56 Iters: 180800/[04], loss: 11.2438, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:54:59 Iters: 180900/[04], loss: 11.4736, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:57:02 Iters: 181000/[04], loss: 11.7253, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-07:59:06 Iters: 181100/[04], loss: 11.4794, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-08:01:09 Iters: 181200/[04], loss: 11.8872, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:03:12 Iters: 181300/[04], loss: 10.6458, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:05:15 Iters: 181400/[04], loss: 12.2417, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-08:07:19 Iters: 181500/[04], loss: 11.1971, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:09:22 Iters: 181600/[04], loss: 11.5574, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:11:25 Iters: 181700/[04], loss: 11.9296, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:13:28 Iters: 181800/[04], loss: 10.6815, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:15:32 Iters: 181900/[04], loss: 11.2887, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-08:16:45 Train Epoch: 5/18 ...
-20220703-08:17:35 Iters: 182000/[05], loss: 11.6471, train_accuracy: 0.0469, time: 0.50 s/iter, learning rate: 0.05
-20220703-08:19:38 Iters: 182100/[05], loss: 10.9358, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:21:41 Iters: 182200/[05], loss: 11.9578, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:23:44 Iters: 182300/[05], loss: 11.4238, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:25:48 Iters: 182400/[05], loss: 10.5044, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:27:51 Iters: 182500/[05], loss: 11.0048, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:29:54 Iters: 182600/[05], loss: 11.6702, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:31:58 Iters: 182700/[05], loss: 11.8312, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220703-08:34:01 Iters: 182800/[05], loss: 10.4389, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:36:04 Iters: 182900/[05], loss: 10.7917, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:38:07 Iters: 183000/[05], loss: 12.2765, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:40:11 Iters: 183100/[05], loss: 11.5779, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:42:14 Iters: 183200/[05], loss: 11.6827, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:44:17 Iters: 183300/[05], loss: 10.1770, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:46:20 Iters: 183400/[05], loss: 10.4916, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:48:23 Iters: 183500/[05], loss: 11.9918, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:50:27 Iters: 183600/[05], loss: 11.7028, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-08:52:30 Iters: 183700/[05], loss: 11.4340, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:54:33 Iters: 183800/[05], loss: 11.7060, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:56:36 Iters: 183900/[05], loss: 12.0949, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-08:58:40 Iters: 184000/[05], loss: 11.5485, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-09:00:43 Iters: 184100/[05], loss: 11.4963, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:02:46 Iters: 184200/[05], loss: 10.4879, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:04:49 Iters: 184300/[05], loss: 12.5609, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:06:52 Iters: 184400/[05], loss: 11.7639, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:08:56 Iters: 184500/[05], loss: 11.6657, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:10:59 Iters: 184600/[05], loss: 11.1299, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:13:02 Iters: 184700/[05], loss: 11.7997, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:15:05 Iters: 184800/[05], loss: 10.6037, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:17:09 Iters: 184900/[05], loss: 11.0114, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-09:19:12 Iters: 185000/[05], loss: 10.3114, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:21:15 Iters: 185100/[05], loss: 11.3640, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:23:19 Iters: 185200/[05], loss: 10.7891, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-09:25:22 Iters: 185300/[05], loss: 11.3621, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:27:25 Iters: 185400/[05], loss: 11.5273, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-09:29:28 Iters: 185500/[05], loss: 11.4832, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:31:32 Iters: 185600/[05], loss: 10.8278, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:33:35 Iters: 185700/[05], loss: 11.3160, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:35:38 Iters: 185800/[05], loss: 11.1070, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:37:41 Iters: 185900/[05], loss: 11.9839, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:39:45 Iters: 186000/[05], loss: 11.2432, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:41:48 Iters: 186100/[05], loss: 10.8420, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:43:51 Iters: 186200/[05], loss: 10.7966, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:45:54 Iters: 186300/[05], loss: 10.9841, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:47:58 Iters: 186400/[05], loss: 11.4375, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-09:50:01 Iters: 186500/[05], loss: 11.4753, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:52:05 Iters: 186600/[05], loss: 11.9785, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-09:54:08 Iters: 186700/[05], loss: 11.3912, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:56:11 Iters: 186800/[05], loss: 10.3592, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-09:58:15 Iters: 186900/[05], loss: 11.3795, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-10:00:18 Iters: 187000/[05], loss: 11.5268, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:02:21 Iters: 187100/[05], loss: 10.7765, train_accuracy: 0.1094, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:04:24 Iters: 187200/[05], loss: 11.4983, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:06:27 Iters: 187300/[05], loss: 11.2624, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:08:31 Iters: 187400/[05], loss: 11.8816, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:10:34 Iters: 187500/[05], loss: 11.4119, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:12:37 Iters: 187600/[05], loss: 10.5647, train_accuracy: 0.0703, time: 1.24 s/iter, learning rate: 0.05
-20220703-10:14:40 Iters: 187700/[05], loss: 11.2162, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:16:43 Iters: 187800/[05], loss: 11.5504, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:18:47 Iters: 187900/[05], loss: 10.8981, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:20:50 Iters: 188000/[05], loss: 11.3028, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:22:53 Iters: 188100/[05], loss: 11.8354, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:24:57 Iters: 188200/[05], loss: 11.3952, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-10:27:00 Iters: 188300/[05], loss: 11.6380, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:29:03 Iters: 188400/[05], loss: 11.9120, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220703-10:31:07 Iters: 188500/[05], loss: 10.9063, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:33:10 Iters: 188600/[05], loss: 11.0454, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:35:13 Iters: 188700/[05], loss: 11.8108, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-10:37:17 Iters: 188800/[05], loss: 10.6296, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:39:20 Iters: 188900/[05], loss: 11.1822, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-10:41:23 Iters: 189000/[05], loss: 10.7059, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:43:27 Iters: 189100/[05], loss: 11.5485, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:45:30 Iters: 189200/[05], loss: 10.8738, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-10:47:33 Iters: 189300/[05], loss: 11.1099, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:49:36 Iters: 189400/[05], loss: 11.2840, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:51:40 Iters: 189500/[05], loss: 11.4167, train_accuracy: 0.0000, time: 1.24 s/iter, learning rate: 0.05
-20220703-10:53:43 Iters: 189600/[05], loss: 11.5198, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:55:46 Iters: 189700/[05], loss: 10.5887, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:57:50 Iters: 189800/[05], loss: 12.0237, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-10:59:53 Iters: 189900/[05], loss: 11.3372, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:01:56 Iters: 190000/[05], loss: 11.3124, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:01:56 Saving checkpoint: 190000
-20220703-11:03:12 LFW Ave Accuracy: 98.9832
-20220703-11:04:28 AgeDB-30 Ave Accuracy: 93.5333
-20220703-11:05:55 CFP-FP Ave Accuracy: 86.8286
-20220703-11:05:55 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.3000 in iters: 120000
-20220703-11:07:58 Iters: 190100/[05], loss: 11.0373, train_accuracy: 0.0234, time: 3.62 s/iter, learning rate: 0.05
-20220703-11:10:01 Iters: 190200/[05], loss: 11.1050, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:12:05 Iters: 190300/[05], loss: 11.5006, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:14:08 Iters: 190400/[05], loss: 10.0798, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:16:11 Iters: 190500/[05], loss: 11.8772, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-11:18:15 Iters: 190600/[05], loss: 11.0624, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-11:20:18 Iters: 190700/[05], loss: 11.3778, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:22:21 Iters: 190800/[05], loss: 10.7428, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:24:25 Iters: 190900/[05], loss: 10.3850, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:26:28 Iters: 191000/[05], loss: 10.9641, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:28:31 Iters: 191100/[05], loss: 11.4269, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:30:34 Iters: 191200/[05], loss: 11.3351, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:32:38 Iters: 191300/[05], loss: 11.6741, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:34:41 Iters: 191400/[05], loss: 11.3100, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220703-11:36:44 Iters: 191500/[05], loss: 11.5573, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:38:48 Iters: 191600/[05], loss: 10.5696, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-11:40:51 Iters: 191700/[05], loss: 11.7624, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:42:54 Iters: 191800/[05], loss: 11.0072, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:44:58 Iters: 191900/[05], loss: 11.3195, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:47:01 Iters: 192000/[05], loss: 10.4423, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:49:04 Iters: 192100/[05], loss: 10.9224, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:51:07 Iters: 192200/[05], loss: 11.3698, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:53:11 Iters: 192300/[05], loss: 11.2588, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:55:14 Iters: 192400/[05], loss: 11.7998, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:57:17 Iters: 192500/[05], loss: 10.7844, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-11:59:21 Iters: 192600/[05], loss: 11.6600, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-12:01:24 Iters: 192700/[05], loss: 10.7030, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:03:28 Iters: 192800/[05], loss: 11.2667, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-12:05:31 Iters: 192900/[05], loss: 11.0460, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:07:34 Iters: 193000/[05], loss: 11.4091, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:09:37 Iters: 193100/[05], loss: 11.3211, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:11:40 Iters: 193200/[05], loss: 10.8389, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:13:44 Iters: 193300/[05], loss: 10.5055, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220703-12:15:47 Iters: 193400/[05], loss: 10.3923, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:17:50 Iters: 193500/[05], loss: 11.3711, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:19:54 Iters: 193600/[05], loss: 11.1879, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-12:21:57 Iters: 193700/[05], loss: 10.8578, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:24:00 Iters: 193800/[05], loss: 11.6116, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:26:04 Iters: 193900/[05], loss: 11.5868, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:28:07 Iters: 194000/[05], loss: 11.5703, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:30:10 Iters: 194100/[05], loss: 10.6251, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:32:14 Iters: 194200/[05], loss: 11.4137, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:34:17 Iters: 194300/[05], loss: 11.0539, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220703-12:36:20 Iters: 194400/[05], loss: 11.9160, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:38:24 Iters: 194500/[05], loss: 10.7493, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-12:40:27 Iters: 194600/[05], loss: 10.7653, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:42:30 Iters: 194700/[05], loss: 11.1271, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220703-12:44:34 Iters: 194800/[05], loss: 11.1881, train_accuracy: 0.0859, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:46:37 Iters: 194900/[05], loss: 11.3898, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:48:40 Iters: 195000/[05], loss: 11.5684, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:50:44 Iters: 195100/[05], loss: 10.7675, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-12:52:47 Iters: 195200/[05], loss: 10.9662, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:54:50 Iters: 195300/[05], loss: 11.4028, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-12:56:53 Iters: 195400/[05], loss: 11.2886, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-12:58:57 Iters: 195500/[05], loss: 10.9861, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:01:00 Iters: 195600/[05], loss: 11.7433, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:03:03 Iters: 195700/[05], loss: 11.2926, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:05:07 Iters: 195800/[05], loss: 10.6673, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-13:07:10 Iters: 195900/[05], loss: 11.8867, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:09:14 Iters: 196000/[05], loss: 10.5690, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-13:11:17 Iters: 196100/[05], loss: 11.1074, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:13:20 Iters: 196200/[05], loss: 11.1482, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:15:23 Iters: 196300/[05], loss: 10.9884, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:17:26 Iters: 196400/[05], loss: 11.4613, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:19:30 Iters: 196500/[05], loss: 11.5699, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:21:33 Iters: 196600/[05], loss: 11.9922, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:23:36 Iters: 196700/[05], loss: 10.8172, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:25:40 Iters: 196800/[05], loss: 10.9627, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-13:27:43 Iters: 196900/[05], loss: 11.3094, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:29:46 Iters: 197000/[05], loss: 12.1259, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-13:31:49 Iters: 197100/[05], loss: 11.4646, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:33:53 Iters: 197200/[05], loss: 12.0267, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:35:56 Iters: 197300/[05], loss: 11.6519, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-13:37:59 Iters: 197400/[05], loss: 11.2833, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:40:03 Iters: 197500/[05], loss: 10.6823, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:42:06 Iters: 197600/[05], loss: 11.2018, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:44:09 Iters: 197700/[05], loss: 11.1676, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:46:12 Iters: 197800/[05], loss: 11.3549, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:48:15 Iters: 197900/[05], loss: 11.4528, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:50:18 Iters: 198000/[05], loss: 11.1823, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:52:22 Iters: 198100/[05], loss: 11.1964, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:54:25 Iters: 198200/[05], loss: 11.7464, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:56:28 Iters: 198300/[05], loss: 10.9442, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-13:58:31 Iters: 198400/[05], loss: 11.2504, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:00:34 Iters: 198500/[05], loss: 11.0741, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:02:38 Iters: 198600/[05], loss: 10.6397, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:04:41 Iters: 198700/[05], loss: 11.3491, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:06:44 Iters: 198800/[05], loss: 10.1516, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:08:48 Iters: 198900/[05], loss: 11.5586, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-14:10:51 Iters: 199000/[05], loss: 12.3363, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:12:54 Iters: 199100/[05], loss: 10.6557, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:14:57 Iters: 199200/[05], loss: 11.2891, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:17:00 Iters: 199300/[05], loss: 11.3552, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:19:04 Iters: 199400/[05], loss: 10.3661, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:21:07 Iters: 199500/[05], loss: 11.3326, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:23:10 Iters: 199600/[05], loss: 10.6499, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:25:19 Iters: 199700/[05], loss: 10.7816, train_accuracy: 0.0312, time: 1.29 s/iter, learning rate: 0.05
-20220703-14:27:22 Iters: 199800/[05], loss: 11.1771, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:29:26 Iters: 199900/[05], loss: 10.8905, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:31:29 Iters: 200000/[05], loss: 11.2633, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:31:29 Saving checkpoint: 200000
-20220703-14:32:46 LFW Ave Accuracy: 99.0999
-20220703-14:34:01 AgeDB-30 Ave Accuracy: 93.4833
-20220703-14:35:28 CFP-FP Ave Accuracy: 86.4571
-20220703-14:35:28 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.3000 in iters: 120000
-20220703-14:37:31 Iters: 200100/[05], loss: 10.9195, train_accuracy: 0.0547, time: 3.62 s/iter, learning rate: 0.05
-20220703-14:39:35 Iters: 200200/[05], loss: 11.2041, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:41:38 Iters: 200300/[05], loss: 10.7501, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:43:41 Iters: 200400/[05], loss: 11.8577, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:45:44 Iters: 200500/[05], loss: 11.0940, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:47:47 Iters: 200600/[05], loss: 10.9965, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:49:51 Iters: 200700/[05], loss: 11.4564, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:51:54 Iters: 200800/[05], loss: 10.8532, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:53:57 Iters: 200900/[05], loss: 11.3249, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:56:00 Iters: 201000/[05], loss: 10.9481, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-14:58:03 Iters: 201100/[05], loss: 12.0128, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:00:07 Iters: 201200/[05], loss: 10.2752, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:02:10 Iters: 201300/[05], loss: 10.4327, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:04:13 Iters: 201400/[05], loss: 11.6176, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:06:17 Iters: 201500/[05], loss: 11.1361, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:08:20 Iters: 201600/[05], loss: 11.6389, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220703-15:10:23 Iters: 201700/[05], loss: 10.7934, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:12:26 Iters: 201800/[05], loss: 11.4217, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:14:30 Iters: 201900/[05], loss: 10.3285, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:16:33 Iters: 202000/[05], loss: 10.9674, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:18:37 Iters: 202100/[05], loss: 11.4713, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-15:20:40 Iters: 202200/[05], loss: 11.3092, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:22:43 Iters: 202300/[05], loss: 10.7739, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-15:24:47 Iters: 202400/[05], loss: 10.7355, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:26:50 Iters: 202500/[05], loss: 11.7081, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220703-15:28:53 Iters: 202600/[05], loss: 12.1495, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:30:56 Iters: 202700/[05], loss: 11.9298, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:33:00 Iters: 202800/[05], loss: 11.3825, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220703-15:35:03 Iters: 202900/[05], loss: 10.5319, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:37:07 Iters: 203000/[05], loss: 11.3859, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220703-15:39:10 Iters: 203100/[05], loss: 11.6414, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:41:13 Iters: 203200/[05], loss: 10.9006, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:43:17 Iters: 203300/[05], loss: 11.2327, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:45:20 Iters: 203400/[05], loss: 10.7373, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:47:23 Iters: 203500/[05], loss: 10.8699, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220703-15:49:27 Iters: 203600/[05], loss: 11.5639, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:51:30 Iters: 203700/[05], loss: 11.3358, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:53:33 Iters: 203800/[05], loss: 11.1344, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:55:36 Iters: 203900/[05], loss: 11.7875, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:57:40 Iters: 204000/[05], loss: 11.0250, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-15:59:43 Iters: 204100/[05], loss: 11.3567, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:01:46 Iters: 204200/[05], loss: 10.8095, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:03:49 Iters: 204300/[05], loss: 10.7877, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:05:53 Iters: 204400/[05], loss: 10.5453, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:07:56 Iters: 204500/[05], loss: 10.6281, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-16:09:59 Iters: 204600/[05], loss: 11.0919, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:12:02 Iters: 204700/[05], loss: 10.5448, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:14:06 Iters: 204800/[05], loss: 10.8844, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-16:16:09 Iters: 204900/[05], loss: 11.6363, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:18:12 Iters: 205000/[05], loss: 11.6821, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:20:16 Iters: 205100/[05], loss: 11.0985, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:22:19 Iters: 205200/[05], loss: 10.7535, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-16:24:22 Iters: 205300/[05], loss: 10.9217, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:26:26 Iters: 205400/[05], loss: 11.3411, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-16:28:29 Iters: 205500/[05], loss: 11.7172, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:30:33 Iters: 205600/[05], loss: 11.4088, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-16:32:36 Iters: 205700/[05], loss: 11.0922, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:34:39 Iters: 205800/[05], loss: 10.3020, train_accuracy: 0.0703, time: 1.24 s/iter, learning rate: 0.05
-20220703-16:36:43 Iters: 205900/[05], loss: 10.0807, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:38:46 Iters: 206000/[05], loss: 11.7189, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:40:49 Iters: 206100/[05], loss: 11.1274, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:42:53 Iters: 206200/[05], loss: 11.5711, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-16:44:56 Iters: 206300/[05], loss: 11.5190, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:47:00 Iters: 206400/[05], loss: 11.3468, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:49:03 Iters: 206500/[05], loss: 10.3036, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-16:51:06 Iters: 206600/[05], loss: 11.3162, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:53:10 Iters: 206700/[05], loss: 11.4297, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-16:55:13 Iters: 206800/[05], loss: 11.0934, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:57:16 Iters: 206900/[05], loss: 11.9399, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-16:59:20 Iters: 207000/[05], loss: 10.8647, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:01:23 Iters: 207100/[05], loss: 10.9696, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:03:26 Iters: 207200/[05], loss: 11.4037, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-17:05:30 Iters: 207300/[05], loss: 10.5171, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:07:33 Iters: 207400/[05], loss: 11.2826, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:09:36 Iters: 207500/[05], loss: 11.7069, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:11:39 Iters: 207600/[05], loss: 11.4155, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:13:42 Iters: 207700/[05], loss: 11.0668, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:15:46 Iters: 207800/[05], loss: 11.4518, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:17:49 Iters: 207900/[05], loss: 10.7824, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:19:53 Iters: 208000/[05], loss: 10.8773, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220703-17:21:56 Iters: 208100/[05], loss: 11.4046, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:23:59 Iters: 208200/[05], loss: 11.6115, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:26:03 Iters: 208300/[05], loss: 12.1360, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-17:28:06 Iters: 208400/[05], loss: 11.8038, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:30:09 Iters: 208500/[05], loss: 11.3278, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:32:13 Iters: 208600/[05], loss: 11.3988, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-17:34:16 Iters: 208700/[05], loss: 11.5811, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:36:19 Iters: 208800/[05], loss: 11.0635, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-17:38:23 Iters: 208900/[05], loss: 11.2300, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:40:26 Iters: 209000/[05], loss: 11.3214, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:42:29 Iters: 209100/[05], loss: 11.1580, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220703-17:44:32 Iters: 209200/[05], loss: 12.1372, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:46:36 Iters: 209300/[05], loss: 11.8813, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-17:48:39 Iters: 209400/[05], loss: 11.1325, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:50:42 Iters: 209500/[05], loss: 11.6469, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:52:46 Iters: 209600/[05], loss: 10.4139, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:54:49 Iters: 209700/[05], loss: 11.4217, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:56:52 Iters: 209800/[05], loss: 10.1514, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-17:58:56 Iters: 209900/[05], loss: 11.8374, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:00:59 Iters: 210000/[05], loss: 11.1232, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:00:59 Saving checkpoint: 210000
-20220703-18:02:16 LFW Ave Accuracy: 98.8666
-20220703-18:03:32 AgeDB-30 Ave Accuracy: 93.3333
-20220703-18:04:58 CFP-FP Ave Accuracy: 88.2286
-20220703-18:04:58 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.3000 in iters: 120000
-20220703-18:07:02 Iters: 210100/[05], loss: 11.1004, train_accuracy: 0.0391, time: 3.62 s/iter, learning rate: 0.05
-20220703-18:09:05 Iters: 210200/[05], loss: 11.2893, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:11:08 Iters: 210300/[05], loss: 10.9137, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:13:11 Iters: 210400/[05], loss: 11.4559, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:15:14 Iters: 210500/[05], loss: 11.1615, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:17:18 Iters: 210600/[05], loss: 10.7788, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:19:21 Iters: 210700/[05], loss: 11.6384, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:21:25 Iters: 210800/[05], loss: 11.4360, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220703-18:23:28 Iters: 210900/[05], loss: 11.1905, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:25:31 Iters: 211000/[05], loss: 11.1733, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:27:34 Iters: 211100/[05], loss: 10.6671, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:29:37 Iters: 211200/[05], loss: 10.3942, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:31:40 Iters: 211300/[05], loss: 10.4261, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:33:44 Iters: 211400/[05], loss: 10.5151, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:35:47 Iters: 211500/[05], loss: 11.9543, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:37:50 Iters: 211600/[05], loss: 11.3796, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:39:53 Iters: 211700/[05], loss: 11.9998, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:41:57 Iters: 211800/[05], loss: 11.5892, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:44:00 Iters: 211900/[05], loss: 11.4080, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:46:03 Iters: 212000/[05], loss: 11.2093, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:48:06 Iters: 212100/[05], loss: 10.7486, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:50:09 Iters: 212200/[05], loss: 11.4182, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:52:12 Iters: 212300/[05], loss: 11.7930, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:54:15 Iters: 212400/[05], loss: 11.6946, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:56:18 Iters: 212500/[05], loss: 12.1675, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-18:58:22 Iters: 212600/[05], loss: 11.8968, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:00:25 Iters: 212700/[05], loss: 11.9826, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:02:28 Iters: 212800/[05], loss: 11.2233, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:04:31 Iters: 212900/[05], loss: 10.7687, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-19:06:35 Iters: 213000/[05], loss: 11.8354, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:08:38 Iters: 213100/[05], loss: 11.0426, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220703-19:10:41 Iters: 213200/[05], loss: 11.8673, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:12:45 Iters: 213300/[05], loss: 11.1884, train_accuracy: 0.0703, time: 1.24 s/iter, learning rate: 0.05
-20220703-19:14:48 Iters: 213400/[05], loss: 10.9420, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:16:51 Iters: 213500/[05], loss: 10.8772, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:18:55 Iters: 213600/[05], loss: 11.3039, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:20:58 Iters: 213700/[05], loss: 11.5344, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:23:01 Iters: 213800/[05], loss: 11.2251, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:25:04 Iters: 213900/[05], loss: 11.1674, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:27:08 Iters: 214000/[05], loss: 11.2032, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220703-19:29:11 Iters: 214100/[05], loss: 11.9791, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:31:14 Iters: 214200/[05], loss: 10.8914, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:33:18 Iters: 214300/[05], loss: 11.0110, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:35:21 Iters: 214400/[05], loss: 11.8097, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:37:24 Iters: 214500/[05], loss: 12.0681, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:39:27 Iters: 214600/[05], loss: 12.1712, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:41:31 Iters: 214700/[05], loss: 11.1042, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-19:43:34 Iters: 214800/[05], loss: 12.1378, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:45:37 Iters: 214900/[05], loss: 10.9086, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:47:40 Iters: 215000/[05], loss: 11.3201, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:49:43 Iters: 215100/[05], loss: 10.5678, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:51:47 Iters: 215200/[05], loss: 11.3607, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:53:50 Iters: 215300/[05], loss: 11.6048, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:55:53 Iters: 215400/[05], loss: 10.9498, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:57:56 Iters: 215500/[05], loss: 11.5446, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-19:59:59 Iters: 215600/[05], loss: 11.2230, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:02:03 Iters: 215700/[05], loss: 10.4573, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:04:06 Iters: 215800/[05], loss: 11.9269, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:06:09 Iters: 215900/[05], loss: 11.3079, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:08:12 Iters: 216000/[05], loss: 10.8979, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:10:15 Iters: 216100/[05], loss: 11.8273, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:12:18 Iters: 216200/[05], loss: 10.8062, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:14:21 Iters: 216300/[05], loss: 11.1587, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:16:24 Iters: 216400/[05], loss: 11.2507, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:18:27 Iters: 216500/[05], loss: 11.3963, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:20:31 Iters: 216600/[05], loss: 11.1651, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:22:34 Iters: 216700/[05], loss: 10.8747, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:24:37 Iters: 216800/[05], loss: 11.1013, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:26:40 Iters: 216900/[05], loss: 11.3460, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:28:44 Iters: 217000/[05], loss: 10.9042, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:30:47 Iters: 217100/[05], loss: 10.5252, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:32:50 Iters: 217200/[05], loss: 10.9833, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:34:53 Iters: 217300/[05], loss: 11.2354, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:36:57 Iters: 217400/[05], loss: 11.5098, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-20:39:00 Iters: 217500/[05], loss: 11.9942, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:41:03 Iters: 217600/[05], loss: 11.0777, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:43:07 Iters: 217700/[05], loss: 11.5688, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:45:10 Iters: 217800/[05], loss: 11.7289, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:47:13 Iters: 217900/[05], loss: 11.0308, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:49:17 Iters: 218000/[05], loss: 10.4288, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:51:20 Iters: 218100/[05], loss: 11.5045, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:53:23 Iters: 218200/[05], loss: 11.3786, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:55:27 Iters: 218300/[05], loss: 12.0132, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-20:57:30 Iters: 218400/[05], loss: 10.6689, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-20:59:33 Iters: 218500/[05], loss: 11.1779, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:01:36 Iters: 218600/[05], loss: 10.7219, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:03:40 Iters: 218700/[05], loss: 11.6872, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:05:43 Iters: 218800/[05], loss: 10.9855, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:07:46 Iters: 218900/[05], loss: 10.8500, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:09:49 Iters: 219000/[05], loss: 11.1684, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:11:53 Iters: 219100/[05], loss: 10.8362, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:13:56 Iters: 219200/[05], loss: 10.7347, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:15:59 Iters: 219300/[05], loss: 11.4426, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:18:03 Iters: 219400/[05], loss: 11.1443, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-21:20:06 Iters: 219500/[05], loss: 10.5520, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:22:10 Iters: 219600/[05], loss: 11.1860, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-21:24:13 Iters: 219700/[05], loss: 11.1044, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:26:17 Iters: 219800/[05], loss: 10.7031, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220703-21:28:20 Iters: 219900/[05], loss: 11.6157, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:30:23 Iters: 220000/[05], loss: 11.5024, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:30:23 Saving checkpoint: 220000
-20220703-21:31:42 LFW Ave Accuracy: 99.0332
-20220703-21:33:00 AgeDB-30 Ave Accuracy: 93.1667
-20220703-21:34:32 CFP-FP Ave Accuracy: 87.4143
-20220703-21:34:32 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.3000 in iters: 120000
-20220703-21:36:35 Iters: 220100/[05], loss: 11.4734, train_accuracy: 0.0469, time: 3.72 s/iter, learning rate: 0.05
-20220703-21:38:38 Iters: 220200/[05], loss: 10.7484, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:40:41 Iters: 220300/[05], loss: 11.4898, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:42:44 Iters: 220400/[05], loss: 11.2891, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:44:48 Iters: 220500/[05], loss: 12.4688, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-21:46:51 Iters: 220600/[05], loss: 10.4077, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:48:55 Iters: 220700/[05], loss: 11.5153, train_accuracy: 0.0703, time: 1.24 s/iter, learning rate: 0.05
-20220703-21:50:58 Iters: 220800/[05], loss: 10.5868, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:53:01 Iters: 220900/[05], loss: 11.9727, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:55:04 Iters: 221000/[05], loss: 10.6374, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:57:07 Iters: 221100/[05], loss: 10.6097, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-21:59:11 Iters: 221200/[05], loss: 10.8195, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:01:14 Iters: 221300/[05], loss: 11.7153, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:03:17 Iters: 221400/[05], loss: 12.0541, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:05:20 Iters: 221500/[05], loss: 10.6753, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-22:07:24 Iters: 221600/[05], loss: 11.1432, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:09:27 Iters: 221700/[05], loss: 11.1046, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:11:30 Iters: 221800/[05], loss: 12.2037, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:13:34 Iters: 221900/[05], loss: 11.2844, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:15:37 Iters: 222000/[05], loss: 11.1684, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-22:17:40 Iters: 222100/[05], loss: 10.6828, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:19:44 Iters: 222200/[05], loss: 11.5448, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:21:47 Iters: 222300/[05], loss: 10.9092, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:23:50 Iters: 222400/[05], loss: 11.1390, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:25:53 Iters: 222500/[05], loss: 11.6150, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:27:56 Iters: 222600/[05], loss: 11.1095, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:30:00 Iters: 222700/[05], loss: 11.6952, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-22:32:03 Iters: 222800/[05], loss: 11.5579, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:34:06 Iters: 222900/[05], loss: 11.1540, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:36:10 Iters: 223000/[05], loss: 10.3572, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:38:13 Iters: 223100/[05], loss: 12.0069, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:40:16 Iters: 223200/[05], loss: 11.4901, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:42:19 Iters: 223300/[05], loss: 10.8080, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:44:23 Iters: 223400/[05], loss: 11.3405, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:46:26 Iters: 223500/[05], loss: 12.1125, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-22:48:29 Iters: 223600/[05], loss: 11.5957, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:50:32 Iters: 223700/[05], loss: 11.7003, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:52:36 Iters: 223800/[05], loss: 11.6777, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220703-22:54:39 Iters: 223900/[05], loss: 11.2978, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-22:56:43 Iters: 224000/[05], loss: 11.4253, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220703-22:58:46 Iters: 224100/[05], loss: 11.5458, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:00:49 Iters: 224200/[05], loss: 11.2670, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:02:53 Iters: 224300/[05], loss: 10.8691, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:04:56 Iters: 224400/[05], loss: 11.0730, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:06:59 Iters: 224500/[05], loss: 11.6275, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:09:03 Iters: 224600/[05], loss: 10.8543, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:11:06 Iters: 224700/[05], loss: 11.6404, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-23:13:09 Iters: 224800/[05], loss: 11.5752, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:15:13 Iters: 224900/[05], loss: 11.4144, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220703-23:17:16 Iters: 225000/[05], loss: 11.8599, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:19:19 Iters: 225100/[05], loss: 11.2964, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:21:23 Iters: 225200/[05], loss: 11.6483, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:23:26 Iters: 225300/[05], loss: 10.7227, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:25:30 Iters: 225400/[05], loss: 10.7505, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220703-23:27:33 Iters: 225500/[05], loss: 10.8718, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:29:36 Iters: 225600/[05], loss: 11.2048, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-23:31:40 Iters: 225700/[05], loss: 11.9139, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:33:43 Iters: 225800/[05], loss: 11.6165, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:35:46 Iters: 225900/[05], loss: 10.9900, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:37:50 Iters: 226000/[05], loss: 12.2365, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:39:53 Iters: 226100/[05], loss: 10.7996, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:41:56 Iters: 226200/[05], loss: 11.4652, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:44:00 Iters: 226300/[05], loss: 10.9007, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-23:46:03 Iters: 226400/[05], loss: 11.5350, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:48:06 Iters: 226500/[05], loss: 12.9009, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:50:10 Iters: 226600/[05], loss: 11.5807, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220703-23:52:13 Iters: 226700/[05], loss: 10.8063, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:54:16 Iters: 226800/[05], loss: 10.9600, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220703-23:56:20 Iters: 226900/[05], loss: 12.0289, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220703-23:58:23 Iters: 227000/[05], loss: 11.3574, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:00:26 Iters: 227100/[05], loss: 11.5347, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:02:30 Iters: 227200/[05], loss: 10.7665, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:04:33 Iters: 227300/[05], loss: 11.0466, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:06:36 Iters: 227400/[05], loss: 11.3992, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:07:37 Train Epoch: 6/18 ...
-20220704-00:08:39 Iters: 227500/[06], loss: 10.6257, train_accuracy: 0.0391, time: 0.62 s/iter, learning rate: 0.05
-20220704-00:10:42 Iters: 227600/[06], loss: 10.0750, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:12:45 Iters: 227700/[06], loss: 11.5047, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:14:49 Iters: 227800/[06], loss: 11.8157, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220704-00:16:52 Iters: 227900/[06], loss: 11.2325, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:18:55 Iters: 228000/[06], loss: 10.8120, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:20:59 Iters: 228100/[06], loss: 11.8947, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220704-00:23:02 Iters: 228200/[06], loss: 11.3191, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:25:06 Iters: 228300/[06], loss: 12.2192, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220704-00:27:09 Iters: 228400/[06], loss: 10.6926, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:29:12 Iters: 228500/[06], loss: 11.2860, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220704-00:31:16 Iters: 228600/[06], loss: 10.7998, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:33:19 Iters: 228700/[06], loss: 11.8966, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220704-00:35:22 Iters: 228800/[06], loss: 11.1468, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:37:25 Iters: 228900/[06], loss: 10.5659, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:39:29 Iters: 229000/[06], loss: 11.2375, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:41:32 Iters: 229100/[06], loss: 11.3678, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220704-00:43:35 Iters: 229200/[06], loss: 10.9030, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:45:39 Iters: 229300/[06], loss: 11.5554, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:47:42 Iters: 229400/[06], loss: 11.2805, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:49:45 Iters: 229500/[06], loss: 10.9039, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:51:48 Iters: 229600/[06], loss: 11.2910, train_accuracy: 0.0000, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:53:51 Iters: 229700/[06], loss: 11.1651, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:55:55 Iters: 229800/[06], loss: 11.5384, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-00:57:58 Iters: 229900/[06], loss: 11.4123, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:00:01 Iters: 230000/[06], loss: 11.6060, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:00:01 Saving checkpoint: 230000
-20220704-01:01:18 LFW Ave Accuracy: 98.9499
-20220704-01:02:33 AgeDB-30 Ave Accuracy: 93.5333
-20220704-01:04:00 CFP-FP Ave Accuracy: 87.9143
-20220704-01:04:00 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.3000 in iters: 120000
-20220704-01:06:02 Iters: 230100/[06], loss: 11.6311, train_accuracy: 0.0625, time: 3.61 s/iter, learning rate: 0.05
-20220704-01:08:06 Iters: 230200/[06], loss: 10.6531, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:10:09 Iters: 230300/[06], loss: 11.7127, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:12:12 Iters: 230400/[06], loss: 11.5849, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220704-01:14:16 Iters: 230500/[06], loss: 10.7046, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:16:19 Iters: 230600/[06], loss: 10.5418, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:18:22 Iters: 230700/[06], loss: 11.7330, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:20:25 Iters: 230800/[06], loss: 11.4882, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:22:28 Iters: 230900/[06], loss: 11.1363, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:24:32 Iters: 231000/[06], loss: 11.0049, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:26:35 Iters: 231100/[06], loss: 11.4553, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:28:39 Iters: 231200/[06], loss: 11.0169, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220704-01:30:42 Iters: 231300/[06], loss: 11.3268, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:32:45 Iters: 231400/[06], loss: 11.4976, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:34:49 Iters: 231500/[06], loss: 11.5014, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:36:52 Iters: 231600/[06], loss: 11.6185, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220704-01:38:56 Iters: 231700/[06], loss: 11.7695, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:40:59 Iters: 231800/[06], loss: 10.9989, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:43:02 Iters: 231900/[06], loss: 11.5605, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:45:06 Iters: 232000/[06], loss: 10.4062, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220704-01:47:09 Iters: 232100/[06], loss: 10.9394, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:49:13 Iters: 232200/[06], loss: 11.1894, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220704-01:51:16 Iters: 232300/[06], loss: 11.4479, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:53:19 Iters: 232400/[06], loss: 10.8519, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:55:22 Iters: 232500/[06], loss: 10.5728, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-01:57:26 Iters: 232600/[06], loss: 11.3176, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220704-01:59:29 Iters: 232700/[06], loss: 11.7472, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:01:33 Iters: 232800/[06], loss: 10.8819, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220704-02:03:36 Iters: 232900/[06], loss: 11.7307, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:05:39 Iters: 233000/[06], loss: 11.0079, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:07:42 Iters: 233100/[06], loss: 11.2401, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:09:46 Iters: 233200/[06], loss: 10.4513, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:11:49 Iters: 233300/[06], loss: 10.2288, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:13:52 Iters: 233400/[06], loss: 11.2771, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:15:55 Iters: 233500/[06], loss: 11.6963, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:17:58 Iters: 233600/[06], loss: 11.7520, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:20:01 Iters: 233700/[06], loss: 11.9936, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:22:04 Iters: 233800/[06], loss: 11.3456, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:24:08 Iters: 233900/[06], loss: 11.1200, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:26:11 Iters: 234000/[06], loss: 10.9643, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:28:14 Iters: 234100/[06], loss: 11.0091, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:30:17 Iters: 234200/[06], loss: 11.1726, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:32:20 Iters: 234300/[06], loss: 12.5291, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:34:23 Iters: 234400/[06], loss: 11.2582, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:36:26 Iters: 234500/[06], loss: 10.9678, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:38:29 Iters: 234600/[06], loss: 11.3184, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:40:33 Iters: 234700/[06], loss: 10.8490, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:42:36 Iters: 234800/[06], loss: 11.0133, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:44:40 Iters: 234900/[06], loss: 10.4819, train_accuracy: 0.0547, time: 1.24 s/iter, learning rate: 0.05
-20220704-02:46:43 Iters: 235000/[06], loss: 11.0599, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:48:46 Iters: 235100/[06], loss: 10.7205, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:50:50 Iters: 235200/[06], loss: 10.7447, train_accuracy: 0.0625, time: 1.24 s/iter, learning rate: 0.05
-20220704-02:52:53 Iters: 235300/[06], loss: 10.6799, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:54:56 Iters: 235400/[06], loss: 11.7260, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:56:59 Iters: 235500/[06], loss: 11.0937, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-02:59:02 Iters: 235600/[06], loss: 11.3487, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:01:06 Iters: 235700/[06], loss: 12.0011, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220704-03:03:09 Iters: 235800/[06], loss: 11.6306, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:05:12 Iters: 235900/[06], loss: 12.6693, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:07:15 Iters: 236000/[06], loss: 11.0246, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:09:19 Iters: 236100/[06], loss: 11.1743, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:11:22 Iters: 236200/[06], loss: 11.0256, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220704-03:13:26 Iters: 236300/[06], loss: 11.3858, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220704-03:15:29 Iters: 236400/[06], loss: 10.6500, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:17:33 Iters: 236500/[06], loss: 11.3414, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220704-03:19:36 Iters: 236600/[06], loss: 11.5769, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:21:39 Iters: 236700/[06], loss: 10.6966, train_accuracy: 0.0703, time: 1.24 s/iter, learning rate: 0.05
-20220704-03:23:43 Iters: 236800/[06], loss: 10.7748, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:25:46 Iters: 236900/[06], loss: 11.4759, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220704-03:27:49 Iters: 237000/[06], loss: 11.8187, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:29:53 Iters: 237100/[06], loss: 11.1876, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:31:56 Iters: 237200/[06], loss: 12.6234, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220704-03:34:00 Iters: 237300/[06], loss: 10.0832, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:36:03 Iters: 237400/[06], loss: 11.3935, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:38:06 Iters: 237500/[06], loss: 11.8831, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:40:09 Iters: 237600/[06], loss: 11.5456, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:42:13 Iters: 237700/[06], loss: 10.9257, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220704-03:44:16 Iters: 237800/[06], loss: 11.5126, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:46:20 Iters: 237900/[06], loss: 11.2600, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:48:23 Iters: 238000/[06], loss: 11.6070, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:50:26 Iters: 238100/[06], loss: 11.1159, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220704-03:52:30 Iters: 238200/[06], loss: 9.9475, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:54:33 Iters: 238300/[06], loss: 12.3845, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-03:56:36 Iters: 238400/[06], loss: 12.1861, train_accuracy: 0.0000, time: 1.24 s/iter, learning rate: 0.05
-20220704-03:58:40 Iters: 238500/[06], loss: 11.4128, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:00:43 Iters: 238600/[06], loss: 11.0617, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:02:46 Iters: 238700/[06], loss: 11.2862, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:04:49 Iters: 238800/[06], loss: 11.5913, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:06:53 Iters: 238900/[06], loss: 10.7311, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:08:56 Iters: 239000/[06], loss: 10.3746, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:10:59 Iters: 239100/[06], loss: 12.2681, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:13:03 Iters: 239200/[06], loss: 11.0141, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:15:06 Iters: 239300/[06], loss: 11.2937, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220704-04:17:09 Iters: 239400/[06], loss: 11.5493, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:19:13 Iters: 239500/[06], loss: 10.9925, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:21:16 Iters: 239600/[06], loss: 11.2964, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220704-04:23:19 Iters: 239700/[06], loss: 11.3940, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:25:23 Iters: 239800/[06], loss: 11.6451, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:27:26 Iters: 239900/[06], loss: 12.1016, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:29:29 Iters: 240000/[06], loss: 10.9809, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:29:29 Saving checkpoint: 240000
-20220704-04:30:46 LFW Ave Accuracy: 99.0998
-20220704-04:32:01 AgeDB-30 Ave Accuracy: 93.3833
-20220704-04:33:27 CFP-FP Ave Accuracy: 87.1571
-20220704-04:33:27 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.3000 in iters: 120000
-20220704-04:35:30 Iters: 240100/[06], loss: 11.8553, train_accuracy: 0.0234, time: 3.61 s/iter, learning rate: 0.05
-20220704-04:37:34 Iters: 240200/[06], loss: 12.4322, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:39:37 Iters: 240300/[06], loss: 12.5225, train_accuracy: 0.0234, time: 1.24 s/iter, learning rate: 0.05
-20220704-04:41:40 Iters: 240400/[06], loss: 11.2777, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:43:44 Iters: 240500/[06], loss: 11.4245, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:45:47 Iters: 240600/[06], loss: 11.1405, train_accuracy: 0.0156, time: 1.24 s/iter, learning rate: 0.05
-20220704-04:47:50 Iters: 240700/[06], loss: 10.7161, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:49:53 Iters: 240800/[06], loss: 10.9458, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:51:57 Iters: 240900/[06], loss: 10.9750, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220704-04:54:00 Iters: 241000/[06], loss: 11.9087, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:56:03 Iters: 241100/[06], loss: 10.8882, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-04:58:07 Iters: 241200/[06], loss: 10.2983, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:00:10 Iters: 241300/[06], loss: 11.4021, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:02:14 Iters: 241400/[06], loss: 12.0877, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220704-05:04:17 Iters: 241500/[06], loss: 11.0337, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:06:20 Iters: 241600/[06], loss: 11.1252, train_accuracy: 0.0078, time: 1.24 s/iter, learning rate: 0.05
-20220704-05:08:23 Iters: 241700/[06], loss: 11.8072, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:10:26 Iters: 241800/[06], loss: 11.1406, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:12:30 Iters: 241900/[06], loss: 11.2321, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:14:33 Iters: 242000/[06], loss: 11.4025, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:16:36 Iters: 242100/[06], loss: 10.7240, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:18:39 Iters: 242200/[06], loss: 11.7133, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:20:42 Iters: 242300/[06], loss: 11.6550, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:22:45 Iters: 242400/[06], loss: 11.2729, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:24:49 Iters: 242500/[06], loss: 10.6847, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:26:52 Iters: 242600/[06], loss: 11.1944, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:28:55 Iters: 242700/[06], loss: 11.5539, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:30:58 Iters: 242800/[06], loss: 10.5228, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:33:02 Iters: 242900/[06], loss: 11.1785, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:35:05 Iters: 243000/[06], loss: 11.0622, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:37:08 Iters: 243100/[06], loss: 11.0752, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:39:11 Iters: 243200/[06], loss: 11.1202, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:41:15 Iters: 243300/[06], loss: 11.8669, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:43:18 Iters: 243400/[06], loss: 11.5312, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:45:21 Iters: 243500/[06], loss: 12.0242, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:47:24 Iters: 243600/[06], loss: 11.4022, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:49:27 Iters: 243700/[06], loss: 10.9716, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:51:31 Iters: 243800/[06], loss: 12.1771, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:53:34 Iters: 243900/[06], loss: 10.9938, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:55:37 Iters: 244000/[06], loss: 11.6640, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:57:40 Iters: 244100/[06], loss: 10.2742, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-05:59:43 Iters: 244200/[06], loss: 10.6527, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:01:47 Iters: 244300/[06], loss: 10.9342, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:03:50 Iters: 244400/[06], loss: 10.8720, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:05:53 Iters: 244500/[06], loss: 11.1906, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:07:56 Iters: 244600/[06], loss: 10.9124, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:10:00 Iters: 244700/[06], loss: 11.4884, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:12:03 Iters: 244800/[06], loss: 10.7333, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:14:06 Iters: 244900/[06], loss: 10.7912, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:16:09 Iters: 245000/[06], loss: 11.2835, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:18:12 Iters: 245100/[06], loss: 10.5105, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:20:16 Iters: 245200/[06], loss: 10.6988, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:22:19 Iters: 245300/[06], loss: 11.7308, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:24:22 Iters: 245400/[06], loss: 11.4900, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:26:25 Iters: 245500/[06], loss: 11.9426, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:28:28 Iters: 245600/[06], loss: 11.2861, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:30:32 Iters: 245700/[06], loss: 11.3018, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:32:35 Iters: 245800/[06], loss: 11.5874, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:34:38 Iters: 245900/[06], loss: 10.9984, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:36:41 Iters: 246000/[06], loss: 11.5283, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:38:44 Iters: 246100/[06], loss: 10.3913, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:40:48 Iters: 246200/[06], loss: 11.1277, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:42:51 Iters: 246300/[06], loss: 11.3241, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:44:54 Iters: 246400/[06], loss: 11.1294, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:46:57 Iters: 246500/[06], loss: 11.3981, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:49:00 Iters: 246600/[06], loss: 10.7724, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:51:04 Iters: 246700/[06], loss: 11.5749, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:53:07 Iters: 246800/[06], loss: 11.4225, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:55:10 Iters: 246900/[06], loss: 11.1744, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:57:13 Iters: 247000/[06], loss: 10.8365, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-06:59:17 Iters: 247100/[06], loss: 11.0258, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:01:20 Iters: 247200/[06], loss: 11.2315, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:03:23 Iters: 247300/[06], loss: 11.7611, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:05:26 Iters: 247400/[06], loss: 11.2772, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:07:30 Iters: 247500/[06], loss: 11.2943, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:09:33 Iters: 247600/[06], loss: 11.1212, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:11:36 Iters: 247700/[06], loss: 11.0259, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:13:39 Iters: 247800/[06], loss: 11.0712, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:15:43 Iters: 247900/[06], loss: 11.9985, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:17:46 Iters: 248000/[06], loss: 10.9488, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:19:49 Iters: 248100/[06], loss: 11.3473, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:21:52 Iters: 248200/[06], loss: 11.1243, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:23:55 Iters: 248300/[06], loss: 11.3197, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:25:59 Iters: 248400/[06], loss: 11.7095, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:28:02 Iters: 248500/[06], loss: 11.1746, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:30:05 Iters: 248600/[06], loss: 11.1667, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:32:08 Iters: 248700/[06], loss: 11.2125, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:34:12 Iters: 248800/[06], loss: 11.5322, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:36:15 Iters: 248900/[06], loss: 10.9965, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:38:18 Iters: 249000/[06], loss: 11.3152, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:40:21 Iters: 249100/[06], loss: 10.9324, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:42:25 Iters: 249200/[06], loss: 10.6801, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:44:28 Iters: 249300/[06], loss: 10.7475, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:46:31 Iters: 249400/[06], loss: 9.9853, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:48:34 Iters: 249500/[06], loss: 11.5325, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:50:38 Iters: 249600/[06], loss: 11.7364, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:52:41 Iters: 249700/[06], loss: 11.0821, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:54:44 Iters: 249800/[06], loss: 11.4169, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:56:47 Iters: 249900/[06], loss: 10.7325, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:58:51 Iters: 250000/[06], loss: 11.3018, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-07:58:51 Saving checkpoint: 250000
-20220704-08:00:08 LFW Ave Accuracy: 99.1332
-20220704-08:01:24 AgeDB-30 Ave Accuracy: 93.7500
-20220704-08:02:52 CFP-FP Ave Accuracy: 88.5143
-20220704-08:02:52 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.5143 in iters: 250000
-20220704-08:04:55 Iters: 250100/[06], loss: 11.3329, train_accuracy: 0.0078, time: 3.64 s/iter, learning rate: 0.05
-20220704-08:06:58 Iters: 250200/[06], loss: 10.8384, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:09:01 Iters: 250300/[06], loss: 11.0649, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:11:04 Iters: 250400/[06], loss: 11.7687, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:13:08 Iters: 250500/[06], loss: 10.8111, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:15:11 Iters: 250600/[06], loss: 10.6718, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:17:14 Iters: 250700/[06], loss: 10.8576, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:19:17 Iters: 250800/[06], loss: 11.4171, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:21:20 Iters: 250900/[06], loss: 10.4826, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:23:24 Iters: 251000/[06], loss: 12.1597, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:25:27 Iters: 251100/[06], loss: 10.9017, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:27:30 Iters: 251200/[06], loss: 11.3048, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:29:33 Iters: 251300/[06], loss: 12.1271, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:31:36 Iters: 251400/[06], loss: 12.0292, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:33:39 Iters: 251500/[06], loss: 10.8617, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:35:43 Iters: 251600/[06], loss: 11.3063, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:37:46 Iters: 251700/[06], loss: 10.3418, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:39:49 Iters: 251800/[06], loss: 11.5613, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:41:52 Iters: 251900/[06], loss: 11.0885, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:43:55 Iters: 252000/[06], loss: 10.7136, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:45:59 Iters: 252100/[06], loss: 10.4458, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:48:02 Iters: 252200/[06], loss: 11.8750, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:50:05 Iters: 252300/[06], loss: 11.2692, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:52:08 Iters: 252400/[06], loss: 11.1923, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:54:11 Iters: 252500/[06], loss: 12.2843, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:56:14 Iters: 252600/[06], loss: 11.0469, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-08:58:18 Iters: 252700/[06], loss: 11.5835, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:00:21 Iters: 252800/[06], loss: 10.1471, train_accuracy: 0.1016, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:02:24 Iters: 252900/[06], loss: 10.6568, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:04:27 Iters: 253000/[06], loss: 11.3428, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:06:31 Iters: 253100/[06], loss: 10.2687, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:08:34 Iters: 253200/[06], loss: 11.8712, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:10:37 Iters: 253300/[06], loss: 11.1890, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:12:40 Iters: 253400/[06], loss: 10.7062, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:14:44 Iters: 253500/[06], loss: 10.7761, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:16:47 Iters: 253600/[06], loss: 10.7524, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:18:50 Iters: 253700/[06], loss: 11.5351, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:20:53 Iters: 253800/[06], loss: 10.7987, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:22:57 Iters: 253900/[06], loss: 11.5344, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:25:00 Iters: 254000/[06], loss: 11.0199, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:27:03 Iters: 254100/[06], loss: 12.3401, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:29:06 Iters: 254200/[06], loss: 11.0951, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:31:10 Iters: 254300/[06], loss: 11.1516, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:33:13 Iters: 254400/[06], loss: 10.6650, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:35:16 Iters: 254500/[06], loss: 10.2173, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:37:19 Iters: 254600/[06], loss: 11.7329, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:39:22 Iters: 254700/[06], loss: 11.4493, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:41:26 Iters: 254800/[06], loss: 11.4924, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:43:29 Iters: 254900/[06], loss: 10.8373, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:45:32 Iters: 255000/[06], loss: 11.2099, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:47:35 Iters: 255100/[06], loss: 12.0800, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:49:38 Iters: 255200/[06], loss: 10.5339, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:51:41 Iters: 255300/[06], loss: 11.5815, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:53:45 Iters: 255400/[06], loss: 10.5906, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:55:48 Iters: 255500/[06], loss: 11.1893, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:57:51 Iters: 255600/[06], loss: 11.0726, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-09:59:54 Iters: 255700/[06], loss: 10.8622, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:01:58 Iters: 255800/[06], loss: 11.3154, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:04:01 Iters: 255900/[06], loss: 11.0156, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:06:04 Iters: 256000/[06], loss: 11.4775, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:08:07 Iters: 256100/[06], loss: 11.4436, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:10:10 Iters: 256200/[06], loss: 10.9711, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:12:14 Iters: 256300/[06], loss: 11.7816, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:14:17 Iters: 256400/[06], loss: 10.8802, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:16:20 Iters: 256500/[06], loss: 10.9457, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:18:23 Iters: 256600/[06], loss: 11.2132, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:20:26 Iters: 256700/[06], loss: 11.4232, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:22:30 Iters: 256800/[06], loss: 11.5609, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:24:33 Iters: 256900/[06], loss: 11.3210, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:26:36 Iters: 257000/[06], loss: 10.0294, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:28:39 Iters: 257100/[06], loss: 11.4733, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:30:42 Iters: 257200/[06], loss: 11.3332, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:32:46 Iters: 257300/[06], loss: 11.7534, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:34:49 Iters: 257400/[06], loss: 11.3240, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:36:52 Iters: 257500/[06], loss: 11.4507, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:38:55 Iters: 257600/[06], loss: 10.7050, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:40:59 Iters: 257700/[06], loss: 10.9997, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:43:02 Iters: 257800/[06], loss: 11.9377, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:45:05 Iters: 257900/[06], loss: 12.0387, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:47:08 Iters: 258000/[06], loss: 10.9002, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:49:12 Iters: 258100/[06], loss: 12.0330, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:51:15 Iters: 258200/[06], loss: 10.5710, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:53:18 Iters: 258300/[06], loss: 10.7763, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:55:21 Iters: 258400/[06], loss: 11.3815, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:57:25 Iters: 258500/[06], loss: 10.7935, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.05
-20220704-10:59:28 Iters: 258600/[06], loss: 11.6685, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:01:31 Iters: 258700/[06], loss: 11.4018, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:03:34 Iters: 258800/[06], loss: 11.2774, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:05:38 Iters: 258900/[06], loss: 11.7579, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:07:41 Iters: 259000/[06], loss: 11.1640, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:09:44 Iters: 259100/[06], loss: 11.4661, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:11:47 Iters: 259200/[06], loss: 10.9543, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:13:50 Iters: 259300/[06], loss: 11.5567, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:15:54 Iters: 259400/[06], loss: 11.7702, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:17:57 Iters: 259500/[06], loss: 12.0188, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:20:00 Iters: 259600/[06], loss: 11.5457, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:22:03 Iters: 259700/[06], loss: 11.3029, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:24:06 Iters: 259800/[06], loss: 11.0827, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:26:10 Iters: 259900/[06], loss: 11.3697, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:28:13 Iters: 260000/[06], loss: 11.1637, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:28:13 Saving checkpoint: 260000
-20220704-11:29:30 LFW Ave Accuracy: 98.9332
-20220704-11:30:47 AgeDB-30 Ave Accuracy: 93.3167
-20220704-11:32:16 CFP-FP Ave Accuracy: 87.9429
-20220704-11:32:16 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9500 in iters: 140000 and CFP-FP: 88.5143 in iters: 250000
-20220704-11:34:18 Iters: 260100/[06], loss: 11.2327, train_accuracy: 0.0391, time: 3.66 s/iter, learning rate: 0.05
-20220704-11:36:21 Iters: 260200/[06], loss: 11.3054, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:38:25 Iters: 260300/[06], loss: 11.0411, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:40:28 Iters: 260400/[06], loss: 11.1530, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:42:31 Iters: 260500/[06], loss: 10.5581, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:44:34 Iters: 260600/[06], loss: 11.1173, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:46:37 Iters: 260700/[06], loss: 11.1801, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:48:40 Iters: 260800/[06], loss: 11.0849, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:50:43 Iters: 260900/[06], loss: 11.0174, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:52:47 Iters: 261000/[06], loss: 11.1730, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:54:50 Iters: 261100/[06], loss: 10.5402, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:56:53 Iters: 261200/[06], loss: 11.3199, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-11:58:56 Iters: 261300/[06], loss: 10.6253, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:00:59 Iters: 261400/[06], loss: 11.6791, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:03:02 Iters: 261500/[06], loss: 11.6463, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:05:05 Iters: 261600/[06], loss: 11.0744, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:07:09 Iters: 261700/[06], loss: 10.9375, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:09:12 Iters: 261800/[06], loss: 11.3906, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:11:15 Iters: 261900/[06], loss: 10.6579, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:13:18 Iters: 262000/[06], loss: 10.4116, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:15:21 Iters: 262100/[06], loss: 10.4419, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:17:24 Iters: 262200/[06], loss: 11.3779, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:19:27 Iters: 262300/[06], loss: 10.7435, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:21:31 Iters: 262400/[06], loss: 11.2717, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:23:34 Iters: 262500/[06], loss: 11.7645, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:25:37 Iters: 262600/[06], loss: 11.1282, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:27:40 Iters: 262700/[06], loss: 10.3897, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:29:43 Iters: 262800/[06], loss: 11.2132, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:31:46 Iters: 262900/[06], loss: 11.2786, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:33:49 Iters: 263000/[06], loss: 11.0751, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:35:53 Iters: 263100/[06], loss: 11.4222, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:37:56 Iters: 263200/[06], loss: 10.9203, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:39:59 Iters: 263300/[06], loss: 11.0148, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:42:02 Iters: 263400/[06], loss: 11.6050, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:44:05 Iters: 263500/[06], loss: 10.4799, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:46:09 Iters: 263600/[06], loss: 10.6094, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:48:12 Iters: 263700/[06], loss: 12.4073, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:50:15 Iters: 263800/[06], loss: 11.5543, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:52:18 Iters: 263900/[06], loss: 12.0351, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:54:22 Iters: 264000/[06], loss: 11.6765, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:56:25 Iters: 264100/[06], loss: 11.0170, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-12:58:28 Iters: 264200/[06], loss: 11.6523, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:00:31 Iters: 264300/[06], loss: 11.2294, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:02:34 Iters: 264400/[06], loss: 11.6202, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:04:38 Iters: 264500/[06], loss: 11.0255, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:06:41 Iters: 264600/[06], loss: 11.1744, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:08:44 Iters: 264700/[06], loss: 11.4946, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:10:47 Iters: 264800/[06], loss: 11.5614, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:12:50 Iters: 264900/[06], loss: 10.8359, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:14:53 Iters: 265000/[06], loss: 12.1427, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:16:57 Iters: 265100/[06], loss: 11.0723, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:19:00 Iters: 265200/[06], loss: 11.5880, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:21:03 Iters: 265300/[06], loss: 10.5142, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:23:06 Iters: 265400/[06], loss: 11.6904, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:25:09 Iters: 265500/[06], loss: 10.8258, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:27:13 Iters: 265600/[06], loss: 11.5935, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:29:16 Iters: 265700/[06], loss: 11.1110, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:31:19 Iters: 265800/[06], loss: 10.2230, train_accuracy: 0.0938, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:33:23 Iters: 265900/[06], loss: 11.3026, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:35:26 Iters: 266000/[06], loss: 12.2552, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:37:29 Iters: 266100/[06], loss: 11.5776, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:39:32 Iters: 266200/[06], loss: 10.7070, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:41:36 Iters: 266300/[06], loss: 10.8086, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:43:39 Iters: 266400/[06], loss: 10.5582, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:45:42 Iters: 266500/[06], loss: 11.4324, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:47:45 Iters: 266600/[06], loss: 10.5606, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:49:48 Iters: 266700/[06], loss: 12.1010, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:51:51 Iters: 266800/[06], loss: 10.8777, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:53:54 Iters: 266900/[06], loss: 11.2954, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:55:58 Iters: 267000/[06], loss: 11.1313, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-13:58:01 Iters: 267100/[06], loss: 10.8937, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:00:04 Iters: 267200/[06], loss: 11.2246, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:02:07 Iters: 267300/[06], loss: 11.6090, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:04:10 Iters: 267400/[06], loss: 11.8248, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:06:13 Iters: 267500/[06], loss: 11.3916, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:08:17 Iters: 267600/[06], loss: 10.7955, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:10:20 Iters: 267700/[06], loss: 11.0611, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:12:23 Iters: 267800/[06], loss: 11.2001, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:14:26 Iters: 267900/[06], loss: 11.4045, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:16:29 Iters: 268000/[06], loss: 11.2396, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:18:33 Iters: 268100/[06], loss: 10.4137, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:20:36 Iters: 268200/[06], loss: 12.4990, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:22:39 Iters: 268300/[06], loss: 11.1172, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:24:42 Iters: 268400/[06], loss: 12.1040, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:26:45 Iters: 268500/[06], loss: 10.9533, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:28:48 Iters: 268600/[06], loss: 11.4982, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:30:52 Iters: 268700/[06], loss: 11.5152, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:32:55 Iters: 268800/[06], loss: 12.1791, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:34:58 Iters: 268900/[06], loss: 11.6722, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:37:01 Iters: 269000/[06], loss: 10.7897, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:39:04 Iters: 269100/[06], loss: 11.1406, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:41:07 Iters: 269200/[06], loss: 11.6408, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:43:10 Iters: 269300/[06], loss: 10.3658, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:45:14 Iters: 269400/[06], loss: 11.8596, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:47:17 Iters: 269500/[06], loss: 11.4754, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:49:20 Iters: 269600/[06], loss: 10.8224, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:51:23 Iters: 269700/[06], loss: 11.0545, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:53:26 Iters: 269800/[06], loss: 11.0707, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:55:29 Iters: 269900/[06], loss: 10.3405, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:57:33 Iters: 270000/[06], loss: 11.4128, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-14:57:33 Saving checkpoint: 270000
-20220704-14:58:51 LFW Ave Accuracy: 99.1666
-20220704-15:00:08 AgeDB-30 Ave Accuracy: 93.9833
-20220704-15:01:37 CFP-FP Ave Accuracy: 88.4000
-20220704-15:01:37 Current Best Accuracy: LFW: 99.2333 in iters: 120000, AgeDB-30: 93.9833 in iters: 270000 and CFP-FP: 88.5143 in iters: 250000
-20220704-15:03:40 Iters: 270100/[06], loss: 11.2566, train_accuracy: 0.0078, time: 3.67 s/iter, learning rate: 0.05
-20220704-15:05:43 Iters: 270200/[06], loss: 11.2872, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:07:46 Iters: 270300/[06], loss: 12.1902, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:09:49 Iters: 270400/[06], loss: 11.4535, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:11:52 Iters: 270500/[06], loss: 11.8669, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:13:55 Iters: 270600/[06], loss: 11.4371, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:15:58 Iters: 270700/[06], loss: 12.1720, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:18:01 Iters: 270800/[06], loss: 11.4423, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:20:04 Iters: 270900/[06], loss: 11.2150, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:22:08 Iters: 271000/[06], loss: 10.8281, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:24:11 Iters: 271100/[06], loss: 11.8874, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:26:14 Iters: 271200/[06], loss: 11.2062, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220704-15:28:17 Iters: 271300/[06], loss: 10.5458, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:30:21 Iters: 271400/[06], loss: 11.2805, train_accuracy: 0.0391, time: 1.24 s/iter, learning rate: 0.05
-20220704-15:32:24 Iters: 271500/[06], loss: 10.6452, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:34:28 Iters: 271600/[06], loss: 12.5898, train_accuracy: 0.0078, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:36:31 Iters: 271700/[06], loss: 11.0624, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:38:34 Iters: 271800/[06], loss: 11.6222, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:40:38 Iters: 271900/[06], loss: 10.1836, train_accuracy: 0.0781, time: 1.24 s/iter, learning rate: 0.05
-20220704-15:42:41 Iters: 272000/[06], loss: 10.7883, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:44:44 Iters: 272100/[06], loss: 11.5306, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:46:47 Iters: 272200/[06], loss: 11.0936, train_accuracy: 0.0312, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:48:51 Iters: 272300/[06], loss: 11.4153, train_accuracy: 0.0469, time: 1.24 s/iter, learning rate: 0.05
-20220704-15:50:54 Iters: 272400/[06], loss: 10.8651, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:52:57 Iters: 272500/[06], loss: 11.4286, train_accuracy: 0.0156, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:55:01 Iters: 272600/[06], loss: 11.2984, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.05
-20220704-15:57:04 Iters: 272700/[06], loss: 11.5191, train_accuracy: 0.0312, time: 1.24 s/iter, learning rate: 0.05
-20220704-15:59:07 Iters: 272800/[06], loss: 12.0750, train_accuracy: 0.0234, time: 1.23 s/iter, learning rate: 0.05
-20220704-16:01:10 Iters: 272900/[06], loss: 11.3029, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.05
-20220704-16:01:59 Train Epoch: 7/18 ...
-20220704-16:03:13 Iters: 273000/[07], loss: 10.5349, train_accuracy: 0.0938, time: 0.74 s/iter, learning rate: 0.0005000000000000001
-20220704-16:05:16 Iters: 273100/[07], loss: 10.1506, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:07:19 Iters: 273200/[07], loss: 9.7129, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:09:22 Iters: 273300/[07], loss: 9.7664, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:11:25 Iters: 273400/[07], loss: 9.7451, train_accuracy: 0.0781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:13:28 Iters: 273500/[07], loss: 9.4928, train_accuracy: 0.0391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:15:31 Iters: 273600/[07], loss: 9.4053, train_accuracy: 0.0547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:17:34 Iters: 273700/[07], loss: 10.0651, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:19:37 Iters: 273800/[07], loss: 9.4433, train_accuracy: 0.1406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:21:40 Iters: 273900/[07], loss: 9.9437, train_accuracy: 0.0703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:23:43 Iters: 274000/[07], loss: 9.1913, train_accuracy: 0.0625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:25:46 Iters: 274100/[07], loss: 8.9044, train_accuracy: 0.0859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:27:49 Iters: 274200/[07], loss: 8.5510, train_accuracy: 0.1406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:29:52 Iters: 274300/[07], loss: 9.9815, train_accuracy: 0.0469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:31:55 Iters: 274400/[07], loss: 8.0824, train_accuracy: 0.1328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:33:59 Iters: 274500/[07], loss: 8.4158, train_accuracy: 0.1094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:36:02 Iters: 274600/[07], loss: 8.3947, train_accuracy: 0.1328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:38:05 Iters: 274700/[07], loss: 8.2179, train_accuracy: 0.1328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:40:09 Iters: 274800/[07], loss: 8.6474, train_accuracy: 0.1172, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220704-16:42:12 Iters: 274900/[07], loss: 8.1019, train_accuracy: 0.1719, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:44:15 Iters: 275000/[07], loss: 8.7569, train_accuracy: 0.1016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:46:18 Iters: 275100/[07], loss: 9.0291, train_accuracy: 0.1484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:48:22 Iters: 275200/[07], loss: 7.9973, train_accuracy: 0.1172, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220704-16:50:25 Iters: 275300/[07], loss: 7.5834, train_accuracy: 0.1953, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:52:28 Iters: 275400/[07], loss: 8.0861, train_accuracy: 0.1094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:54:31 Iters: 275500/[07], loss: 7.9512, train_accuracy: 0.1406, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220704-16:56:34 Iters: 275600/[07], loss: 7.4492, train_accuracy: 0.1484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-16:58:38 Iters: 275700/[07], loss: 7.7917, train_accuracy: 0.1250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:00:41 Iters: 275800/[07], loss: 8.2875, train_accuracy: 0.1641, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:02:44 Iters: 275900/[07], loss: 7.8552, train_accuracy: 0.1406, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220704-17:04:47 Iters: 276000/[07], loss: 7.6145, train_accuracy: 0.1328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:06:51 Iters: 276100/[07], loss: 7.3199, train_accuracy: 0.1719, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:08:54 Iters: 276200/[07], loss: 8.6045, train_accuracy: 0.1250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:10:57 Iters: 276300/[07], loss: 7.9690, train_accuracy: 0.1406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:13:00 Iters: 276400/[07], loss: 7.1673, train_accuracy: 0.1719, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220704-17:15:04 Iters: 276500/[07], loss: 7.1216, train_accuracy: 0.1406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:17:07 Iters: 276600/[07], loss: 7.2737, train_accuracy: 0.1875, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:19:10 Iters: 276700/[07], loss: 7.6641, train_accuracy: 0.1250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:21:13 Iters: 276800/[07], loss: 6.8040, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:23:17 Iters: 276900/[07], loss: 7.3441, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:25:20 Iters: 277000/[07], loss: 8.2234, train_accuracy: 0.1406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:27:23 Iters: 277100/[07], loss: 7.8567, train_accuracy: 0.1719, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:29:27 Iters: 277200/[07], loss: 6.8518, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:31:30 Iters: 277300/[07], loss: 6.5514, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:33:33 Iters: 277400/[07], loss: 6.8700, train_accuracy: 0.1875, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:35:37 Iters: 277500/[07], loss: 7.5759, train_accuracy: 0.1484, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220704-17:37:40 Iters: 277600/[07], loss: 7.0618, train_accuracy: 0.1953, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:39:43 Iters: 277700/[07], loss: 6.8067, train_accuracy: 0.1562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:41:46 Iters: 277800/[07], loss: 7.0910, train_accuracy: 0.1875, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:43:49 Iters: 277900/[07], loss: 7.2597, train_accuracy: 0.1875, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:45:52 Iters: 278000/[07], loss: 6.6515, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:47:56 Iters: 278100/[07], loss: 6.7371, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:49:59 Iters: 278200/[07], loss: 7.0919, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:52:02 Iters: 278300/[07], loss: 7.2923, train_accuracy: 0.1953, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:54:06 Iters: 278400/[07], loss: 6.7394, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-17:56:09 Iters: 278500/[07], loss: 7.0743, train_accuracy: 0.1953, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220704-17:58:12 Iters: 278600/[07], loss: 6.5242, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:00:16 Iters: 278700/[07], loss: 8.0552, train_accuracy: 0.1641, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:02:19 Iters: 278800/[07], loss: 6.5873, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:04:22 Iters: 278900/[07], loss: 6.7548, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:06:26 Iters: 279000/[07], loss: 6.5080, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:08:29 Iters: 279100/[07], loss: 7.3077, train_accuracy: 0.1953, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:10:32 Iters: 279200/[07], loss: 6.8304, train_accuracy: 0.1953, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:12:36 Iters: 279300/[07], loss: 7.5819, train_accuracy: 0.1797, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220704-18:14:39 Iters: 279400/[07], loss: 6.7911, train_accuracy: 0.1875, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:16:43 Iters: 279500/[07], loss: 6.3844, train_accuracy: 0.2500, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220704-18:18:46 Iters: 279600/[07], loss: 6.3367, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:20:49 Iters: 279700/[07], loss: 7.7020, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:22:52 Iters: 279800/[07], loss: 7.1129, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:24:55 Iters: 279900/[07], loss: 7.2561, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:26:59 Iters: 280000/[07], loss: 6.9344, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:26:59 Saving checkpoint: 280000
-20220704-18:28:15 LFW Ave Accuracy: 99.4165
-20220704-18:29:30 AgeDB-30 Ave Accuracy: 95.6333
-20220704-18:30:58 CFP-FP Ave Accuracy: 91.9429
-20220704-18:30:58 Current Best Accuracy: LFW: 99.4165 in iters: 280000, AgeDB-30: 95.6333 in iters: 280000 and CFP-FP: 91.9429 in iters: 280000
-20220704-18:33:01 Iters: 280100/[07], loss: 6.7312, train_accuracy: 0.1875, time: 3.62 s/iter, learning rate: 0.0005000000000000001
-20220704-18:35:04 Iters: 280200/[07], loss: 6.9800, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:37:08 Iters: 280300/[07], loss: 6.2361, train_accuracy: 0.2109, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220704-18:39:11 Iters: 280400/[07], loss: 6.1980, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:41:14 Iters: 280500/[07], loss: 6.1669, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:43:18 Iters: 280600/[07], loss: 7.2176, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:45:21 Iters: 280700/[07], loss: 6.3589, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:47:24 Iters: 280800/[07], loss: 7.2025, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:49:27 Iters: 280900/[07], loss: 5.6165, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:51:31 Iters: 281000/[07], loss: 6.2845, train_accuracy: 0.2656, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220704-18:53:34 Iters: 281100/[07], loss: 5.4726, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:55:38 Iters: 281200/[07], loss: 5.7618, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:57:41 Iters: 281300/[07], loss: 5.5972, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-18:59:44 Iters: 281400/[07], loss: 6.4657, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:01:47 Iters: 281500/[07], loss: 6.1775, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:03:50 Iters: 281600/[07], loss: 6.3707, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:05:53 Iters: 281700/[07], loss: 6.4730, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:07:57 Iters: 281800/[07], loss: 6.4723, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:10:00 Iters: 281900/[07], loss: 6.2239, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:12:03 Iters: 282000/[07], loss: 6.6604, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:14:06 Iters: 282100/[07], loss: 6.0415, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:16:10 Iters: 282200/[07], loss: 6.7041, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:18:13 Iters: 282300/[07], loss: 5.9640, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:20:16 Iters: 282400/[07], loss: 6.4076, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:22:19 Iters: 282500/[07], loss: 6.8115, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:24:23 Iters: 282600/[07], loss: 5.0755, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:26:26 Iters: 282700/[07], loss: 5.6420, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:28:29 Iters: 282800/[07], loss: 5.6986, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:30:32 Iters: 282900/[07], loss: 6.0465, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:32:36 Iters: 283000/[07], loss: 5.6722, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:34:39 Iters: 283100/[07], loss: 5.9159, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:36:42 Iters: 283200/[07], loss: 5.8805, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:38:46 Iters: 283300/[07], loss: 5.3827, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:40:49 Iters: 283400/[07], loss: 6.9292, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:42:52 Iters: 283500/[07], loss: 5.9433, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:44:55 Iters: 283600/[07], loss: 6.4808, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:46:59 Iters: 283700/[07], loss: 5.6572, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:49:02 Iters: 283800/[07], loss: 5.6217, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:51:05 Iters: 283900/[07], loss: 6.2648, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:53:08 Iters: 284000/[07], loss: 5.5069, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:55:12 Iters: 284100/[07], loss: 5.5006, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:57:15 Iters: 284200/[07], loss: 5.6336, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-19:59:18 Iters: 284300/[07], loss: 5.4188, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:01:21 Iters: 284400/[07], loss: 6.2855, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:03:24 Iters: 284500/[07], loss: 5.6107, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:05:28 Iters: 284600/[07], loss: 5.9283, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:07:31 Iters: 284700/[07], loss: 6.1865, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:09:34 Iters: 284800/[07], loss: 6.0519, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:11:37 Iters: 284900/[07], loss: 5.8359, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:13:40 Iters: 285000/[07], loss: 5.5213, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:15:44 Iters: 285100/[07], loss: 6.0864, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:17:47 Iters: 285200/[07], loss: 5.4329, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:19:50 Iters: 285300/[07], loss: 6.3232, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:21:53 Iters: 285400/[07], loss: 5.7547, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:23:56 Iters: 285500/[07], loss: 5.4194, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:26:00 Iters: 285600/[07], loss: 5.4958, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:28:03 Iters: 285700/[07], loss: 6.0637, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:30:06 Iters: 285800/[07], loss: 5.7523, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:32:09 Iters: 285900/[07], loss: 6.3727, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:34:13 Iters: 286000/[07], loss: 5.8268, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:36:16 Iters: 286100/[07], loss: 5.9367, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:38:19 Iters: 286200/[07], loss: 5.0472, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:40:22 Iters: 286300/[07], loss: 4.8049, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:42:26 Iters: 286400/[07], loss: 6.8482, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:44:29 Iters: 286500/[07], loss: 5.4679, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:46:32 Iters: 286600/[07], loss: 5.7856, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:48:35 Iters: 286700/[07], loss: 5.4913, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:50:38 Iters: 286800/[07], loss: 6.5894, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:52:41 Iters: 286900/[07], loss: 6.0459, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:54:45 Iters: 287000/[07], loss: 6.2403, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:56:48 Iters: 287100/[07], loss: 5.0177, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-20:58:51 Iters: 287200/[07], loss: 6.3451, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:00:54 Iters: 287300/[07], loss: 6.3818, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:02:57 Iters: 287400/[07], loss: 5.8443, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:05:01 Iters: 287500/[07], loss: 5.7929, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:07:04 Iters: 287600/[07], loss: 5.4749, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:09:07 Iters: 287700/[07], loss: 6.2180, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:11:10 Iters: 287800/[07], loss: 5.9278, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:13:13 Iters: 287900/[07], loss: 6.1218, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:15:17 Iters: 288000/[07], loss: 5.3824, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:17:20 Iters: 288100/[07], loss: 5.2571, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:19:23 Iters: 288200/[07], loss: 5.0104, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:21:26 Iters: 288300/[07], loss: 4.3779, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:23:30 Iters: 288400/[07], loss: 5.7850, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:25:33 Iters: 288500/[07], loss: 6.0429, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:27:36 Iters: 288600/[07], loss: 5.6084, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:29:40 Iters: 288700/[07], loss: 6.2063, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:31:43 Iters: 288800/[07], loss: 5.1714, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:33:46 Iters: 288900/[07], loss: 5.7380, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:35:49 Iters: 289000/[07], loss: 5.3591, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:37:53 Iters: 289100/[07], loss: 5.5491, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:39:56 Iters: 289200/[07], loss: 5.6316, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:41:59 Iters: 289300/[07], loss: 5.6731, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:44:02 Iters: 289400/[07], loss: 5.2360, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:46:06 Iters: 289500/[07], loss: 5.9289, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:48:09 Iters: 289600/[07], loss: 5.8802, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:50:12 Iters: 289700/[07], loss: 5.2597, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:52:15 Iters: 289800/[07], loss: 4.9709, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:54:18 Iters: 289900/[07], loss: 5.4980, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:56:22 Iters: 290000/[07], loss: 5.4401, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-21:56:22 Saving checkpoint: 290000
-20220704-21:57:38 LFW Ave Accuracy: 99.4833
-20220704-21:58:53 AgeDB-30 Ave Accuracy: 95.8000
-20220704-22:00:20 CFP-FP Ave Accuracy: 92.8143
-20220704-22:00:20 Current Best Accuracy: LFW: 99.4833 in iters: 290000, AgeDB-30: 95.8000 in iters: 290000 and CFP-FP: 92.8143 in iters: 290000
-20220704-22:02:23 Iters: 290100/[07], loss: 5.9006, train_accuracy: 0.2891, time: 3.61 s/iter, learning rate: 0.0005000000000000001
-20220704-22:04:26 Iters: 290200/[07], loss: 5.0880, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:06:29 Iters: 290300/[07], loss: 5.5520, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:08:33 Iters: 290400/[07], loss: 5.3634, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:10:36 Iters: 290500/[07], loss: 5.4190, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:12:39 Iters: 290600/[07], loss: 5.3288, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:14:42 Iters: 290700/[07], loss: 5.5081, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:16:46 Iters: 290800/[07], loss: 5.3034, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:18:49 Iters: 290900/[07], loss: 5.8261, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:20:52 Iters: 291000/[07], loss: 6.1737, train_accuracy: 0.1797, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:22:55 Iters: 291100/[07], loss: 5.6662, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:24:58 Iters: 291200/[07], loss: 6.1791, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:27:02 Iters: 291300/[07], loss: 5.5681, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:29:05 Iters: 291400/[07], loss: 5.9825, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:31:08 Iters: 291500/[07], loss: 5.6309, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:33:11 Iters: 291600/[07], loss: 5.0448, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:35:14 Iters: 291700/[07], loss: 5.2502, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:37:18 Iters: 291800/[07], loss: 6.2180, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:39:21 Iters: 291900/[07], loss: 5.8852, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:41:24 Iters: 292000/[07], loss: 5.9619, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:43:27 Iters: 292100/[07], loss: 6.3892, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:45:31 Iters: 292200/[07], loss: 5.6102, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:47:34 Iters: 292300/[07], loss: 5.2456, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:49:37 Iters: 292400/[07], loss: 5.8799, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:51:40 Iters: 292500/[07], loss: 4.8486, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:53:43 Iters: 292600/[07], loss: 5.4462, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:55:47 Iters: 292700/[07], loss: 4.9158, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:57:50 Iters: 292800/[07], loss: 6.2756, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-22:59:53 Iters: 292900/[07], loss: 5.1306, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:01:56 Iters: 293000/[07], loss: 5.8809, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:03:59 Iters: 293100/[07], loss: 4.5307, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:06:03 Iters: 293200/[07], loss: 4.9149, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:08:06 Iters: 293300/[07], loss: 5.0192, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:10:09 Iters: 293400/[07], loss: 5.2837, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:12:12 Iters: 293500/[07], loss: 5.8334, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:14:16 Iters: 293600/[07], loss: 5.0658, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:16:19 Iters: 293700/[07], loss: 5.2385, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:18:22 Iters: 293800/[07], loss: 5.6631, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:20:25 Iters: 293900/[07], loss: 5.3735, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:22:29 Iters: 294000/[07], loss: 5.1804, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:24:32 Iters: 294100/[07], loss: 5.6817, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:26:35 Iters: 294200/[07], loss: 5.7109, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:28:38 Iters: 294300/[07], loss: 5.5733, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:30:42 Iters: 294400/[07], loss: 4.8643, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:32:45 Iters: 294500/[07], loss: 5.9744, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:34:48 Iters: 294600/[07], loss: 5.3083, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:36:51 Iters: 294700/[07], loss: 5.2998, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:38:54 Iters: 294800/[07], loss: 5.9242, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:40:57 Iters: 294900/[07], loss: 5.3856, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:43:01 Iters: 295000/[07], loss: 5.1034, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:45:04 Iters: 295100/[07], loss: 5.7383, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:47:07 Iters: 295200/[07], loss: 5.7420, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:49:10 Iters: 295300/[07], loss: 5.4763, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:51:13 Iters: 295400/[07], loss: 6.1533, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:53:17 Iters: 295500/[07], loss: 5.3643, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:55:20 Iters: 295600/[07], loss: 5.6871, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:57:23 Iters: 295700/[07], loss: 5.7769, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220704-23:59:26 Iters: 295800/[07], loss: 6.0454, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:01:29 Iters: 295900/[07], loss: 5.7672, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:03:33 Iters: 296000/[07], loss: 5.3767, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:05:36 Iters: 296100/[07], loss: 6.1940, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:07:39 Iters: 296200/[07], loss: 5.5422, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:09:42 Iters: 296300/[07], loss: 5.1451, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:11:45 Iters: 296400/[07], loss: 5.3577, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:13:48 Iters: 296500/[07], loss: 5.3620, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:15:52 Iters: 296600/[07], loss: 5.9447, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:17:55 Iters: 296700/[07], loss: 4.7793, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:19:58 Iters: 296800/[07], loss: 5.9823, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:22:01 Iters: 296900/[07], loss: 6.3507, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:24:05 Iters: 297000/[07], loss: 5.8361, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:26:08 Iters: 297100/[07], loss: 6.0573, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:28:11 Iters: 297200/[07], loss: 6.1822, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:30:14 Iters: 297300/[07], loss: 5.7172, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:32:17 Iters: 297400/[07], loss: 5.2017, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:34:21 Iters: 297500/[07], loss: 6.4664, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:36:24 Iters: 297600/[07], loss: 6.1625, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:38:27 Iters: 297700/[07], loss: 5.2390, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:40:30 Iters: 297800/[07], loss: 5.8014, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:42:33 Iters: 297900/[07], loss: 5.6847, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:44:37 Iters: 298000/[07], loss: 6.2382, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:46:40 Iters: 298100/[07], loss: 5.6479, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:48:43 Iters: 298200/[07], loss: 6.2950, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:50:46 Iters: 298300/[07], loss: 5.0603, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:52:50 Iters: 298400/[07], loss: 5.3100, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:54:53 Iters: 298500/[07], loss: 5.3387, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:56:56 Iters: 298600/[07], loss: 5.1310, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-00:58:59 Iters: 298700/[07], loss: 5.5569, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:01:02 Iters: 298800/[07], loss: 5.8497, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:03:06 Iters: 298900/[07], loss: 5.4139, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:05:09 Iters: 299000/[07], loss: 5.1348, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:07:12 Iters: 299100/[07], loss: 5.3889, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:09:15 Iters: 299200/[07], loss: 5.0473, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:11:19 Iters: 299300/[07], loss: 6.1061, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:13:22 Iters: 299400/[07], loss: 5.0234, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:15:25 Iters: 299500/[07], loss: 5.1307, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:17:28 Iters: 299600/[07], loss: 5.4756, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:19:32 Iters: 299700/[07], loss: 5.4766, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:21:35 Iters: 299800/[07], loss: 5.2390, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:23:38 Iters: 299900/[07], loss: 5.0194, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:25:41 Iters: 300000/[07], loss: 5.7247, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:25:41 Saving checkpoint: 300000
-20220705-01:26:58 LFW Ave Accuracy: 99.5333
-20220705-01:28:14 AgeDB-30 Ave Accuracy: 95.8833
-20220705-01:29:42 CFP-FP Ave Accuracy: 92.7429
-20220705-01:29:42 Current Best Accuracy: LFW: 99.5333 in iters: 300000, AgeDB-30: 95.8833 in iters: 300000 and CFP-FP: 92.8143 in iters: 290000
-20220705-01:31:45 Iters: 300100/[07], loss: 7.1182, train_accuracy: 0.2188, time: 3.63 s/iter, learning rate: 0.0005000000000000001
-20220705-01:33:48 Iters: 300200/[07], loss: 5.0560, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:35:51 Iters: 300300/[07], loss: 5.9284, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:37:54 Iters: 300400/[07], loss: 5.9469, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:39:57 Iters: 300500/[07], loss: 5.0810, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:42:01 Iters: 300600/[07], loss: 5.4829, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:44:04 Iters: 300700/[07], loss: 6.0172, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:46:07 Iters: 300800/[07], loss: 5.6112, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:48:10 Iters: 300900/[07], loss: 6.8803, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:50:13 Iters: 301000/[07], loss: 5.6822, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:52:16 Iters: 301100/[07], loss: 5.4816, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:54:20 Iters: 301200/[07], loss: 5.0249, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:56:23 Iters: 301300/[07], loss: 5.4253, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-01:58:26 Iters: 301400/[07], loss: 5.4765, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:00:29 Iters: 301500/[07], loss: 5.8296, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:02:32 Iters: 301600/[07], loss: 5.6025, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:04:35 Iters: 301700/[07], loss: 5.5229, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:06:39 Iters: 301800/[07], loss: 5.2985, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:08:42 Iters: 301900/[07], loss: 5.5061, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:10:45 Iters: 302000/[07], loss: 5.8405, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:12:48 Iters: 302100/[07], loss: 5.3088, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:14:51 Iters: 302200/[07], loss: 5.8930, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:16:55 Iters: 302300/[07], loss: 5.0589, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:18:58 Iters: 302400/[07], loss: 5.5293, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:21:01 Iters: 302500/[07], loss: 5.3306, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:23:04 Iters: 302600/[07], loss: 5.8046, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:25:08 Iters: 302700/[07], loss: 5.7874, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:27:11 Iters: 302800/[07], loss: 5.0882, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:29:14 Iters: 302900/[07], loss: 5.8273, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:31:17 Iters: 303000/[07], loss: 5.0883, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:33:20 Iters: 303100/[07], loss: 5.2088, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:35:24 Iters: 303200/[07], loss: 5.2997, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:37:27 Iters: 303300/[07], loss: 5.1955, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:39:30 Iters: 303400/[07], loss: 6.0569, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:41:34 Iters: 303500/[07], loss: 5.4920, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:43:37 Iters: 303600/[07], loss: 5.2898, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:45:40 Iters: 303700/[07], loss: 4.7115, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:47:43 Iters: 303800/[07], loss: 5.4758, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:49:47 Iters: 303900/[07], loss: 5.2187, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:51:50 Iters: 304000/[07], loss: 5.8791, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:53:53 Iters: 304100/[07], loss: 5.5558, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:55:56 Iters: 304200/[07], loss: 5.8910, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-02:58:00 Iters: 304300/[07], loss: 5.7212, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:00:03 Iters: 304400/[07], loss: 5.5289, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:02:06 Iters: 304500/[07], loss: 5.4983, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:04:09 Iters: 304600/[07], loss: 5.6978, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:06:13 Iters: 304700/[07], loss: 6.1016, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:08:16 Iters: 304800/[07], loss: 5.7348, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:10:19 Iters: 304900/[07], loss: 6.3761, train_accuracy: 0.1953, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:12:22 Iters: 305000/[07], loss: 4.6835, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:14:25 Iters: 305100/[07], loss: 5.2155, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:16:29 Iters: 305200/[07], loss: 5.1861, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:18:32 Iters: 305300/[07], loss: 5.9300, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:20:35 Iters: 305400/[07], loss: 5.4062, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:22:38 Iters: 305500/[07], loss: 6.4094, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:24:42 Iters: 305600/[07], loss: 5.4604, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:26:45 Iters: 305700/[07], loss: 5.1227, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:28:48 Iters: 305800/[07], loss: 5.3641, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:30:51 Iters: 305900/[07], loss: 5.6595, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:32:55 Iters: 306000/[07], loss: 5.1337, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:34:58 Iters: 306100/[07], loss: 5.5846, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:37:01 Iters: 306200/[07], loss: 5.9554, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:39:05 Iters: 306300/[07], loss: 5.1490, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:41:08 Iters: 306400/[07], loss: 5.5602, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:43:11 Iters: 306500/[07], loss: 6.0361, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:45:14 Iters: 306600/[07], loss: 5.4282, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:47:18 Iters: 306700/[07], loss: 5.3809, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:49:21 Iters: 306800/[07], loss: 5.3872, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:51:24 Iters: 306900/[07], loss: 5.3466, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:53:27 Iters: 307000/[07], loss: 5.6051, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:55:30 Iters: 307100/[07], loss: 6.0762, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:57:34 Iters: 307200/[07], loss: 5.5541, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-03:59:37 Iters: 307300/[07], loss: 5.7578, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:01:40 Iters: 307400/[07], loss: 5.2574, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:03:43 Iters: 307500/[07], loss: 5.3457, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:05:47 Iters: 307600/[07], loss: 5.3400, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:07:50 Iters: 307700/[07], loss: 5.8904, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:09:53 Iters: 307800/[07], loss: 5.8215, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:11:56 Iters: 307900/[07], loss: 7.1742, train_accuracy: 0.1797, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:14:00 Iters: 308000/[07], loss: 6.2160, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:16:03 Iters: 308100/[07], loss: 5.7122, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:18:06 Iters: 308200/[07], loss: 5.6638, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:20:09 Iters: 308300/[07], loss: 5.3087, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:22:12 Iters: 308400/[07], loss: 5.6563, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:24:15 Iters: 308500/[07], loss: 5.9798, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:26:19 Iters: 308600/[07], loss: 5.7484, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:28:22 Iters: 308700/[07], loss: 5.4732, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:30:25 Iters: 308800/[07], loss: 5.6772, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:32:28 Iters: 308900/[07], loss: 5.9471, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:34:31 Iters: 309000/[07], loss: 5.3966, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:36:35 Iters: 309100/[07], loss: 6.0276, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:38:38 Iters: 309200/[07], loss: 5.9061, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:40:41 Iters: 309300/[07], loss: 5.5698, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:42:44 Iters: 309400/[07], loss: 5.7427, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:44:47 Iters: 309500/[07], loss: 6.5201, train_accuracy: 0.1875, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:46:50 Iters: 309600/[07], loss: 6.1076, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:48:53 Iters: 309700/[07], loss: 5.8960, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:50:57 Iters: 309800/[07], loss: 5.7113, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:53:00 Iters: 309900/[07], loss: 6.0760, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:55:03 Iters: 310000/[07], loss: 5.7462, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-04:55:03 Saving checkpoint: 310000
-20220705-04:56:22 LFW Ave Accuracy: 99.5166
-20220705-04:57:39 AgeDB-30 Ave Accuracy: 96.0000
-20220705-04:59:09 CFP-FP Ave Accuracy: 93.1143
-20220705-04:59:09 Current Best Accuracy: LFW: 99.5333 in iters: 300000, AgeDB-30: 96.0000 in iters: 310000 and CFP-FP: 93.1143 in iters: 310000
-20220705-05:01:12 Iters: 310100/[07], loss: 6.1017, train_accuracy: 0.2344, time: 3.69 s/iter, learning rate: 0.0005000000000000001
-20220705-05:03:15 Iters: 310200/[07], loss: 5.3410, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:05:18 Iters: 310300/[07], loss: 5.7530, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:07:21 Iters: 310400/[07], loss: 5.4611, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:09:25 Iters: 310500/[07], loss: 5.6100, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:11:28 Iters: 310600/[07], loss: 6.8097, train_accuracy: 0.1719, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:13:31 Iters: 310700/[07], loss: 5.6855, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:15:34 Iters: 310800/[07], loss: 6.3954, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:17:38 Iters: 310900/[07], loss: 5.9524, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:19:41 Iters: 311000/[07], loss: 5.8300, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:21:44 Iters: 311100/[07], loss: 4.8188, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:23:47 Iters: 311200/[07], loss: 5.7702, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:25:50 Iters: 311300/[07], loss: 5.6436, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:27:53 Iters: 311400/[07], loss: 5.3234, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:29:57 Iters: 311500/[07], loss: 5.5216, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:32:00 Iters: 311600/[07], loss: 4.9947, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:34:03 Iters: 311700/[07], loss: 6.2107, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:36:06 Iters: 311800/[07], loss: 5.9139, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:38:09 Iters: 311900/[07], loss: 5.2755, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:40:12 Iters: 312000/[07], loss: 5.7471, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:42:16 Iters: 312100/[07], loss: 5.5982, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:44:19 Iters: 312200/[07], loss: 5.9469, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:46:22 Iters: 312300/[07], loss: 5.8586, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:48:25 Iters: 312400/[07], loss: 4.8015, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:50:28 Iters: 312500/[07], loss: 5.0429, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:52:31 Iters: 312600/[07], loss: 6.3873, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:54:35 Iters: 312700/[07], loss: 5.6512, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:56:38 Iters: 312800/[07], loss: 5.7992, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-05:58:41 Iters: 312900/[07], loss: 5.9713, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:00:44 Iters: 313000/[07], loss: 5.7770, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:02:48 Iters: 313100/[07], loss: 5.4878, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:04:51 Iters: 313200/[07], loss: 5.9826, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:06:54 Iters: 313300/[07], loss: 5.3474, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:08:57 Iters: 313400/[07], loss: 4.9810, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:11:01 Iters: 313500/[07], loss: 6.1492, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:13:04 Iters: 313600/[07], loss: 6.0790, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:15:07 Iters: 313700/[07], loss: 6.0226, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:17:10 Iters: 313800/[07], loss: 6.4248, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:19:14 Iters: 313900/[07], loss: 6.3180, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:21:17 Iters: 314000/[07], loss: 5.8744, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:23:20 Iters: 314100/[07], loss: 6.6608, train_accuracy: 0.1406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:25:23 Iters: 314200/[07], loss: 6.1421, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:27:27 Iters: 314300/[07], loss: 5.9868, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:29:30 Iters: 314400/[07], loss: 6.7756, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:31:33 Iters: 314500/[07], loss: 5.5398, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:33:36 Iters: 314600/[07], loss: 5.1145, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:35:39 Iters: 314700/[07], loss: 5.6375, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:37:43 Iters: 314800/[07], loss: 5.1814, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:39:46 Iters: 314900/[07], loss: 5.4342, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:41:49 Iters: 315000/[07], loss: 5.1648, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:43:52 Iters: 315100/[07], loss: 5.9238, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:45:55 Iters: 315200/[07], loss: 5.7384, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:47:59 Iters: 315300/[07], loss: 6.0444, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:50:02 Iters: 315400/[07], loss: 5.3613, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:52:05 Iters: 315500/[07], loss: 5.7858, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:54:08 Iters: 315600/[07], loss: 5.6025, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:56:12 Iters: 315700/[07], loss: 5.6231, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-06:58:15 Iters: 315800/[07], loss: 5.5521, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:00:18 Iters: 315900/[07], loss: 5.2202, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:02:21 Iters: 316000/[07], loss: 5.1169, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:04:24 Iters: 316100/[07], loss: 6.4937, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:06:28 Iters: 316200/[07], loss: 4.7637, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:08:31 Iters: 316300/[07], loss: 5.2628, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:10:34 Iters: 316400/[07], loss: 6.3956, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:12:37 Iters: 316500/[07], loss: 5.8019, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:14:41 Iters: 316600/[07], loss: 5.7880, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:16:44 Iters: 316700/[07], loss: 5.1952, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:18:47 Iters: 316800/[07], loss: 5.7971, train_accuracy: 0.2578, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220705-07:20:51 Iters: 316900/[07], loss: 5.2303, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:22:54 Iters: 317000/[07], loss: 5.9315, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:24:56 Iters: 317100/[07], loss: 5.5978, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:26:59 Iters: 317200/[07], loss: 6.6847, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:29:03 Iters: 317300/[07], loss: 5.2928, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:31:06 Iters: 317400/[07], loss: 5.6212, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:33:09 Iters: 317500/[07], loss: 5.8977, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:35:12 Iters: 317600/[07], loss: 5.7709, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:37:15 Iters: 317700/[07], loss: 5.7656, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:39:18 Iters: 317800/[07], loss: 5.3647, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:41:21 Iters: 317900/[07], loss: 5.5668, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:43:24 Iters: 318000/[07], loss: 5.4775, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:45:27 Iters: 318100/[07], loss: 6.5163, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:47:30 Iters: 318200/[07], loss: 6.2435, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:49:34 Iters: 318300/[07], loss: 5.2163, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:51:37 Iters: 318400/[07], loss: 5.9920, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220705-07:52:13 Train Epoch: 8/18 ...
-20220705-07:53:46 Iters: 318500/[08], loss: 5.4484, train_accuracy: 0.3203, time: 0.93 s/iter, learning rate: 0.005000000000000001
-20220705-07:55:50 Iters: 318600/[08], loss: 5.6252, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-07:57:53 Iters: 318700/[08], loss: 5.0364, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-07:59:56 Iters: 318800/[08], loss: 5.4360, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:01:59 Iters: 318900/[08], loss: 5.7512, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:04:02 Iters: 319000/[08], loss: 4.7812, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:06:06 Iters: 319100/[08], loss: 5.9076, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:08:09 Iters: 319200/[08], loss: 5.6344, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:10:12 Iters: 319300/[08], loss: 5.2008, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:12:15 Iters: 319400/[08], loss: 4.9987, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:14:18 Iters: 319500/[08], loss: 5.7742, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:16:22 Iters: 319600/[08], loss: 5.2789, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:18:25 Iters: 319700/[08], loss: 5.5766, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:20:28 Iters: 319800/[08], loss: 6.2851, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:22:31 Iters: 319900/[08], loss: 5.2415, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:24:34 Iters: 320000/[08], loss: 4.7394, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:24:34 Saving checkpoint: 320000
-20220705-08:25:50 LFW Ave Accuracy: 99.5833
-20220705-08:27:05 AgeDB-30 Ave Accuracy: 95.9000
-20220705-08:28:31 CFP-FP Ave Accuracy: 93.3857
-20220705-08:28:31 Current Best Accuracy: LFW: 99.5833 in iters: 320000, AgeDB-30: 96.0000 in iters: 310000 and CFP-FP: 93.3857 in iters: 320000
-20220705-08:30:34 Iters: 320100/[08], loss: 5.3464, train_accuracy: 0.3125, time: 3.60 s/iter, learning rate: 0.005000000000000001
-20220705-08:32:37 Iters: 320200/[08], loss: 5.2213, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:34:40 Iters: 320300/[08], loss: 5.1047, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:36:43 Iters: 320400/[08], loss: 5.5429, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:38:47 Iters: 320500/[08], loss: 5.9825, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:40:50 Iters: 320600/[08], loss: 5.3378, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:42:53 Iters: 320700/[08], loss: 5.5687, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:44:56 Iters: 320800/[08], loss: 6.4439, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:46:59 Iters: 320900/[08], loss: 5.9520, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:49:02 Iters: 321000/[08], loss: 5.2733, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:51:06 Iters: 321100/[08], loss: 5.6412, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:53:09 Iters: 321200/[08], loss: 5.3777, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:55:12 Iters: 321300/[08], loss: 5.8311, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:57:15 Iters: 321400/[08], loss: 5.9187, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-08:59:19 Iters: 321500/[08], loss: 5.7158, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:01:22 Iters: 321600/[08], loss: 5.2666, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:03:25 Iters: 321700/[08], loss: 5.7184, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:05:28 Iters: 321800/[08], loss: 5.5794, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:07:32 Iters: 321900/[08], loss: 5.2613, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:09:35 Iters: 322000/[08], loss: 5.8015, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:11:38 Iters: 322100/[08], loss: 4.9840, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:13:41 Iters: 322200/[08], loss: 5.2062, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:15:44 Iters: 322300/[08], loss: 5.8024, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:17:48 Iters: 322400/[08], loss: 6.8150, train_accuracy: 0.1719, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:19:51 Iters: 322500/[08], loss: 5.2692, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:21:54 Iters: 322600/[08], loss: 5.0826, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:23:57 Iters: 322700/[08], loss: 5.6661, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:26:00 Iters: 322800/[08], loss: 5.5977, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:28:04 Iters: 322900/[08], loss: 5.7341, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:30:07 Iters: 323000/[08], loss: 5.0968, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:32:10 Iters: 323100/[08], loss: 5.2671, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:34:13 Iters: 323200/[08], loss: 5.7784, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:36:17 Iters: 323300/[08], loss: 5.0491, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:38:20 Iters: 323400/[08], loss: 5.1876, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:40:23 Iters: 323500/[08], loss: 5.8798, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:42:26 Iters: 323600/[08], loss: 5.7277, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:44:29 Iters: 323700/[08], loss: 5.6967, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:46:33 Iters: 323800/[08], loss: 5.1031, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:48:36 Iters: 323900/[08], loss: 6.4460, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:50:39 Iters: 324000/[08], loss: 6.0268, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:52:42 Iters: 324100/[08], loss: 5.8253, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:54:46 Iters: 324200/[08], loss: 5.5511, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:56:49 Iters: 324300/[08], loss: 5.2030, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-09:58:52 Iters: 324400/[08], loss: 5.3031, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:00:55 Iters: 324500/[08], loss: 5.8333, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:02:58 Iters: 324600/[08], loss: 5.2001, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:05:02 Iters: 324700/[08], loss: 5.6093, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:07:05 Iters: 324800/[08], loss: 6.1200, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:09:08 Iters: 324900/[08], loss: 5.9840, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:11:11 Iters: 325000/[08], loss: 6.0519, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:13:14 Iters: 325100/[08], loss: 5.3979, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:15:17 Iters: 325200/[08], loss: 5.4049, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:17:21 Iters: 325300/[08], loss: 5.3285, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:19:24 Iters: 325400/[08], loss: 6.1051, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:21:27 Iters: 325500/[08], loss: 5.3105, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:23:30 Iters: 325600/[08], loss: 5.8467, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:25:33 Iters: 325700/[08], loss: 5.2133, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:27:37 Iters: 325800/[08], loss: 5.2727, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:29:40 Iters: 325900/[08], loss: 5.8098, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:31:43 Iters: 326000/[08], loss: 5.7120, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:33:46 Iters: 326100/[08], loss: 6.2295, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:35:49 Iters: 326200/[08], loss: 6.0921, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:37:53 Iters: 326300/[08], loss: 5.4461, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:39:56 Iters: 326400/[08], loss: 5.8484, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:41:59 Iters: 326500/[08], loss: 6.1301, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:44:02 Iters: 326600/[08], loss: 5.4742, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:46:05 Iters: 326700/[08], loss: 5.4140, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:48:09 Iters: 326800/[08], loss: 5.1829, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:50:12 Iters: 326900/[08], loss: 5.6053, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:52:15 Iters: 327000/[08], loss: 6.0430, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:54:18 Iters: 327100/[08], loss: 5.7673, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:56:21 Iters: 327200/[08], loss: 5.8179, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-10:58:24 Iters: 327300/[08], loss: 6.2303, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:00:27 Iters: 327400/[08], loss: 5.5887, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:02:30 Iters: 327500/[08], loss: 6.1290, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:04:33 Iters: 327600/[08], loss: 6.5664, train_accuracy: 0.1797, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:06:36 Iters: 327700/[08], loss: 4.9715, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:08:40 Iters: 327800/[08], loss: 5.9117, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:10:43 Iters: 327900/[08], loss: 5.9484, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:12:46 Iters: 328000/[08], loss: 6.0461, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:14:49 Iters: 328100/[08], loss: 6.2819, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:16:52 Iters: 328200/[08], loss: 6.3181, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:18:55 Iters: 328300/[08], loss: 5.5032, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:20:58 Iters: 328400/[08], loss: 5.6446, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:23:02 Iters: 328500/[08], loss: 6.4006, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:25:05 Iters: 328600/[08], loss: 5.6779, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:27:08 Iters: 328700/[08], loss: 5.9147, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:29:11 Iters: 328800/[08], loss: 5.2788, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:31:14 Iters: 328900/[08], loss: 5.5495, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:33:18 Iters: 329000/[08], loss: 6.1908, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:35:21 Iters: 329100/[08], loss: 5.3364, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:37:24 Iters: 329200/[08], loss: 6.4534, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:39:27 Iters: 329300/[08], loss: 5.7542, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:41:30 Iters: 329400/[08], loss: 5.5077, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:43:34 Iters: 329500/[08], loss: 5.7414, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:45:37 Iters: 329600/[08], loss: 6.2639, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:47:40 Iters: 329700/[08], loss: 5.4578, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:49:43 Iters: 329800/[08], loss: 6.2189, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:51:46 Iters: 329900/[08], loss: 5.5581, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:53:50 Iters: 330000/[08], loss: 5.3562, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-11:53:50 Saving checkpoint: 330000
-20220705-11:55:06 LFW Ave Accuracy: 99.4999
-20220705-11:56:22 AgeDB-30 Ave Accuracy: 96.2667
-20220705-11:57:49 CFP-FP Ave Accuracy: 93.1286
-20220705-11:57:49 Current Best Accuracy: LFW: 99.5833 in iters: 320000, AgeDB-30: 96.2667 in iters: 330000 and CFP-FP: 93.3857 in iters: 320000
-20220705-11:59:52 Iters: 330100/[08], loss: 5.4973, train_accuracy: 0.3281, time: 3.62 s/iter, learning rate: 0.005000000000000001
-20220705-12:01:55 Iters: 330200/[08], loss: 5.4531, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:03:58 Iters: 330300/[08], loss: 5.0804, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:06:01 Iters: 330400/[08], loss: 5.8615, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:08:04 Iters: 330500/[08], loss: 6.5983, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:10:08 Iters: 330600/[08], loss: 5.6433, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:12:11 Iters: 330700/[08], loss: 6.6048, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:14:14 Iters: 330800/[08], loss: 6.4952, train_accuracy: 0.1875, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:16:17 Iters: 330900/[08], loss: 5.6917, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:18:21 Iters: 331000/[08], loss: 6.1720, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:20:24 Iters: 331100/[08], loss: 5.8599, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:22:27 Iters: 331200/[08], loss: 5.4565, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:24:30 Iters: 331300/[08], loss: 5.9031, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:26:33 Iters: 331400/[08], loss: 5.8793, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:28:37 Iters: 331500/[08], loss: 5.7464, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:30:40 Iters: 331600/[08], loss: 5.5150, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:32:43 Iters: 331700/[08], loss: 5.9503, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:34:46 Iters: 331800/[08], loss: 5.5289, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:36:49 Iters: 331900/[08], loss: 5.7708, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:38:52 Iters: 332000/[08], loss: 5.1521, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:40:56 Iters: 332100/[08], loss: 5.6439, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:42:59 Iters: 332200/[08], loss: 4.9127, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:45:02 Iters: 332300/[08], loss: 5.7576, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:47:05 Iters: 332400/[08], loss: 5.9901, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:49:08 Iters: 332500/[08], loss: 5.4316, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:51:12 Iters: 332600/[08], loss: 5.6933, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:53:15 Iters: 332700/[08], loss: 5.6260, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:55:18 Iters: 332800/[08], loss: 6.2200, train_accuracy: 0.1953, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:57:21 Iters: 332900/[08], loss: 6.1743, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-12:59:24 Iters: 333000/[08], loss: 6.4542, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:01:27 Iters: 333100/[08], loss: 6.1439, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:03:31 Iters: 333200/[08], loss: 5.5915, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:05:34 Iters: 333300/[08], loss: 6.0092, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:07:37 Iters: 333400/[08], loss: 5.1289, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:09:40 Iters: 333500/[08], loss: 5.5101, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:11:43 Iters: 333600/[08], loss: 5.5969, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:13:47 Iters: 333700/[08], loss: 5.2199, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:15:50 Iters: 333800/[08], loss: 6.0485, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:17:53 Iters: 333900/[08], loss: 5.3883, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:19:56 Iters: 334000/[08], loss: 5.6167, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:21:59 Iters: 334100/[08], loss: 5.9121, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:24:03 Iters: 334200/[08], loss: 5.6763, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:26:06 Iters: 334300/[08], loss: 5.8487, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:28:09 Iters: 334400/[08], loss: 5.9090, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:30:12 Iters: 334500/[08], loss: 5.5973, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:32:16 Iters: 334600/[08], loss: 5.6933, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:34:19 Iters: 334700/[08], loss: 5.9313, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:36:22 Iters: 334800/[08], loss: 6.0300, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:38:25 Iters: 334900/[08], loss: 6.0206, train_accuracy: 0.1719, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:40:29 Iters: 335000/[08], loss: 6.0991, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:42:32 Iters: 335100/[08], loss: 6.2882, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:44:35 Iters: 335200/[08], loss: 5.8262, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:46:38 Iters: 335300/[08], loss: 5.8368, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:48:41 Iters: 335400/[08], loss: 6.4885, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:50:45 Iters: 335500/[08], loss: 5.2968, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:52:48 Iters: 335600/[08], loss: 5.4155, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:54:51 Iters: 335700/[08], loss: 5.8658, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:56:54 Iters: 335800/[08], loss: 5.3011, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-13:58:57 Iters: 335900/[08], loss: 5.4635, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:01:01 Iters: 336000/[08], loss: 6.2850, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:03:04 Iters: 336100/[08], loss: 5.8216, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:05:07 Iters: 336200/[08], loss: 5.8295, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:07:10 Iters: 336300/[08], loss: 5.8099, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:09:13 Iters: 336400/[08], loss: 5.1743, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:11:17 Iters: 336500/[08], loss: 5.2287, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:13:20 Iters: 336600/[08], loss: 6.0322, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:15:23 Iters: 336700/[08], loss: 4.7795, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:17:26 Iters: 336800/[08], loss: 5.7362, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:19:30 Iters: 336900/[08], loss: 5.2314, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:21:33 Iters: 337000/[08], loss: 5.3424, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:23:36 Iters: 337100/[08], loss: 6.0244, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:25:39 Iters: 337200/[08], loss: 5.4916, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:27:42 Iters: 337300/[08], loss: 5.8241, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:29:45 Iters: 337400/[08], loss: 5.9748, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:31:49 Iters: 337500/[08], loss: 6.3299, train_accuracy: 0.1875, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:33:52 Iters: 337600/[08], loss: 5.5896, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:35:55 Iters: 337700/[08], loss: 5.3090, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:37:58 Iters: 337800/[08], loss: 5.3847, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:40:01 Iters: 337900/[08], loss: 5.5519, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:42:05 Iters: 338000/[08], loss: 5.7121, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:44:08 Iters: 338100/[08], loss: 5.6488, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:46:11 Iters: 338200/[08], loss: 5.0960, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:48:14 Iters: 338300/[08], loss: 5.2617, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:50:17 Iters: 338400/[08], loss: 5.7081, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:52:20 Iters: 338500/[08], loss: 5.5287, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:54:24 Iters: 338600/[08], loss: 5.9960, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:56:27 Iters: 338700/[08], loss: 6.1680, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-14:58:30 Iters: 338800/[08], loss: 5.5879, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:00:33 Iters: 338900/[08], loss: 6.5621, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:02:36 Iters: 339000/[08], loss: 5.8214, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:04:40 Iters: 339100/[08], loss: 5.9742, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:06:43 Iters: 339200/[08], loss: 5.8708, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:08:46 Iters: 339300/[08], loss: 5.6730, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:10:49 Iters: 339400/[08], loss: 5.9025, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:12:53 Iters: 339500/[08], loss: 5.5814, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:14:56 Iters: 339600/[08], loss: 5.6483, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:16:59 Iters: 339700/[08], loss: 5.5739, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:19:02 Iters: 339800/[08], loss: 6.7208, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:21:06 Iters: 339900/[08], loss: 5.6405, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:23:09 Iters: 340000/[08], loss: 5.4617, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:23:09 Saving checkpoint: 340000
-20220705-15:24:26 LFW Ave Accuracy: 99.5500
-20220705-15:25:44 AgeDB-30 Ave Accuracy: 95.9167
-20220705-15:27:13 CFP-FP Ave Accuracy: 92.8714
-20220705-15:27:13 Current Best Accuracy: LFW: 99.5833 in iters: 320000, AgeDB-30: 96.2667 in iters: 330000 and CFP-FP: 93.3857 in iters: 320000
-20220705-15:29:15 Iters: 340100/[08], loss: 5.2485, train_accuracy: 0.2734, time: 3.66 s/iter, learning rate: 0.005000000000000001
-20220705-15:31:18 Iters: 340200/[08], loss: 6.1555, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:33:21 Iters: 340300/[08], loss: 6.0967, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:35:24 Iters: 340400/[08], loss: 5.5594, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:37:28 Iters: 340500/[08], loss: 4.8834, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:39:31 Iters: 340600/[08], loss: 5.3183, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:41:34 Iters: 340700/[08], loss: 5.4013, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:43:37 Iters: 340800/[08], loss: 5.3776, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:45:41 Iters: 340900/[08], loss: 5.9737, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:47:44 Iters: 341000/[08], loss: 4.9407, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:49:47 Iters: 341100/[08], loss: 5.2067, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:51:50 Iters: 341200/[08], loss: 4.8144, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:53:54 Iters: 341300/[08], loss: 5.5172, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:55:57 Iters: 341400/[08], loss: 6.5253, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-15:58:00 Iters: 341500/[08], loss: 5.9201, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:00:03 Iters: 341600/[08], loss: 5.5749, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:02:06 Iters: 341700/[08], loss: 5.4001, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:04:10 Iters: 341800/[08], loss: 6.2446, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:06:13 Iters: 341900/[08], loss: 5.8224, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:08:16 Iters: 342000/[08], loss: 5.6895, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:10:19 Iters: 342100/[08], loss: 5.3054, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:12:22 Iters: 342200/[08], loss: 4.3802, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:14:26 Iters: 342300/[08], loss: 5.7602, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:16:29 Iters: 342400/[08], loss: 5.7058, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:18:32 Iters: 342500/[08], loss: 4.7485, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:20:35 Iters: 342600/[08], loss: 6.1698, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:22:38 Iters: 342700/[08], loss: 5.6875, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:24:41 Iters: 342800/[08], loss: 6.0148, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:26:44 Iters: 342900/[08], loss: 6.2714, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:28:48 Iters: 343000/[08], loss: 5.7517, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:30:51 Iters: 343100/[08], loss: 6.2036, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:32:54 Iters: 343200/[08], loss: 5.6248, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:34:57 Iters: 343300/[08], loss: 5.9410, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:37:00 Iters: 343400/[08], loss: 6.2799, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:39:03 Iters: 343500/[08], loss: 6.0669, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:41:07 Iters: 343600/[08], loss: 5.7512, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:43:10 Iters: 343700/[08], loss: 6.1078, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:45:13 Iters: 343800/[08], loss: 5.6110, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:47:16 Iters: 343900/[08], loss: 5.8476, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:49:19 Iters: 344000/[08], loss: 5.6396, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:51:23 Iters: 344100/[08], loss: 4.8942, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:53:26 Iters: 344200/[08], loss: 6.5993, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:55:29 Iters: 344300/[08], loss: 5.2176, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:57:32 Iters: 344400/[08], loss: 5.9365, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-16:59:35 Iters: 344500/[08], loss: 5.6368, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:01:38 Iters: 344600/[08], loss: 5.4106, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:03:42 Iters: 344700/[08], loss: 6.4568, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:05:45 Iters: 344800/[08], loss: 5.5561, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:07:48 Iters: 344900/[08], loss: 5.5070, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:09:51 Iters: 345000/[08], loss: 5.5330, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:11:54 Iters: 345100/[08], loss: 5.4934, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:13:57 Iters: 345200/[08], loss: 6.1354, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:16:01 Iters: 345300/[08], loss: 5.8719, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:18:04 Iters: 345400/[08], loss: 6.1663, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:20:07 Iters: 345500/[08], loss: 5.7809, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:22:10 Iters: 345600/[08], loss: 6.2549, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:24:13 Iters: 345700/[08], loss: 5.0706, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:26:17 Iters: 345800/[08], loss: 5.1815, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:28:20 Iters: 345900/[08], loss: 5.3765, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:30:23 Iters: 346000/[08], loss: 5.9572, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:32:26 Iters: 346100/[08], loss: 6.9255, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:34:29 Iters: 346200/[08], loss: 6.0093, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:36:32 Iters: 346300/[08], loss: 5.3481, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:38:36 Iters: 346400/[08], loss: 5.4372, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:40:39 Iters: 346500/[08], loss: 6.0215, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:42:42 Iters: 346600/[08], loss: 5.6935, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:44:45 Iters: 346700/[08], loss: 5.7609, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:46:48 Iters: 346800/[08], loss: 5.7045, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:48:52 Iters: 346900/[08], loss: 6.0318, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:50:55 Iters: 347000/[08], loss: 5.6391, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:52:58 Iters: 347100/[08], loss: 5.1922, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:55:01 Iters: 347200/[08], loss: 4.9411, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:57:05 Iters: 347300/[08], loss: 5.7393, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-17:59:08 Iters: 347400/[08], loss: 5.9031, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:01:11 Iters: 347500/[08], loss: 5.7235, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:03:14 Iters: 347600/[08], loss: 4.7333, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:05:17 Iters: 347700/[08], loss: 6.5719, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:07:20 Iters: 347800/[08], loss: 5.8202, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:09:24 Iters: 347900/[08], loss: 5.7699, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:11:27 Iters: 348000/[08], loss: 5.4839, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:13:30 Iters: 348100/[08], loss: 5.7827, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:15:33 Iters: 348200/[08], loss: 5.7703, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:17:37 Iters: 348300/[08], loss: 5.1382, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:19:40 Iters: 348400/[08], loss: 6.4002, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:21:43 Iters: 348500/[08], loss: 5.3350, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:23:46 Iters: 348600/[08], loss: 5.3596, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:25:49 Iters: 348700/[08], loss: 4.6905, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:27:53 Iters: 348800/[08], loss: 5.8956, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:29:56 Iters: 348900/[08], loss: 5.8398, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:31:59 Iters: 349000/[08], loss: 5.3044, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:34:02 Iters: 349100/[08], loss: 5.6768, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:36:06 Iters: 349200/[08], loss: 5.9040, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:38:09 Iters: 349300/[08], loss: 5.4913, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:40:12 Iters: 349400/[08], loss: 5.0379, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:42:15 Iters: 349500/[08], loss: 6.1899, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:44:19 Iters: 349600/[08], loss: 5.2141, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:46:22 Iters: 349700/[08], loss: 5.9968, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:48:25 Iters: 349800/[08], loss: 5.4071, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:50:28 Iters: 349900/[08], loss: 6.0098, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:52:32 Iters: 350000/[08], loss: 5.2264, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-18:52:32 Saving checkpoint: 350000
-20220705-18:53:48 LFW Ave Accuracy: 99.5333
-20220705-18:55:05 AgeDB-30 Ave Accuracy: 96.1167
-20220705-18:56:33 CFP-FP Ave Accuracy: 93.0143
-20220705-18:56:33 Current Best Accuracy: LFW: 99.5833 in iters: 320000, AgeDB-30: 96.2667 in iters: 330000 and CFP-FP: 93.3857 in iters: 320000
-20220705-18:58:36 Iters: 350100/[08], loss: 5.4128, train_accuracy: 0.3125, time: 3.64 s/iter, learning rate: 0.005000000000000001
-20220705-19:00:38 Iters: 350200/[08], loss: 5.3944, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:02:42 Iters: 350300/[08], loss: 5.1411, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:04:45 Iters: 350400/[08], loss: 4.9022, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:06:48 Iters: 350500/[08], loss: 5.1051, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:08:51 Iters: 350600/[08], loss: 5.8490, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:10:54 Iters: 350700/[08], loss: 5.0304, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:12:58 Iters: 350800/[08], loss: 5.6046, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:15:01 Iters: 350900/[08], loss: 5.3511, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:17:04 Iters: 351000/[08], loss: 5.4609, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:19:07 Iters: 351100/[08], loss: 5.2712, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:21:10 Iters: 351200/[08], loss: 5.9317, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:23:13 Iters: 351300/[08], loss: 6.5403, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:25:16 Iters: 351400/[08], loss: 5.6576, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:27:19 Iters: 351500/[08], loss: 5.3584, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:29:22 Iters: 351600/[08], loss: 6.5057, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:31:25 Iters: 351700/[08], loss: 6.2078, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:33:29 Iters: 351800/[08], loss: 5.9885, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:35:32 Iters: 351900/[08], loss: 5.4294, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:37:35 Iters: 352000/[08], loss: 5.9834, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:39:38 Iters: 352100/[08], loss: 5.4854, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:41:41 Iters: 352200/[08], loss: 5.3563, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:43:44 Iters: 352300/[08], loss: 5.5045, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:45:47 Iters: 352400/[08], loss: 4.8032, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:47:51 Iters: 352500/[08], loss: 5.1647, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:49:54 Iters: 352600/[08], loss: 5.7201, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:51:57 Iters: 352700/[08], loss: 6.3415, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:54:00 Iters: 352800/[08], loss: 5.9396, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:56:03 Iters: 352900/[08], loss: 5.2362, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-19:58:06 Iters: 353000/[08], loss: 5.4851, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:00:09 Iters: 353100/[08], loss: 6.3634, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:02:13 Iters: 353200/[08], loss: 5.7763, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:04:16 Iters: 353300/[08], loss: 5.2862, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:06:19 Iters: 353400/[08], loss: 5.4389, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:08:22 Iters: 353500/[08], loss: 5.6718, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:10:25 Iters: 353600/[08], loss: 5.5531, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:12:29 Iters: 353700/[08], loss: 6.4928, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:14:32 Iters: 353800/[08], loss: 5.5778, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:16:35 Iters: 353900/[08], loss: 5.5280, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:18:38 Iters: 354000/[08], loss: 5.9992, train_accuracy: 0.1797, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:20:42 Iters: 354100/[08], loss: 5.5009, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:22:45 Iters: 354200/[08], loss: 5.6457, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:24:48 Iters: 354300/[08], loss: 5.1617, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:26:51 Iters: 354400/[08], loss: 5.6291, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:28:54 Iters: 354500/[08], loss: 5.8491, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:30:58 Iters: 354600/[08], loss: 5.1548, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:33:01 Iters: 354700/[08], loss: 6.0710, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:35:04 Iters: 354800/[08], loss: 5.6286, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:37:07 Iters: 354900/[08], loss: 5.6685, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:39:10 Iters: 355000/[08], loss: 5.5338, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:41:13 Iters: 355100/[08], loss: 5.8115, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:43:16 Iters: 355200/[08], loss: 5.2790, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:45:20 Iters: 355300/[08], loss: 5.8634, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:47:23 Iters: 355400/[08], loss: 5.1946, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:49:26 Iters: 355500/[08], loss: 5.2435, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:51:29 Iters: 355600/[08], loss: 5.1769, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:53:32 Iters: 355700/[08], loss: 5.9697, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:55:36 Iters: 355800/[08], loss: 5.0524, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:57:39 Iters: 355900/[08], loss: 5.9471, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-20:59:42 Iters: 356000/[08], loss: 6.0761, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:01:45 Iters: 356100/[08], loss: 6.0476, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:03:48 Iters: 356200/[08], loss: 5.6010, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:05:52 Iters: 356300/[08], loss: 5.1205, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:07:55 Iters: 356400/[08], loss: 6.1809, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:09:58 Iters: 356500/[08], loss: 6.1093, train_accuracy: 0.1953, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:12:01 Iters: 356600/[08], loss: 5.5835, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:14:05 Iters: 356700/[08], loss: 5.0695, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:16:08 Iters: 356800/[08], loss: 5.7272, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:18:11 Iters: 356900/[08], loss: 5.6380, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:20:14 Iters: 357000/[08], loss: 6.1594, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:22:17 Iters: 357100/[08], loss: 5.5650, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:24:20 Iters: 357200/[08], loss: 6.0157, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:26:24 Iters: 357300/[08], loss: 5.0878, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:28:27 Iters: 357400/[08], loss: 6.5622, train_accuracy: 0.1875, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:30:30 Iters: 357500/[08], loss: 5.4687, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:32:33 Iters: 357600/[08], loss: 5.9121, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:34:36 Iters: 357700/[08], loss: 5.5991, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:36:39 Iters: 357800/[08], loss: 6.0818, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:38:43 Iters: 357900/[08], loss: 5.2792, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:40:46 Iters: 358000/[08], loss: 5.8927, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:42:49 Iters: 358100/[08], loss: 6.3831, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:44:52 Iters: 358200/[08], loss: 5.4663, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:46:55 Iters: 358300/[08], loss: 5.5644, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:48:58 Iters: 358400/[08], loss: 6.3464, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:51:01 Iters: 358500/[08], loss: 5.8439, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:53:05 Iters: 358600/[08], loss: 5.5783, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:55:08 Iters: 358700/[08], loss: 6.0940, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:57:11 Iters: 358800/[08], loss: 5.8302, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-21:59:14 Iters: 358900/[08], loss: 5.5808, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:01:17 Iters: 359000/[08], loss: 6.3718, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:03:21 Iters: 359100/[08], loss: 5.2378, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:05:24 Iters: 359200/[08], loss: 5.5281, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:07:27 Iters: 359300/[08], loss: 5.8943, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:09:30 Iters: 359400/[08], loss: 5.9385, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:11:33 Iters: 359500/[08], loss: 5.1174, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:13:36 Iters: 359600/[08], loss: 6.1668, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:15:40 Iters: 359700/[08], loss: 5.7863, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:17:43 Iters: 359800/[08], loss: 5.5558, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:19:46 Iters: 359900/[08], loss: 5.9442, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:21:49 Iters: 360000/[08], loss: 5.9789, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:21:49 Saving checkpoint: 360000
-20220705-22:23:07 LFW Ave Accuracy: 99.4999
-20220705-22:24:24 AgeDB-30 Ave Accuracy: 96.1500
-20220705-22:25:55 CFP-FP Ave Accuracy: 93.1714
-20220705-22:25:55 Current Best Accuracy: LFW: 99.5833 in iters: 320000, AgeDB-30: 96.2667 in iters: 330000 and CFP-FP: 93.3857 in iters: 320000
-20220705-22:27:57 Iters: 360100/[08], loss: 5.7161, train_accuracy: 0.2734, time: 3.68 s/iter, learning rate: 0.005000000000000001
-20220705-22:30:00 Iters: 360200/[08], loss: 5.9010, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:32:03 Iters: 360300/[08], loss: 5.2533, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:34:07 Iters: 360400/[08], loss: 5.4014, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:36:10 Iters: 360500/[08], loss: 6.6322, train_accuracy: 0.1953, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:38:13 Iters: 360600/[08], loss: 4.9004, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:40:16 Iters: 360700/[08], loss: 5.3414, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:42:19 Iters: 360800/[08], loss: 5.7578, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:44:23 Iters: 360900/[08], loss: 5.3305, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:46:26 Iters: 361000/[08], loss: 5.5385, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:48:29 Iters: 361100/[08], loss: 4.9628, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:50:32 Iters: 361200/[08], loss: 5.3952, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:52:35 Iters: 361300/[08], loss: 6.0581, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:54:38 Iters: 361400/[08], loss: 5.3970, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:56:41 Iters: 361500/[08], loss: 5.8235, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-22:58:45 Iters: 361600/[08], loss: 5.8126, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:00:48 Iters: 361700/[08], loss: 5.1856, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:02:51 Iters: 361800/[08], loss: 6.0530, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:04:54 Iters: 361900/[08], loss: 6.8844, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:06:57 Iters: 362000/[08], loss: 5.3755, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:09:01 Iters: 362100/[08], loss: 5.5220, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:11:04 Iters: 362200/[08], loss: 5.2999, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:13:07 Iters: 362300/[08], loss: 4.9026, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:15:10 Iters: 362400/[08], loss: 5.1850, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:17:13 Iters: 362500/[08], loss: 5.4589, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:19:17 Iters: 362600/[08], loss: 6.2434, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:21:20 Iters: 362700/[08], loss: 5.5240, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:23:23 Iters: 362800/[08], loss: 5.3399, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:25:26 Iters: 362900/[08], loss: 6.2796, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:27:29 Iters: 363000/[08], loss: 5.8222, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:29:32 Iters: 363100/[08], loss: 5.9896, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:31:35 Iters: 363200/[08], loss: 5.2122, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:33:39 Iters: 363300/[08], loss: 6.2415, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:35:42 Iters: 363400/[08], loss: 6.1433, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:37:45 Iters: 363500/[08], loss: 5.9765, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:39:48 Iters: 363600/[08], loss: 5.7516, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:41:52 Iters: 363700/[08], loss: 5.6646, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:43:55 Iters: 363800/[08], loss: 5.1021, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:45:58 Iters: 363900/[08], loss: 5.9853, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:46:22 Train Epoch: 9/18 ...
-20220705-23:48:01 Iters: 364000/[09], loss: 5.3878, train_accuracy: 0.3438, time: 0.99 s/iter, learning rate: 0.005000000000000001
-20220705-23:50:04 Iters: 364100/[09], loss: 6.0858, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:52:07 Iters: 364200/[09], loss: 5.4394, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:54:11 Iters: 364300/[09], loss: 5.9887, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:56:14 Iters: 364400/[09], loss: 5.4018, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220705-23:58:17 Iters: 364500/[09], loss: 5.5206, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:00:20 Iters: 364600/[09], loss: 5.8112, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:02:24 Iters: 364700/[09], loss: 5.8508, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:04:27 Iters: 364800/[09], loss: 5.0663, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:06:30 Iters: 364900/[09], loss: 5.8400, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:08:33 Iters: 365000/[09], loss: 6.4495, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:10:37 Iters: 365100/[09], loss: 5.0111, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:12:40 Iters: 365200/[09], loss: 4.8578, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:14:43 Iters: 365300/[09], loss: 5.1111, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:16:46 Iters: 365400/[09], loss: 4.8892, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:18:49 Iters: 365500/[09], loss: 5.3958, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:20:52 Iters: 365600/[09], loss: 5.8554, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:22:55 Iters: 365700/[09], loss: 5.7284, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:24:58 Iters: 365800/[09], loss: 5.4037, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:27:02 Iters: 365900/[09], loss: 4.7398, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:29:05 Iters: 366000/[09], loss: 5.6530, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:31:08 Iters: 366100/[09], loss: 5.6798, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:33:11 Iters: 366200/[09], loss: 5.5187, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:35:14 Iters: 366300/[09], loss: 4.9246, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:37:17 Iters: 366400/[09], loss: 5.6243, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:39:20 Iters: 366500/[09], loss: 5.2840, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:41:24 Iters: 366600/[09], loss: 6.2190, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:43:27 Iters: 366700/[09], loss: 5.6290, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:45:30 Iters: 366800/[09], loss: 6.0767, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:47:33 Iters: 366900/[09], loss: 5.1727, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:49:36 Iters: 367000/[09], loss: 5.8072, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:51:40 Iters: 367100/[09], loss: 5.9060, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:53:43 Iters: 367200/[09], loss: 5.8992, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:55:46 Iters: 367300/[09], loss: 5.6870, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:57:49 Iters: 367400/[09], loss: 5.7402, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-00:59:53 Iters: 367500/[09], loss: 4.9574, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:01:56 Iters: 367600/[09], loss: 5.3231, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:03:59 Iters: 367700/[09], loss: 5.4855, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:06:02 Iters: 367800/[09], loss: 5.4419, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:08:06 Iters: 367900/[09], loss: 5.0824, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:10:09 Iters: 368000/[09], loss: 5.6559, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:12:12 Iters: 368100/[09], loss: 5.4995, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:14:15 Iters: 368200/[09], loss: 5.1258, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:16:19 Iters: 368300/[09], loss: 5.2415, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:18:22 Iters: 368400/[09], loss: 5.4460, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:20:25 Iters: 368500/[09], loss: 5.8529, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:22:28 Iters: 368600/[09], loss: 5.1528, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:24:31 Iters: 368700/[09], loss: 6.3570, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:26:34 Iters: 368800/[09], loss: 5.2049, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:28:38 Iters: 368900/[09], loss: 5.4845, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:30:41 Iters: 369000/[09], loss: 5.5370, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:32:44 Iters: 369100/[09], loss: 5.9275, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:34:47 Iters: 369200/[09], loss: 5.4896, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:36:50 Iters: 369300/[09], loss: 5.3293, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:38:54 Iters: 369400/[09], loss: 5.2504, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:40:57 Iters: 369500/[09], loss: 5.4324, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:43:00 Iters: 369600/[09], loss: 4.8639, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:45:04 Iters: 369700/[09], loss: 5.8234, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:47:07 Iters: 369800/[09], loss: 5.0756, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:49:10 Iters: 369900/[09], loss: 6.0250, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:51:13 Iters: 370000/[09], loss: 4.9973, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-01:51:13 Saving checkpoint: 370000
-20220706-01:52:30 LFW Ave Accuracy: 99.4999
-20220706-01:53:45 AgeDB-30 Ave Accuracy: 96.2167
-20220706-01:55:11 CFP-FP Ave Accuracy: 93.6143
-20220706-01:55:11 Current Best Accuracy: LFW: 99.5833 in iters: 320000, AgeDB-30: 96.2667 in iters: 330000 and CFP-FP: 93.6143 in iters: 370000
-20220706-01:57:13 Iters: 370100/[09], loss: 5.1610, train_accuracy: 0.3047, time: 3.60 s/iter, learning rate: 0.005000000000000001
-20220706-01:59:17 Iters: 370200/[09], loss: 5.6226, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:01:20 Iters: 370300/[09], loss: 5.7924, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:03:23 Iters: 370400/[09], loss: 5.2430, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:05:26 Iters: 370500/[09], loss: 5.0514, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:07:29 Iters: 370600/[09], loss: 5.1062, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:09:33 Iters: 370700/[09], loss: 5.5461, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:11:36 Iters: 370800/[09], loss: 6.0509, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:13:39 Iters: 370900/[09], loss: 5.2304, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:15:42 Iters: 371000/[09], loss: 4.9531, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:17:45 Iters: 371100/[09], loss: 5.3987, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:19:48 Iters: 371200/[09], loss: 6.4060, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:21:51 Iters: 371300/[09], loss: 5.7573, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:23:54 Iters: 371400/[09], loss: 5.5984, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:25:58 Iters: 371500/[09], loss: 6.2392, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:28:01 Iters: 371600/[09], loss: 5.2794, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:30:04 Iters: 371700/[09], loss: 5.4631, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:32:07 Iters: 371800/[09], loss: 5.4451, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:34:10 Iters: 371900/[09], loss: 5.1861, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:36:13 Iters: 372000/[09], loss: 4.9104, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:38:17 Iters: 372100/[09], loss: 5.6305, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:40:20 Iters: 372200/[09], loss: 4.8279, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:42:23 Iters: 372300/[09], loss: 6.0898, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:44:26 Iters: 372400/[09], loss: 5.6919, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:46:29 Iters: 372500/[09], loss: 4.6022, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:48:32 Iters: 372600/[09], loss: 5.0385, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:50:35 Iters: 372700/[09], loss: 5.4740, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:52:39 Iters: 372800/[09], loss: 5.0690, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:54:42 Iters: 372900/[09], loss: 5.3041, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:56:45 Iters: 373000/[09], loss: 5.7740, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-02:58:48 Iters: 373100/[09], loss: 5.5794, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:00:51 Iters: 373200/[09], loss: 5.4324, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:02:54 Iters: 373300/[09], loss: 6.0654, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:04:58 Iters: 373400/[09], loss: 5.9893, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:07:01 Iters: 373500/[09], loss: 6.5634, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:09:04 Iters: 373600/[09], loss: 5.7654, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:11:07 Iters: 373700/[09], loss: 5.2221, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:13:10 Iters: 373800/[09], loss: 5.0700, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:15:14 Iters: 373900/[09], loss: 5.4259, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:17:17 Iters: 374000/[09], loss: 5.3865, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:19:20 Iters: 374100/[09], loss: 5.4198, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:21:23 Iters: 374200/[09], loss: 5.5920, train_accuracy: 0.1953, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:23:26 Iters: 374300/[09], loss: 5.3982, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:25:30 Iters: 374400/[09], loss: 4.9142, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:27:33 Iters: 374500/[09], loss: 5.5310, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:29:36 Iters: 374600/[09], loss: 5.3781, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:31:39 Iters: 374700/[09], loss: 5.1936, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:33:42 Iters: 374800/[09], loss: 5.5113, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:35:46 Iters: 374900/[09], loss: 5.6649, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:37:49 Iters: 375000/[09], loss: 5.3374, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:39:52 Iters: 375100/[09], loss: 5.1669, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:41:55 Iters: 375200/[09], loss: 5.6905, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:43:58 Iters: 375300/[09], loss: 5.4615, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:46:01 Iters: 375400/[09], loss: 5.1740, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:48:05 Iters: 375500/[09], loss: 5.0329, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:50:08 Iters: 375600/[09], loss: 5.3671, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:52:11 Iters: 375700/[09], loss: 5.2969, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:54:14 Iters: 375800/[09], loss: 5.2838, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:56:18 Iters: 375900/[09], loss: 4.9007, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-03:58:21 Iters: 376000/[09], loss: 5.0295, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:00:24 Iters: 376100/[09], loss: 6.2503, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:02:27 Iters: 376200/[09], loss: 6.0378, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:04:30 Iters: 376300/[09], loss: 5.1824, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:06:34 Iters: 376400/[09], loss: 4.5748, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:08:37 Iters: 376500/[09], loss: 5.3077, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:10:40 Iters: 376600/[09], loss: 4.4963, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:12:43 Iters: 376700/[09], loss: 5.4865, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:14:46 Iters: 376800/[09], loss: 5.7666, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:16:49 Iters: 376900/[09], loss: 5.4595, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:18:53 Iters: 377000/[09], loss: 5.4804, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:20:56 Iters: 377100/[09], loss: 5.9357, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:22:59 Iters: 377200/[09], loss: 5.9569, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:25:02 Iters: 377300/[09], loss: 5.3769, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:27:05 Iters: 377400/[09], loss: 5.9495, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:29:09 Iters: 377500/[09], loss: 5.7603, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:31:12 Iters: 377600/[09], loss: 5.6515, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:33:15 Iters: 377700/[09], loss: 5.0209, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:35:18 Iters: 377800/[09], loss: 5.4417, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:37:21 Iters: 377900/[09], loss: 5.0504, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:39:25 Iters: 378000/[09], loss: 5.4888, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:41:28 Iters: 378100/[09], loss: 5.4610, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:43:31 Iters: 378200/[09], loss: 5.6702, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:45:34 Iters: 378300/[09], loss: 6.1643, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:47:38 Iters: 378400/[09], loss: 5.5415, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:49:41 Iters: 378500/[09], loss: 5.2088, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:51:44 Iters: 378600/[09], loss: 6.1357, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:53:47 Iters: 378700/[09], loss: 5.6721, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:55:50 Iters: 378800/[09], loss: 5.5272, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:57:54 Iters: 378900/[09], loss: 5.1137, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-04:59:57 Iters: 379000/[09], loss: 5.8070, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:02:00 Iters: 379100/[09], loss: 5.9044, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:04:03 Iters: 379200/[09], loss: 5.9736, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:06:07 Iters: 379300/[09], loss: 5.3494, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:08:10 Iters: 379400/[09], loss: 5.2438, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:10:13 Iters: 379500/[09], loss: 5.3047, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:12:16 Iters: 379600/[09], loss: 5.8294, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:14:19 Iters: 379700/[09], loss: 4.7346, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:16:23 Iters: 379800/[09], loss: 6.0829, train_accuracy: 0.1953, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:18:26 Iters: 379900/[09], loss: 5.0514, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:20:29 Iters: 380000/[09], loss: 5.5363, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:20:29 Saving checkpoint: 380000
-20220706-05:21:46 LFW Ave Accuracy: 99.5666
-20220706-05:23:00 AgeDB-30 Ave Accuracy: 95.8000
-20220706-05:24:27 CFP-FP Ave Accuracy: 92.8429
-20220706-05:24:27 Current Best Accuracy: LFW: 99.5833 in iters: 320000, AgeDB-30: 96.2667 in iters: 330000 and CFP-FP: 93.6143 in iters: 370000
-20220706-05:26:29 Iters: 380100/[09], loss: 5.5461, train_accuracy: 0.2812, time: 3.60 s/iter, learning rate: 0.005000000000000001
-20220706-05:28:32 Iters: 380200/[09], loss: 5.3157, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:30:36 Iters: 380300/[09], loss: 6.1228, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:32:39 Iters: 380400/[09], loss: 4.7346, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:34:42 Iters: 380500/[09], loss: 5.1990, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:36:45 Iters: 380600/[09], loss: 6.2369, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:38:48 Iters: 380700/[09], loss: 5.5240, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:40:51 Iters: 380800/[09], loss: 5.9173, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:42:55 Iters: 380900/[09], loss: 5.7012, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:44:58 Iters: 381000/[09], loss: 4.6113, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:47:01 Iters: 381100/[09], loss: 5.0876, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:49:04 Iters: 381200/[09], loss: 5.8714, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:51:07 Iters: 381300/[09], loss: 5.0467, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:53:10 Iters: 381400/[09], loss: 5.9395, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:55:14 Iters: 381500/[09], loss: 5.7660, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:57:17 Iters: 381600/[09], loss: 5.9441, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-05:59:20 Iters: 381700/[09], loss: 5.8843, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:01:23 Iters: 381800/[09], loss: 5.6359, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:03:26 Iters: 381900/[09], loss: 5.6155, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:05:29 Iters: 382000/[09], loss: 5.7245, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:07:33 Iters: 382100/[09], loss: 5.7366, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:09:36 Iters: 382200/[09], loss: 5.0672, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:11:39 Iters: 382300/[09], loss: 6.6831, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:13:42 Iters: 382400/[09], loss: 5.3907, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:15:45 Iters: 382500/[09], loss: 5.6297, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:17:49 Iters: 382600/[09], loss: 4.9307, train_accuracy: 0.3984, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:19:52 Iters: 382700/[09], loss: 6.3028, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:21:55 Iters: 382800/[09], loss: 5.5202, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:23:58 Iters: 382900/[09], loss: 5.4413, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:26:02 Iters: 383000/[09], loss: 5.2240, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:28:05 Iters: 383100/[09], loss: 5.8372, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:30:08 Iters: 383200/[09], loss: 5.3441, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:32:11 Iters: 383300/[09], loss: 5.2597, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:34:14 Iters: 383400/[09], loss: 4.7769, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:36:17 Iters: 383500/[09], loss: 4.7604, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:38:21 Iters: 383600/[09], loss: 5.0482, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:40:24 Iters: 383700/[09], loss: 5.7439, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:42:27 Iters: 383800/[09], loss: 5.6678, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:44:30 Iters: 383900/[09], loss: 4.9006, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:46:34 Iters: 384000/[09], loss: 5.6006, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:48:37 Iters: 384100/[09], loss: 6.0373, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:50:40 Iters: 384200/[09], loss: 5.7099, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:52:43 Iters: 384300/[09], loss: 6.0402, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:54:46 Iters: 384400/[09], loss: 5.6447, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:56:50 Iters: 384500/[09], loss: 5.8030, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-06:58:53 Iters: 384600/[09], loss: 6.5465, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:00:56 Iters: 384700/[09], loss: 5.1845, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:02:59 Iters: 384800/[09], loss: 5.9507, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:05:02 Iters: 384900/[09], loss: 6.4566, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:07:06 Iters: 385000/[09], loss: 5.3872, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:09:09 Iters: 385100/[09], loss: 6.3577, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:11:12 Iters: 385200/[09], loss: 5.9235, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:13:15 Iters: 385300/[09], loss: 6.1113, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:15:18 Iters: 385400/[09], loss: 5.7976, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:17:22 Iters: 385500/[09], loss: 5.7039, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:19:25 Iters: 385600/[09], loss: 4.9807, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:21:28 Iters: 385700/[09], loss: 5.5663, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:23:31 Iters: 385800/[09], loss: 6.0396, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:25:34 Iters: 385900/[09], loss: 5.1492, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:27:37 Iters: 386000/[09], loss: 5.1488, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:29:41 Iters: 386100/[09], loss: 5.8462, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:31:44 Iters: 386200/[09], loss: 5.8285, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:33:47 Iters: 386300/[09], loss: 5.2224, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:35:50 Iters: 386400/[09], loss: 5.5364, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:37:53 Iters: 386500/[09], loss: 5.4030, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:39:57 Iters: 386600/[09], loss: 5.5617, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:42:00 Iters: 386700/[09], loss: 4.4395, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:44:03 Iters: 386800/[09], loss: 4.8868, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:46:06 Iters: 386900/[09], loss: 5.2201, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:48:10 Iters: 387000/[09], loss: 5.4833, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:50:13 Iters: 387100/[09], loss: 5.3037, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:52:16 Iters: 387200/[09], loss: 5.2143, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:54:19 Iters: 387300/[09], loss: 4.9557, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:56:23 Iters: 387400/[09], loss: 6.0676, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-07:58:26 Iters: 387500/[09], loss: 5.4370, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:00:29 Iters: 387600/[09], loss: 5.2299, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:02:33 Iters: 387700/[09], loss: 6.3662, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:04:36 Iters: 387800/[09], loss: 4.9827, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:06:39 Iters: 387900/[09], loss: 5.2082, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:08:42 Iters: 388000/[09], loss: 6.0331, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:10:46 Iters: 388100/[09], loss: 4.9085, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:12:49 Iters: 388200/[09], loss: 6.2396, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:14:52 Iters: 388300/[09], loss: 6.0634, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:16:55 Iters: 388400/[09], loss: 4.4336, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:18:59 Iters: 388500/[09], loss: 4.7939, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:21:02 Iters: 388600/[09], loss: 5.6146, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:23:05 Iters: 388700/[09], loss: 5.5134, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:25:08 Iters: 388800/[09], loss: 5.6059, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:27:12 Iters: 388900/[09], loss: 4.6878, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:29:15 Iters: 389000/[09], loss: 5.0280, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:31:18 Iters: 389100/[09], loss: 5.5525, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:33:21 Iters: 389200/[09], loss: 5.6883, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:35:25 Iters: 389300/[09], loss: 4.7830, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:37:28 Iters: 389400/[09], loss: 5.3716, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:39:31 Iters: 389500/[09], loss: 5.9292, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:41:34 Iters: 389600/[09], loss: 5.9472, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:43:38 Iters: 389700/[09], loss: 5.1529, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:45:41 Iters: 389800/[09], loss: 5.6929, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:47:44 Iters: 389900/[09], loss: 5.6565, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:49:47 Iters: 390000/[09], loss: 5.8521, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:49:47 Saving checkpoint: 390000
-20220706-08:51:05 LFW Ave Accuracy: 99.5333
-20220706-08:52:21 AgeDB-30 Ave Accuracy: 96.2833
-20220706-08:53:49 CFP-FP Ave Accuracy: 93.3286
-20220706-08:53:49 Current Best Accuracy: LFW: 99.5833 in iters: 320000, AgeDB-30: 96.2833 in iters: 390000 and CFP-FP: 93.6143 in iters: 370000
-20220706-08:55:52 Iters: 390100/[09], loss: 5.3900, train_accuracy: 0.2656, time: 3.65 s/iter, learning rate: 0.005000000000000001
-20220706-08:57:55 Iters: 390200/[09], loss: 5.6028, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-08:59:58 Iters: 390300/[09], loss: 5.3489, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:02:01 Iters: 390400/[09], loss: 5.3955, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:04:05 Iters: 390500/[09], loss: 5.7464, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:06:08 Iters: 390600/[09], loss: 5.4202, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:08:11 Iters: 390700/[09], loss: 5.9472, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:10:14 Iters: 390800/[09], loss: 6.1688, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:12:18 Iters: 390900/[09], loss: 4.6467, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:14:21 Iters: 391000/[09], loss: 5.1382, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:16:24 Iters: 391100/[09], loss: 5.6043, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:18:27 Iters: 391200/[09], loss: 6.0890, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:20:30 Iters: 391300/[09], loss: 6.0159, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:22:34 Iters: 391400/[09], loss: 5.9748, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:24:37 Iters: 391500/[09], loss: 5.8086, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:26:40 Iters: 391600/[09], loss: 5.5393, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:28:43 Iters: 391700/[09], loss: 6.4114, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:30:46 Iters: 391800/[09], loss: 5.9544, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:32:49 Iters: 391900/[09], loss: 4.8079, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:34:53 Iters: 392000/[09], loss: 5.2847, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:36:56 Iters: 392100/[09], loss: 4.6861, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:38:59 Iters: 392200/[09], loss: 6.3373, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:41:02 Iters: 392300/[09], loss: 5.2092, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:43:05 Iters: 392400/[09], loss: 5.7775, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:45:08 Iters: 392500/[09], loss: 5.3382, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:47:12 Iters: 392600/[09], loss: 6.7480, train_accuracy: 0.1719, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:49:15 Iters: 392700/[09], loss: 5.4466, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:51:18 Iters: 392800/[09], loss: 5.0313, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:53:22 Iters: 392900/[09], loss: 5.9723, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:55:25 Iters: 393000/[09], loss: 5.0451, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:57:28 Iters: 393100/[09], loss: 4.9328, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-09:59:31 Iters: 393200/[09], loss: 5.8370, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:01:35 Iters: 393300/[09], loss: 5.8118, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:03:38 Iters: 393400/[09], loss: 5.3345, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:05:41 Iters: 393500/[09], loss: 5.6872, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:07:44 Iters: 393600/[09], loss: 5.6676, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:09:48 Iters: 393700/[09], loss: 4.8659, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:11:51 Iters: 393800/[09], loss: 5.4693, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:13:54 Iters: 393900/[09], loss: 5.6317, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:15:57 Iters: 394000/[09], loss: 5.5896, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:18:01 Iters: 394100/[09], loss: 5.7193, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:20:04 Iters: 394200/[09], loss: 5.2700, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:22:07 Iters: 394300/[09], loss: 5.6511, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:24:10 Iters: 394400/[09], loss: 5.3646, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:26:14 Iters: 394500/[09], loss: 5.4832, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:28:17 Iters: 394600/[09], loss: 5.4090, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:30:20 Iters: 394700/[09], loss: 5.5127, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:32:24 Iters: 394800/[09], loss: 5.4539, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:34:27 Iters: 394900/[09], loss: 6.2431, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:36:30 Iters: 395000/[09], loss: 4.9108, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:38:33 Iters: 395100/[09], loss: 5.6183, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:40:36 Iters: 395200/[09], loss: 6.2848, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:42:39 Iters: 395300/[09], loss: 6.5731, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:44:43 Iters: 395400/[09], loss: 4.8898, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:46:46 Iters: 395500/[09], loss: 5.1890, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:48:49 Iters: 395600/[09], loss: 6.0481, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:50:52 Iters: 395700/[09], loss: 5.6650, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:52:55 Iters: 395800/[09], loss: 5.0964, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:54:59 Iters: 395900/[09], loss: 5.4130, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:57:02 Iters: 396000/[09], loss: 5.9513, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-10:59:05 Iters: 396100/[09], loss: 4.9723, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:01:08 Iters: 396200/[09], loss: 5.3931, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:03:12 Iters: 396300/[09], loss: 5.3864, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:05:15 Iters: 396400/[09], loss: 6.4189, train_accuracy: 0.1875, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:07:18 Iters: 396500/[09], loss: 6.3723, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:09:21 Iters: 396600/[09], loss: 5.9646, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:11:24 Iters: 396700/[09], loss: 4.7178, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:13:28 Iters: 396800/[09], loss: 5.6849, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:15:31 Iters: 396900/[09], loss: 5.3160, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:17:34 Iters: 397000/[09], loss: 5.4296, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:19:37 Iters: 397100/[09], loss: 5.5044, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:21:41 Iters: 397200/[09], loss: 5.7890, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:23:44 Iters: 397300/[09], loss: 5.6795, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:25:47 Iters: 397400/[09], loss: 5.5738, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:27:50 Iters: 397500/[09], loss: 5.5643, train_accuracy: 0.2891, time: 1.24 s/iter, learning rate: 0.005000000000000001
-20220706-11:29:54 Iters: 397600/[09], loss: 5.5629, train_accuracy: 0.3047, time: 1.24 s/iter, learning rate: 0.005000000000000001
-20220706-11:31:58 Iters: 397700/[09], loss: 5.5784, train_accuracy: 0.2734, time: 1.24 s/iter, learning rate: 0.005000000000000001
-20220706-11:34:01 Iters: 397800/[09], loss: 5.2347, train_accuracy: 0.2812, time: 1.24 s/iter, learning rate: 0.005000000000000001
-20220706-11:36:05 Iters: 397900/[09], loss: 5.4074, train_accuracy: 0.2500, time: 1.24 s/iter, learning rate: 0.005000000000000001
-20220706-11:38:08 Iters: 398000/[09], loss: 5.0177, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:40:12 Iters: 398100/[09], loss: 5.6123, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:42:15 Iters: 398200/[09], loss: 6.0247, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:44:18 Iters: 398300/[09], loss: 5.2890, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:46:21 Iters: 398400/[09], loss: 5.2626, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:48:25 Iters: 398500/[09], loss: 6.1808, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:50:28 Iters: 398600/[09], loss: 5.6915, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:52:31 Iters: 398700/[09], loss: 6.1187, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:54:34 Iters: 398800/[09], loss: 4.8438, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:56:37 Iters: 398900/[09], loss: 5.1666, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-11:58:41 Iters: 399000/[09], loss: 5.9980, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:00:44 Iters: 399100/[09], loss: 5.5076, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:02:47 Iters: 399200/[09], loss: 6.5213, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:04:51 Iters: 399300/[09], loss: 5.4668, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:06:54 Iters: 399400/[09], loss: 5.6875, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:08:57 Iters: 399500/[09], loss: 5.7727, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:11:00 Iters: 399600/[09], loss: 5.6393, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:13:03 Iters: 399700/[09], loss: 5.8751, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:15:07 Iters: 399800/[09], loss: 5.3753, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:17:10 Iters: 399900/[09], loss: 5.7983, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:19:13 Iters: 400000/[09], loss: 5.2553, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:19:13 Saving checkpoint: 400000
-20220706-12:20:30 LFW Ave Accuracy: 99.6000
-20220706-12:21:46 AgeDB-30 Ave Accuracy: 96.5000
-20220706-12:23:15 CFP-FP Ave Accuracy: 93.3000
-20220706-12:23:15 Current Best Accuracy: LFW: 99.6000 in iters: 400000, AgeDB-30: 96.5000 in iters: 400000 and CFP-FP: 93.6143 in iters: 370000
-20220706-12:25:17 Iters: 400100/[09], loss: 5.1502, train_accuracy: 0.3125, time: 3.65 s/iter, learning rate: 0.005000000000000001
-20220706-12:27:21 Iters: 400200/[09], loss: 5.5620, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:29:24 Iters: 400300/[09], loss: 5.2319, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:31:27 Iters: 400400/[09], loss: 5.4844, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:33:30 Iters: 400500/[09], loss: 4.8917, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:35:34 Iters: 400600/[09], loss: 5.5218, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:37:37 Iters: 400700/[09], loss: 5.9868, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:39:40 Iters: 400800/[09], loss: 5.8604, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:41:43 Iters: 400900/[09], loss: 6.0762, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:43:47 Iters: 401000/[09], loss: 5.7878, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:45:50 Iters: 401100/[09], loss: 5.5151, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:47:53 Iters: 401200/[09], loss: 5.8962, train_accuracy: 0.1953, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:49:57 Iters: 401300/[09], loss: 5.5792, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:52:00 Iters: 401400/[09], loss: 5.2775, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:54:03 Iters: 401500/[09], loss: 5.3536, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:56:06 Iters: 401600/[09], loss: 6.1007, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-12:58:10 Iters: 401700/[09], loss: 5.7473, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:00:13 Iters: 401800/[09], loss: 5.5658, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:02:16 Iters: 401900/[09], loss: 5.9901, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:04:19 Iters: 402000/[09], loss: 6.1024, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:06:23 Iters: 402100/[09], loss: 5.6988, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:08:26 Iters: 402200/[09], loss: 5.0130, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:10:29 Iters: 402300/[09], loss: 5.3796, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:12:32 Iters: 402400/[09], loss: 6.2265, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:14:36 Iters: 402500/[09], loss: 4.8971, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:16:39 Iters: 402600/[09], loss: 5.6254, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:18:42 Iters: 402700/[09], loss: 5.3604, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:20:46 Iters: 402800/[09], loss: 4.7901, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:22:49 Iters: 402900/[09], loss: 5.1113, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:24:52 Iters: 403000/[09], loss: 5.1681, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:26:56 Iters: 403100/[09], loss: 5.6791, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:28:59 Iters: 403200/[09], loss: 5.9430, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:31:02 Iters: 403300/[09], loss: 4.9324, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:33:06 Iters: 403400/[09], loss: 5.2111, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:35:09 Iters: 403500/[09], loss: 6.6720, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:37:12 Iters: 403600/[09], loss: 5.4041, train_accuracy: 0.3984, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:39:15 Iters: 403700/[09], loss: 5.5886, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:41:19 Iters: 403800/[09], loss: 4.8532, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:43:22 Iters: 403900/[09], loss: 4.6402, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:45:25 Iters: 404000/[09], loss: 5.4390, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:47:28 Iters: 404100/[09], loss: 5.5043, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:49:31 Iters: 404200/[09], loss: 5.7077, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:51:35 Iters: 404300/[09], loss: 5.3110, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:53:38 Iters: 404400/[09], loss: 5.3880, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:55:41 Iters: 404500/[09], loss: 5.3982, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:57:44 Iters: 404600/[09], loss: 5.7917, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-13:59:48 Iters: 404700/[09], loss: 5.7315, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:01:51 Iters: 404800/[09], loss: 5.5307, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:03:54 Iters: 404900/[09], loss: 4.9972, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:05:57 Iters: 405000/[09], loss: 4.7727, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:08:01 Iters: 405100/[09], loss: 5.1617, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:10:04 Iters: 405200/[09], loss: 5.0877, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:12:07 Iters: 405300/[09], loss: 6.0646, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:14:10 Iters: 405400/[09], loss: 5.7159, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:16:14 Iters: 405500/[09], loss: 5.6758, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:18:17 Iters: 405600/[09], loss: 4.8455, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:20:20 Iters: 405700/[09], loss: 5.0198, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:22:23 Iters: 405800/[09], loss: 5.3100, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:24:27 Iters: 405900/[09], loss: 5.0689, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:26:30 Iters: 406000/[09], loss: 4.8936, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:28:33 Iters: 406100/[09], loss: 4.9192, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:30:37 Iters: 406200/[09], loss: 5.1534, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:32:40 Iters: 406300/[09], loss: 5.1381, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:34:43 Iters: 406400/[09], loss: 4.9971, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:36:46 Iters: 406500/[09], loss: 5.2562, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:38:50 Iters: 406600/[09], loss: 5.8395, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:40:53 Iters: 406700/[09], loss: 6.0199, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:42:56 Iters: 406800/[09], loss: 5.4056, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:44:59 Iters: 406900/[09], loss: 5.4879, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:47:03 Iters: 407000/[09], loss: 4.9335, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:49:06 Iters: 407100/[09], loss: 5.5787, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:51:09 Iters: 407200/[09], loss: 5.1057, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:53:12 Iters: 407300/[09], loss: 6.0883, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:55:16 Iters: 407400/[09], loss: 5.8218, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:57:19 Iters: 407500/[09], loss: 5.5388, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-14:59:22 Iters: 407600/[09], loss: 5.0292, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:01:26 Iters: 407700/[09], loss: 5.6816, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:03:29 Iters: 407800/[09], loss: 4.9271, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:05:32 Iters: 407900/[09], loss: 4.8813, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:07:35 Iters: 408000/[09], loss: 5.2784, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:09:39 Iters: 408100/[09], loss: 5.9947, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:11:42 Iters: 408200/[09], loss: 5.4926, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:13:45 Iters: 408300/[09], loss: 5.7092, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:15:48 Iters: 408400/[09], loss: 5.7083, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:17:52 Iters: 408500/[09], loss: 5.7913, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:19:55 Iters: 408600/[09], loss: 5.0983, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:21:58 Iters: 408700/[09], loss: 5.3800, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:24:01 Iters: 408800/[09], loss: 6.0694, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:26:05 Iters: 408900/[09], loss: 5.5017, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:28:08 Iters: 409000/[09], loss: 5.5765, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:30:11 Iters: 409100/[09], loss: 5.6460, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:32:14 Iters: 409200/[09], loss: 5.0200, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:34:18 Iters: 409300/[09], loss: 5.7850, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:36:21 Iters: 409400/[09], loss: 5.1829, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:36:32 Train Epoch: 10/18 ...
-20220706-15:38:24 Iters: 409500/[10], loss: 5.0457, train_accuracy: 0.3594, time: 1.11 s/iter, learning rate: 0.005000000000000001
-20220706-15:40:27 Iters: 409600/[10], loss: 5.5892, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:42:30 Iters: 409700/[10], loss: 6.0127, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:44:34 Iters: 409800/[10], loss: 5.2521, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:46:37 Iters: 409900/[10], loss: 5.2989, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:48:40 Iters: 410000/[10], loss: 5.1748, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:48:40 Saving checkpoint: 410000
-20220706-15:49:56 LFW Ave Accuracy: 99.4499
-20220706-15:51:11 AgeDB-30 Ave Accuracy: 96.3833
-20220706-15:52:37 CFP-FP Ave Accuracy: 93.4714
-20220706-15:52:37 Current Best Accuracy: LFW: 99.6000 in iters: 400000, AgeDB-30: 96.5000 in iters: 400000 and CFP-FP: 93.6143 in iters: 370000
-20220706-15:54:39 Iters: 410100/[10], loss: 4.8597, train_accuracy: 0.4062, time: 3.59 s/iter, learning rate: 0.005000000000000001
-20220706-15:56:43 Iters: 410200/[10], loss: 5.3681, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-15:58:46 Iters: 410300/[10], loss: 4.8542, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:00:49 Iters: 410400/[10], loss: 5.9834, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:02:52 Iters: 410500/[10], loss: 5.6225, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:04:55 Iters: 410600/[10], loss: 5.4916, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:06:59 Iters: 410700/[10], loss: 4.3947, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:09:02 Iters: 410800/[10], loss: 5.3716, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:11:05 Iters: 410900/[10], loss: 5.7001, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:13:08 Iters: 411000/[10], loss: 5.7705, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:15:11 Iters: 411100/[10], loss: 5.3877, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:17:15 Iters: 411200/[10], loss: 5.6250, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:19:18 Iters: 411300/[10], loss: 5.7684, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:21:21 Iters: 411400/[10], loss: 4.6922, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:23:24 Iters: 411500/[10], loss: 5.5796, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:25:28 Iters: 411600/[10], loss: 5.5304, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:27:31 Iters: 411700/[10], loss: 5.9617, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:29:34 Iters: 411800/[10], loss: 5.6162, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:31:38 Iters: 411900/[10], loss: 6.2973, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:33:41 Iters: 412000/[10], loss: 5.4273, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:35:44 Iters: 412100/[10], loss: 4.6885, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:37:48 Iters: 412200/[10], loss: 5.8006, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:39:51 Iters: 412300/[10], loss: 5.2889, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:41:54 Iters: 412400/[10], loss: 4.9443, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:43:58 Iters: 412500/[10], loss: 5.2830, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:46:01 Iters: 412600/[10], loss: 5.8878, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:48:04 Iters: 412700/[10], loss: 5.6555, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:50:08 Iters: 412800/[10], loss: 5.4405, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:52:11 Iters: 412900/[10], loss: 5.0768, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:54:14 Iters: 413000/[10], loss: 5.6756, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:56:18 Iters: 413100/[10], loss: 5.4767, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-16:58:21 Iters: 413200/[10], loss: 4.9451, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:00:24 Iters: 413300/[10], loss: 5.5415, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:02:28 Iters: 413400/[10], loss: 4.8126, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:04:31 Iters: 413500/[10], loss: 4.8262, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:06:34 Iters: 413600/[10], loss: 5.2721, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:08:38 Iters: 413700/[10], loss: 5.3051, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:10:41 Iters: 413800/[10], loss: 5.5185, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:12:44 Iters: 413900/[10], loss: 4.9846, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:14:48 Iters: 414000/[10], loss: 5.3912, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:16:51 Iters: 414100/[10], loss: 4.9149, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:18:54 Iters: 414200/[10], loss: 5.8842, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:20:58 Iters: 414300/[10], loss: 4.9835, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:23:01 Iters: 414400/[10], loss: 5.5868, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:25:05 Iters: 414500/[10], loss: 5.7910, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:27:08 Iters: 414600/[10], loss: 6.1907, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:29:11 Iters: 414700/[10], loss: 5.8821, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:31:15 Iters: 414800/[10], loss: 5.0830, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:33:18 Iters: 414900/[10], loss: 5.8238, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:35:21 Iters: 415000/[10], loss: 5.4917, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:37:25 Iters: 415100/[10], loss: 5.5507, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:39:28 Iters: 415200/[10], loss: 5.7481, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:41:31 Iters: 415300/[10], loss: 5.7304, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:43:34 Iters: 415400/[10], loss: 5.4939, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:45:38 Iters: 415500/[10], loss: 6.2315, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:47:41 Iters: 415600/[10], loss: 5.5945, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:49:44 Iters: 415700/[10], loss: 5.1529, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:51:48 Iters: 415800/[10], loss: 5.6166, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:53:51 Iters: 415900/[10], loss: 4.8280, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:55:54 Iters: 416000/[10], loss: 6.3417, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-17:57:58 Iters: 416100/[10], loss: 5.4308, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:00:01 Iters: 416200/[10], loss: 5.5883, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:02:04 Iters: 416300/[10], loss: 5.1247, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:04:07 Iters: 416400/[10], loss: 5.4634, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:06:11 Iters: 416500/[10], loss: 5.7429, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:08:14 Iters: 416600/[10], loss: 5.3009, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:10:17 Iters: 416700/[10], loss: 5.3162, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:12:20 Iters: 416800/[10], loss: 5.3562, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:14:24 Iters: 416900/[10], loss: 5.5669, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:16:27 Iters: 417000/[10], loss: 5.6305, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:18:30 Iters: 417100/[10], loss: 5.5696, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:20:34 Iters: 417200/[10], loss: 5.3616, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:22:37 Iters: 417300/[10], loss: 5.1353, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:24:40 Iters: 417400/[10], loss: 5.2733, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:26:43 Iters: 417500/[10], loss: 5.9051, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:28:47 Iters: 417600/[10], loss: 5.3955, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:30:50 Iters: 417700/[10], loss: 4.8073, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:32:53 Iters: 417800/[10], loss: 4.8592, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:34:57 Iters: 417900/[10], loss: 5.7471, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:37:00 Iters: 418000/[10], loss: 5.3051, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:39:03 Iters: 418100/[10], loss: 6.0722, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:41:07 Iters: 418200/[10], loss: 5.1401, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:43:10 Iters: 418300/[10], loss: 4.5208, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:45:13 Iters: 418400/[10], loss: 5.3545, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:47:17 Iters: 418500/[10], loss: 5.2110, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:49:20 Iters: 418600/[10], loss: 4.8760, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:51:23 Iters: 418700/[10], loss: 5.7682, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:53:27 Iters: 418800/[10], loss: 4.4523, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:55:30 Iters: 418900/[10], loss: 5.4311, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:57:33 Iters: 419000/[10], loss: 5.0484, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-18:59:37 Iters: 419100/[10], loss: 6.1850, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:01:40 Iters: 419200/[10], loss: 5.2808, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:03:43 Iters: 419300/[10], loss: 5.2522, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:05:46 Iters: 419400/[10], loss: 4.9348, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:07:50 Iters: 419500/[10], loss: 5.6573, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:09:53 Iters: 419600/[10], loss: 4.9686, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:11:56 Iters: 419700/[10], loss: 5.8205, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:14:00 Iters: 419800/[10], loss: 6.6195, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:16:03 Iters: 419900/[10], loss: 5.0905, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:18:06 Iters: 420000/[10], loss: 4.8182, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:18:06 Saving checkpoint: 420000
-20220706-19:19:23 LFW Ave Accuracy: 99.5666
-20220706-19:20:38 AgeDB-30 Ave Accuracy: 96.4833
-20220706-19:22:04 CFP-FP Ave Accuracy: 93.4714
-20220706-19:22:04 Current Best Accuracy: LFW: 99.6000 in iters: 400000, AgeDB-30: 96.5000 in iters: 400000 and CFP-FP: 93.6143 in iters: 370000
-20220706-19:24:06 Iters: 420100/[10], loss: 5.6224, train_accuracy: 0.2734, time: 3.60 s/iter, learning rate: 0.005000000000000001
-20220706-19:26:09 Iters: 420200/[10], loss: 5.9759, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:28:13 Iters: 420300/[10], loss: 6.1261, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:30:16 Iters: 420400/[10], loss: 5.0073, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:32:19 Iters: 420500/[10], loss: 5.7784, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:34:22 Iters: 420600/[10], loss: 5.7498, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:36:25 Iters: 420700/[10], loss: 5.9473, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:38:29 Iters: 420800/[10], loss: 5.2711, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:40:32 Iters: 420900/[10], loss: 5.9991, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:42:35 Iters: 421000/[10], loss: 5.4583, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:44:38 Iters: 421100/[10], loss: 6.1255, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:46:41 Iters: 421200/[10], loss: 5.0115, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:48:45 Iters: 421300/[10], loss: 5.4572, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:50:48 Iters: 421400/[10], loss: 5.3579, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:52:51 Iters: 421500/[10], loss: 5.3984, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:54:54 Iters: 421600/[10], loss: 4.7527, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:56:57 Iters: 421700/[10], loss: 5.4051, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-19:59:01 Iters: 421800/[10], loss: 5.4448, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:01:04 Iters: 421900/[10], loss: 5.3415, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:03:07 Iters: 422000/[10], loss: 5.2869, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:05:11 Iters: 422100/[10], loss: 5.6934, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:07:14 Iters: 422200/[10], loss: 5.3704, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:09:17 Iters: 422300/[10], loss: 4.9547, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:11:20 Iters: 422400/[10], loss: 4.8532, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:13:24 Iters: 422500/[10], loss: 5.4365, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:15:27 Iters: 422600/[10], loss: 5.6926, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:17:30 Iters: 422700/[10], loss: 6.2134, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:19:33 Iters: 422800/[10], loss: 5.6684, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:21:37 Iters: 422900/[10], loss: 5.4027, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:23:40 Iters: 423000/[10], loss: 6.7495, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:25:43 Iters: 423100/[10], loss: 5.9770, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:27:47 Iters: 423200/[10], loss: 5.0763, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:29:50 Iters: 423300/[10], loss: 6.1921, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:31:53 Iters: 423400/[10], loss: 4.4677, train_accuracy: 0.4219, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:33:56 Iters: 423500/[10], loss: 5.2827, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:36:00 Iters: 423600/[10], loss: 5.5688, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:38:03 Iters: 423700/[10], loss: 5.0379, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:40:06 Iters: 423800/[10], loss: 5.2398, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:42:10 Iters: 423900/[10], loss: 5.9981, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:44:13 Iters: 424000/[10], loss: 5.3379, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:46:16 Iters: 424100/[10], loss: 5.6029, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:48:20 Iters: 424200/[10], loss: 6.0070, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:50:23 Iters: 424300/[10], loss: 6.0110, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:52:26 Iters: 424400/[10], loss: 5.5128, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:54:30 Iters: 424500/[10], loss: 6.1923, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:56:33 Iters: 424600/[10], loss: 5.2471, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-20:58:36 Iters: 424700/[10], loss: 5.5032, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:00:39 Iters: 424800/[10], loss: 5.5304, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:02:43 Iters: 424900/[10], loss: 5.9304, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:04:46 Iters: 425000/[10], loss: 5.1989, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:06:49 Iters: 425100/[10], loss: 5.7595, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:08:52 Iters: 425200/[10], loss: 6.1795, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:10:56 Iters: 425300/[10], loss: 5.0385, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:12:59 Iters: 425400/[10], loss: 6.1391, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:15:02 Iters: 425500/[10], loss: 4.5581, train_accuracy: 0.3984, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:17:05 Iters: 425600/[10], loss: 5.5897, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:19:08 Iters: 425700/[10], loss: 4.6628, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:21:12 Iters: 425800/[10], loss: 5.0154, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:23:15 Iters: 425900/[10], loss: 4.7563, train_accuracy: 0.3984, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:25:18 Iters: 426000/[10], loss: 4.6036, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:27:21 Iters: 426100/[10], loss: 6.5572, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:29:25 Iters: 426200/[10], loss: 6.0245, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:31:28 Iters: 426300/[10], loss: 5.6614, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:33:31 Iters: 426400/[10], loss: 5.6308, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:35:34 Iters: 426500/[10], loss: 5.8432, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:37:37 Iters: 426600/[10], loss: 5.0584, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:39:41 Iters: 426700/[10], loss: 6.0196, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:41:44 Iters: 426800/[10], loss: 5.3123, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:43:47 Iters: 426900/[10], loss: 5.5056, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:45:50 Iters: 427000/[10], loss: 5.7236, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:47:54 Iters: 427100/[10], loss: 4.4309, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:49:57 Iters: 427200/[10], loss: 5.4276, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:52:00 Iters: 427300/[10], loss: 5.4925, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:54:03 Iters: 427400/[10], loss: 5.5479, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:56:06 Iters: 427500/[10], loss: 5.3216, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-21:58:10 Iters: 427600/[10], loss: 5.4297, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:00:13 Iters: 427700/[10], loss: 5.5272, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:02:16 Iters: 427800/[10], loss: 5.6229, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:04:19 Iters: 427900/[10], loss: 4.8555, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:06:23 Iters: 428000/[10], loss: 4.8998, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:08:26 Iters: 428100/[10], loss: 6.0304, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:10:29 Iters: 428200/[10], loss: 5.5000, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:12:32 Iters: 428300/[10], loss: 4.4052, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:14:35 Iters: 428400/[10], loss: 4.8911, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:16:39 Iters: 428500/[10], loss: 5.1885, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:18:42 Iters: 428600/[10], loss: 6.9416, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:20:45 Iters: 428700/[10], loss: 6.3854, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:22:48 Iters: 428800/[10], loss: 5.3711, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:24:52 Iters: 428900/[10], loss: 5.1874, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:26:55 Iters: 429000/[10], loss: 5.6402, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:28:58 Iters: 429100/[10], loss: 6.0797, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:31:01 Iters: 429200/[10], loss: 5.1128, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:33:05 Iters: 429300/[10], loss: 6.0303, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:35:08 Iters: 429400/[10], loss: 5.0005, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:37:11 Iters: 429500/[10], loss: 5.1500, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:39:14 Iters: 429600/[10], loss: 5.8151, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:41:18 Iters: 429700/[10], loss: 5.3351, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:43:21 Iters: 429800/[10], loss: 5.9651, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:45:24 Iters: 429900/[10], loss: 5.5370, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:47:28 Iters: 430000/[10], loss: 5.7774, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:47:28 Saving checkpoint: 430000
-20220706-22:48:44 LFW Ave Accuracy: 99.5666
-20220706-22:49:59 AgeDB-30 Ave Accuracy: 96.2667
-20220706-22:51:26 CFP-FP Ave Accuracy: 93.3714
-20220706-22:51:26 Current Best Accuracy: LFW: 99.6000 in iters: 400000, AgeDB-30: 96.5000 in iters: 400000 and CFP-FP: 93.6143 in iters: 370000
-20220706-22:53:28 Iters: 430100/[10], loss: 5.9526, train_accuracy: 0.2500, time: 3.61 s/iter, learning rate: 0.005000000000000001
-20220706-22:55:32 Iters: 430200/[10], loss: 5.0146, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:57:35 Iters: 430300/[10], loss: 5.2617, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-22:59:38 Iters: 430400/[10], loss: 5.2432, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:01:41 Iters: 430500/[10], loss: 5.2612, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:03:45 Iters: 430600/[10], loss: 5.3561, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:05:48 Iters: 430700/[10], loss: 5.5446, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:07:51 Iters: 430800/[10], loss: 5.7679, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:09:54 Iters: 430900/[10], loss: 5.4063, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:11:58 Iters: 431000/[10], loss: 5.3731, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:14:01 Iters: 431100/[10], loss: 5.4812, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:16:04 Iters: 431200/[10], loss: 6.4585, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:18:07 Iters: 431300/[10], loss: 5.4412, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:20:10 Iters: 431400/[10], loss: 5.6861, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:22:14 Iters: 431500/[10], loss: 5.7636, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:24:17 Iters: 431600/[10], loss: 4.8788, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:26:20 Iters: 431700/[10], loss: 4.8694, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:28:23 Iters: 431800/[10], loss: 5.0738, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:30:27 Iters: 431900/[10], loss: 5.3883, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:32:30 Iters: 432000/[10], loss: 4.5440, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:34:33 Iters: 432100/[10], loss: 4.7267, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:36:37 Iters: 432200/[10], loss: 4.9080, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:38:40 Iters: 432300/[10], loss: 5.2654, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:40:43 Iters: 432400/[10], loss: 5.9735, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:42:46 Iters: 432500/[10], loss: 5.2543, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:44:50 Iters: 432600/[10], loss: 4.9036, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:46:53 Iters: 432700/[10], loss: 5.5385, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:48:56 Iters: 432800/[10], loss: 5.5562, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:50:59 Iters: 432900/[10], loss: 5.4575, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:53:03 Iters: 433000/[10], loss: 5.9738, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:55:06 Iters: 433100/[10], loss: 5.7692, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:57:09 Iters: 433200/[10], loss: 5.4205, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220706-23:59:12 Iters: 433300/[10], loss: 5.2734, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:01:16 Iters: 433400/[10], loss: 4.5723, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:03:19 Iters: 433500/[10], loss: 5.7110, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:05:22 Iters: 433600/[10], loss: 5.2973, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:07:25 Iters: 433700/[10], loss: 5.9982, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:09:29 Iters: 433800/[10], loss: 5.6202, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:11:32 Iters: 433900/[10], loss: 5.7328, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:13:35 Iters: 434000/[10], loss: 5.7594, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:15:38 Iters: 434100/[10], loss: 5.2584, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:17:41 Iters: 434200/[10], loss: 5.4536, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:19:45 Iters: 434300/[10], loss: 5.7136, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:21:48 Iters: 434400/[10], loss: 5.3277, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:23:51 Iters: 434500/[10], loss: 4.1458, train_accuracy: 0.3984, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:25:54 Iters: 434600/[10], loss: 5.1500, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:27:58 Iters: 434700/[10], loss: 4.6567, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:30:01 Iters: 434800/[10], loss: 4.6666, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:32:04 Iters: 434900/[10], loss: 6.0473, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:34:07 Iters: 435000/[10], loss: 5.6752, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:36:11 Iters: 435100/[10], loss: 5.9353, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:38:14 Iters: 435200/[10], loss: 5.5168, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:40:17 Iters: 435300/[10], loss: 5.3897, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:42:20 Iters: 435400/[10], loss: 4.8080, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:44:24 Iters: 435500/[10], loss: 5.2355, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:46:27 Iters: 435600/[10], loss: 4.8099, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:48:30 Iters: 435700/[10], loss: 5.8653, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:50:33 Iters: 435800/[10], loss: 5.6802, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:52:37 Iters: 435900/[10], loss: 4.5188, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:54:40 Iters: 436000/[10], loss: 5.8713, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:56:43 Iters: 436100/[10], loss: 5.1176, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-00:58:46 Iters: 436200/[10], loss: 5.2141, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:00:50 Iters: 436300/[10], loss: 5.8085, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:02:53 Iters: 436400/[10], loss: 5.1207, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:04:56 Iters: 436500/[10], loss: 4.6079, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:06:59 Iters: 436600/[10], loss: 5.7074, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:09:02 Iters: 436700/[10], loss: 5.7936, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:11:06 Iters: 436800/[10], loss: 5.1693, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:13:09 Iters: 436900/[10], loss: 5.8711, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:15:12 Iters: 437000/[10], loss: 4.7768, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:17:15 Iters: 437100/[10], loss: 6.2540, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:19:19 Iters: 437200/[10], loss: 5.8083, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:21:22 Iters: 437300/[10], loss: 5.6117, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:23:25 Iters: 437400/[10], loss: 5.1115, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:25:28 Iters: 437500/[10], loss: 5.3578, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:27:31 Iters: 437600/[10], loss: 4.8013, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:29:35 Iters: 437700/[10], loss: 4.6142, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:31:38 Iters: 437800/[10], loss: 5.5412, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:33:41 Iters: 437900/[10], loss: 4.5387, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:35:44 Iters: 438000/[10], loss: 5.1610, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:37:47 Iters: 438100/[10], loss: 5.7838, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:39:51 Iters: 438200/[10], loss: 5.3226, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:41:54 Iters: 438300/[10], loss: 5.3137, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:43:57 Iters: 438400/[10], loss: 5.3009, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:46:00 Iters: 438500/[10], loss: 5.4739, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:48:04 Iters: 438600/[10], loss: 5.2809, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:50:07 Iters: 438700/[10], loss: 5.5804, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:52:10 Iters: 438800/[10], loss: 5.4815, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:54:13 Iters: 438900/[10], loss: 5.4328, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:56:17 Iters: 439000/[10], loss: 5.6760, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-01:58:20 Iters: 439100/[10], loss: 5.9833, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:00:23 Iters: 439200/[10], loss: 6.2226, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:02:27 Iters: 439300/[10], loss: 5.2247, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:04:30 Iters: 439400/[10], loss: 6.1390, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:06:33 Iters: 439500/[10], loss: 5.9796, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:08:36 Iters: 439600/[10], loss: 5.2972, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:10:40 Iters: 439700/[10], loss: 4.9527, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:12:43 Iters: 439800/[10], loss: 5.5583, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:14:46 Iters: 439900/[10], loss: 5.2502, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:16:49 Iters: 440000/[10], loss: 5.8688, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:16:49 Saving checkpoint: 440000
-20220707-02:18:08 LFW Ave Accuracy: 99.5666
-20220707-02:19:26 AgeDB-30 Ave Accuracy: 96.4833
-20220707-02:20:56 CFP-FP Ave Accuracy: 93.1286
-20220707-02:20:56 Current Best Accuracy: LFW: 99.6000 in iters: 400000, AgeDB-30: 96.5000 in iters: 400000 and CFP-FP: 93.6143 in iters: 370000
-20220707-02:22:58 Iters: 440100/[10], loss: 5.4992, train_accuracy: 0.3203, time: 3.69 s/iter, learning rate: 0.005000000000000001
-20220707-02:25:01 Iters: 440200/[10], loss: 5.4902, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:27:04 Iters: 440300/[10], loss: 5.5896, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:29:08 Iters: 440400/[10], loss: 5.0429, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:31:11 Iters: 440500/[10], loss: 5.6236, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:33:14 Iters: 440600/[10], loss: 5.7949, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:35:17 Iters: 440700/[10], loss: 4.9597, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:37:21 Iters: 440800/[10], loss: 5.5153, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:39:24 Iters: 440900/[10], loss: 5.0931, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:41:27 Iters: 441000/[10], loss: 5.6699, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:43:30 Iters: 441100/[10], loss: 5.1997, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:45:33 Iters: 441200/[10], loss: 5.6288, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:47:36 Iters: 441300/[10], loss: 5.8093, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:49:40 Iters: 441400/[10], loss: 5.3489, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:51:43 Iters: 441500/[10], loss: 6.1258, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:53:46 Iters: 441600/[10], loss: 5.4471, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:55:49 Iters: 441700/[10], loss: 5.4418, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:57:53 Iters: 441800/[10], loss: 5.0724, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-02:59:56 Iters: 441900/[10], loss: 5.2068, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:01:59 Iters: 442000/[10], loss: 5.5070, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:04:02 Iters: 442100/[10], loss: 5.3850, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:06:06 Iters: 442200/[10], loss: 5.5049, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:08:09 Iters: 442300/[10], loss: 5.8753, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:10:12 Iters: 442400/[10], loss: 5.6120, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:12:15 Iters: 442500/[10], loss: 5.7885, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:14:18 Iters: 442600/[10], loss: 5.0939, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:16:22 Iters: 442700/[10], loss: 5.4886, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:18:25 Iters: 442800/[10], loss: 5.4018, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:20:28 Iters: 442900/[10], loss: 5.1621, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:22:31 Iters: 443000/[10], loss: 5.9087, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:24:34 Iters: 443100/[10], loss: 5.8460, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:26:38 Iters: 443200/[10], loss: 5.8675, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:28:41 Iters: 443300/[10], loss: 5.6686, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:30:44 Iters: 443400/[10], loss: 5.4034, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:32:47 Iters: 443500/[10], loss: 5.7127, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:34:50 Iters: 443600/[10], loss: 5.5534, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:36:53 Iters: 443700/[10], loss: 5.5810, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:38:57 Iters: 443800/[10], loss: 5.8231, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:41:00 Iters: 443900/[10], loss: 5.7726, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:43:03 Iters: 444000/[10], loss: 5.1132, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:45:06 Iters: 444100/[10], loss: 4.8909, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:47:10 Iters: 444200/[10], loss: 5.4852, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:49:13 Iters: 444300/[10], loss: 5.7494, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:51:16 Iters: 444400/[10], loss: 5.8351, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:53:20 Iters: 444500/[10], loss: 5.0593, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:55:23 Iters: 444600/[10], loss: 5.6208, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:57:26 Iters: 444700/[10], loss: 5.7229, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-03:59:29 Iters: 444800/[10], loss: 5.5814, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:01:32 Iters: 444900/[10], loss: 5.2486, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:03:36 Iters: 445000/[10], loss: 5.7065, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:05:39 Iters: 445100/[10], loss: 5.9253, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:07:42 Iters: 445200/[10], loss: 6.2401, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:09:45 Iters: 445300/[10], loss: 4.8413, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:11:49 Iters: 445400/[10], loss: 5.6093, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:13:52 Iters: 445500/[10], loss: 5.5055, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:15:55 Iters: 445600/[10], loss: 5.8562, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:17:58 Iters: 445700/[10], loss: 5.4982, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:20:02 Iters: 445800/[10], loss: 5.9663, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:22:05 Iters: 445900/[10], loss: 5.7719, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:24:08 Iters: 446000/[10], loss: 5.9701, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:26:11 Iters: 446100/[10], loss: 5.2207, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:28:15 Iters: 446200/[10], loss: 5.6443, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:30:18 Iters: 446300/[10], loss: 4.8850, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:32:22 Iters: 446400/[10], loss: 5.3745, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:34:25 Iters: 446500/[10], loss: 6.3054, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:36:28 Iters: 446600/[10], loss: 4.9380, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:38:31 Iters: 446700/[10], loss: 5.4593, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:40:35 Iters: 446800/[10], loss: 6.1816, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:42:38 Iters: 446900/[10], loss: 5.1790, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:44:42 Iters: 447000/[10], loss: 5.9051, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:46:45 Iters: 447100/[10], loss: 5.8669, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:48:48 Iters: 447200/[10], loss: 5.5288, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:50:51 Iters: 447300/[10], loss: 4.8230, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:52:55 Iters: 447400/[10], loss: 4.9854, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:54:58 Iters: 447500/[10], loss: 5.3959, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:57:01 Iters: 447600/[10], loss: 5.5825, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-04:59:04 Iters: 447700/[10], loss: 4.9348, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:01:08 Iters: 447800/[10], loss: 5.4034, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:03:11 Iters: 447900/[10], loss: 5.6726, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:05:14 Iters: 448000/[10], loss: 5.7430, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:07:17 Iters: 448100/[10], loss: 5.8581, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:09:21 Iters: 448200/[10], loss: 5.8431, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:11:24 Iters: 448300/[10], loss: 5.1941, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:13:27 Iters: 448400/[10], loss: 5.7917, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:15:30 Iters: 448500/[10], loss: 5.4011, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:17:33 Iters: 448600/[10], loss: 5.8368, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:19:37 Iters: 448700/[10], loss: 4.5112, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:21:40 Iters: 448800/[10], loss: 5.5718, train_accuracy: 0.2031, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:23:43 Iters: 448900/[10], loss: 5.2615, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:25:46 Iters: 449000/[10], loss: 5.4319, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:27:49 Iters: 449100/[10], loss: 5.5263, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:29:53 Iters: 449200/[10], loss: 5.3030, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:31:56 Iters: 449300/[10], loss: 5.1550, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:33:59 Iters: 449400/[10], loss: 6.2430, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:36:02 Iters: 449500/[10], loss: 5.7197, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:38:06 Iters: 449600/[10], loss: 5.0727, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:40:09 Iters: 449700/[10], loss: 6.0503, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:42:12 Iters: 449800/[10], loss: 4.8430, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:44:15 Iters: 449900/[10], loss: 5.6025, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:46:19 Iters: 450000/[10], loss: 5.7591, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:46:19 Saving checkpoint: 450000
-20220707-05:47:35 LFW Ave Accuracy: 99.5666
-20220707-05:48:50 AgeDB-30 Ave Accuracy: 96.3000
-20220707-05:50:18 CFP-FP Ave Accuracy: 93.4143
-20220707-05:50:18 Current Best Accuracy: LFW: 99.6000 in iters: 400000, AgeDB-30: 96.5000 in iters: 400000 and CFP-FP: 93.6143 in iters: 370000
-20220707-05:52:20 Iters: 450100/[10], loss: 4.9451, train_accuracy: 0.2812, time: 3.61 s/iter, learning rate: 0.005000000000000001
-20220707-05:54:23 Iters: 450200/[10], loss: 5.0982, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:56:26 Iters: 450300/[10], loss: 5.3657, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-05:58:29 Iters: 450400/[10], loss: 5.1253, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:00:33 Iters: 450500/[10], loss: 5.6830, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:02:36 Iters: 450600/[10], loss: 5.0742, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:04:39 Iters: 450700/[10], loss: 5.6950, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:06:42 Iters: 450800/[10], loss: 5.7861, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:08:46 Iters: 450900/[10], loss: 5.1682, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:10:49 Iters: 451000/[10], loss: 5.3210, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:12:52 Iters: 451100/[10], loss: 4.6802, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:14:55 Iters: 451200/[10], loss: 5.2049, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:16:59 Iters: 451300/[10], loss: 5.5625, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:19:02 Iters: 451400/[10], loss: 4.9593, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:21:05 Iters: 451500/[10], loss: 5.4732, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:23:08 Iters: 451600/[10], loss: 5.6993, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:25:11 Iters: 451700/[10], loss: 5.5274, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:27:15 Iters: 451800/[10], loss: 4.9427, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:29:18 Iters: 451900/[10], loss: 5.3715, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:31:21 Iters: 452000/[10], loss: 5.2376, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:33:24 Iters: 452100/[10], loss: 5.6387, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:35:28 Iters: 452200/[10], loss: 5.1017, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:37:31 Iters: 452300/[10], loss: 5.9462, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:39:34 Iters: 452400/[10], loss: 5.9375, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:41:38 Iters: 452500/[10], loss: 6.6783, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:43:41 Iters: 452600/[10], loss: 5.2298, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:45:44 Iters: 452700/[10], loss: 5.6890, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:47:47 Iters: 452800/[10], loss: 5.6504, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:49:51 Iters: 452900/[10], loss: 5.2245, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:51:54 Iters: 453000/[10], loss: 5.5724, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:53:57 Iters: 453100/[10], loss: 5.8387, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:56:00 Iters: 453200/[10], loss: 6.0830, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-06:58:04 Iters: 453300/[10], loss: 5.3293, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:00:07 Iters: 453400/[10], loss: 5.6070, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:02:10 Iters: 453500/[10], loss: 4.9226, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:04:14 Iters: 453600/[10], loss: 5.5499, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:06:17 Iters: 453700/[10], loss: 5.5067, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:08:20 Iters: 453800/[10], loss: 5.8949, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:10:23 Iters: 453900/[10], loss: 5.3694, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:12:26 Iters: 454000/[10], loss: 5.6097, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:14:30 Iters: 454100/[10], loss: 5.6927, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:16:33 Iters: 454200/[10], loss: 5.3208, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:18:36 Iters: 454300/[10], loss: 5.7923, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:20:39 Iters: 454400/[10], loss: 4.8753, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:22:43 Iters: 454500/[10], loss: 4.9987, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:24:46 Iters: 454600/[10], loss: 4.8352, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:26:49 Iters: 454700/[10], loss: 5.2144, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:28:52 Iters: 454800/[10], loss: 4.8669, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:30:55 Iters: 454900/[10], loss: 5.1183, train_accuracy: 0.3934, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:30:55 Train Epoch: 11/18 ...
-20220707-07:32:59 Iters: 455000/[11], loss: 4.8943, train_accuracy: 0.3906, time: 1.24 s/iter, learning rate: 0.005000000000000001
-20220707-07:35:02 Iters: 455100/[11], loss: 4.8602, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:37:06 Iters: 455200/[11], loss: 6.1110, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:39:09 Iters: 455300/[11], loss: 5.1687, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:41:12 Iters: 455400/[11], loss: 5.0898, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:43:15 Iters: 455500/[11], loss: 5.3607, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:45:19 Iters: 455600/[11], loss: 5.4724, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:47:22 Iters: 455700/[11], loss: 5.4680, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:49:25 Iters: 455800/[11], loss: 5.2900, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:51:28 Iters: 455900/[11], loss: 5.7596, train_accuracy: 0.2188, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:53:32 Iters: 456000/[11], loss: 5.6499, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:55:35 Iters: 456100/[11], loss: 4.9632, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:57:38 Iters: 456200/[11], loss: 4.9952, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-07:59:41 Iters: 456300/[11], loss: 4.8447, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:01:45 Iters: 456400/[11], loss: 4.6903, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:03:48 Iters: 456500/[11], loss: 5.1954, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:05:51 Iters: 456600/[11], loss: 4.8599, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:07:54 Iters: 456700/[11], loss: 5.1432, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:09:57 Iters: 456800/[11], loss: 5.2344, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:12:01 Iters: 456900/[11], loss: 5.5326, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:14:04 Iters: 457000/[11], loss: 5.7714, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:16:07 Iters: 457100/[11], loss: 5.5870, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:18:10 Iters: 457200/[11], loss: 5.1863, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:20:13 Iters: 457300/[11], loss: 5.2186, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:22:17 Iters: 457400/[11], loss: 5.8401, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:24:20 Iters: 457500/[11], loss: 5.3295, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:26:23 Iters: 457600/[11], loss: 5.5744, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:28:26 Iters: 457700/[11], loss: 5.1260, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:30:30 Iters: 457800/[11], loss: 5.1611, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:32:33 Iters: 457900/[11], loss: 5.1791, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:34:36 Iters: 458000/[11], loss: 4.9432, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:36:39 Iters: 458100/[11], loss: 5.7908, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:38:42 Iters: 458200/[11], loss: 5.5581, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:40:46 Iters: 458300/[11], loss: 5.1261, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:42:49 Iters: 458400/[11], loss: 4.4755, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:44:52 Iters: 458500/[11], loss: 5.2684, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:46:55 Iters: 458600/[11], loss: 5.3747, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:48:58 Iters: 458700/[11], loss: 5.9042, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:51:02 Iters: 458800/[11], loss: 5.7349, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:53:05 Iters: 458900/[11], loss: 5.1520, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:55:08 Iters: 459000/[11], loss: 4.8220, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:57:11 Iters: 459100/[11], loss: 5.0201, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-08:59:15 Iters: 459200/[11], loss: 5.2108, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:01:18 Iters: 459300/[11], loss: 5.3132, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:03:21 Iters: 459400/[11], loss: 5.3184, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:05:24 Iters: 459500/[11], loss: 5.1621, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:07:28 Iters: 459600/[11], loss: 5.6613, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:09:31 Iters: 459700/[11], loss: 5.3161, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:11:34 Iters: 459800/[11], loss: 5.2968, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:13:38 Iters: 459900/[11], loss: 5.6364, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:15:41 Iters: 460000/[11], loss: 5.3161, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:15:41 Saving checkpoint: 460000
-20220707-09:16:57 LFW Ave Accuracy: 99.6333
-20220707-09:18:12 AgeDB-30 Ave Accuracy: 96.4667
-20220707-09:19:39 CFP-FP Ave Accuracy: 93.6000
-20220707-09:19:39 Current Best Accuracy: LFW: 99.6333 in iters: 460000, AgeDB-30: 96.5000 in iters: 400000 and CFP-FP: 93.6143 in iters: 370000
-20220707-09:21:42 Iters: 460100/[11], loss: 5.3516, train_accuracy: 0.3125, time: 3.61 s/iter, learning rate: 0.005000000000000001
-20220707-09:23:45 Iters: 460200/[11], loss: 4.6994, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:25:48 Iters: 460300/[11], loss: 5.5972, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:27:51 Iters: 460400/[11], loss: 5.5603, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:29:55 Iters: 460500/[11], loss: 6.2794, train_accuracy: 0.1875, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:31:58 Iters: 460600/[11], loss: 5.2358, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:34:01 Iters: 460700/[11], loss: 4.4456, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:36:04 Iters: 460800/[11], loss: 6.2862, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:38:08 Iters: 460900/[11], loss: 5.3493, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:40:11 Iters: 461000/[11], loss: 5.2785, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:42:14 Iters: 461100/[11], loss: 5.2321, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:44:17 Iters: 461200/[11], loss: 4.9977, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:46:21 Iters: 461300/[11], loss: 5.4082, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:48:24 Iters: 461400/[11], loss: 5.2672, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:50:27 Iters: 461500/[11], loss: 4.4671, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:52:30 Iters: 461600/[11], loss: 5.2942, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:54:34 Iters: 461700/[11], loss: 6.0276, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:56:37 Iters: 461800/[11], loss: 5.4965, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-09:58:40 Iters: 461900/[11], loss: 5.0820, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:00:43 Iters: 462000/[11], loss: 5.7497, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:02:47 Iters: 462100/[11], loss: 5.3595, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:04:50 Iters: 462200/[11], loss: 5.3767, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:06:53 Iters: 462300/[11], loss: 4.9695, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:08:57 Iters: 462400/[11], loss: 5.0226, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:11:00 Iters: 462500/[11], loss: 5.8295, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:13:03 Iters: 462600/[11], loss: 4.5937, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:15:06 Iters: 462700/[11], loss: 5.5761, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:17:10 Iters: 462800/[11], loss: 5.2412, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:19:13 Iters: 462900/[11], loss: 5.8190, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:21:16 Iters: 463000/[11], loss: 4.9520, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:23:20 Iters: 463100/[11], loss: 5.6048, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:25:23 Iters: 463200/[11], loss: 5.3365, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:27:26 Iters: 463300/[11], loss: 4.9820, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:29:29 Iters: 463400/[11], loss: 5.3829, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:31:33 Iters: 463500/[11], loss: 5.7681, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:33:36 Iters: 463600/[11], loss: 5.5300, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:35:39 Iters: 463700/[11], loss: 6.5945, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:37:42 Iters: 463800/[11], loss: 6.0673, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:39:45 Iters: 463900/[11], loss: 5.2466, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:41:49 Iters: 464000/[11], loss: 5.4698, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:43:52 Iters: 464100/[11], loss: 5.7191, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:45:55 Iters: 464200/[11], loss: 5.0310, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:47:58 Iters: 464300/[11], loss: 5.2304, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:50:02 Iters: 464400/[11], loss: 6.0047, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:52:05 Iters: 464500/[11], loss: 5.5150, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:54:08 Iters: 464600/[11], loss: 5.2616, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:56:11 Iters: 464700/[11], loss: 5.4305, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-10:58:14 Iters: 464800/[11], loss: 5.1744, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:00:18 Iters: 464900/[11], loss: 4.7236, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:02:21 Iters: 465000/[11], loss: 5.7522, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:04:24 Iters: 465100/[11], loss: 5.4309, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:06:28 Iters: 465200/[11], loss: 5.4794, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:08:31 Iters: 465300/[11], loss: 5.4277, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:10:34 Iters: 465400/[11], loss: 5.1723, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:12:37 Iters: 465500/[11], loss: 4.5351, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:14:41 Iters: 465600/[11], loss: 6.2361, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:16:44 Iters: 465700/[11], loss: 5.3375, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:18:47 Iters: 465800/[11], loss: 6.0938, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:20:50 Iters: 465900/[11], loss: 5.5825, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:22:54 Iters: 466000/[11], loss: 5.6493, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:24:57 Iters: 466100/[11], loss: 5.0642, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:27:00 Iters: 466200/[11], loss: 6.5985, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:29:04 Iters: 466300/[11], loss: 4.9649, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:31:07 Iters: 466400/[11], loss: 4.5714, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:33:10 Iters: 466500/[11], loss: 4.9483, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:35:13 Iters: 466600/[11], loss: 4.9600, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:37:17 Iters: 466700/[11], loss: 4.6970, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:39:20 Iters: 466800/[11], loss: 4.8109, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:41:23 Iters: 466900/[11], loss: 5.6416, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:43:26 Iters: 467000/[11], loss: 5.1572, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:45:30 Iters: 467100/[11], loss: 5.8610, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:47:33 Iters: 467200/[11], loss: 5.1774, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:49:36 Iters: 467300/[11], loss: 5.4189, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:51:39 Iters: 467400/[11], loss: 6.2421, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:53:42 Iters: 467500/[11], loss: 5.3057, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:55:46 Iters: 467600/[11], loss: 5.3559, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:57:49 Iters: 467700/[11], loss: 5.8391, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-11:59:52 Iters: 467800/[11], loss: 5.5148, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:01:55 Iters: 467900/[11], loss: 5.6439, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:03:59 Iters: 468000/[11], loss: 5.2336, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:06:02 Iters: 468100/[11], loss: 5.2595, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:08:05 Iters: 468200/[11], loss: 5.6445, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:10:08 Iters: 468300/[11], loss: 5.3145, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:12:12 Iters: 468400/[11], loss: 5.5857, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:14:15 Iters: 468500/[11], loss: 5.5464, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:16:18 Iters: 468600/[11], loss: 5.2214, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:18:21 Iters: 468700/[11], loss: 5.3316, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:20:24 Iters: 468800/[11], loss: 5.3989, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:22:28 Iters: 468900/[11], loss: 5.1674, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:24:31 Iters: 469000/[11], loss: 5.0217, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:26:34 Iters: 469100/[11], loss: 5.8476, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:28:37 Iters: 469200/[11], loss: 4.8248, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:30:41 Iters: 469300/[11], loss: 5.8135, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:32:44 Iters: 469400/[11], loss: 5.6216, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:34:47 Iters: 469500/[11], loss: 5.5950, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:36:50 Iters: 469600/[11], loss: 5.9078, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:38:54 Iters: 469700/[11], loss: 6.3896, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:40:57 Iters: 469800/[11], loss: 5.3401, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:43:00 Iters: 469900/[11], loss: 5.4261, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:45:03 Iters: 470000/[11], loss: 4.9032, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:45:03 Saving checkpoint: 470000
-20220707-12:46:21 LFW Ave Accuracy: 99.5833
-20220707-12:47:36 AgeDB-30 Ave Accuracy: 96.2500
-20220707-12:49:02 CFP-FP Ave Accuracy: 93.3571
-20220707-12:49:02 Current Best Accuracy: LFW: 99.6333 in iters: 460000, AgeDB-30: 96.5000 in iters: 400000 and CFP-FP: 93.6143 in iters: 370000
-20220707-12:51:05 Iters: 470100/[11], loss: 5.2432, train_accuracy: 0.3359, time: 3.62 s/iter, learning rate: 0.005000000000000001
-20220707-12:53:08 Iters: 470200/[11], loss: 6.0191, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:55:11 Iters: 470300/[11], loss: 5.3209, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:57:14 Iters: 470400/[11], loss: 5.2475, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-12:59:18 Iters: 470500/[11], loss: 5.0704, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:01:21 Iters: 470600/[11], loss: 5.9693, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:03:24 Iters: 470700/[11], loss: 4.9861, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:05:27 Iters: 470800/[11], loss: 4.5116, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:07:30 Iters: 470900/[11], loss: 4.8953, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:09:34 Iters: 471000/[11], loss: 5.7794, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:11:37 Iters: 471100/[11], loss: 4.8736, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:13:40 Iters: 471200/[11], loss: 6.3340, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:15:43 Iters: 471300/[11], loss: 5.4169, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:17:46 Iters: 471400/[11], loss: 5.6612, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:19:50 Iters: 471500/[11], loss: 6.2199, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:21:53 Iters: 471600/[11], loss: 5.6405, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:23:56 Iters: 471700/[11], loss: 5.7912, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:25:59 Iters: 471800/[11], loss: 5.3992, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:28:03 Iters: 471900/[11], loss: 5.6762, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:30:06 Iters: 472000/[11], loss: 5.8175, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:32:09 Iters: 472100/[11], loss: 6.4511, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:34:12 Iters: 472200/[11], loss: 5.4579, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:36:16 Iters: 472300/[11], loss: 5.1851, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:38:19 Iters: 472400/[11], loss: 5.8080, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:40:22 Iters: 472500/[11], loss: 5.4829, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:42:25 Iters: 472600/[11], loss: 5.3643, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:44:29 Iters: 472700/[11], loss: 5.9461, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:46:32 Iters: 472800/[11], loss: 4.7108, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:48:35 Iters: 472900/[11], loss: 5.7660, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:50:39 Iters: 473000/[11], loss: 5.5376, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:52:42 Iters: 473100/[11], loss: 5.2145, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:54:45 Iters: 473200/[11], loss: 5.7302, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:56:48 Iters: 473300/[11], loss: 5.0993, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-13:58:52 Iters: 473400/[11], loss: 5.5379, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:00:55 Iters: 473500/[11], loss: 5.0441, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:02:58 Iters: 473600/[11], loss: 6.1266, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:05:01 Iters: 473700/[11], loss: 4.7934, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:07:05 Iters: 473800/[11], loss: 4.4065, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:09:08 Iters: 473900/[11], loss: 5.6597, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:11:11 Iters: 474000/[11], loss: 5.1931, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:13:14 Iters: 474100/[11], loss: 5.9618, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:15:18 Iters: 474200/[11], loss: 4.9627, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:17:21 Iters: 474300/[11], loss: 6.3885, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:19:24 Iters: 474400/[11], loss: 4.9283, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:21:27 Iters: 474500/[11], loss: 6.0584, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:23:30 Iters: 474600/[11], loss: 5.0195, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:25:34 Iters: 474700/[11], loss: 5.2718, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:27:37 Iters: 474800/[11], loss: 5.7276, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:29:40 Iters: 474900/[11], loss: 5.3885, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:31:44 Iters: 475000/[11], loss: 4.9780, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:33:47 Iters: 475100/[11], loss: 4.9106, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:35:50 Iters: 475200/[11], loss: 4.6521, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:37:54 Iters: 475300/[11], loss: 5.5402, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:39:57 Iters: 475400/[11], loss: 4.8665, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:42:00 Iters: 475500/[11], loss: 5.0319, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:44:03 Iters: 475600/[11], loss: 4.3321, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:46:06 Iters: 475700/[11], loss: 5.3769, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:48:10 Iters: 475800/[11], loss: 4.9730, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:50:13 Iters: 475900/[11], loss: 5.7272, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:52:16 Iters: 476000/[11], loss: 5.8109, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:54:20 Iters: 476100/[11], loss: 5.5683, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:56:23 Iters: 476200/[11], loss: 4.9717, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-14:58:26 Iters: 476300/[11], loss: 5.1953, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:00:29 Iters: 476400/[11], loss: 6.0561, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:02:33 Iters: 476500/[11], loss: 5.9453, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:04:36 Iters: 476600/[11], loss: 4.9705, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:06:39 Iters: 476700/[11], loss: 5.4648, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:08:43 Iters: 476800/[11], loss: 5.4359, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:10:46 Iters: 476900/[11], loss: 5.4936, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:12:49 Iters: 477000/[11], loss: 5.2615, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:14:52 Iters: 477100/[11], loss: 5.5160, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:16:55 Iters: 477200/[11], loss: 5.5415, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:18:59 Iters: 477300/[11], loss: 5.2350, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:21:02 Iters: 477400/[11], loss: 5.3306, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:23:05 Iters: 477500/[11], loss: 5.0943, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:25:09 Iters: 477600/[11], loss: 4.6953, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:27:12 Iters: 477700/[11], loss: 5.8385, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:29:15 Iters: 477800/[11], loss: 5.8648, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:31:18 Iters: 477900/[11], loss: 5.5083, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:33:22 Iters: 478000/[11], loss: 5.6888, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:35:25 Iters: 478100/[11], loss: 4.5802, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:37:28 Iters: 478200/[11], loss: 4.8672, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:39:32 Iters: 478300/[11], loss: 5.5394, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:41:35 Iters: 478400/[11], loss: 6.0360, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:43:38 Iters: 478500/[11], loss: 5.6109, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:45:41 Iters: 478600/[11], loss: 5.5452, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:47:45 Iters: 478700/[11], loss: 5.8498, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:49:48 Iters: 478800/[11], loss: 5.9171, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:51:51 Iters: 478900/[11], loss: 5.5267, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:53:54 Iters: 479000/[11], loss: 4.4589, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:55:58 Iters: 479100/[11], loss: 5.1116, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-15:58:01 Iters: 479200/[11], loss: 5.3324, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:00:05 Iters: 479300/[11], loss: 5.7531, train_accuracy: 0.2969, time: 1.24 s/iter, learning rate: 0.005000000000000001
-20220707-16:02:08 Iters: 479400/[11], loss: 5.1110, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:04:11 Iters: 479500/[11], loss: 5.3546, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:06:14 Iters: 479600/[11], loss: 5.2720, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:08:18 Iters: 479700/[11], loss: 5.6845, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:10:21 Iters: 479800/[11], loss: 5.1142, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:12:24 Iters: 479900/[11], loss: 5.1523, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:14:28 Iters: 480000/[11], loss: 6.0421, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:14:28 Saving checkpoint: 480000
-20220707-16:15:45 LFW Ave Accuracy: 99.5166
-20220707-16:17:03 AgeDB-30 Ave Accuracy: 96.5333
-20220707-16:18:33 CFP-FP Ave Accuracy: 93.2857
-20220707-16:18:33 Current Best Accuracy: LFW: 99.6333 in iters: 460000, AgeDB-30: 96.5333 in iters: 480000 and CFP-FP: 93.6143 in iters: 370000
-20220707-16:20:35 Iters: 480100/[11], loss: 5.5761, train_accuracy: 0.2344, time: 3.67 s/iter, learning rate: 0.005000000000000001
-20220707-16:22:38 Iters: 480200/[11], loss: 5.4200, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:24:41 Iters: 480300/[11], loss: 5.1173, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:26:44 Iters: 480400/[11], loss: 5.3901, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:28:48 Iters: 480500/[11], loss: 5.3082, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:30:51 Iters: 480600/[11], loss: 5.9640, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:32:54 Iters: 480700/[11], loss: 5.3075, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:34:57 Iters: 480800/[11], loss: 5.1820, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:37:00 Iters: 480900/[11], loss: 5.0724, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:39:03 Iters: 481000/[11], loss: 5.5157, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:41:07 Iters: 481100/[11], loss: 5.5073, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:43:10 Iters: 481200/[11], loss: 5.8124, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:45:13 Iters: 481300/[11], loss: 4.8397, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:47:16 Iters: 481400/[11], loss: 5.6182, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:49:19 Iters: 481500/[11], loss: 4.9952, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:51:22 Iters: 481600/[11], loss: 5.4337, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:53:26 Iters: 481700/[11], loss: 5.2577, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:55:29 Iters: 481800/[11], loss: 4.4975, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:57:32 Iters: 481900/[11], loss: 5.6530, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-16:59:35 Iters: 482000/[11], loss: 5.2414, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:01:38 Iters: 482100/[11], loss: 4.7965, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:03:42 Iters: 482200/[11], loss: 4.8506, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:05:45 Iters: 482300/[11], loss: 5.0948, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:07:48 Iters: 482400/[11], loss: 5.9171, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:09:51 Iters: 482500/[11], loss: 5.2924, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:11:55 Iters: 482600/[11], loss: 5.0347, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:13:58 Iters: 482700/[11], loss: 5.1253, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:16:01 Iters: 482800/[11], loss: 5.8462, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:18:04 Iters: 482900/[11], loss: 5.6839, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:20:08 Iters: 483000/[11], loss: 4.4749, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:22:11 Iters: 483100/[11], loss: 5.3242, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:24:14 Iters: 483200/[11], loss: 5.3742, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:26:17 Iters: 483300/[11], loss: 5.0976, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:28:21 Iters: 483400/[11], loss: 5.2949, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:30:24 Iters: 483500/[11], loss: 5.6919, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:32:27 Iters: 483600/[11], loss: 5.4947, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:34:30 Iters: 483700/[11], loss: 5.2241, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:36:33 Iters: 483800/[11], loss: 4.7543, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:38:37 Iters: 483900/[11], loss: 5.7311, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:40:40 Iters: 484000/[11], loss: 5.9675, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:42:43 Iters: 484100/[11], loss: 5.6391, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:44:46 Iters: 484200/[11], loss: 6.2707, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:46:49 Iters: 484300/[11], loss: 5.3145, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:48:52 Iters: 484400/[11], loss: 5.4259, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:50:55 Iters: 484500/[11], loss: 6.3953, train_accuracy: 0.1641, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:52:58 Iters: 484600/[11], loss: 5.1580, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:55:01 Iters: 484700/[11], loss: 4.8291, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:57:05 Iters: 484800/[11], loss: 5.2974, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-17:59:08 Iters: 484900/[11], loss: 6.1730, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:01:11 Iters: 485000/[11], loss: 5.1193, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:03:14 Iters: 485100/[11], loss: 5.0800, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:05:17 Iters: 485200/[11], loss: 4.9438, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:07:20 Iters: 485300/[11], loss: 6.3331, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:09:23 Iters: 485400/[11], loss: 5.4074, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:11:26 Iters: 485500/[11], loss: 6.1077, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:13:30 Iters: 485600/[11], loss: 5.5098, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:15:33 Iters: 485700/[11], loss: 5.5955, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:17:36 Iters: 485800/[11], loss: 5.5800, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:19:39 Iters: 485900/[11], loss: 5.2412, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:21:42 Iters: 486000/[11], loss: 5.0293, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:23:45 Iters: 486100/[11], loss: 4.9545, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:25:48 Iters: 486200/[11], loss: 6.0748, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:27:51 Iters: 486300/[11], loss: 5.7684, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:29:54 Iters: 486400/[11], loss: 4.6853, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:31:57 Iters: 486500/[11], loss: 5.1734, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:34:00 Iters: 486600/[11], loss: 5.9474, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:36:03 Iters: 486700/[11], loss: 5.1875, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:38:06 Iters: 486800/[11], loss: 5.0706, train_accuracy: 0.3984, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:40:09 Iters: 486900/[11], loss: 4.7856, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:42:12 Iters: 487000/[11], loss: 5.8904, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:44:15 Iters: 487100/[11], loss: 5.2772, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:46:18 Iters: 487200/[11], loss: 5.7156, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:48:21 Iters: 487300/[11], loss: 5.9883, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:50:24 Iters: 487400/[11], loss: 5.6225, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:52:27 Iters: 487500/[11], loss: 4.7747, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:54:30 Iters: 487600/[11], loss: 5.6486, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:56:33 Iters: 487700/[11], loss: 5.2492, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-18:58:36 Iters: 487800/[11], loss: 5.1079, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:00:39 Iters: 487900/[11], loss: 6.4975, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:02:42 Iters: 488000/[11], loss: 5.2556, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:04:45 Iters: 488100/[11], loss: 5.8234, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:06:48 Iters: 488200/[11], loss: 5.2923, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:08:51 Iters: 488300/[11], loss: 5.0636, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:10:54 Iters: 488400/[11], loss: 5.5363, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:12:57 Iters: 488500/[11], loss: 5.8637, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:15:00 Iters: 488600/[11], loss: 4.9333, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:17:03 Iters: 488700/[11], loss: 5.3279, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:19:06 Iters: 488800/[11], loss: 4.9237, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:21:09 Iters: 488900/[11], loss: 5.0516, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:23:12 Iters: 489000/[11], loss: 5.1873, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:25:15 Iters: 489100/[11], loss: 5.5486, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:27:18 Iters: 489200/[11], loss: 5.1923, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:29:21 Iters: 489300/[11], loss: 5.3006, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:31:24 Iters: 489400/[11], loss: 5.0884, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:33:27 Iters: 489500/[11], loss: 5.5073, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:35:30 Iters: 489600/[11], loss: 5.1648, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:37:34 Iters: 489700/[11], loss: 5.2416, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:39:37 Iters: 489800/[11], loss: 5.1651, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:41:40 Iters: 489900/[11], loss: 5.5772, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:43:43 Iters: 490000/[11], loss: 4.5694, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:43:43 Saving checkpoint: 490000
-20220707-19:45:00 LFW Ave Accuracy: 99.5165
-20220707-19:46:17 AgeDB-30 Ave Accuracy: 96.5167
-20220707-19:47:46 CFP-FP Ave Accuracy: 93.5571
-20220707-19:47:46 Current Best Accuracy: LFW: 99.6333 in iters: 460000, AgeDB-30: 96.5333 in iters: 480000 and CFP-FP: 93.6143 in iters: 370000
-20220707-19:49:48 Iters: 490100/[11], loss: 5.0954, train_accuracy: 0.2969, time: 3.65 s/iter, learning rate: 0.005000000000000001
-20220707-19:51:51 Iters: 490200/[11], loss: 5.4295, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:53:54 Iters: 490300/[11], loss: 5.9747, train_accuracy: 0.2422, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:55:57 Iters: 490400/[11], loss: 5.8303, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-19:58:00 Iters: 490500/[11], loss: 5.4452, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:00:03 Iters: 490600/[11], loss: 5.1889, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:02:06 Iters: 490700/[11], loss: 5.4822, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:04:09 Iters: 490800/[11], loss: 6.0764, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:06:12 Iters: 490900/[11], loss: 5.0735, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:08:15 Iters: 491000/[11], loss: 5.7044, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:10:18 Iters: 491100/[11], loss: 5.0865, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:12:21 Iters: 491200/[11], loss: 5.9784, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:14:24 Iters: 491300/[11], loss: 5.1185, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:16:28 Iters: 491400/[11], loss: 5.9982, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:18:31 Iters: 491500/[11], loss: 5.4441, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:20:34 Iters: 491600/[11], loss: 5.4349, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:22:37 Iters: 491700/[11], loss: 6.2597, train_accuracy: 0.2266, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:24:40 Iters: 491800/[11], loss: 4.9083, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:26:44 Iters: 491900/[11], loss: 5.6708, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:28:47 Iters: 492000/[11], loss: 6.0602, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:30:50 Iters: 492100/[11], loss: 5.5329, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:32:53 Iters: 492200/[11], loss: 4.7443, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:34:56 Iters: 492300/[11], loss: 5.5147, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:37:00 Iters: 492400/[11], loss: 5.3097, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:39:03 Iters: 492500/[11], loss: 5.0942, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:41:06 Iters: 492600/[11], loss: 5.2429, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:43:09 Iters: 492700/[11], loss: 6.3494, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:45:13 Iters: 492800/[11], loss: 6.1103, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:47:16 Iters: 492900/[11], loss: 5.2217, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:49:19 Iters: 493000/[11], loss: 5.1325, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:51:22 Iters: 493100/[11], loss: 5.1400, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:53:25 Iters: 493200/[11], loss: 5.0466, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:55:28 Iters: 493300/[11], loss: 5.8173, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:57:32 Iters: 493400/[11], loss: 5.2914, train_accuracy: 0.3594, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-20:59:35 Iters: 493500/[11], loss: 5.2247, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:01:38 Iters: 493600/[11], loss: 4.8864, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:03:41 Iters: 493700/[11], loss: 5.1505, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:05:45 Iters: 493800/[11], loss: 5.6448, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:07:48 Iters: 493900/[11], loss: 5.7010, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:09:51 Iters: 494000/[11], loss: 5.7588, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:11:55 Iters: 494100/[11], loss: 5.1911, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:13:58 Iters: 494200/[11], loss: 5.5131, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:16:01 Iters: 494300/[11], loss: 5.3401, train_accuracy: 0.2656, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:18:04 Iters: 494400/[11], loss: 5.4750, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:20:08 Iters: 494500/[11], loss: 5.3062, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:22:11 Iters: 494600/[11], loss: 5.6459, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:24:14 Iters: 494700/[11], loss: 5.5970, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:26:17 Iters: 494800/[11], loss: 5.3602, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:28:21 Iters: 494900/[11], loss: 5.2405, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:30:24 Iters: 495000/[11], loss: 5.9222, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:32:27 Iters: 495100/[11], loss: 5.5178, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:34:30 Iters: 495200/[11], loss: 5.0618, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:36:34 Iters: 495300/[11], loss: 5.1847, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:38:37 Iters: 495400/[11], loss: 5.9694, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:40:40 Iters: 495500/[11], loss: 5.5064, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:42:43 Iters: 495600/[11], loss: 5.4586, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:44:47 Iters: 495700/[11], loss: 5.4207, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:46:50 Iters: 495800/[11], loss: 5.5042, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:48:53 Iters: 495900/[11], loss: 5.5473, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:50:56 Iters: 496000/[11], loss: 4.8887, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:53:00 Iters: 496100/[11], loss: 4.9818, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:55:03 Iters: 496200/[11], loss: 5.5717, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:57:06 Iters: 496300/[11], loss: 5.7386, train_accuracy: 0.2109, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-21:59:09 Iters: 496400/[11], loss: 5.2738, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:01:13 Iters: 496500/[11], loss: 5.5421, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:03:16 Iters: 496600/[11], loss: 6.0075, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:05:19 Iters: 496700/[11], loss: 4.9070, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:07:22 Iters: 496800/[11], loss: 5.1895, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:09:25 Iters: 496900/[11], loss: 5.6243, train_accuracy: 0.3125, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:11:29 Iters: 497000/[11], loss: 6.6365, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:13:32 Iters: 497100/[11], loss: 4.6826, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:15:35 Iters: 497200/[11], loss: 5.7838, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:17:38 Iters: 497300/[11], loss: 4.9419, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:19:41 Iters: 497400/[11], loss: 5.1454, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:21:45 Iters: 497500/[11], loss: 5.1266, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:23:48 Iters: 497600/[11], loss: 4.7404, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:25:51 Iters: 497700/[11], loss: 5.2193, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:27:54 Iters: 497800/[11], loss: 5.0557, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:29:58 Iters: 497900/[11], loss: 5.9342, train_accuracy: 0.2578, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:32:01 Iters: 498000/[11], loss: 5.3454, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:34:04 Iters: 498100/[11], loss: 5.2987, train_accuracy: 0.2891, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:36:07 Iters: 498200/[11], loss: 4.8685, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:38:10 Iters: 498300/[11], loss: 5.1317, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:40:14 Iters: 498400/[11], loss: 4.9875, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:42:17 Iters: 498500/[11], loss: 5.0087, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:44:20 Iters: 498600/[11], loss: 4.9226, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:46:23 Iters: 498700/[11], loss: 5.6348, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:48:27 Iters: 498800/[11], loss: 5.1181, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:50:30 Iters: 498900/[11], loss: 5.5975, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:52:33 Iters: 499000/[11], loss: 5.5130, train_accuracy: 0.2344, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:54:36 Iters: 499100/[11], loss: 4.2478, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:56:40 Iters: 499200/[11], loss: 5.6281, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-22:58:43 Iters: 499300/[11], loss: 5.6994, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-23:00:46 Iters: 499400/[11], loss: 5.8441, train_accuracy: 0.2500, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-23:02:49 Iters: 499500/[11], loss: 5.7657, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-23:04:53 Iters: 499600/[11], loss: 5.1938, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-23:06:56 Iters: 499700/[11], loss: 5.2907, train_accuracy: 0.2734, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-23:08:59 Iters: 499800/[11], loss: 5.6695, train_accuracy: 0.3047, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-23:11:03 Iters: 499900/[11], loss: 5.5174, train_accuracy: 0.2969, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-23:13:06 Iters: 500000/[11], loss: 5.4552, train_accuracy: 0.3359, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-23:13:06 Saving checkpoint: 500000
-20220707-23:14:24 LFW Ave Accuracy: 99.5666
-20220707-23:15:41 AgeDB-30 Ave Accuracy: 95.9000
-20220707-23:17:11 CFP-FP Ave Accuracy: 93.5857
-20220707-23:17:11 Current Best Accuracy: LFW: 99.6333 in iters: 460000, AgeDB-30: 96.5333 in iters: 480000 and CFP-FP: 93.6143 in iters: 370000
-20220707-23:19:13 Iters: 500100/[11], loss: 4.4125, train_accuracy: 0.3906, time: 3.67 s/iter, learning rate: 0.005000000000000001
-20220707-23:21:16 Iters: 500200/[11], loss: 5.3389, train_accuracy: 0.3203, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-23:23:19 Iters: 500300/[11], loss: 5.0035, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 0.005000000000000001
-20220707-23:25:09 Train Epoch: 12/18 ...
-20220707-23:25:22 Iters: 500400/[12], loss: 4.9105, train_accuracy: 0.3438, time: 0.13 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:27:25 Iters: 500500/[12], loss: 4.6982, train_accuracy: 0.3984, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:29:29 Iters: 500600/[12], loss: 4.4895, train_accuracy: 0.3984, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:31:32 Iters: 500700/[12], loss: 4.8026, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:33:35 Iters: 500800/[12], loss: 4.3025, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:35:39 Iters: 500900/[12], loss: 4.7232, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:37:42 Iters: 501000/[12], loss: 3.8666, train_accuracy: 0.4219, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:39:45 Iters: 501100/[12], loss: 4.2215, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:41:48 Iters: 501200/[12], loss: 4.1172, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:43:52 Iters: 501300/[12], loss: 4.2172, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:45:55 Iters: 501400/[12], loss: 4.4437, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:47:58 Iters: 501500/[12], loss: 4.2005, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:50:01 Iters: 501600/[12], loss: 4.7081, train_accuracy: 0.3281, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:52:05 Iters: 501700/[12], loss: 5.3833, train_accuracy: 0.2812, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:54:08 Iters: 501800/[12], loss: 3.8497, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:56:11 Iters: 501900/[12], loss: 3.7752, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220707-23:58:14 Iters: 502000/[12], loss: 4.2698, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:00:18 Iters: 502100/[12], loss: 4.1617, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:02:21 Iters: 502200/[12], loss: 4.6605, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:04:24 Iters: 502300/[12], loss: 4.4697, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:06:28 Iters: 502400/[12], loss: 5.1106, train_accuracy: 0.3984, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:08:31 Iters: 502500/[12], loss: 4.5623, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:10:34 Iters: 502600/[12], loss: 4.3414, train_accuracy: 0.3750, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:12:37 Iters: 502700/[12], loss: 4.2058, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:14:41 Iters: 502800/[12], loss: 4.2330, train_accuracy: 0.4219, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:16:44 Iters: 502900/[12], loss: 4.4657, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:18:47 Iters: 503000/[12], loss: 4.7995, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:20:50 Iters: 503100/[12], loss: 3.9044, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:22:53 Iters: 503200/[12], loss: 4.4666, train_accuracy: 0.3984, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:24:57 Iters: 503300/[12], loss: 3.7412, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:27:00 Iters: 503400/[12], loss: 3.7659, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:29:03 Iters: 503500/[12], loss: 4.9893, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:31:06 Iters: 503600/[12], loss: 4.3642, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:33:09 Iters: 503700/[12], loss: 4.7351, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:35:13 Iters: 503800/[12], loss: 4.1379, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:37:16 Iters: 503900/[12], loss: 4.0958, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:39:19 Iters: 504000/[12], loss: 3.7945, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:41:22 Iters: 504100/[12], loss: 4.2439, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:43:26 Iters: 504200/[12], loss: 4.4575, train_accuracy: 0.3984, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:45:29 Iters: 504300/[12], loss: 4.2549, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:47:32 Iters: 504400/[12], loss: 3.6290, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:49:35 Iters: 504500/[12], loss: 3.9043, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:51:38 Iters: 504600/[12], loss: 4.3846, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:53:42 Iters: 504700/[12], loss: 3.9054, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:55:45 Iters: 504800/[12], loss: 3.6869, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:57:48 Iters: 504900/[12], loss: 4.3043, train_accuracy: 0.4141, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-00:59:51 Iters: 505000/[12], loss: 4.4996, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:01:55 Iters: 505100/[12], loss: 3.7250, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:03:58 Iters: 505200/[12], loss: 4.2451, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:06:01 Iters: 505300/[12], loss: 4.3670, train_accuracy: 0.3516, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:08:04 Iters: 505400/[12], loss: 4.1730, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:10:08 Iters: 505500/[12], loss: 3.8483, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:12:11 Iters: 505600/[12], loss: 4.6724, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:14:14 Iters: 505700/[12], loss: 3.8542, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:16:17 Iters: 505800/[12], loss: 3.6159, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:18:21 Iters: 505900/[12], loss: 3.9621, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:20:24 Iters: 506000/[12], loss: 4.1625, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:22:27 Iters: 506100/[12], loss: 4.6853, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:24:31 Iters: 506200/[12], loss: 4.1515, train_accuracy: 0.4141, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:26:34 Iters: 506300/[12], loss: 3.9713, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:28:37 Iters: 506400/[12], loss: 3.9582, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:30:40 Iters: 506500/[12], loss: 4.1404, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:32:44 Iters: 506600/[12], loss: 4.3213, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:34:47 Iters: 506700/[12], loss: 4.2091, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:36:50 Iters: 506800/[12], loss: 4.1754, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:38:53 Iters: 506900/[12], loss: 4.2558, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:40:56 Iters: 507000/[12], loss: 3.4892, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:43:00 Iters: 507100/[12], loss: 3.9671, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:45:03 Iters: 507200/[12], loss: 3.7660, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:47:06 Iters: 507300/[12], loss: 3.7882, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:49:09 Iters: 507400/[12], loss: 4.6336, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:51:12 Iters: 507500/[12], loss: 4.1848, train_accuracy: 0.3438, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:53:16 Iters: 507600/[12], loss: 4.4492, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:55:19 Iters: 507700/[12], loss: 4.5866, train_accuracy: 0.3672, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:57:22 Iters: 507800/[12], loss: 3.7418, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-01:59:25 Iters: 507900/[12], loss: 3.4318, train_accuracy: 0.4219, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:01:29 Iters: 508000/[12], loss: 3.8035, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:03:32 Iters: 508100/[12], loss: 4.2869, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:05:35 Iters: 508200/[12], loss: 3.4668, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:07:38 Iters: 508300/[12], loss: 4.4921, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:09:42 Iters: 508400/[12], loss: 4.1111, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:11:45 Iters: 508500/[12], loss: 3.6666, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:13:48 Iters: 508600/[12], loss: 4.2737, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:15:52 Iters: 508700/[12], loss: 4.9771, train_accuracy: 0.3828, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:17:55 Iters: 508800/[12], loss: 3.9414, train_accuracy: 0.4141, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:19:58 Iters: 508900/[12], loss: 3.7661, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:22:02 Iters: 509000/[12], loss: 4.3718, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:24:05 Iters: 509100/[12], loss: 3.6488, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:26:09 Iters: 509200/[12], loss: 4.1959, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:28:12 Iters: 509300/[12], loss: 3.9373, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:30:15 Iters: 509400/[12], loss: 3.7154, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:32:18 Iters: 509500/[12], loss: 3.3347, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:34:21 Iters: 509600/[12], loss: 4.2143, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:36:24 Iters: 509700/[12], loss: 4.0542, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:38:27 Iters: 509800/[12], loss: 3.2625, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:40:30 Iters: 509900/[12], loss: 4.6548, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:42:33 Iters: 510000/[12], loss: 3.9993, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:42:33 Saving checkpoint: 510000
-20220708-02:43:49 LFW Ave Accuracy: 99.6000
-20220708-02:45:06 AgeDB-30 Ave Accuracy: 97.0167
-20220708-02:46:34 CFP-FP Ave Accuracy: 94.7143
-20220708-02:46:34 Current Best Accuracy: LFW: 99.6333 in iters: 460000, AgeDB-30: 97.0167 in iters: 510000 and CFP-FP: 94.7143 in iters: 510000
-20220708-02:48:37 Iters: 510100/[12], loss: 4.2740, train_accuracy: 0.4844, time: 3.64 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:50:40 Iters: 510200/[12], loss: 3.3865, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:52:43 Iters: 510300/[12], loss: 3.5658, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:54:45 Iters: 510400/[12], loss: 4.3553, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:56:48 Iters: 510500/[12], loss: 4.5393, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-02:58:51 Iters: 510600/[12], loss: 4.2130, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:00:54 Iters: 510700/[12], loss: 4.3845, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:02:57 Iters: 510800/[12], loss: 4.0886, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:05:00 Iters: 510900/[12], loss: 3.5711, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:07:03 Iters: 511000/[12], loss: 4.2083, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:09:06 Iters: 511100/[12], loss: 3.3718, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:11:09 Iters: 511200/[12], loss: 3.4204, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:13:12 Iters: 511300/[12], loss: 3.7623, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:15:15 Iters: 511400/[12], loss: 3.6431, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:17:18 Iters: 511500/[12], loss: 4.1133, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:19:21 Iters: 511600/[12], loss: 3.2772, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:21:24 Iters: 511700/[12], loss: 3.9678, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:23:27 Iters: 511800/[12], loss: 3.6323, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:25:29 Iters: 511900/[12], loss: 3.3380, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:27:32 Iters: 512000/[12], loss: 3.7517, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:29:35 Iters: 512100/[12], loss: 3.8524, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:31:39 Iters: 512200/[12], loss: 3.7746, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:33:42 Iters: 512300/[12], loss: 4.0064, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:35:45 Iters: 512400/[12], loss: 3.9372, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:37:48 Iters: 512500/[12], loss: 3.9973, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:39:52 Iters: 512600/[12], loss: 3.7327, train_accuracy: 0.5000, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:41:55 Iters: 512700/[12], loss: 3.6711, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:43:58 Iters: 512800/[12], loss: 3.3818, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:46:02 Iters: 512900/[12], loss: 3.5309, train_accuracy: 0.5156, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:48:05 Iters: 513000/[12], loss: 4.2535, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:50:09 Iters: 513100/[12], loss: 3.0142, train_accuracy: 0.5000, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:52:12 Iters: 513200/[12], loss: 3.1880, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:54:15 Iters: 513300/[12], loss: 4.1675, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:56:19 Iters: 513400/[12], loss: 3.8649, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-03:58:22 Iters: 513500/[12], loss: 4.3519, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:00:26 Iters: 513600/[12], loss: 3.7852, train_accuracy: 0.4922, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:02:29 Iters: 513700/[12], loss: 3.6997, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:04:32 Iters: 513800/[12], loss: 3.1791, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:06:36 Iters: 513900/[12], loss: 3.2463, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:08:39 Iters: 514000/[12], loss: 3.6300, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:10:42 Iters: 514100/[12], loss: 3.6579, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:12:46 Iters: 514200/[12], loss: 3.3845, train_accuracy: 0.5391, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:14:50 Iters: 514300/[12], loss: 3.4865, train_accuracy: 0.4609, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:16:53 Iters: 514400/[12], loss: 3.4391, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:18:56 Iters: 514500/[12], loss: 3.5603, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:21:00 Iters: 514600/[12], loss: 3.9507, train_accuracy: 0.4766, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:23:04 Iters: 514700/[12], loss: 3.6655, train_accuracy: 0.4531, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:25:07 Iters: 514800/[12], loss: 3.2235, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:27:10 Iters: 514900/[12], loss: 3.2679, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:29:13 Iters: 515000/[12], loss: 3.0586, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:31:16 Iters: 515100/[12], loss: 3.8078, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:33:19 Iters: 515200/[12], loss: 3.3923, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:35:22 Iters: 515300/[12], loss: 3.5409, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:37:25 Iters: 515400/[12], loss: 3.2441, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:39:28 Iters: 515500/[12], loss: 3.9492, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:41:31 Iters: 515600/[12], loss: 4.0181, train_accuracy: 0.4141, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:43:34 Iters: 515700/[12], loss: 4.6566, train_accuracy: 0.3906, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:45:37 Iters: 515800/[12], loss: 3.3865, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:47:40 Iters: 515900/[12], loss: 3.8249, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:49:43 Iters: 516000/[12], loss: 3.6235, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:51:45 Iters: 516100/[12], loss: 4.3341, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:53:48 Iters: 516200/[12], loss: 3.7117, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:55:51 Iters: 516300/[12], loss: 3.9127, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:57:54 Iters: 516400/[12], loss: 3.8612, train_accuracy: 0.4219, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-04:59:57 Iters: 516500/[12], loss: 3.5685, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:02:00 Iters: 516600/[12], loss: 4.0672, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:04:03 Iters: 516700/[12], loss: 4.1272, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:06:06 Iters: 516800/[12], loss: 3.2464, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:08:09 Iters: 516900/[12], loss: 4.2821, train_accuracy: 0.3984, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:10:12 Iters: 517000/[12], loss: 3.5115, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:12:15 Iters: 517100/[12], loss: 4.3220, train_accuracy: 0.4062, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:14:18 Iters: 517200/[12], loss: 3.8911, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:16:21 Iters: 517300/[12], loss: 3.3484, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:18:24 Iters: 517400/[12], loss: 3.8849, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:20:27 Iters: 517500/[12], loss: 3.8331, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:22:30 Iters: 517600/[12], loss: 4.1165, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:24:33 Iters: 517700/[12], loss: 3.7508, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:26:36 Iters: 517800/[12], loss: 3.4877, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:28:39 Iters: 517900/[12], loss: 3.3053, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:30:42 Iters: 518000/[12], loss: 3.6605, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:32:45 Iters: 518100/[12], loss: 3.8208, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:34:48 Iters: 518200/[12], loss: 3.7756, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:36:51 Iters: 518300/[12], loss: 3.8348, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:38:53 Iters: 518400/[12], loss: 3.3956, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:40:56 Iters: 518500/[12], loss: 2.8435, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:42:59 Iters: 518600/[12], loss: 2.9828, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:45:02 Iters: 518700/[12], loss: 3.1438, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:47:05 Iters: 518800/[12], loss: 4.0483, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:49:08 Iters: 518900/[12], loss: 3.4859, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:51:11 Iters: 519000/[12], loss: 3.8176, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:53:14 Iters: 519100/[12], loss: 3.2157, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:55:17 Iters: 519200/[12], loss: 3.3027, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:57:20 Iters: 519300/[12], loss: 3.8531, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-05:59:23 Iters: 519400/[12], loss: 4.5191, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:01:26 Iters: 519500/[12], loss: 3.0816, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:03:30 Iters: 519600/[12], loss: 3.8236, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:05:33 Iters: 519700/[12], loss: 4.0633, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:07:36 Iters: 519800/[12], loss: 3.5127, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:09:39 Iters: 519900/[12], loss: 3.9804, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:11:42 Iters: 520000/[12], loss: 3.1086, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:11:42 Saving checkpoint: 520000
-20220708-06:13:01 LFW Ave Accuracy: 99.6000
-20220708-06:14:20 AgeDB-30 Ave Accuracy: 97.0333
-20220708-06:15:50 CFP-FP Ave Accuracy: 94.6571
-20220708-06:15:50 Current Best Accuracy: LFW: 99.6333 in iters: 460000, AgeDB-30: 97.0333 in iters: 520000 and CFP-FP: 94.7143 in iters: 510000
-20220708-06:17:52 Iters: 520100/[12], loss: 3.3695, train_accuracy: 0.5312, time: 3.71 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:19:55 Iters: 520200/[12], loss: 3.8556, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:21:59 Iters: 520300/[12], loss: 4.0299, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:24:02 Iters: 520400/[12], loss: 3.7772, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:26:05 Iters: 520500/[12], loss: 3.5381, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:28:08 Iters: 520600/[12], loss: 4.5420, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:30:12 Iters: 520700/[12], loss: 3.7863, train_accuracy: 0.4844, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:32:15 Iters: 520800/[12], loss: 2.9080, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:34:19 Iters: 520900/[12], loss: 3.5370, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:36:22 Iters: 521000/[12], loss: 3.9331, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:38:25 Iters: 521100/[12], loss: 3.3592, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:40:28 Iters: 521200/[12], loss: 3.6804, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:42:31 Iters: 521300/[12], loss: 3.8937, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:44:34 Iters: 521400/[12], loss: 3.2752, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:46:37 Iters: 521500/[12], loss: 3.3242, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:48:40 Iters: 521600/[12], loss: 2.9937, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:50:43 Iters: 521700/[12], loss: 4.0667, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:52:46 Iters: 521800/[12], loss: 2.8120, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:54:49 Iters: 521900/[12], loss: 3.3237, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:56:51 Iters: 522000/[12], loss: 3.7177, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-06:58:54 Iters: 522100/[12], loss: 3.1949, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:00:57 Iters: 522200/[12], loss: 3.1283, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:03:00 Iters: 522300/[12], loss: 3.2841, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:05:03 Iters: 522400/[12], loss: 3.4813, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:07:06 Iters: 522500/[12], loss: 3.4149, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:09:09 Iters: 522600/[12], loss: 4.4441, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:11:12 Iters: 522700/[12], loss: 3.6693, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:13:15 Iters: 522800/[12], loss: 3.0067, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:15:18 Iters: 522900/[12], loss: 3.4320, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:17:21 Iters: 523000/[12], loss: 4.1577, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:19:24 Iters: 523100/[12], loss: 3.1737, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:21:27 Iters: 523200/[12], loss: 3.4038, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:23:30 Iters: 523300/[12], loss: 3.5602, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:25:32 Iters: 523400/[12], loss: 4.0029, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:27:36 Iters: 523500/[12], loss: 3.8885, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:29:39 Iters: 523600/[12], loss: 3.3178, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:31:42 Iters: 523700/[12], loss: 3.1362, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:33:45 Iters: 523800/[12], loss: 3.1815, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:35:48 Iters: 523900/[12], loss: 3.2132, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:37:51 Iters: 524000/[12], loss: 3.0657, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:39:54 Iters: 524100/[12], loss: 3.2931, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:41:57 Iters: 524200/[12], loss: 3.2155, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:44:00 Iters: 524300/[12], loss: 3.7368, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:46:03 Iters: 524400/[12], loss: 3.6495, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:48:06 Iters: 524500/[12], loss: 3.8442, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:50:09 Iters: 524600/[12], loss: 3.8468, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:52:12 Iters: 524700/[12], loss: 4.1362, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:54:15 Iters: 524800/[12], loss: 3.3755, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:56:18 Iters: 524900/[12], loss: 2.9236, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-07:58:21 Iters: 525000/[12], loss: 4.0502, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:00:24 Iters: 525100/[12], loss: 3.4574, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:02:27 Iters: 525200/[12], loss: 3.2426, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:04:30 Iters: 525300/[12], loss: 3.7143, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:06:33 Iters: 525400/[12], loss: 3.7474, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:08:36 Iters: 525500/[12], loss: 3.4617, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:10:39 Iters: 525600/[12], loss: 3.9148, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:12:42 Iters: 525700/[12], loss: 2.4111, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:14:45 Iters: 525800/[12], loss: 3.6610, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:16:48 Iters: 525900/[12], loss: 3.1508, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:18:51 Iters: 526000/[12], loss: 3.6059, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:20:54 Iters: 526100/[12], loss: 3.8213, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:22:58 Iters: 526200/[12], loss: 3.3295, train_accuracy: 0.4922, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:25:01 Iters: 526300/[12], loss: 3.5529, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:27:05 Iters: 526400/[12], loss: 3.5204, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:29:08 Iters: 526500/[12], loss: 3.5576, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:31:11 Iters: 526600/[12], loss: 4.1535, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:33:14 Iters: 526700/[12], loss: 3.7029, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:35:17 Iters: 526800/[12], loss: 3.0354, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:37:21 Iters: 526900/[12], loss: 3.6490, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:39:24 Iters: 527000/[12], loss: 2.7465, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:41:27 Iters: 527100/[12], loss: 3.2952, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:43:30 Iters: 527200/[12], loss: 3.4348, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:45:34 Iters: 527300/[12], loss: 4.4294, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:47:37 Iters: 527400/[12], loss: 2.9798, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:49:40 Iters: 527500/[12], loss: 3.9167, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:51:43 Iters: 527600/[12], loss: 3.5199, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:53:46 Iters: 527700/[12], loss: 3.7986, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:55:49 Iters: 527800/[12], loss: 2.9081, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:57:52 Iters: 527900/[12], loss: 3.1081, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-08:59:56 Iters: 528000/[12], loss: 3.0981, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:01:59 Iters: 528100/[12], loss: 3.5912, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:04:02 Iters: 528200/[12], loss: 3.9277, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:06:05 Iters: 528300/[12], loss: 3.8298, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:08:09 Iters: 528400/[12], loss: 3.6492, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:10:12 Iters: 528500/[12], loss: 4.6560, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:12:15 Iters: 528600/[12], loss: 3.5150, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:14:18 Iters: 528700/[12], loss: 2.7262, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:16:22 Iters: 528800/[12], loss: 4.1461, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:18:25 Iters: 528900/[12], loss: 3.9513, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:20:28 Iters: 529000/[12], loss: 3.8066, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:22:32 Iters: 529100/[12], loss: 3.4681, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:24:35 Iters: 529200/[12], loss: 4.0496, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:26:38 Iters: 529300/[12], loss: 2.7971, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:28:41 Iters: 529400/[12], loss: 3.1981, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:30:45 Iters: 529500/[12], loss: 3.3993, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:32:48 Iters: 529600/[12], loss: 3.5305, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:34:51 Iters: 529700/[12], loss: 2.9218, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:36:54 Iters: 529800/[12], loss: 3.7414, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:38:58 Iters: 529900/[12], loss: 3.1911, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:41:01 Iters: 530000/[12], loss: 3.4987, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:41:01 Saving checkpoint: 530000
-20220708-09:42:19 LFW Ave Accuracy: 99.6333
-20220708-09:43:35 AgeDB-30 Ave Accuracy: 97.0500
-20220708-09:45:05 CFP-FP Ave Accuracy: 94.5143
-20220708-09:45:05 Current Best Accuracy: LFW: 99.6333 in iters: 530000, AgeDB-30: 97.0500 in iters: 530000 and CFP-FP: 94.7143 in iters: 510000
-20220708-09:47:08 Iters: 530100/[12], loss: 3.7724, train_accuracy: 0.4922, time: 3.68 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:49:12 Iters: 530200/[12], loss: 3.0806, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:51:15 Iters: 530300/[12], loss: 3.0470, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:53:18 Iters: 530400/[12], loss: 3.2699, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:55:21 Iters: 530500/[12], loss: 3.9905, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:57:24 Iters: 530600/[12], loss: 3.3155, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-09:59:27 Iters: 530700/[12], loss: 3.0176, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:01:31 Iters: 530800/[12], loss: 3.9901, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:03:34 Iters: 530900/[12], loss: 3.0669, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:05:37 Iters: 531000/[12], loss: 4.5584, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:07:40 Iters: 531100/[12], loss: 3.4305, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:09:43 Iters: 531200/[12], loss: 3.8173, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:11:47 Iters: 531300/[12], loss: 3.4217, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:13:50 Iters: 531400/[12], loss: 2.9055, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:15:53 Iters: 531500/[12], loss: 2.1942, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:17:56 Iters: 531600/[12], loss: 4.1232, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:19:59 Iters: 531700/[12], loss: 3.3881, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:22:02 Iters: 531800/[12], loss: 3.2824, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:24:05 Iters: 531900/[12], loss: 3.5067, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:26:09 Iters: 532000/[12], loss: 3.8612, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:28:12 Iters: 532100/[12], loss: 3.4392, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:30:15 Iters: 532200/[12], loss: 3.7561, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:32:18 Iters: 532300/[12], loss: 3.4361, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:34:21 Iters: 532400/[12], loss: 3.6435, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:36:24 Iters: 532500/[12], loss: 3.9320, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:38:27 Iters: 532600/[12], loss: 2.8930, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:40:31 Iters: 532700/[12], loss: 3.4935, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:42:34 Iters: 532800/[12], loss: 3.4652, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:44:37 Iters: 532900/[12], loss: 3.6012, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:46:40 Iters: 533000/[12], loss: 3.4861, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:48:43 Iters: 533100/[12], loss: 3.6873, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:50:46 Iters: 533200/[12], loss: 3.7098, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:52:49 Iters: 533300/[12], loss: 3.3487, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:54:52 Iters: 533400/[12], loss: 3.3013, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:56:56 Iters: 533500/[12], loss: 4.0831, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-10:58:59 Iters: 533600/[12], loss: 3.6850, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:01:02 Iters: 533700/[12], loss: 3.1826, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:03:05 Iters: 533800/[12], loss: 3.8824, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:05:08 Iters: 533900/[12], loss: 2.9400, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:07:12 Iters: 534000/[12], loss: 3.4864, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:09:15 Iters: 534100/[12], loss: 3.6171, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:11:18 Iters: 534200/[12], loss: 3.7534, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:13:21 Iters: 534300/[12], loss: 4.1229, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:15:24 Iters: 534400/[12], loss: 3.9858, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:17:27 Iters: 534500/[12], loss: 3.7987, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:19:30 Iters: 534600/[12], loss: 3.2659, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:21:34 Iters: 534700/[12], loss: 3.4520, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:23:37 Iters: 534800/[12], loss: 2.6340, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:25:40 Iters: 534900/[12], loss: 3.4273, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:27:43 Iters: 535000/[12], loss: 3.1260, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:29:46 Iters: 535100/[12], loss: 2.9569, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:31:49 Iters: 535200/[12], loss: 3.1587, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:33:52 Iters: 535300/[12], loss: 2.9853, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:35:56 Iters: 535400/[12], loss: 3.2445, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:37:59 Iters: 535500/[12], loss: 3.6083, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:40:02 Iters: 535600/[12], loss: 3.5127, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:42:05 Iters: 535700/[12], loss: 2.9207, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:44:08 Iters: 535800/[12], loss: 2.9108, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:46:11 Iters: 535900/[12], loss: 3.4444, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:48:15 Iters: 536000/[12], loss: 2.8802, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:50:18 Iters: 536100/[12], loss: 4.1135, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:52:21 Iters: 536200/[12], loss: 3.0698, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:54:24 Iters: 536300/[12], loss: 3.3661, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:56:27 Iters: 536400/[12], loss: 3.5864, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-11:58:30 Iters: 536500/[12], loss: 3.3649, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:00:34 Iters: 536600/[12], loss: 3.4705, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:02:37 Iters: 536700/[12], loss: 2.9423, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:04:40 Iters: 536800/[12], loss: 2.8304, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:06:43 Iters: 536900/[12], loss: 3.4102, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:08:46 Iters: 537000/[12], loss: 3.1550, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:10:49 Iters: 537100/[12], loss: 3.2472, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:12:53 Iters: 537200/[12], loss: 3.4774, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:14:56 Iters: 537300/[12], loss: 3.3882, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:16:59 Iters: 537400/[12], loss: 3.8455, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:19:02 Iters: 537500/[12], loss: 3.2747, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:21:05 Iters: 537600/[12], loss: 3.8852, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:23:08 Iters: 537700/[12], loss: 3.4209, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:25:11 Iters: 537800/[12], loss: 3.3440, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:27:15 Iters: 537900/[12], loss: 3.8564, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:29:18 Iters: 538000/[12], loss: 3.4972, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:31:21 Iters: 538100/[12], loss: 3.4300, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:33:25 Iters: 538200/[12], loss: 4.4237, train_accuracy: 0.4297, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:35:28 Iters: 538300/[12], loss: 3.5656, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:37:31 Iters: 538400/[12], loss: 3.8241, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:39:34 Iters: 538500/[12], loss: 3.7981, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:41:37 Iters: 538600/[12], loss: 2.8329, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:43:40 Iters: 538700/[12], loss: 3.0563, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:45:44 Iters: 538800/[12], loss: 3.3858, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:47:47 Iters: 538900/[12], loss: 3.1838, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:49:50 Iters: 539000/[12], loss: 3.0677, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:51:53 Iters: 539100/[12], loss: 3.4848, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:53:56 Iters: 539200/[12], loss: 3.3732, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:56:00 Iters: 539300/[12], loss: 3.3008, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-12:58:03 Iters: 539400/[12], loss: 3.9783, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:00:06 Iters: 539500/[12], loss: 3.2235, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:02:09 Iters: 539600/[12], loss: 3.9870, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:04:13 Iters: 539700/[12], loss: 3.5537, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:06:16 Iters: 539800/[12], loss: 3.4234, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:08:19 Iters: 539900/[12], loss: 3.5838, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:10:22 Iters: 540000/[12], loss: 3.5072, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:10:22 Saving checkpoint: 540000
-20220708-13:11:41 LFW Ave Accuracy: 99.6333
-20220708-13:12:58 AgeDB-30 Ave Accuracy: 96.9667
-20220708-13:14:29 CFP-FP Ave Accuracy: 94.7857
-20220708-13:14:29 Current Best Accuracy: LFW: 99.6333 in iters: 540000, AgeDB-30: 97.0500 in iters: 530000 and CFP-FP: 94.7857 in iters: 540000
-20220708-13:16:32 Iters: 540100/[12], loss: 2.9476, train_accuracy: 0.5703, time: 3.70 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:18:35 Iters: 540200/[12], loss: 3.5375, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:20:38 Iters: 540300/[12], loss: 3.4141, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:22:41 Iters: 540400/[12], loss: 2.8290, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:24:45 Iters: 540500/[12], loss: 3.8258, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:26:48 Iters: 540600/[12], loss: 3.0204, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:28:51 Iters: 540700/[12], loss: 3.1065, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:30:54 Iters: 540800/[12], loss: 3.5650, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:32:57 Iters: 540900/[12], loss: 3.3094, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:35:00 Iters: 541000/[12], loss: 3.7390, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:37:03 Iters: 541100/[12], loss: 2.7926, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:39:07 Iters: 541200/[12], loss: 3.4441, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:41:10 Iters: 541300/[12], loss: 3.5014, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:43:13 Iters: 541400/[12], loss: 3.5559, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:45:16 Iters: 541500/[12], loss: 2.9021, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:47:19 Iters: 541600/[12], loss: 3.4972, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:49:22 Iters: 541700/[12], loss: 3.1263, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:51:26 Iters: 541800/[12], loss: 2.9789, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:53:29 Iters: 541900/[12], loss: 3.4708, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:55:32 Iters: 542000/[12], loss: 3.6584, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:57:35 Iters: 542100/[12], loss: 3.3531, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-13:59:38 Iters: 542200/[12], loss: 3.2166, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:01:41 Iters: 542300/[12], loss: 2.8615, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:03:45 Iters: 542400/[12], loss: 3.0999, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:05:48 Iters: 542500/[12], loss: 2.9674, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:07:51 Iters: 542600/[12], loss: 3.8240, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:09:54 Iters: 542700/[12], loss: 3.6342, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:11:57 Iters: 542800/[12], loss: 3.1042, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:14:00 Iters: 542900/[12], loss: 4.1338, train_accuracy: 0.4141, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:16:03 Iters: 543000/[12], loss: 2.7791, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:18:06 Iters: 543100/[12], loss: 3.6449, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:20:09 Iters: 543200/[12], loss: 3.4932, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:22:13 Iters: 543300/[12], loss: 3.4816, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:24:16 Iters: 543400/[12], loss: 2.9773, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:26:19 Iters: 543500/[12], loss: 3.1545, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:28:22 Iters: 543600/[12], loss: 3.7921, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:30:25 Iters: 543700/[12], loss: 3.7831, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:32:29 Iters: 543800/[12], loss: 3.1895, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:34:32 Iters: 543900/[12], loss: 4.0786, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:36:35 Iters: 544000/[12], loss: 2.8560, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:38:38 Iters: 544100/[12], loss: 3.2577, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:40:41 Iters: 544200/[12], loss: 3.4217, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:42:45 Iters: 544300/[12], loss: 3.6040, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:44:48 Iters: 544400/[12], loss: 2.8560, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:46:51 Iters: 544500/[12], loss: 2.7916, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:48:55 Iters: 544600/[12], loss: 3.2711, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:50:58 Iters: 544700/[12], loss: 3.7305, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:53:01 Iters: 544800/[12], loss: 3.0191, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:55:04 Iters: 544900/[12], loss: 3.6148, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:57:08 Iters: 545000/[12], loss: 2.8795, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-14:59:11 Iters: 545100/[12], loss: 3.4069, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-15:01:14 Iters: 545200/[12], loss: 3.3179, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-15:03:17 Iters: 545300/[12], loss: 3.1639, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-15:05:21 Iters: 545400/[12], loss: 3.4076, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-15:07:24 Iters: 545500/[12], loss: 3.2415, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-15:09:27 Iters: 545600/[12], loss: 3.3982, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-15:11:30 Iters: 545700/[12], loss: 3.1028, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-15:13:34 Iters: 545800/[12], loss: 3.8440, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220708-15:15:11 Train Epoch: 13/18 ...
-20220708-15:15:37 Iters: 545900/[13], loss: 2.7690, train_accuracy: 0.5703, time: 0.26 s/iter, learning rate: 0.0005000000000000001
-20220708-15:17:40 Iters: 546000/[13], loss: 3.2982, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:19:44 Iters: 546100/[13], loss: 3.0298, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:21:47 Iters: 546200/[13], loss: 3.1800, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:23:50 Iters: 546300/[13], loss: 3.3276, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:25:54 Iters: 546400/[13], loss: 3.2401, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:27:57 Iters: 546500/[13], loss: 3.0621, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:30:00 Iters: 546600/[13], loss: 3.0777, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:32:03 Iters: 546700/[13], loss: 2.4689, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:34:07 Iters: 546800/[13], loss: 4.4592, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:36:10 Iters: 546900/[13], loss: 3.4790, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:38:13 Iters: 547000/[13], loss: 3.0617, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:40:16 Iters: 547100/[13], loss: 2.8435, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:42:19 Iters: 547200/[13], loss: 2.9959, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:44:23 Iters: 547300/[13], loss: 3.7850, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:46:26 Iters: 547400/[13], loss: 3.2607, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:48:29 Iters: 547500/[13], loss: 2.8956, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:50:32 Iters: 547600/[13], loss: 3.5172, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:52:35 Iters: 547700/[13], loss: 2.7656, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:54:38 Iters: 547800/[13], loss: 2.9057, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:56:42 Iters: 547900/[13], loss: 2.9383, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-15:58:45 Iters: 548000/[13], loss: 2.7564, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:00:48 Iters: 548100/[13], loss: 3.3900, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:02:51 Iters: 548200/[13], loss: 3.7974, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:04:54 Iters: 548300/[13], loss: 2.5897, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:06:57 Iters: 548400/[13], loss: 3.1201, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:09:01 Iters: 548500/[13], loss: 3.0897, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:11:04 Iters: 548600/[13], loss: 2.9622, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:13:07 Iters: 548700/[13], loss: 3.3209, train_accuracy: 0.5547, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220708-16:15:11 Iters: 548800/[13], loss: 3.3944, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:17:14 Iters: 548900/[13], loss: 2.6343, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:19:17 Iters: 549000/[13], loss: 3.4641, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:21:21 Iters: 549100/[13], loss: 3.0399, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:23:24 Iters: 549200/[13], loss: 2.8585, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:25:27 Iters: 549300/[13], loss: 2.9576, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:27:30 Iters: 549400/[13], loss: 3.6218, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:29:33 Iters: 549500/[13], loss: 3.1640, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:31:36 Iters: 549600/[13], loss: 3.3073, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:33:40 Iters: 549700/[13], loss: 3.3561, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:35:43 Iters: 549800/[13], loss: 2.9024, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:37:46 Iters: 549900/[13], loss: 3.6948, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:39:49 Iters: 550000/[13], loss: 2.9487, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:39:49 Saving checkpoint: 550000
-20220708-16:41:05 LFW Ave Accuracy: 99.6166
-20220708-16:42:20 AgeDB-30 Ave Accuracy: 96.9333
-20220708-16:43:46 CFP-FP Ave Accuracy: 94.9714
-20220708-16:43:46 Current Best Accuracy: LFW: 99.6333 in iters: 540000, AgeDB-30: 97.0500 in iters: 530000 and CFP-FP: 94.9714 in iters: 550000
-20220708-16:45:49 Iters: 550100/[13], loss: 3.3208, train_accuracy: 0.5391, time: 3.60 s/iter, learning rate: 0.0005000000000000001
-20220708-16:47:52 Iters: 550200/[13], loss: 3.3728, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:49:55 Iters: 550300/[13], loss: 3.2119, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:51:58 Iters: 550400/[13], loss: 3.2787, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:54:01 Iters: 550500/[13], loss: 3.1430, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:56:04 Iters: 550600/[13], loss: 3.1111, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-16:58:08 Iters: 550700/[13], loss: 3.3240, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:00:11 Iters: 550800/[13], loss: 3.1909, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:02:14 Iters: 550900/[13], loss: 2.6840, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:04:17 Iters: 551000/[13], loss: 3.3816, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:06:20 Iters: 551100/[13], loss: 3.3496, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:08:24 Iters: 551200/[13], loss: 3.8466, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:10:27 Iters: 551300/[13], loss: 2.7119, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:12:30 Iters: 551400/[13], loss: 2.5593, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:14:33 Iters: 551500/[13], loss: 3.4218, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:16:36 Iters: 551600/[13], loss: 3.4325, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:18:40 Iters: 551700/[13], loss: 3.4909, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:20:43 Iters: 551800/[13], loss: 3.6939, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:22:46 Iters: 551900/[13], loss: 3.3687, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:24:49 Iters: 552000/[13], loss: 3.1744, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:26:52 Iters: 552100/[13], loss: 3.3105, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:28:55 Iters: 552200/[13], loss: 2.7966, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:30:58 Iters: 552300/[13], loss: 3.1024, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:33:01 Iters: 552400/[13], loss: 3.2992, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:35:05 Iters: 552500/[13], loss: 3.2982, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:37:08 Iters: 552600/[13], loss: 3.5486, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:39:11 Iters: 552700/[13], loss: 3.3705, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:41:14 Iters: 552800/[13], loss: 2.8626, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:43:17 Iters: 552900/[13], loss: 3.2630, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:45:20 Iters: 553000/[13], loss: 3.0408, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:47:23 Iters: 553100/[13], loss: 2.8218, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:49:27 Iters: 553200/[13], loss: 3.1394, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:51:30 Iters: 553300/[13], loss: 3.5649, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:53:33 Iters: 553400/[13], loss: 3.6012, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:55:36 Iters: 553500/[13], loss: 3.1530, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:57:39 Iters: 553600/[13], loss: 3.6841, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-17:59:43 Iters: 553700/[13], loss: 3.6692, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:01:46 Iters: 553800/[13], loss: 3.0526, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:03:49 Iters: 553900/[13], loss: 2.6457, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:05:52 Iters: 554000/[13], loss: 2.5896, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:07:55 Iters: 554100/[13], loss: 3.6426, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:09:58 Iters: 554200/[13], loss: 2.8517, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:12:01 Iters: 554300/[13], loss: 3.3215, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:14:04 Iters: 554400/[13], loss: 3.3559, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:16:07 Iters: 554500/[13], loss: 3.5413, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:18:10 Iters: 554600/[13], loss: 2.7406, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:20:14 Iters: 554700/[13], loss: 3.6034, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:22:17 Iters: 554800/[13], loss: 3.0800, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:24:20 Iters: 554900/[13], loss: 2.4500, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:26:23 Iters: 555000/[13], loss: 2.5991, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:28:26 Iters: 555100/[13], loss: 3.0647, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:30:29 Iters: 555200/[13], loss: 2.6695, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:32:32 Iters: 555300/[13], loss: 3.4229, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:34:35 Iters: 555400/[13], loss: 3.1616, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:36:38 Iters: 555500/[13], loss: 3.0310, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:38:41 Iters: 555600/[13], loss: 2.4361, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:40:44 Iters: 555700/[13], loss: 3.0259, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:42:47 Iters: 555800/[13], loss: 2.6239, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:44:50 Iters: 555900/[13], loss: 3.1680, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:46:53 Iters: 556000/[13], loss: 3.0153, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:48:56 Iters: 556100/[13], loss: 3.1480, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:50:58 Iters: 556200/[13], loss: 3.9841, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:53:01 Iters: 556300/[13], loss: 3.7178, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:55:04 Iters: 556400/[13], loss: 3.2490, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:57:07 Iters: 556500/[13], loss: 3.7554, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-18:59:10 Iters: 556600/[13], loss: 2.4589, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:01:13 Iters: 556700/[13], loss: 3.2803, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:03:16 Iters: 556800/[13], loss: 3.0028, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:05:19 Iters: 556900/[13], loss: 3.1763, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:07:22 Iters: 557000/[13], loss: 2.9443, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:09:25 Iters: 557100/[13], loss: 2.8866, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:11:28 Iters: 557200/[13], loss: 3.3582, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:13:31 Iters: 557300/[13], loss: 3.4129, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:15:33 Iters: 557400/[13], loss: 3.0224, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:17:36 Iters: 557500/[13], loss: 3.4068, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:19:39 Iters: 557600/[13], loss: 2.8342, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:21:42 Iters: 557700/[13], loss: 3.4718, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:23:45 Iters: 557800/[13], loss: 3.0665, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:25:48 Iters: 557900/[13], loss: 3.1682, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:27:51 Iters: 558000/[13], loss: 3.0202, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:29:54 Iters: 558100/[13], loss: 3.7339, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:31:57 Iters: 558200/[13], loss: 3.6646, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:34:00 Iters: 558300/[13], loss: 3.4664, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:36:03 Iters: 558400/[13], loss: 2.9276, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:38:06 Iters: 558500/[13], loss: 2.7800, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:40:09 Iters: 558600/[13], loss: 3.1776, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:42:11 Iters: 558700/[13], loss: 2.9136, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:44:14 Iters: 558800/[13], loss: 3.1490, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:46:17 Iters: 558900/[13], loss: 3.5306, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:48:20 Iters: 559000/[13], loss: 2.8379, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:50:24 Iters: 559100/[13], loss: 3.1952, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:52:27 Iters: 559200/[13], loss: 2.9256, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:54:30 Iters: 559300/[13], loss: 2.3865, train_accuracy: 0.7109, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:56:33 Iters: 559400/[13], loss: 2.9232, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-19:58:36 Iters: 559500/[13], loss: 3.3960, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:00:39 Iters: 559600/[13], loss: 3.0069, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:02:43 Iters: 559700/[13], loss: 2.9036, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:04:46 Iters: 559800/[13], loss: 3.6985, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:06:49 Iters: 559900/[13], loss: 2.6513, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:08:53 Iters: 560000/[13], loss: 2.8748, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:08:53 Saving checkpoint: 560000
-20220708-20:10:10 LFW Ave Accuracy: 99.6166
-20220708-20:11:26 AgeDB-30 Ave Accuracy: 97.0833
-20220708-20:12:52 CFP-FP Ave Accuracy: 95.0714
-20220708-20:12:52 Current Best Accuracy: LFW: 99.6333 in iters: 540000, AgeDB-30: 97.0833 in iters: 560000 and CFP-FP: 95.0714 in iters: 560000
-20220708-20:14:55 Iters: 560100/[13], loss: 3.2549, train_accuracy: 0.5938, time: 3.62 s/iter, learning rate: 0.0005000000000000001
-20220708-20:16:58 Iters: 560200/[13], loss: 3.8192, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:19:01 Iters: 560300/[13], loss: 3.5325, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:21:05 Iters: 560400/[13], loss: 2.5392, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:23:08 Iters: 560500/[13], loss: 3.0924, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:25:11 Iters: 560600/[13], loss: 2.9350, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:27:14 Iters: 560700/[13], loss: 3.2834, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:29:18 Iters: 560800/[13], loss: 3.3322, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:31:21 Iters: 560900/[13], loss: 2.9139, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:33:24 Iters: 561000/[13], loss: 2.6975, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:35:27 Iters: 561100/[13], loss: 3.1987, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:37:31 Iters: 561200/[13], loss: 3.2886, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:39:34 Iters: 561300/[13], loss: 2.8331, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:41:37 Iters: 561400/[13], loss: 3.4247, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:43:40 Iters: 561500/[13], loss: 3.0167, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:45:43 Iters: 561600/[13], loss: 3.7289, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:47:47 Iters: 561700/[13], loss: 3.1421, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:49:49 Iters: 561800/[13], loss: 3.2955, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:51:52 Iters: 561900/[13], loss: 3.0522, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:53:55 Iters: 562000/[13], loss: 3.2255, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:55:58 Iters: 562100/[13], loss: 3.1313, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-20:58:01 Iters: 562200/[13], loss: 3.1751, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:00:04 Iters: 562300/[13], loss: 3.1439, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:02:07 Iters: 562400/[13], loss: 3.3064, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:04:10 Iters: 562500/[13], loss: 2.6000, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:06:13 Iters: 562600/[13], loss: 3.4032, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:08:16 Iters: 562700/[13], loss: 3.4082, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:10:19 Iters: 562800/[13], loss: 3.5027, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:12:21 Iters: 562900/[13], loss: 3.2367, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:14:24 Iters: 563000/[13], loss: 2.8757, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:16:27 Iters: 563100/[13], loss: 2.8103, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:18:30 Iters: 563200/[13], loss: 2.9140, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:20:33 Iters: 563300/[13], loss: 3.4990, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:22:36 Iters: 563400/[13], loss: 3.4945, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:24:38 Iters: 563500/[13], loss: 3.6282, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:26:41 Iters: 563600/[13], loss: 3.6896, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:28:44 Iters: 563700/[13], loss: 2.6557, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:30:47 Iters: 563800/[13], loss: 3.6816, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:32:50 Iters: 563900/[13], loss: 2.9585, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:34:53 Iters: 564000/[13], loss: 3.5686, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:36:56 Iters: 564100/[13], loss: 3.0687, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:38:59 Iters: 564200/[13], loss: 3.6105, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:41:01 Iters: 564300/[13], loss: 3.6227, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:43:04 Iters: 564400/[13], loss: 2.7303, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:45:07 Iters: 564500/[13], loss: 2.7806, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:47:10 Iters: 564600/[13], loss: 3.4537, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:49:13 Iters: 564700/[13], loss: 3.2247, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:51:16 Iters: 564800/[13], loss: 2.9144, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:53:18 Iters: 564900/[13], loss: 3.7259, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:55:21 Iters: 565000/[13], loss: 3.2804, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:57:24 Iters: 565100/[13], loss: 3.6476, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-21:59:27 Iters: 565200/[13], loss: 3.1920, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:01:30 Iters: 565300/[13], loss: 2.9821, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:03:32 Iters: 565400/[13], loss: 2.9352, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:05:35 Iters: 565500/[13], loss: 3.4072, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:07:38 Iters: 565600/[13], loss: 2.9444, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:09:41 Iters: 565700/[13], loss: 3.2127, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:11:44 Iters: 565800/[13], loss: 3.1635, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:13:47 Iters: 565900/[13], loss: 2.6370, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:15:50 Iters: 566000/[13], loss: 2.7864, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:17:53 Iters: 566100/[13], loss: 3.1616, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:19:55 Iters: 566200/[13], loss: 3.3234, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:21:58 Iters: 566300/[13], loss: 2.7501, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:24:01 Iters: 566400/[13], loss: 2.8169, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:26:04 Iters: 566500/[13], loss: 3.2601, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:28:07 Iters: 566600/[13], loss: 3.0439, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:30:10 Iters: 566700/[13], loss: 3.5201, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:32:13 Iters: 566800/[13], loss: 3.3527, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:34:16 Iters: 566900/[13], loss: 2.6771, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:36:19 Iters: 567000/[13], loss: 3.3724, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:38:22 Iters: 567100/[13], loss: 2.6643, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:40:25 Iters: 567200/[13], loss: 3.0966, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:42:28 Iters: 567300/[13], loss: 3.7286, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:44:31 Iters: 567400/[13], loss: 3.8216, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:46:34 Iters: 567500/[13], loss: 3.4829, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:48:37 Iters: 567600/[13], loss: 3.6835, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:50:39 Iters: 567700/[13], loss: 2.9696, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:52:42 Iters: 567800/[13], loss: 3.2053, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:54:45 Iters: 567900/[13], loss: 2.9939, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:56:48 Iters: 568000/[13], loss: 3.1027, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-22:58:51 Iters: 568100/[13], loss: 3.2892, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:00:54 Iters: 568200/[13], loss: 3.0518, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:02:57 Iters: 568300/[13], loss: 3.4673, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:05:00 Iters: 568400/[13], loss: 2.3880, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:07:03 Iters: 568500/[13], loss: 3.2725, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:09:06 Iters: 568600/[13], loss: 2.7605, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:11:09 Iters: 568700/[13], loss: 3.9596, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:13:12 Iters: 568800/[13], loss: 2.3851, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:15:15 Iters: 568900/[13], loss: 3.2666, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:17:18 Iters: 569000/[13], loss: 2.7029, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:19:21 Iters: 569100/[13], loss: 2.6695, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:21:24 Iters: 569200/[13], loss: 3.5665, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:23:28 Iters: 569300/[13], loss: 3.5128, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:25:31 Iters: 569400/[13], loss: 3.3669, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:27:34 Iters: 569500/[13], loss: 3.1152, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:29:37 Iters: 569600/[13], loss: 3.0437, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:31:40 Iters: 569700/[13], loss: 3.5774, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:33:43 Iters: 569800/[13], loss: 2.7550, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:35:45 Iters: 569900/[13], loss: 2.9212, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:37:48 Iters: 570000/[13], loss: 3.8033, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:37:48 Saving checkpoint: 570000
-20220708-23:39:07 LFW Ave Accuracy: 99.6333
-20220708-23:40:25 AgeDB-30 Ave Accuracy: 97.2167
-20220708-23:41:58 CFP-FP Ave Accuracy: 94.9571
-20220708-23:41:58 Current Best Accuracy: LFW: 99.6333 in iters: 570000, AgeDB-30: 97.2167 in iters: 570000 and CFP-FP: 95.0714 in iters: 560000
-20220708-23:44:00 Iters: 570100/[13], loss: 2.9519, train_accuracy: 0.6016, time: 3.72 s/iter, learning rate: 0.0005000000000000001
-20220708-23:46:03 Iters: 570200/[13], loss: 3.3638, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:48:07 Iters: 570300/[13], loss: 3.1501, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:50:10 Iters: 570400/[13], loss: 3.4138, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:52:13 Iters: 570500/[13], loss: 2.7183, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:54:16 Iters: 570600/[13], loss: 3.2666, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:56:19 Iters: 570700/[13], loss: 3.3274, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220708-23:58:22 Iters: 570800/[13], loss: 3.2458, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:00:25 Iters: 570900/[13], loss: 3.0952, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:02:28 Iters: 571000/[13], loss: 3.2384, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:04:31 Iters: 571100/[13], loss: 3.8934, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:06:34 Iters: 571200/[13], loss: 3.3160, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:08:37 Iters: 571300/[13], loss: 3.7116, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:10:40 Iters: 571400/[13], loss: 3.4504, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:12:44 Iters: 571500/[13], loss: 3.1922, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:14:47 Iters: 571600/[13], loss: 3.1776, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:16:50 Iters: 571700/[13], loss: 3.3238, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:18:52 Iters: 571800/[13], loss: 3.0753, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:20:55 Iters: 571900/[13], loss: 3.2041, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:22:58 Iters: 572000/[13], loss: 2.6278, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:25:01 Iters: 572100/[13], loss: 3.4377, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:27:04 Iters: 572200/[13], loss: 2.8231, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:29:07 Iters: 572300/[13], loss: 3.4708, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:31:10 Iters: 572400/[13], loss: 2.7130, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:33:13 Iters: 572500/[13], loss: 3.2694, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:35:16 Iters: 572600/[13], loss: 3.8669, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:37:19 Iters: 572700/[13], loss: 3.6833, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:39:22 Iters: 572800/[13], loss: 2.9422, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:41:25 Iters: 572900/[13], loss: 2.8729, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:43:28 Iters: 573000/[13], loss: 3.4753, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:45:31 Iters: 573100/[13], loss: 3.5200, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:47:34 Iters: 573200/[13], loss: 2.8721, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:49:37 Iters: 573300/[13], loss: 2.9355, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:51:40 Iters: 573400/[13], loss: 3.3783, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:53:43 Iters: 573500/[13], loss: 3.1785, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:55:46 Iters: 573600/[13], loss: 3.0761, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:57:49 Iters: 573700/[13], loss: 2.1440, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-00:59:52 Iters: 573800/[13], loss: 3.1350, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:01:56 Iters: 573900/[13], loss: 2.8469, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:03:59 Iters: 574000/[13], loss: 3.2407, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:06:02 Iters: 574100/[13], loss: 3.5923, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:08:05 Iters: 574200/[13], loss: 3.6763, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:10:08 Iters: 574300/[13], loss: 3.2765, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:12:11 Iters: 574400/[13], loss: 2.8856, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:14:15 Iters: 574500/[13], loss: 2.9957, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:16:18 Iters: 574600/[13], loss: 3.6594, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:18:21 Iters: 574700/[13], loss: 2.9432, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:20:25 Iters: 574800/[13], loss: 3.4627, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:22:28 Iters: 574900/[13], loss: 3.2074, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:24:31 Iters: 575000/[13], loss: 3.3283, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:26:34 Iters: 575100/[13], loss: 3.3600, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:28:38 Iters: 575200/[13], loss: 2.9741, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:30:41 Iters: 575300/[13], loss: 3.3044, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:32:44 Iters: 575400/[13], loss: 3.1200, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:34:47 Iters: 575500/[13], loss: 3.6809, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:36:50 Iters: 575600/[13], loss: 4.0742, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:38:53 Iters: 575700/[13], loss: 2.6990, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:40:56 Iters: 575800/[13], loss: 2.5884, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:43:00 Iters: 575900/[13], loss: 3.1097, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:45:03 Iters: 576000/[13], loss: 3.7320, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:47:06 Iters: 576100/[13], loss: 3.0668, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:49:09 Iters: 576200/[13], loss: 3.6037, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:51:12 Iters: 576300/[13], loss: 3.7275, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:53:15 Iters: 576400/[13], loss: 3.1265, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:55:18 Iters: 576500/[13], loss: 3.4105, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:57:21 Iters: 576600/[13], loss: 2.9322, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-01:59:24 Iters: 576700/[13], loss: 3.4912, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:01:27 Iters: 576800/[13], loss: 2.8959, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:03:31 Iters: 576900/[13], loss: 3.8201, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:05:34 Iters: 577000/[13], loss: 3.0344, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:07:37 Iters: 577100/[13], loss: 2.9543, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:09:40 Iters: 577200/[13], loss: 3.2921, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:11:43 Iters: 577300/[13], loss: 3.1844, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:13:46 Iters: 577400/[13], loss: 2.6670, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:15:49 Iters: 577500/[13], loss: 2.9340, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:17:52 Iters: 577600/[13], loss: 2.8591, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:19:55 Iters: 577700/[13], loss: 3.3134, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:21:58 Iters: 577800/[13], loss: 2.7842, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:24:01 Iters: 577900/[13], loss: 3.1002, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:26:04 Iters: 578000/[13], loss: 3.4108, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:28:07 Iters: 578100/[13], loss: 3.4555, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:30:10 Iters: 578200/[13], loss: 3.3020, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:32:13 Iters: 578300/[13], loss: 3.2162, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:34:16 Iters: 578400/[13], loss: 2.8263, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:36:19 Iters: 578500/[13], loss: 3.2963, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:38:22 Iters: 578600/[13], loss: 2.7881, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:40:26 Iters: 578700/[13], loss: 2.8065, train_accuracy: 0.5859, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220709-02:42:29 Iters: 578800/[13], loss: 2.9750, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:44:32 Iters: 578900/[13], loss: 2.6062, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:46:35 Iters: 579000/[13], loss: 3.1690, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:48:38 Iters: 579100/[13], loss: 3.2425, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:50:41 Iters: 579200/[13], loss: 3.4346, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:52:44 Iters: 579300/[13], loss: 2.9069, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:54:47 Iters: 579400/[13], loss: 3.0003, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:56:50 Iters: 579500/[13], loss: 2.7132, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-02:58:52 Iters: 579600/[13], loss: 2.9121, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:00:55 Iters: 579700/[13], loss: 3.2476, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:02:58 Iters: 579800/[13], loss: 2.4134, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:05:01 Iters: 579900/[13], loss: 3.3700, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:07:04 Iters: 580000/[13], loss: 3.1507, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:07:04 Saving checkpoint: 580000
-20220709-03:08:21 LFW Ave Accuracy: 99.6166
-20220709-03:09:36 AgeDB-30 Ave Accuracy: 97.2000
-20220709-03:11:03 CFP-FP Ave Accuracy: 95.0571
-20220709-03:11:03 Current Best Accuracy: LFW: 99.6333 in iters: 570000, AgeDB-30: 97.2167 in iters: 570000 and CFP-FP: 95.0714 in iters: 560000
-20220709-03:13:06 Iters: 580100/[13], loss: 3.8088, train_accuracy: 0.5703, time: 3.61 s/iter, learning rate: 0.0005000000000000001
-20220709-03:15:09 Iters: 580200/[13], loss: 3.5336, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:17:11 Iters: 580300/[13], loss: 3.1797, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:19:14 Iters: 580400/[13], loss: 3.4489, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:21:18 Iters: 580500/[13], loss: 3.2848, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:23:21 Iters: 580600/[13], loss: 3.3819, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:25:24 Iters: 580700/[13], loss: 2.5523, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:27:27 Iters: 580800/[13], loss: 2.9747, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:29:30 Iters: 580900/[13], loss: 2.4942, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:31:33 Iters: 581000/[13], loss: 3.7447, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:33:36 Iters: 581100/[13], loss: 3.3319, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:35:39 Iters: 581200/[13], loss: 4.0387, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:37:42 Iters: 581300/[13], loss: 3.0692, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:39:45 Iters: 581400/[13], loss: 3.3809, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:41:48 Iters: 581500/[13], loss: 3.1140, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:43:51 Iters: 581600/[13], loss: 2.8097, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:45:54 Iters: 581700/[13], loss: 3.2941, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:47:57 Iters: 581800/[13], loss: 3.1005, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:50:00 Iters: 581900/[13], loss: 3.6680, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:52:03 Iters: 582000/[13], loss: 2.9866, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:54:07 Iters: 582100/[13], loss: 3.1110, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:56:10 Iters: 582200/[13], loss: 2.9174, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-03:58:13 Iters: 582300/[13], loss: 3.5897, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:00:16 Iters: 582400/[13], loss: 3.5313, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:02:19 Iters: 582500/[13], loss: 3.3953, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:04:22 Iters: 582600/[13], loss: 3.3450, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:06:25 Iters: 582700/[13], loss: 2.8890, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:08:28 Iters: 582800/[13], loss: 3.0490, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:10:31 Iters: 582900/[13], loss: 3.1339, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:12:34 Iters: 583000/[13], loss: 3.0763, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:14:37 Iters: 583100/[13], loss: 3.2021, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:16:40 Iters: 583200/[13], loss: 3.0799, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:18:43 Iters: 583300/[13], loss: 3.4180, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:20:46 Iters: 583400/[13], loss: 3.1979, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:22:49 Iters: 583500/[13], loss: 3.4750, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:24:52 Iters: 583600/[13], loss: 2.9536, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:26:55 Iters: 583700/[13], loss: 3.3007, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:28:58 Iters: 583800/[13], loss: 3.4305, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:31:01 Iters: 583900/[13], loss: 3.3457, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:33:05 Iters: 584000/[13], loss: 3.6745, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:35:08 Iters: 584100/[13], loss: 3.0659, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:37:11 Iters: 584200/[13], loss: 3.3546, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:39:14 Iters: 584300/[13], loss: 3.5635, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:41:17 Iters: 584400/[13], loss: 3.2861, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:43:20 Iters: 584500/[13], loss: 3.3244, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:45:23 Iters: 584600/[13], loss: 2.4938, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:47:26 Iters: 584700/[13], loss: 2.9484, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:49:29 Iters: 584800/[13], loss: 3.1801, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:51:32 Iters: 584900/[13], loss: 2.8890, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:53:35 Iters: 585000/[13], loss: 2.9988, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:55:38 Iters: 585100/[13], loss: 3.7518, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:57:42 Iters: 585200/[13], loss: 3.4473, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-04:59:45 Iters: 585300/[13], loss: 2.8996, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:01:48 Iters: 585400/[13], loss: 3.5145, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:03:51 Iters: 585500/[13], loss: 3.4692, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:05:54 Iters: 585600/[13], loss: 2.6847, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:07:57 Iters: 585700/[13], loss: 3.0420, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:09:59 Iters: 585800/[13], loss: 3.1688, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:12:02 Iters: 585900/[13], loss: 3.2274, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:14:05 Iters: 586000/[13], loss: 3.2901, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:16:08 Iters: 586100/[13], loss: 2.9996, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:18:11 Iters: 586200/[13], loss: 3.3275, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:20:14 Iters: 586300/[13], loss: 2.8019, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:22:17 Iters: 586400/[13], loss: 3.5593, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:24:20 Iters: 586500/[13], loss: 3.0662, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:26:23 Iters: 586600/[13], loss: 3.2284, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:28:26 Iters: 586700/[13], loss: 2.7706, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:30:29 Iters: 586800/[13], loss: 2.6295, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:32:32 Iters: 586900/[13], loss: 3.4240, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:34:35 Iters: 587000/[13], loss: 3.3017, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:36:38 Iters: 587100/[13], loss: 3.2638, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:38:41 Iters: 587200/[13], loss: 2.9017, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:40:43 Iters: 587300/[13], loss: 3.7549, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:42:46 Iters: 587400/[13], loss: 3.2549, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:44:49 Iters: 587500/[13], loss: 3.2113, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:46:52 Iters: 587600/[13], loss: 2.5497, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:48:55 Iters: 587700/[13], loss: 3.1278, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:50:58 Iters: 587800/[13], loss: 2.8169, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:53:01 Iters: 587900/[13], loss: 3.1486, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:55:04 Iters: 588000/[13], loss: 3.1668, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:57:08 Iters: 588100/[13], loss: 3.4026, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-05:59:11 Iters: 588200/[13], loss: 3.1051, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:01:14 Iters: 588300/[13], loss: 3.5573, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:03:18 Iters: 588400/[13], loss: 3.0497, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:05:21 Iters: 588500/[13], loss: 2.9619, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:07:24 Iters: 588600/[13], loss: 3.2176, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:09:27 Iters: 588700/[13], loss: 3.0630, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:11:30 Iters: 588800/[13], loss: 3.0236, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:13:33 Iters: 588900/[13], loss: 3.2936, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:15:36 Iters: 589000/[13], loss: 3.5114, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:17:39 Iters: 589100/[13], loss: 2.9218, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:19:42 Iters: 589200/[13], loss: 2.7707, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:21:45 Iters: 589300/[13], loss: 3.8978, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:23:48 Iters: 589400/[13], loss: 3.4250, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:25:51 Iters: 589500/[13], loss: 3.1138, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:27:54 Iters: 589600/[13], loss: 3.2626, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:29:58 Iters: 589700/[13], loss: 3.4273, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:32:01 Iters: 589800/[13], loss: 3.6746, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:34:04 Iters: 589900/[13], loss: 2.6497, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:36:07 Iters: 590000/[13], loss: 2.9710, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:36:07 Saving checkpoint: 590000
-20220709-06:37:26 LFW Ave Accuracy: 99.6500
-20220709-06:38:45 AgeDB-30 Ave Accuracy: 97.1667
-20220709-06:40:17 CFP-FP Ave Accuracy: 94.8714
-20220709-06:40:17 Current Best Accuracy: LFW: 99.6500 in iters: 590000, AgeDB-30: 97.2167 in iters: 570000 and CFP-FP: 95.0714 in iters: 560000
-20220709-06:42:20 Iters: 590100/[13], loss: 3.7771, train_accuracy: 0.5312, time: 3.73 s/iter, learning rate: 0.0005000000000000001
-20220709-06:44:23 Iters: 590200/[13], loss: 3.3010, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:46:27 Iters: 590300/[13], loss: 3.1683, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:48:30 Iters: 590400/[13], loss: 3.2930, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:50:33 Iters: 590500/[13], loss: 3.1827, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:52:36 Iters: 590600/[13], loss: 3.3761, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:54:39 Iters: 590700/[13], loss: 2.4834, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:56:42 Iters: 590800/[13], loss: 3.7662, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-06:58:45 Iters: 590900/[13], loss: 3.1639, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:00:49 Iters: 591000/[13], loss: 3.8619, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:02:52 Iters: 591100/[13], loss: 3.0734, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:04:55 Iters: 591200/[13], loss: 3.1408, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:06:58 Iters: 591300/[13], loss: 2.8131, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:08:24 Train Epoch: 14/18 ...
-20220709-07:09:01 Iters: 591400/[14], loss: 2.6771, train_accuracy: 0.6328, time: 0.38 s/iter, learning rate: 0.0005000000000000001
-20220709-07:11:05 Iters: 591500/[14], loss: 2.8399, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:13:08 Iters: 591600/[14], loss: 3.6744, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:15:11 Iters: 591700/[14], loss: 3.2697, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:17:14 Iters: 591800/[14], loss: 3.5016, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:19:18 Iters: 591900/[14], loss: 3.1012, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:21:21 Iters: 592000/[14], loss: 2.9844, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:23:24 Iters: 592100/[14], loss: 3.2107, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:25:27 Iters: 592200/[14], loss: 3.3858, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:27:31 Iters: 592300/[14], loss: 2.8368, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:29:34 Iters: 592400/[14], loss: 3.1326, train_accuracy: 0.5391, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220709-07:31:37 Iters: 592500/[14], loss: 2.7969, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:33:41 Iters: 592600/[14], loss: 3.1048, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:35:44 Iters: 592700/[14], loss: 2.9726, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:37:47 Iters: 592800/[14], loss: 3.6476, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:39:50 Iters: 592900/[14], loss: 3.5173, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:41:53 Iters: 593000/[14], loss: 2.8491, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:43:56 Iters: 593100/[14], loss: 2.7434, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:45:59 Iters: 593200/[14], loss: 2.9499, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:48:02 Iters: 593300/[14], loss: 2.1376, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:50:06 Iters: 593400/[14], loss: 2.9579, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:52:09 Iters: 593500/[14], loss: 3.2884, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:54:12 Iters: 593600/[14], loss: 3.1248, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:56:15 Iters: 593700/[14], loss: 2.7254, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-07:58:18 Iters: 593800/[14], loss: 3.3039, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:00:21 Iters: 593900/[14], loss: 2.9137, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:02:24 Iters: 594000/[14], loss: 3.8798, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:04:27 Iters: 594100/[14], loss: 2.8208, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:06:30 Iters: 594200/[14], loss: 3.3653, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:08:33 Iters: 594300/[14], loss: 2.7797, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:10:36 Iters: 594400/[14], loss: 2.8489, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:12:39 Iters: 594500/[14], loss: 3.0824, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:14:42 Iters: 594600/[14], loss: 3.6395, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:16:45 Iters: 594700/[14], loss: 3.4120, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:18:49 Iters: 594800/[14], loss: 3.1523, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:20:52 Iters: 594900/[14], loss: 2.8651, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:22:55 Iters: 595000/[14], loss: 2.9520, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:24:58 Iters: 595100/[14], loss: 3.2539, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:27:01 Iters: 595200/[14], loss: 2.4288, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:29:04 Iters: 595300/[14], loss: 3.1235, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:31:07 Iters: 595400/[14], loss: 2.9779, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:33:11 Iters: 595500/[14], loss: 2.7932, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:35:14 Iters: 595600/[14], loss: 3.0092, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:37:17 Iters: 595700/[14], loss: 2.8079, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:39:20 Iters: 595800/[14], loss: 2.9847, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:41:23 Iters: 595900/[14], loss: 2.9674, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:43:26 Iters: 596000/[14], loss: 3.2368, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:45:29 Iters: 596100/[14], loss: 2.9130, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:47:32 Iters: 596200/[14], loss: 3.6132, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:49:35 Iters: 596300/[14], loss: 3.2148, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:51:38 Iters: 596400/[14], loss: 2.8245, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:53:41 Iters: 596500/[14], loss: 3.4488, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:55:44 Iters: 596600/[14], loss: 3.0894, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:57:47 Iters: 596700/[14], loss: 3.6485, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-08:59:50 Iters: 596800/[14], loss: 2.6241, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:01:54 Iters: 596900/[14], loss: 3.2394, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:03:57 Iters: 597000/[14], loss: 2.5949, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:06:00 Iters: 597100/[14], loss: 3.0232, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:08:03 Iters: 597200/[14], loss: 2.9142, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:10:06 Iters: 597300/[14], loss: 2.7734, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:12:09 Iters: 597400/[14], loss: 3.3594, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:14:12 Iters: 597500/[14], loss: 3.2097, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:16:15 Iters: 597600/[14], loss: 2.7069, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:18:18 Iters: 597700/[14], loss: 3.2376, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:20:21 Iters: 597800/[14], loss: 2.9281, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:22:24 Iters: 597900/[14], loss: 3.2055, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:24:27 Iters: 598000/[14], loss: 2.4608, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:26:30 Iters: 598100/[14], loss: 2.9603, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:28:33 Iters: 598200/[14], loss: 2.9800, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:30:36 Iters: 598300/[14], loss: 2.4410, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:32:39 Iters: 598400/[14], loss: 3.1383, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:34:42 Iters: 598500/[14], loss: 3.5748, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:36:45 Iters: 598600/[14], loss: 3.2090, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:38:48 Iters: 598700/[14], loss: 3.0295, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:40:51 Iters: 598800/[14], loss: 3.4384, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:42:54 Iters: 598900/[14], loss: 2.9461, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:44:57 Iters: 599000/[14], loss: 3.4736, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:47:00 Iters: 599100/[14], loss: 3.2519, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:49:04 Iters: 599200/[14], loss: 3.2472, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:51:07 Iters: 599300/[14], loss: 3.3813, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:53:10 Iters: 599400/[14], loss: 3.3245, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:55:14 Iters: 599500/[14], loss: 2.9510, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:57:17 Iters: 599600/[14], loss: 3.0432, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-09:59:20 Iters: 599700/[14], loss: 3.0715, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:01:24 Iters: 599800/[14], loss: 3.5193, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:03:27 Iters: 599900/[14], loss: 3.3485, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:05:30 Iters: 600000/[14], loss: 3.0502, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:05:30 Saving checkpoint: 600000
-20220709-10:06:47 LFW Ave Accuracy: 99.6666
-20220709-10:08:02 AgeDB-30 Ave Accuracy: 97.2833
-20220709-10:09:29 CFP-FP Ave Accuracy: 95.1286
-20220709-10:09:29 Current Best Accuracy: LFW: 99.6666 in iters: 600000, AgeDB-30: 97.2833 in iters: 600000 and CFP-FP: 95.1286 in iters: 600000
-20220709-10:11:32 Iters: 600100/[14], loss: 3.4286, train_accuracy: 0.5703, time: 3.62 s/iter, learning rate: 0.0005000000000000001
-20220709-10:13:35 Iters: 600200/[14], loss: 2.5018, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:15:39 Iters: 600300/[14], loss: 3.0295, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:17:42 Iters: 600400/[14], loss: 3.5669, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:19:45 Iters: 600500/[14], loss: 3.1980, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:21:49 Iters: 600600/[14], loss: 3.4768, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:23:52 Iters: 600700/[14], loss: 3.5689, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:25:55 Iters: 600800/[14], loss: 2.4753, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:27:59 Iters: 600900/[14], loss: 3.2682, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:30:02 Iters: 601000/[14], loss: 3.4180, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:32:05 Iters: 601100/[14], loss: 2.7064, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:34:08 Iters: 601200/[14], loss: 3.1104, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:36:12 Iters: 601300/[14], loss: 2.6096, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:38:15 Iters: 601400/[14], loss: 2.9503, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:40:18 Iters: 601500/[14], loss: 3.6450, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:42:22 Iters: 601600/[14], loss: 3.4333, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:44:25 Iters: 601700/[14], loss: 3.5753, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:46:28 Iters: 601800/[14], loss: 3.1425, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:48:32 Iters: 601900/[14], loss: 3.3012, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:50:35 Iters: 602000/[14], loss: 3.3202, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:52:38 Iters: 602100/[14], loss: 2.9457, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:54:42 Iters: 602200/[14], loss: 3.1182, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:56:45 Iters: 602300/[14], loss: 2.6721, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-10:58:49 Iters: 602400/[14], loss: 2.8463, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:00:52 Iters: 602500/[14], loss: 3.1204, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:02:55 Iters: 602600/[14], loss: 2.8839, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:04:59 Iters: 602700/[14], loss: 2.6780, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:07:02 Iters: 602800/[14], loss: 2.9013, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:09:05 Iters: 602900/[14], loss: 2.6891, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:11:09 Iters: 603000/[14], loss: 2.6576, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:13:12 Iters: 603100/[14], loss: 2.7662, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:15:15 Iters: 603200/[14], loss: 3.0377, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:17:19 Iters: 603300/[14], loss: 2.6150, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:19:22 Iters: 603400/[14], loss: 2.8605, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:21:25 Iters: 603500/[14], loss: 2.7132, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:23:29 Iters: 603600/[14], loss: 2.8352, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220709-11:25:32 Iters: 603700/[14], loss: 3.1467, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:27:36 Iters: 603800/[14], loss: 3.2065, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:29:39 Iters: 603900/[14], loss: 3.1367, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:31:42 Iters: 604000/[14], loss: 2.8090, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:33:46 Iters: 604100/[14], loss: 3.3177, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:35:49 Iters: 604200/[14], loss: 3.2600, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:37:52 Iters: 604300/[14], loss: 2.4912, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:39:55 Iters: 604400/[14], loss: 2.7985, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:41:59 Iters: 604500/[14], loss: 2.8401, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:44:02 Iters: 604600/[14], loss: 3.1397, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:46:05 Iters: 604700/[14], loss: 3.0959, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:48:09 Iters: 604800/[14], loss: 2.8924, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:50:12 Iters: 604900/[14], loss: 3.3947, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:52:15 Iters: 605000/[14], loss: 3.1544, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:54:19 Iters: 605100/[14], loss: 3.0536, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:56:22 Iters: 605200/[14], loss: 2.7900, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-11:58:25 Iters: 605300/[14], loss: 2.9996, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:00:29 Iters: 605400/[14], loss: 2.6793, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:02:32 Iters: 605500/[14], loss: 3.1633, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:04:35 Iters: 605600/[14], loss: 3.3813, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:06:39 Iters: 605700/[14], loss: 3.1408, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:08:42 Iters: 605800/[14], loss: 2.8813, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:10:45 Iters: 605900/[14], loss: 2.5866, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:12:49 Iters: 606000/[14], loss: 3.5322, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:14:52 Iters: 606100/[14], loss: 2.7192, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:16:55 Iters: 606200/[14], loss: 3.0285, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:18:59 Iters: 606300/[14], loss: 3.5989, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:21:02 Iters: 606400/[14], loss: 3.3536, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:23:05 Iters: 606500/[14], loss: 3.0833, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220709-12:25:09 Iters: 606600/[14], loss: 2.9272, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:27:12 Iters: 606700/[14], loss: 2.7626, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:29:15 Iters: 606800/[14], loss: 2.9860, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:31:19 Iters: 606900/[14], loss: 3.4734, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:33:22 Iters: 607000/[14], loss: 2.6543, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:35:26 Iters: 607100/[14], loss: 3.2918, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:37:29 Iters: 607200/[14], loss: 2.5361, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:39:32 Iters: 607300/[14], loss: 2.9315, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:41:36 Iters: 607400/[14], loss: 3.5520, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:43:39 Iters: 607500/[14], loss: 3.2783, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:45:42 Iters: 607600/[14], loss: 4.0574, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:47:46 Iters: 607700/[14], loss: 2.8459, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:49:49 Iters: 607800/[14], loss: 3.2417, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:51:52 Iters: 607900/[14], loss: 3.3291, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:53:55 Iters: 608000/[14], loss: 2.9709, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:55:59 Iters: 608100/[14], loss: 2.8459, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-12:58:02 Iters: 608200/[14], loss: 2.8771, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:00:05 Iters: 608300/[14], loss: 3.1411, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:02:09 Iters: 608400/[14], loss: 2.7864, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:04:12 Iters: 608500/[14], loss: 3.0132, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:06:15 Iters: 608600/[14], loss: 2.5683, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:08:19 Iters: 608700/[14], loss: 2.8128, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:10:22 Iters: 608800/[14], loss: 3.1193, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:12:25 Iters: 608900/[14], loss: 2.9749, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:14:29 Iters: 609000/[14], loss: 3.7638, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:16:32 Iters: 609100/[14], loss: 3.0866, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:18:35 Iters: 609200/[14], loss: 2.9505, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:20:39 Iters: 609300/[14], loss: 3.1841, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:22:42 Iters: 609400/[14], loss: 3.4253, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:24:45 Iters: 609500/[14], loss: 2.9002, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:26:48 Iters: 609600/[14], loss: 2.6845, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:28:52 Iters: 609700/[14], loss: 2.7861, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:30:55 Iters: 609800/[14], loss: 3.4582, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:32:59 Iters: 609900/[14], loss: 3.4207, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:35:02 Iters: 610000/[14], loss: 3.5836, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:35:02 Saving checkpoint: 610000
-20220709-13:36:21 LFW Ave Accuracy: 99.6500
-20220709-13:37:37 AgeDB-30 Ave Accuracy: 97.2500
-20220709-13:39:05 CFP-FP Ave Accuracy: 95.0571
-20220709-13:39:05 Current Best Accuracy: LFW: 99.6666 in iters: 600000, AgeDB-30: 97.2833 in iters: 600000 and CFP-FP: 95.1286 in iters: 600000
-20220709-13:41:08 Iters: 610100/[14], loss: 3.2902, train_accuracy: 0.5859, time: 3.66 s/iter, learning rate: 0.0005000000000000001
-20220709-13:43:11 Iters: 610200/[14], loss: 2.8781, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:45:15 Iters: 610300/[14], loss: 3.1686, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:47:18 Iters: 610400/[14], loss: 2.6364, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:49:21 Iters: 610500/[14], loss: 2.6420, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:51:25 Iters: 610600/[14], loss: 2.8623, train_accuracy: 0.5391, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220709-13:53:28 Iters: 610700/[14], loss: 3.1295, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:55:32 Iters: 610800/[14], loss: 3.1396, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:57:35 Iters: 610900/[14], loss: 2.9753, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-13:59:38 Iters: 611000/[14], loss: 2.8950, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:01:41 Iters: 611100/[14], loss: 2.6832, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:03:44 Iters: 611200/[14], loss: 3.3263, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:05:48 Iters: 611300/[14], loss: 2.9206, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:07:51 Iters: 611400/[14], loss: 3.0832, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:09:54 Iters: 611500/[14], loss: 2.4560, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:11:57 Iters: 611600/[14], loss: 3.5505, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:14:00 Iters: 611700/[14], loss: 2.9564, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:16:03 Iters: 611800/[14], loss: 3.0118, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:18:06 Iters: 611900/[14], loss: 3.3578, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:20:08 Iters: 612000/[14], loss: 3.0957, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:22:11 Iters: 612100/[14], loss: 3.4378, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:24:14 Iters: 612200/[14], loss: 2.9082, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:26:17 Iters: 612300/[14], loss: 3.1910, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:28:20 Iters: 612400/[14], loss: 2.5100, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:30:23 Iters: 612500/[14], loss: 2.7507, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:32:26 Iters: 612600/[14], loss: 2.6722, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:34:28 Iters: 612700/[14], loss: 3.6106, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:36:31 Iters: 612800/[14], loss: 3.3498, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:38:34 Iters: 612900/[14], loss: 3.0842, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:40:37 Iters: 613000/[14], loss: 3.5989, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:42:40 Iters: 613100/[14], loss: 2.9438, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:44:43 Iters: 613200/[14], loss: 3.2017, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:46:46 Iters: 613300/[14], loss: 2.7175, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:48:49 Iters: 613400/[14], loss: 2.7700, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:50:51 Iters: 613500/[14], loss: 4.0367, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:52:54 Iters: 613600/[14], loss: 2.7030, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:54:57 Iters: 613700/[14], loss: 3.0543, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:57:00 Iters: 613800/[14], loss: 3.1826, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-14:59:03 Iters: 613900/[14], loss: 3.4679, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:01:06 Iters: 614000/[14], loss: 3.2217, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:03:09 Iters: 614100/[14], loss: 3.2831, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:05:12 Iters: 614200/[14], loss: 2.6711, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:07:14 Iters: 614300/[14], loss: 2.9125, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:09:17 Iters: 614400/[14], loss: 2.9492, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:11:20 Iters: 614500/[14], loss: 3.3908, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:13:23 Iters: 614600/[14], loss: 3.2771, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:15:26 Iters: 614700/[14], loss: 2.9109, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:17:29 Iters: 614800/[14], loss: 3.5405, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:19:32 Iters: 614900/[14], loss: 2.8019, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:21:35 Iters: 615000/[14], loss: 2.7127, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:23:38 Iters: 615100/[14], loss: 3.2471, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:25:41 Iters: 615200/[14], loss: 3.7511, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:27:43 Iters: 615300/[14], loss: 2.8175, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:29:46 Iters: 615400/[14], loss: 3.7329, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:31:49 Iters: 615500/[14], loss: 3.3674, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:33:52 Iters: 615600/[14], loss: 3.4473, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:35:54 Iters: 615700/[14], loss: 2.5858, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:37:57 Iters: 615800/[14], loss: 3.3044, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:40:00 Iters: 615900/[14], loss: 2.7334, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:42:03 Iters: 616000/[14], loss: 3.4520, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:44:05 Iters: 616100/[14], loss: 3.3198, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:46:08 Iters: 616200/[14], loss: 3.0871, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:48:11 Iters: 616300/[14], loss: 3.1834, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:50:14 Iters: 616400/[14], loss: 2.8872, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:52:17 Iters: 616500/[14], loss: 3.3014, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:54:19 Iters: 616600/[14], loss: 4.0065, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:56:22 Iters: 616700/[14], loss: 2.8298, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-15:58:25 Iters: 616800/[14], loss: 3.4891, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:00:28 Iters: 616900/[14], loss: 2.6257, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:02:31 Iters: 617000/[14], loss: 3.9859, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:04:33 Iters: 617100/[14], loss: 2.7410, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:06:36 Iters: 617200/[14], loss: 3.3278, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:08:39 Iters: 617300/[14], loss: 2.8714, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:10:42 Iters: 617400/[14], loss: 3.8549, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:12:45 Iters: 617500/[14], loss: 2.8546, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:14:48 Iters: 617600/[14], loss: 3.0866, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:16:51 Iters: 617700/[14], loss: 2.7664, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:18:53 Iters: 617800/[14], loss: 2.9929, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:20:56 Iters: 617900/[14], loss: 2.6761, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:22:59 Iters: 618000/[14], loss: 3.1066, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:25:02 Iters: 618100/[14], loss: 3.2617, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:27:05 Iters: 618200/[14], loss: 2.8930, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:29:08 Iters: 618300/[14], loss: 2.6065, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:31:10 Iters: 618400/[14], loss: 2.5837, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:33:13 Iters: 618500/[14], loss: 2.8873, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:35:16 Iters: 618600/[14], loss: 2.8427, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:37:19 Iters: 618700/[14], loss: 2.7079, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:39:22 Iters: 618800/[14], loss: 3.4230, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:41:24 Iters: 618900/[14], loss: 3.2699, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:43:27 Iters: 619000/[14], loss: 2.9234, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:45:30 Iters: 619100/[14], loss: 3.0598, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:47:33 Iters: 619200/[14], loss: 2.8822, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:49:36 Iters: 619300/[14], loss: 3.1070, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:51:39 Iters: 619400/[14], loss: 2.6427, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:53:42 Iters: 619500/[14], loss: 3.0712, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:55:44 Iters: 619600/[14], loss: 3.3505, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:57:47 Iters: 619700/[14], loss: 3.3540, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-16:59:50 Iters: 619800/[14], loss: 2.8394, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:01:53 Iters: 619900/[14], loss: 2.2169, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:03:56 Iters: 620000/[14], loss: 3.0551, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:03:56 Saving checkpoint: 620000
-20220709-17:05:14 LFW Ave Accuracy: 99.6500
-20220709-17:06:31 AgeDB-30 Ave Accuracy: 97.2500
-20220709-17:08:01 CFP-FP Ave Accuracy: 94.8286
-20220709-17:08:01 Current Best Accuracy: LFW: 99.6666 in iters: 600000, AgeDB-30: 97.2833 in iters: 600000 and CFP-FP: 95.1286 in iters: 600000
-20220709-17:10:03 Iters: 620100/[14], loss: 3.6303, train_accuracy: 0.5000, time: 3.68 s/iter, learning rate: 0.0005000000000000001
-20220709-17:12:06 Iters: 620200/[14], loss: 3.1588, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:14:09 Iters: 620300/[14], loss: 2.7426, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:16:12 Iters: 620400/[14], loss: 3.5926, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:18:15 Iters: 620500/[14], loss: 3.6818, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:20:18 Iters: 620600/[14], loss: 3.4750, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:22:21 Iters: 620700/[14], loss: 2.9616, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:24:24 Iters: 620800/[14], loss: 3.8640, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:26:26 Iters: 620900/[14], loss: 3.2488, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:28:29 Iters: 621000/[14], loss: 3.0671, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:30:32 Iters: 621100/[14], loss: 3.2244, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:32:35 Iters: 621200/[14], loss: 3.0139, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:34:38 Iters: 621300/[14], loss: 2.2686, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:36:41 Iters: 621400/[14], loss: 3.6168, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:38:44 Iters: 621500/[14], loss: 2.9380, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:40:47 Iters: 621600/[14], loss: 2.9200, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:42:50 Iters: 621700/[14], loss: 3.4899, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:44:53 Iters: 621800/[14], loss: 2.7551, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:46:56 Iters: 621900/[14], loss: 3.0364, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:48:58 Iters: 622000/[14], loss: 3.2374, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:51:01 Iters: 622100/[14], loss: 3.0914, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:53:04 Iters: 622200/[14], loss: 3.4377, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:55:07 Iters: 622300/[14], loss: 2.9979, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:57:10 Iters: 622400/[14], loss: 3.8193, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-17:59:13 Iters: 622500/[14], loss: 3.3280, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:01:15 Iters: 622600/[14], loss: 3.2725, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:03:18 Iters: 622700/[14], loss: 2.8892, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:05:21 Iters: 622800/[14], loss: 2.5863, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:07:24 Iters: 622900/[14], loss: 2.8094, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:09:27 Iters: 623000/[14], loss: 3.3163, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:11:30 Iters: 623100/[14], loss: 3.5893, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:13:33 Iters: 623200/[14], loss: 3.0952, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:15:35 Iters: 623300/[14], loss: 3.1344, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:17:38 Iters: 623400/[14], loss: 3.3117, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:19:41 Iters: 623500/[14], loss: 3.7030, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:21:44 Iters: 623600/[14], loss: 2.7511, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:23:47 Iters: 623700/[14], loss: 3.1918, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:25:50 Iters: 623800/[14], loss: 2.9052, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:27:52 Iters: 623900/[14], loss: 3.2642, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:29:55 Iters: 624000/[14], loss: 3.3692, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:31:58 Iters: 624100/[14], loss: 3.2856, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:34:01 Iters: 624200/[14], loss: 2.9088, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:36:04 Iters: 624300/[14], loss: 2.9715, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:38:06 Iters: 624400/[14], loss: 3.6071, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:40:09 Iters: 624500/[14], loss: 2.9206, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:42:12 Iters: 624600/[14], loss: 2.5788, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:44:15 Iters: 624700/[14], loss: 3.3062, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:46:18 Iters: 624800/[14], loss: 2.9907, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:48:21 Iters: 624900/[14], loss: 3.5391, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:50:23 Iters: 625000/[14], loss: 3.5138, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:52:26 Iters: 625100/[14], loss: 2.9929, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:54:29 Iters: 625200/[14], loss: 3.1280, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:56:32 Iters: 625300/[14], loss: 3.5068, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-18:58:35 Iters: 625400/[14], loss: 3.1206, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:00:38 Iters: 625500/[14], loss: 3.3528, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:02:41 Iters: 625600/[14], loss: 2.7165, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:04:44 Iters: 625700/[14], loss: 2.9732, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:06:46 Iters: 625800/[14], loss: 2.7716, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:08:49 Iters: 625900/[14], loss: 2.9763, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:10:52 Iters: 626000/[14], loss: 3.9800, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:12:55 Iters: 626100/[14], loss: 3.1047, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:14:58 Iters: 626200/[14], loss: 3.1972, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:17:01 Iters: 626300/[14], loss: 2.5844, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:19:04 Iters: 626400/[14], loss: 3.0317, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:21:07 Iters: 626500/[14], loss: 2.6909, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:23:09 Iters: 626600/[14], loss: 3.7200, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:25:12 Iters: 626700/[14], loss: 2.9672, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:27:15 Iters: 626800/[14], loss: 3.8378, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:29:18 Iters: 626900/[14], loss: 3.4753, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:31:21 Iters: 627000/[14], loss: 3.1979, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:33:24 Iters: 627100/[14], loss: 2.6625, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:35:27 Iters: 627200/[14], loss: 2.7665, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:37:30 Iters: 627300/[14], loss: 2.9628, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:39:33 Iters: 627400/[14], loss: 3.3627, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:41:35 Iters: 627500/[14], loss: 3.2260, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:43:38 Iters: 627600/[14], loss: 2.9293, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:45:41 Iters: 627700/[14], loss: 2.7873, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:47:44 Iters: 627800/[14], loss: 3.5277, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:49:47 Iters: 627900/[14], loss: 2.8608, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:51:50 Iters: 628000/[14], loss: 3.4370, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:53:53 Iters: 628100/[14], loss: 2.9459, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:55:56 Iters: 628200/[14], loss: 3.2557, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-19:57:59 Iters: 628300/[14], loss: 2.9600, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:00:02 Iters: 628400/[14], loss: 3.1652, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:02:05 Iters: 628500/[14], loss: 3.1422, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:04:07 Iters: 628600/[14], loss: 3.0738, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:06:10 Iters: 628700/[14], loss: 3.2099, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:08:13 Iters: 628800/[14], loss: 3.5107, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:10:16 Iters: 628900/[14], loss: 2.7066, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:12:19 Iters: 629000/[14], loss: 2.6727, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:14:22 Iters: 629100/[14], loss: 2.3250, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:16:24 Iters: 629200/[14], loss: 3.3360, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:18:27 Iters: 629300/[14], loss: 2.7638, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:20:30 Iters: 629400/[14], loss: 3.9672, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:22:33 Iters: 629500/[14], loss: 2.6808, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:24:37 Iters: 629600/[14], loss: 3.1298, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:26:40 Iters: 629700/[14], loss: 3.5143, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:28:43 Iters: 629800/[14], loss: 2.9186, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:30:45 Iters: 629900/[14], loss: 3.3004, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:32:48 Iters: 630000/[14], loss: 2.8684, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:32:48 Saving checkpoint: 630000
-20220709-20:34:06 LFW Ave Accuracy: 99.5833
-20220709-20:35:23 AgeDB-30 Ave Accuracy: 97.3333
-20220709-20:36:53 CFP-FP Ave Accuracy: 95.2000
-20220709-20:36:53 Current Best Accuracy: LFW: 99.6666 in iters: 600000, AgeDB-30: 97.3333 in iters: 630000 and CFP-FP: 95.2000 in iters: 630000
-20220709-20:38:56 Iters: 630100/[14], loss: 3.1870, train_accuracy: 0.5625, time: 3.67 s/iter, learning rate: 0.0005000000000000001
-20220709-20:40:59 Iters: 630200/[14], loss: 2.9040, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:43:02 Iters: 630300/[14], loss: 3.3034, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:45:05 Iters: 630400/[14], loss: 3.8878, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:47:08 Iters: 630500/[14], loss: 3.0231, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:49:11 Iters: 630600/[14], loss: 3.4556, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:51:14 Iters: 630700/[14], loss: 2.9903, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:53:17 Iters: 630800/[14], loss: 3.6984, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:55:20 Iters: 630900/[14], loss: 3.3739, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:57:23 Iters: 631000/[14], loss: 3.1628, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-20:59:26 Iters: 631100/[14], loss: 3.4982, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:01:29 Iters: 631200/[14], loss: 2.6830, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:03:33 Iters: 631300/[14], loss: 3.5960, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:05:36 Iters: 631400/[14], loss: 3.0488, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:07:39 Iters: 631500/[14], loss: 3.3228, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:09:43 Iters: 631600/[14], loss: 3.5531, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:11:46 Iters: 631700/[14], loss: 2.9649, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:13:49 Iters: 631800/[14], loss: 3.3182, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:15:52 Iters: 631900/[14], loss: 2.8685, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:17:56 Iters: 632000/[14], loss: 2.5408, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220709-21:19:59 Iters: 632100/[14], loss: 3.2175, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:22:02 Iters: 632200/[14], loss: 3.1819, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:24:05 Iters: 632300/[14], loss: 2.9580, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:26:09 Iters: 632400/[14], loss: 3.3545, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:28:12 Iters: 632500/[14], loss: 3.2112, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:30:15 Iters: 632600/[14], loss: 3.3029, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:32:18 Iters: 632700/[14], loss: 2.6243, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:34:22 Iters: 632800/[14], loss: 3.3560, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:36:25 Iters: 632900/[14], loss: 3.2001, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:38:28 Iters: 633000/[14], loss: 3.5171, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:40:31 Iters: 633100/[14], loss: 2.8612, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:42:34 Iters: 633200/[14], loss: 3.4312, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:44:37 Iters: 633300/[14], loss: 2.9575, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:46:40 Iters: 633400/[14], loss: 2.8178, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:48:44 Iters: 633500/[14], loss: 3.4072, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:50:47 Iters: 633600/[14], loss: 3.1285, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:52:50 Iters: 633700/[14], loss: 3.3825, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:54:53 Iters: 633800/[14], loss: 3.3599, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:56:56 Iters: 633900/[14], loss: 3.2026, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-21:58:59 Iters: 634000/[14], loss: 2.7360, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:01:03 Iters: 634100/[14], loss: 3.7904, train_accuracy: 0.4844, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220709-22:03:06 Iters: 634200/[14], loss: 2.7909, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:05:09 Iters: 634300/[14], loss: 3.0340, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:07:13 Iters: 634400/[14], loss: 2.9133, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:09:16 Iters: 634500/[14], loss: 2.7528, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:11:19 Iters: 634600/[14], loss: 3.1957, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:13:22 Iters: 634700/[14], loss: 3.0990, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:15:25 Iters: 634800/[14], loss: 2.9897, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:17:28 Iters: 634900/[14], loss: 3.1399, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:19:32 Iters: 635000/[14], loss: 3.1947, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:21:35 Iters: 635100/[14], loss: 3.5715, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:23:38 Iters: 635200/[14], loss: 3.1614, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:25:41 Iters: 635300/[14], loss: 2.7525, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:27:45 Iters: 635400/[14], loss: 3.2669, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:29:48 Iters: 635500/[14], loss: 2.6636, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:31:51 Iters: 635600/[14], loss: 3.0415, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:33:54 Iters: 635700/[14], loss: 3.7882, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:35:58 Iters: 635800/[14], loss: 2.9856, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:38:01 Iters: 635900/[14], loss: 3.9160, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:40:04 Iters: 636000/[14], loss: 3.7077, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:42:07 Iters: 636100/[14], loss: 3.1813, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:44:10 Iters: 636200/[14], loss: 2.9054, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:46:14 Iters: 636300/[14], loss: 3.1857, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:48:17 Iters: 636400/[14], loss: 2.8826, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:50:20 Iters: 636500/[14], loss: 3.1044, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:52:24 Iters: 636600/[14], loss: 2.5784, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:54:27 Iters: 636700/[14], loss: 3.2868, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:56:30 Iters: 636800/[14], loss: 3.1654, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-22:57:43 Train Epoch: 15/18 ...
-20220709-22:58:33 Iters: 636900/[15], loss: 2.0534, train_accuracy: 0.6406, time: 0.50 s/iter, learning rate: 0.0005000000000000001
-20220709-23:00:36 Iters: 637000/[15], loss: 3.2710, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:02:39 Iters: 637100/[15], loss: 2.9517, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:04:43 Iters: 637200/[15], loss: 3.2644, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:06:46 Iters: 637300/[15], loss: 2.4244, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:08:49 Iters: 637400/[15], loss: 3.4143, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:10:52 Iters: 637500/[15], loss: 3.7499, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:12:55 Iters: 637600/[15], loss: 2.5760, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:14:59 Iters: 637700/[15], loss: 2.8924, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:17:02 Iters: 637800/[15], loss: 2.9731, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:19:05 Iters: 637900/[15], loss: 2.4691, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:21:08 Iters: 638000/[15], loss: 3.3839, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:23:11 Iters: 638100/[15], loss: 4.1004, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:25:14 Iters: 638200/[15], loss: 3.2666, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:27:17 Iters: 638300/[15], loss: 2.7844, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:29:20 Iters: 638400/[15], loss: 3.0100, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:31:23 Iters: 638500/[15], loss: 2.1732, train_accuracy: 0.7109, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:33:26 Iters: 638600/[15], loss: 2.9902, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:35:29 Iters: 638700/[15], loss: 3.9692, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:37:32 Iters: 638800/[15], loss: 2.5629, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:39:35 Iters: 638900/[15], loss: 2.8481, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:41:39 Iters: 639000/[15], loss: 3.1068, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:43:42 Iters: 639100/[15], loss: 2.5182, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:45:45 Iters: 639200/[15], loss: 3.4404, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:47:48 Iters: 639300/[15], loss: 3.0820, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:49:51 Iters: 639400/[15], loss: 3.1689, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:51:54 Iters: 639500/[15], loss: 2.7047, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:53:57 Iters: 639600/[15], loss: 2.6657, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:56:00 Iters: 639700/[15], loss: 2.3375, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220709-23:58:03 Iters: 639800/[15], loss: 2.7698, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:00:06 Iters: 639900/[15], loss: 2.5542, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:02:09 Iters: 640000/[15], loss: 3.4961, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:02:09 Saving checkpoint: 640000
-20220710-00:03:25 LFW Ave Accuracy: 99.6666
-20220710-00:04:40 AgeDB-30 Ave Accuracy: 97.2167
-20220710-00:06:06 CFP-FP Ave Accuracy: 95.1714
-20220710-00:06:06 Current Best Accuracy: LFW: 99.6666 in iters: 640000, AgeDB-30: 97.3333 in iters: 630000 and CFP-FP: 95.2000 in iters: 630000
-20220710-00:08:09 Iters: 640100/[15], loss: 3.6250, train_accuracy: 0.5156, time: 3.60 s/iter, learning rate: 0.0005000000000000001
-20220710-00:10:12 Iters: 640200/[15], loss: 3.2572, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:12:15 Iters: 640300/[15], loss: 2.7236, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:14:19 Iters: 640400/[15], loss: 3.1219, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:16:22 Iters: 640500/[15], loss: 2.7345, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:18:25 Iters: 640600/[15], loss: 3.2594, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:20:28 Iters: 640700/[15], loss: 2.6509, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:22:32 Iters: 640800/[15], loss: 3.0248, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:24:35 Iters: 640900/[15], loss: 2.9236, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:26:38 Iters: 641000/[15], loss: 2.4488, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:28:41 Iters: 641100/[15], loss: 3.0045, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:30:45 Iters: 641200/[15], loss: 3.3930, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:32:48 Iters: 641300/[15], loss: 2.6016, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:34:51 Iters: 641400/[15], loss: 2.8885, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:36:54 Iters: 641500/[15], loss: 3.0862, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:38:58 Iters: 641600/[15], loss: 2.8037, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:41:01 Iters: 641700/[15], loss: 2.8874, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:43:04 Iters: 641800/[15], loss: 2.7923, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:45:07 Iters: 641900/[15], loss: 3.4639, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:47:11 Iters: 642000/[15], loss: 2.5083, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:49:14 Iters: 642100/[15], loss: 3.5048, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:51:17 Iters: 642200/[15], loss: 3.4265, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:53:20 Iters: 642300/[15], loss: 2.7980, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:55:23 Iters: 642400/[15], loss: 3.3365, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:57:26 Iters: 642500/[15], loss: 2.6658, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-00:59:30 Iters: 642600/[15], loss: 2.9408, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:01:33 Iters: 642700/[15], loss: 3.2226, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:03:36 Iters: 642800/[15], loss: 2.9657, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:05:39 Iters: 642900/[15], loss: 3.2155, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:07:42 Iters: 643000/[15], loss: 3.0792, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:09:46 Iters: 643100/[15], loss: 3.6962, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:11:49 Iters: 643200/[15], loss: 2.8649, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:13:52 Iters: 643300/[15], loss: 3.7558, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:15:55 Iters: 643400/[15], loss: 3.5643, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:17:59 Iters: 643500/[15], loss: 2.8690, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:20:02 Iters: 643600/[15], loss: 3.2660, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:22:05 Iters: 643700/[15], loss: 3.2294, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:24:08 Iters: 643800/[15], loss: 2.6654, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:26:11 Iters: 643900/[15], loss: 2.9990, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:28:14 Iters: 644000/[15], loss: 3.2658, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:30:17 Iters: 644100/[15], loss: 3.8156, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:32:21 Iters: 644200/[15], loss: 2.9607, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:34:24 Iters: 644300/[15], loss: 2.6361, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:36:27 Iters: 644400/[15], loss: 2.7576, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:38:30 Iters: 644500/[15], loss: 3.6456, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:40:33 Iters: 644600/[15], loss: 2.9512, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:42:37 Iters: 644700/[15], loss: 2.8461, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:44:40 Iters: 644800/[15], loss: 2.6973, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:46:43 Iters: 644900/[15], loss: 2.6549, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:48:46 Iters: 645000/[15], loss: 2.4489, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:50:49 Iters: 645100/[15], loss: 3.4870, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:52:53 Iters: 645200/[15], loss: 3.1217, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:54:56 Iters: 645300/[15], loss: 3.4310, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:56:59 Iters: 645400/[15], loss: 3.7197, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-01:59:02 Iters: 645500/[15], loss: 2.5509, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:01:06 Iters: 645600/[15], loss: 3.0520, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:03:09 Iters: 645700/[15], loss: 3.2806, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:05:12 Iters: 645800/[15], loss: 2.6815, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:07:15 Iters: 645900/[15], loss: 3.2258, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:09:18 Iters: 646000/[15], loss: 3.7008, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:11:22 Iters: 646100/[15], loss: 3.4281, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:13:25 Iters: 646200/[15], loss: 2.6242, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:15:28 Iters: 646300/[15], loss: 3.3786, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:17:31 Iters: 646400/[15], loss: 2.9982, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:19:35 Iters: 646500/[15], loss: 2.7587, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:21:38 Iters: 646600/[15], loss: 3.1923, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:23:41 Iters: 646700/[15], loss: 3.4028, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:25:45 Iters: 646800/[15], loss: 3.2087, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:27:48 Iters: 646900/[15], loss: 2.6128, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:29:51 Iters: 647000/[15], loss: 3.0494, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:31:54 Iters: 647100/[15], loss: 2.5992, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:33:58 Iters: 647200/[15], loss: 3.0937, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:36:01 Iters: 647300/[15], loss: 3.1759, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:38:04 Iters: 647400/[15], loss: 3.2787, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:40:07 Iters: 647500/[15], loss: 2.7310, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:42:10 Iters: 647600/[15], loss: 2.9664, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:44:14 Iters: 647700/[15], loss: 3.2336, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:46:17 Iters: 647800/[15], loss: 2.7647, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:48:20 Iters: 647900/[15], loss: 3.1066, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:50:23 Iters: 648000/[15], loss: 3.4906, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:52:26 Iters: 648100/[15], loss: 2.8935, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:54:30 Iters: 648200/[15], loss: 3.4905, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:56:33 Iters: 648300/[15], loss: 2.4945, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-02:58:36 Iters: 648400/[15], loss: 3.1154, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:00:39 Iters: 648500/[15], loss: 2.9620, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:02:42 Iters: 648600/[15], loss: 2.7259, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:04:45 Iters: 648700/[15], loss: 2.7562, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:06:49 Iters: 648800/[15], loss: 2.6585, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:08:52 Iters: 648900/[15], loss: 2.6399, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:10:55 Iters: 649000/[15], loss: 3.2605, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:12:58 Iters: 649100/[15], loss: 3.1669, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:15:01 Iters: 649200/[15], loss: 2.8579, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:17:04 Iters: 649300/[15], loss: 2.8684, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:19:07 Iters: 649400/[15], loss: 2.7674, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:21:10 Iters: 649500/[15], loss: 2.7326, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:23:13 Iters: 649600/[15], loss: 3.5302, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:25:16 Iters: 649700/[15], loss: 3.6700, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:27:19 Iters: 649800/[15], loss: 2.9042, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:29:22 Iters: 649900/[15], loss: 2.4018, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:31:25 Iters: 650000/[15], loss: 3.4015, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:31:25 Saving checkpoint: 650000
-20220710-03:32:42 LFW Ave Accuracy: 99.6666
-20220710-03:33:57 AgeDB-30 Ave Accuracy: 97.2000
-20220710-03:35:24 CFP-FP Ave Accuracy: 95.2143
-20220710-03:35:24 Current Best Accuracy: LFW: 99.6666 in iters: 640000, AgeDB-30: 97.3333 in iters: 630000 and CFP-FP: 95.2143 in iters: 650000
-20220710-03:37:27 Iters: 650100/[15], loss: 2.8568, train_accuracy: 0.5703, time: 3.61 s/iter, learning rate: 0.0005000000000000001
-20220710-03:39:30 Iters: 650200/[15], loss: 3.7349, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:41:33 Iters: 650300/[15], loss: 2.9174, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:43:36 Iters: 650400/[15], loss: 3.2063, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:45:39 Iters: 650500/[15], loss: 2.6444, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:47:42 Iters: 650600/[15], loss: 3.3917, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:49:45 Iters: 650700/[15], loss: 2.1337, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:51:48 Iters: 650800/[15], loss: 2.9572, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:53:51 Iters: 650900/[15], loss: 2.8896, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:55:53 Iters: 651000/[15], loss: 2.9354, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:57:56 Iters: 651100/[15], loss: 3.4927, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-03:59:59 Iters: 651200/[15], loss: 3.8510, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:02:02 Iters: 651300/[15], loss: 3.0057, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:04:05 Iters: 651400/[15], loss: 3.1854, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:06:08 Iters: 651500/[15], loss: 3.8548, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:08:11 Iters: 651600/[15], loss: 2.9388, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:10:14 Iters: 651700/[15], loss: 3.8737, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:12:17 Iters: 651800/[15], loss: 2.9808, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:14:20 Iters: 651900/[15], loss: 2.7073, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:16:23 Iters: 652000/[15], loss: 3.0451, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:18:27 Iters: 652100/[15], loss: 2.8520, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:20:30 Iters: 652200/[15], loss: 3.5143, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:22:33 Iters: 652300/[15], loss: 3.3163, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:24:36 Iters: 652400/[15], loss: 3.0108, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:26:38 Iters: 652500/[15], loss: 2.9450, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:28:41 Iters: 652600/[15], loss: 2.7141, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:30:44 Iters: 652700/[15], loss: 2.9970, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:32:47 Iters: 652800/[15], loss: 2.9094, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:34:50 Iters: 652900/[15], loss: 2.7881, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:36:53 Iters: 653000/[15], loss: 2.9369, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:38:56 Iters: 653100/[15], loss: 3.1210, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:40:59 Iters: 653200/[15], loss: 3.0963, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:43:02 Iters: 653300/[15], loss: 3.9268, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:45:05 Iters: 653400/[15], loss: 3.0608, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:47:08 Iters: 653500/[15], loss: 2.9435, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:49:11 Iters: 653600/[15], loss: 2.8257, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:51:14 Iters: 653700/[15], loss: 3.2065, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:53:17 Iters: 653800/[15], loss: 2.7566, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:55:20 Iters: 653900/[15], loss: 2.9258, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:57:23 Iters: 654000/[15], loss: 2.8742, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-04:59:26 Iters: 654100/[15], loss: 3.1402, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:01:29 Iters: 654200/[15], loss: 3.1546, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:03:32 Iters: 654300/[15], loss: 3.6824, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:05:35 Iters: 654400/[15], loss: 3.0525, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:07:38 Iters: 654500/[15], loss: 2.7050, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:09:41 Iters: 654600/[15], loss: 3.1299, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:11:44 Iters: 654700/[15], loss: 3.5471, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:13:47 Iters: 654800/[15], loss: 2.6380, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:15:50 Iters: 654900/[15], loss: 3.1755, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:17:54 Iters: 655000/[15], loss: 2.9981, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:19:57 Iters: 655100/[15], loss: 3.2991, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:22:00 Iters: 655200/[15], loss: 3.4199, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:24:03 Iters: 655300/[15], loss: 2.9370, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:26:06 Iters: 655400/[15], loss: 3.6490, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:28:09 Iters: 655500/[15], loss: 2.7979, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:30:13 Iters: 655600/[15], loss: 3.4902, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:32:16 Iters: 655700/[15], loss: 3.3463, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:34:19 Iters: 655800/[15], loss: 3.0096, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:36:22 Iters: 655900/[15], loss: 3.3527, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:38:25 Iters: 656000/[15], loss: 3.1397, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:40:28 Iters: 656100/[15], loss: 3.5201, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:42:31 Iters: 656200/[15], loss: 3.0602, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:44:34 Iters: 656300/[15], loss: 3.1421, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:46:38 Iters: 656400/[15], loss: 2.6490, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:48:41 Iters: 656500/[15], loss: 2.5134, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:50:44 Iters: 656600/[15], loss: 2.5819, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:52:47 Iters: 656700/[15], loss: 3.5414, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:54:50 Iters: 656800/[15], loss: 3.1607, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:56:53 Iters: 656900/[15], loss: 2.7015, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-05:58:56 Iters: 657000/[15], loss: 3.0761, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:00:59 Iters: 657100/[15], loss: 2.4683, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:03:02 Iters: 657200/[15], loss: 2.9371, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:05:06 Iters: 657300/[15], loss: 3.0022, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:07:09 Iters: 657400/[15], loss: 3.2613, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:09:12 Iters: 657500/[15], loss: 3.2323, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:11:15 Iters: 657600/[15], loss: 3.4795, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:13:18 Iters: 657700/[15], loss: 2.9786, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:15:21 Iters: 657800/[15], loss: 3.2875, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:17:24 Iters: 657900/[15], loss: 3.4846, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:19:27 Iters: 658000/[15], loss: 3.1802, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:21:30 Iters: 658100/[15], loss: 2.7421, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:23:34 Iters: 658200/[15], loss: 2.9387, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:25:37 Iters: 658300/[15], loss: 3.0271, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:27:40 Iters: 658400/[15], loss: 3.1828, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:29:43 Iters: 658500/[15], loss: 4.0962, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:31:46 Iters: 658600/[15], loss: 3.2946, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:33:49 Iters: 658700/[15], loss: 3.2093, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:35:52 Iters: 658800/[15], loss: 2.8201, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:37:55 Iters: 658900/[15], loss: 3.6480, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:39:58 Iters: 659000/[15], loss: 3.2572, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:42:01 Iters: 659100/[15], loss: 4.2607, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:44:04 Iters: 659200/[15], loss: 3.2901, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:46:08 Iters: 659300/[15], loss: 3.5502, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:48:11 Iters: 659400/[15], loss: 3.0628, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:50:14 Iters: 659500/[15], loss: 3.2552, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:52:17 Iters: 659600/[15], loss: 2.7063, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:54:20 Iters: 659700/[15], loss: 2.7815, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:56:23 Iters: 659800/[15], loss: 2.7724, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-06:58:26 Iters: 659900/[15], loss: 3.4058, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:00:29 Iters: 660000/[15], loss: 3.1693, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:00:29 Saving checkpoint: 660000
-20220710-07:01:46 LFW Ave Accuracy: 99.5832
-20220710-07:03:02 AgeDB-30 Ave Accuracy: 97.3000
-20220710-07:04:29 CFP-FP Ave Accuracy: 94.8571
-20220710-07:04:29 Current Best Accuracy: LFW: 99.6666 in iters: 640000, AgeDB-30: 97.3333 in iters: 630000 and CFP-FP: 95.2143 in iters: 650000
-20220710-07:06:31 Iters: 660100/[15], loss: 3.1393, train_accuracy: 0.5312, time: 3.62 s/iter, learning rate: 0.0005000000000000001
-20220710-07:08:34 Iters: 660200/[15], loss: 2.9099, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:10:38 Iters: 660300/[15], loss: 3.0883, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:12:41 Iters: 660400/[15], loss: 3.3934, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:14:44 Iters: 660500/[15], loss: 3.6151, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:16:47 Iters: 660600/[15], loss: 2.9474, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:18:50 Iters: 660700/[15], loss: 2.9344, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:20:53 Iters: 660800/[15], loss: 2.9881, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:22:57 Iters: 660900/[15], loss: 2.9268, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:25:00 Iters: 661000/[15], loss: 3.6674, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:27:03 Iters: 661100/[15], loss: 2.9317, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:29:06 Iters: 661200/[15], loss: 3.3236, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:31:10 Iters: 661300/[15], loss: 3.0529, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:33:13 Iters: 661400/[15], loss: 2.7922, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:35:16 Iters: 661500/[15], loss: 4.0394, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:37:20 Iters: 661600/[15], loss: 3.4012, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:39:23 Iters: 661700/[15], loss: 2.6933, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:41:26 Iters: 661800/[15], loss: 3.0125, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:43:29 Iters: 661900/[15], loss: 2.9849, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:45:33 Iters: 662000/[15], loss: 3.2609, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:47:36 Iters: 662100/[15], loss: 3.5273, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:49:39 Iters: 662200/[15], loss: 3.4153, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:51:43 Iters: 662300/[15], loss: 3.1367, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:53:46 Iters: 662400/[15], loss: 2.8657, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:55:49 Iters: 662500/[15], loss: 3.1123, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:57:52 Iters: 662600/[15], loss: 3.4455, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-07:59:56 Iters: 662700/[15], loss: 2.8913, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:01:59 Iters: 662800/[15], loss: 3.3790, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:04:02 Iters: 662900/[15], loss: 2.9751, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:06:05 Iters: 663000/[15], loss: 3.3568, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:08:08 Iters: 663100/[15], loss: 2.9429, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:10:12 Iters: 663200/[15], loss: 3.5059, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:12:15 Iters: 663300/[15], loss: 3.2912, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:14:18 Iters: 663400/[15], loss: 2.7517, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:16:21 Iters: 663500/[15], loss: 3.3957, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:18:25 Iters: 663600/[15], loss: 3.0837, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:20:28 Iters: 663700/[15], loss: 3.4693, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:22:32 Iters: 663800/[15], loss: 3.1576, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:24:35 Iters: 663900/[15], loss: 3.4004, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:26:38 Iters: 664000/[15], loss: 3.7998, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:28:42 Iters: 664100/[15], loss: 3.0824, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:30:45 Iters: 664200/[15], loss: 3.1602, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:32:48 Iters: 664300/[15], loss: 3.5099, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:34:51 Iters: 664400/[15], loss: 3.1313, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:36:55 Iters: 664500/[15], loss: 2.5981, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:38:58 Iters: 664600/[15], loss: 2.9155, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:41:01 Iters: 664700/[15], loss: 3.1946, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:43:05 Iters: 664800/[15], loss: 3.0907, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:45:08 Iters: 664900/[15], loss: 2.8849, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:47:11 Iters: 665000/[15], loss: 3.0023, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:49:14 Iters: 665100/[15], loss: 3.3426, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:51:18 Iters: 665200/[15], loss: 3.2007, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:53:21 Iters: 665300/[15], loss: 2.8696, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:55:24 Iters: 665400/[15], loss: 3.1633, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:57:28 Iters: 665500/[15], loss: 2.6425, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-08:59:31 Iters: 665600/[15], loss: 2.9529, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:01:34 Iters: 665700/[15], loss: 3.7476, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:03:38 Iters: 665800/[15], loss: 3.3836, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:05:41 Iters: 665900/[15], loss: 3.3943, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:07:44 Iters: 666000/[15], loss: 3.5803, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:09:48 Iters: 666100/[15], loss: 2.8776, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:11:51 Iters: 666200/[15], loss: 2.8139, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:13:54 Iters: 666300/[15], loss: 2.8947, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:15:58 Iters: 666400/[15], loss: 2.7281, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:18:01 Iters: 666500/[15], loss: 3.2084, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:20:04 Iters: 666600/[15], loss: 3.3508, train_accuracy: 0.5859, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220710-09:22:08 Iters: 666700/[15], loss: 2.9232, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:24:11 Iters: 666800/[15], loss: 2.9796, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:26:15 Iters: 666900/[15], loss: 2.9861, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:28:18 Iters: 667000/[15], loss: 2.5244, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:30:21 Iters: 667100/[15], loss: 3.2814, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:32:25 Iters: 667200/[15], loss: 3.6935, train_accuracy: 0.4922, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220710-09:34:28 Iters: 667300/[15], loss: 3.1404, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:36:32 Iters: 667400/[15], loss: 2.7201, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:38:35 Iters: 667500/[15], loss: 3.7789, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:40:38 Iters: 667600/[15], loss: 2.8969, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:42:41 Iters: 667700/[15], loss: 3.2782, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:44:45 Iters: 667800/[15], loss: 3.0815, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:46:48 Iters: 667900/[15], loss: 3.8428, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:48:51 Iters: 668000/[15], loss: 3.1408, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:50:55 Iters: 668100/[15], loss: 2.5349, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:52:58 Iters: 668200/[15], loss: 3.0037, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:55:01 Iters: 668300/[15], loss: 3.0203, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:57:04 Iters: 668400/[15], loss: 2.9433, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-09:59:07 Iters: 668500/[15], loss: 3.2938, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:01:11 Iters: 668600/[15], loss: 3.5713, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:03:14 Iters: 668700/[15], loss: 3.3234, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:05:17 Iters: 668800/[15], loss: 3.0625, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:07:20 Iters: 668900/[15], loss: 3.3237, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:09:24 Iters: 669000/[15], loss: 2.8800, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:11:27 Iters: 669100/[15], loss: 2.7194, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:13:30 Iters: 669200/[15], loss: 3.4058, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:15:33 Iters: 669300/[15], loss: 3.7103, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:17:37 Iters: 669400/[15], loss: 3.4548, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:19:40 Iters: 669500/[15], loss: 3.1027, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:21:43 Iters: 669600/[15], loss: 3.5006, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:23:46 Iters: 669700/[15], loss: 3.1975, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:25:50 Iters: 669800/[15], loss: 2.6742, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:27:53 Iters: 669900/[15], loss: 3.6233, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:29:56 Iters: 670000/[15], loss: 3.3522, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:29:56 Saving checkpoint: 670000
-20220710-10:31:14 LFW Ave Accuracy: 99.6333
-20220710-10:32:31 AgeDB-30 Ave Accuracy: 97.2500
-20220710-10:34:01 CFP-FP Ave Accuracy: 95.0286
-20220710-10:34:01 Current Best Accuracy: LFW: 99.6666 in iters: 640000, AgeDB-30: 97.3333 in iters: 630000 and CFP-FP: 95.2143 in iters: 650000
-20220710-10:36:04 Iters: 670100/[15], loss: 3.3208, train_accuracy: 0.5312, time: 3.68 s/iter, learning rate: 0.0005000000000000001
-20220710-10:38:07 Iters: 670200/[15], loss: 3.7130, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:40:10 Iters: 670300/[15], loss: 2.6806, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:42:14 Iters: 670400/[15], loss: 3.6708, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:44:17 Iters: 670500/[15], loss: 3.9205, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:46:21 Iters: 670600/[15], loss: 3.6679, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:48:24 Iters: 670700/[15], loss: 3.7787, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:50:27 Iters: 670800/[15], loss: 3.1394, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:52:30 Iters: 670900/[15], loss: 3.4652, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:54:34 Iters: 671000/[15], loss: 3.3619, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:56:37 Iters: 671100/[15], loss: 3.5077, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-10:58:40 Iters: 671200/[15], loss: 2.7342, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:00:43 Iters: 671300/[15], loss: 3.6138, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:02:47 Iters: 671400/[15], loss: 4.4781, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:04:50 Iters: 671500/[15], loss: 3.3466, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:06:53 Iters: 671600/[15], loss: 3.4014, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:08:56 Iters: 671700/[15], loss: 4.1017, train_accuracy: 0.4609, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:11:00 Iters: 671800/[15], loss: 3.4229, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:13:03 Iters: 671900/[15], loss: 3.1172, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:15:06 Iters: 672000/[15], loss: 3.3352, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:17:09 Iters: 672100/[15], loss: 2.9260, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:19:12 Iters: 672200/[15], loss: 3.0336, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:21:16 Iters: 672300/[15], loss: 3.2268, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:23:19 Iters: 672400/[15], loss: 3.1395, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:25:22 Iters: 672500/[15], loss: 3.5186, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:27:25 Iters: 672600/[15], loss: 2.6605, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:29:29 Iters: 672700/[15], loss: 3.2029, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:31:32 Iters: 672800/[15], loss: 2.9569, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:33:35 Iters: 672900/[15], loss: 3.4347, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:35:39 Iters: 673000/[15], loss: 3.1489, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:37:42 Iters: 673100/[15], loss: 2.9542, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:39:45 Iters: 673200/[15], loss: 3.5444, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:41:48 Iters: 673300/[15], loss: 2.9689, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:43:52 Iters: 673400/[15], loss: 2.9661, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:45:55 Iters: 673500/[15], loss: 3.1494, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:47:58 Iters: 673600/[15], loss: 3.1613, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:50:01 Iters: 673700/[15], loss: 3.0062, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:52:05 Iters: 673800/[15], loss: 3.4693, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:54:08 Iters: 673900/[15], loss: 3.7216, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:56:11 Iters: 674000/[15], loss: 3.2504, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-11:58:14 Iters: 674100/[15], loss: 3.1780, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:00:18 Iters: 674200/[15], loss: 3.2031, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:02:21 Iters: 674300/[15], loss: 3.2009, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:04:24 Iters: 674400/[15], loss: 2.7969, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:06:27 Iters: 674500/[15], loss: 2.6046, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:08:31 Iters: 674600/[15], loss: 2.9693, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:10:34 Iters: 674700/[15], loss: 3.1509, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:12:37 Iters: 674800/[15], loss: 3.0610, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:14:40 Iters: 674900/[15], loss: 2.9879, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:16:44 Iters: 675000/[15], loss: 3.0569, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:18:47 Iters: 675100/[15], loss: 2.9669, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:20:50 Iters: 675200/[15], loss: 2.9916, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:22:53 Iters: 675300/[15], loss: 3.1262, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:24:57 Iters: 675400/[15], loss: 3.1046, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:27:00 Iters: 675500/[15], loss: 3.3364, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:29:03 Iters: 675600/[15], loss: 3.0541, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:31:07 Iters: 675700/[15], loss: 3.0857, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:33:10 Iters: 675800/[15], loss: 3.6106, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:35:13 Iters: 675900/[15], loss: 2.7771, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:37:16 Iters: 676000/[15], loss: 3.1548, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:39:20 Iters: 676100/[15], loss: 2.9730, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:41:23 Iters: 676200/[15], loss: 3.3790, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:43:26 Iters: 676300/[15], loss: 3.1260, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:45:30 Iters: 676400/[15], loss: 3.1901, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:47:33 Iters: 676500/[15], loss: 3.1736, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:49:36 Iters: 676600/[15], loss: 2.8964, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:51:39 Iters: 676700/[15], loss: 3.3517, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:53:43 Iters: 676800/[15], loss: 3.3006, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:55:46 Iters: 676900/[15], loss: 3.1532, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:57:49 Iters: 677000/[15], loss: 3.0865, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-12:59:53 Iters: 677100/[15], loss: 2.6200, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:01:56 Iters: 677200/[15], loss: 3.4823, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:03:59 Iters: 677300/[15], loss: 3.2364, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:06:03 Iters: 677400/[15], loss: 3.3196, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:08:06 Iters: 677500/[15], loss: 3.3153, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:10:09 Iters: 677600/[15], loss: 3.1220, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:12:12 Iters: 677700/[15], loss: 3.1554, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:14:16 Iters: 677800/[15], loss: 3.2664, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:16:19 Iters: 677900/[15], loss: 3.1650, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:18:22 Iters: 678000/[15], loss: 2.8781, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:20:25 Iters: 678100/[15], loss: 2.7044, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:22:29 Iters: 678200/[15], loss: 3.0618, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:24:32 Iters: 678300/[15], loss: 3.2549, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:26:35 Iters: 678400/[15], loss: 3.1001, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:28:38 Iters: 678500/[15], loss: 3.2030, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:30:42 Iters: 678600/[15], loss: 3.3499, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:32:45 Iters: 678700/[15], loss: 3.1800, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:34:48 Iters: 678800/[15], loss: 3.3315, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:36:51 Iters: 678900/[15], loss: 3.9429, train_accuracy: 0.4453, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:38:55 Iters: 679000/[15], loss: 2.7801, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:40:58 Iters: 679100/[15], loss: 3.4899, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:43:01 Iters: 679200/[15], loss: 3.5283, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:45:04 Iters: 679300/[15], loss: 3.0653, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:47:07 Iters: 679400/[15], loss: 2.9869, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:49:11 Iters: 679500/[15], loss: 2.9491, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:51:14 Iters: 679600/[15], loss: 2.6835, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:53:17 Iters: 679700/[15], loss: 2.9142, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:55:20 Iters: 679800/[15], loss: 2.9766, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:57:24 Iters: 679900/[15], loss: 4.0207, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:59:27 Iters: 680000/[15], loss: 2.8391, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-13:59:27 Saving checkpoint: 680000
-20220710-14:00:46 LFW Ave Accuracy: 99.6833
-20220710-14:02:03 AgeDB-30 Ave Accuracy: 97.3333
-20220710-14:03:33 CFP-FP Ave Accuracy: 95.2571
-20220710-14:03:33 Current Best Accuracy: LFW: 99.6833 in iters: 680000, AgeDB-30: 97.3333 in iters: 680000 and CFP-FP: 95.2571 in iters: 680000
-20220710-14:05:36 Iters: 680100/[15], loss: 2.5498, train_accuracy: 0.6016, time: 3.68 s/iter, learning rate: 0.0005000000000000001
-20220710-14:07:39 Iters: 680200/[15], loss: 3.4237, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:09:42 Iters: 680300/[15], loss: 3.2614, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:11:45 Iters: 680400/[15], loss: 3.1607, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:13:48 Iters: 680500/[15], loss: 2.8456, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:15:51 Iters: 680600/[15], loss: 3.1369, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:17:55 Iters: 680700/[15], loss: 3.1578, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:19:58 Iters: 680800/[15], loss: 3.4368, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:22:01 Iters: 680900/[15], loss: 3.4771, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:24:04 Iters: 681000/[15], loss: 3.0897, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:26:07 Iters: 681100/[15], loss: 3.2995, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:28:11 Iters: 681200/[15], loss: 3.2713, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:30:14 Iters: 681300/[15], loss: 3.2538, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:32:17 Iters: 681400/[15], loss: 3.9775, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:34:20 Iters: 681500/[15], loss: 3.8621, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:36:23 Iters: 681600/[15], loss: 3.6311, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:38:27 Iters: 681700/[15], loss: 3.3274, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:40:30 Iters: 681800/[15], loss: 3.3418, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:42:33 Iters: 681900/[15], loss: 2.8542, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:44:36 Iters: 682000/[15], loss: 3.3927, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:46:39 Iters: 682100/[15], loss: 3.7238, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:48:42 Iters: 682200/[15], loss: 3.2195, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:50:45 Iters: 682300/[15], loss: 2.9356, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:51:46 Train Epoch: 16/18 ...
-20220710-14:52:49 Iters: 682400/[16], loss: 2.7417, train_accuracy: 0.5625, time: 0.62 s/iter, learning rate: 0.0005000000000000001
-20220710-14:54:52 Iters: 682500/[16], loss: 3.1591, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:56:55 Iters: 682600/[16], loss: 2.6614, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-14:58:59 Iters: 682700/[16], loss: 2.8570, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:01:02 Iters: 682800/[16], loss: 2.9941, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:03:05 Iters: 682900/[16], loss: 2.4171, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:05:08 Iters: 683000/[16], loss: 2.9163, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:07:12 Iters: 683100/[16], loss: 2.8944, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:09:15 Iters: 683200/[16], loss: 2.5007, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:11:18 Iters: 683300/[16], loss: 2.6667, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:13:21 Iters: 683400/[16], loss: 2.5361, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:15:24 Iters: 683500/[16], loss: 2.5474, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:17:28 Iters: 683600/[16], loss: 3.1226, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:19:31 Iters: 683700/[16], loss: 2.4143, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:21:34 Iters: 683800/[16], loss: 2.9842, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:23:37 Iters: 683900/[16], loss: 3.1083, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:25:40 Iters: 684000/[16], loss: 3.2818, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:27:43 Iters: 684100/[16], loss: 2.6729, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:29:46 Iters: 684200/[16], loss: 3.0342, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:31:50 Iters: 684300/[16], loss: 2.4287, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:33:53 Iters: 684400/[16], loss: 2.3637, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:35:56 Iters: 684500/[16], loss: 2.4466, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:37:59 Iters: 684600/[16], loss: 2.0935, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:40:02 Iters: 684700/[16], loss: 2.3839, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:42:05 Iters: 684800/[16], loss: 2.7988, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:44:08 Iters: 684900/[16], loss: 3.1355, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:46:11 Iters: 685000/[16], loss: 2.6073, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:48:15 Iters: 685100/[16], loss: 2.7863, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:50:18 Iters: 685200/[16], loss: 3.2908, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:52:21 Iters: 685300/[16], loss: 3.4203, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:54:24 Iters: 685400/[16], loss: 2.8362, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:56:27 Iters: 685500/[16], loss: 2.1226, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-15:58:30 Iters: 685600/[16], loss: 2.9338, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:00:34 Iters: 685700/[16], loss: 2.7758, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:02:37 Iters: 685800/[16], loss: 2.8744, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:04:40 Iters: 685900/[16], loss: 2.9446, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:06:43 Iters: 686000/[16], loss: 2.9405, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:08:47 Iters: 686100/[16], loss: 2.8308, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:10:50 Iters: 686200/[16], loss: 3.0661, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:12:53 Iters: 686300/[16], loss: 2.7632, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:14:56 Iters: 686400/[16], loss: 3.0098, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:17:00 Iters: 686500/[16], loss: 2.9077, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:19:03 Iters: 686600/[16], loss: 3.2621, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:21:06 Iters: 686700/[16], loss: 2.6130, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:23:10 Iters: 686800/[16], loss: 2.9398, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:25:13 Iters: 686900/[16], loss: 2.8715, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:27:16 Iters: 687000/[16], loss: 3.2372, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:29:19 Iters: 687100/[16], loss: 3.3146, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:31:23 Iters: 687200/[16], loss: 2.7487, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:33:26 Iters: 687300/[16], loss: 3.1247, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:35:29 Iters: 687400/[16], loss: 2.9265, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:37:32 Iters: 687500/[16], loss: 2.7523, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:39:36 Iters: 687600/[16], loss: 3.1839, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:41:39 Iters: 687700/[16], loss: 3.2200, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:43:42 Iters: 687800/[16], loss: 2.4260, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:45:45 Iters: 687900/[16], loss: 2.4572, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:47:48 Iters: 688000/[16], loss: 3.0326, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:49:52 Iters: 688100/[16], loss: 2.6529, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:51:55 Iters: 688200/[16], loss: 2.8997, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:53:59 Iters: 688300/[16], loss: 3.2718, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:56:02 Iters: 688400/[16], loss: 2.6646, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-16:58:05 Iters: 688500/[16], loss: 2.6085, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:00:09 Iters: 688600/[16], loss: 2.8177, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:02:12 Iters: 688700/[16], loss: 2.7774, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:04:16 Iters: 688800/[16], loss: 3.3576, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:06:19 Iters: 688900/[16], loss: 3.0168, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:08:22 Iters: 689000/[16], loss: 2.7130, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:10:26 Iters: 689100/[16], loss: 2.8302, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:12:29 Iters: 689200/[16], loss: 2.9724, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:14:32 Iters: 689300/[16], loss: 3.5249, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:16:35 Iters: 689400/[16], loss: 3.1709, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:18:39 Iters: 689500/[16], loss: 3.0663, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:20:42 Iters: 689600/[16], loss: 3.0265, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:22:45 Iters: 689700/[16], loss: 3.0870, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:24:48 Iters: 689800/[16], loss: 2.7918, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:26:52 Iters: 689900/[16], loss: 2.9507, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:28:55 Iters: 690000/[16], loss: 3.7372, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:28:55 Saving checkpoint: 690000
-20220710-17:30:11 LFW Ave Accuracy: 99.7000
-20220710-17:31:26 AgeDB-30 Ave Accuracy: 97.3167
-20220710-17:32:53 CFP-FP Ave Accuracy: 94.9714
-20220710-17:32:53 Current Best Accuracy: LFW: 99.7000 in iters: 690000, AgeDB-30: 97.3333 in iters: 680000 and CFP-FP: 95.2571 in iters: 680000
-20220710-17:34:56 Iters: 690100/[16], loss: 3.2736, train_accuracy: 0.5234, time: 3.61 s/iter, learning rate: 0.0005000000000000001
-20220710-17:36:59 Iters: 690200/[16], loss: 2.3429, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:39:03 Iters: 690300/[16], loss: 3.0335, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:41:06 Iters: 690400/[16], loss: 3.0812, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:43:09 Iters: 690500/[16], loss: 2.5672, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:45:12 Iters: 690600/[16], loss: 3.0245, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:47:16 Iters: 690700/[16], loss: 2.9025, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:49:19 Iters: 690800/[16], loss: 3.4295, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:51:22 Iters: 690900/[16], loss: 3.3319, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:53:25 Iters: 691000/[16], loss: 3.3311, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:55:29 Iters: 691100/[16], loss: 3.1536, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:57:32 Iters: 691200/[16], loss: 2.8877, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-17:59:35 Iters: 691300/[16], loss: 2.9429, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:01:38 Iters: 691400/[16], loss: 3.5587, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:03:42 Iters: 691500/[16], loss: 2.7675, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:05:45 Iters: 691600/[16], loss: 3.0580, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:07:48 Iters: 691700/[16], loss: 3.4620, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:09:51 Iters: 691800/[16], loss: 3.2843, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:11:55 Iters: 691900/[16], loss: 3.0415, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:13:58 Iters: 692000/[16], loss: 3.0942, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:16:01 Iters: 692100/[16], loss: 2.7330, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:18:04 Iters: 692200/[16], loss: 2.8187, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:20:08 Iters: 692300/[16], loss: 2.7379, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:22:11 Iters: 692400/[16], loss: 3.1795, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:24:14 Iters: 692500/[16], loss: 3.0053, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:26:17 Iters: 692600/[16], loss: 3.0826, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:28:21 Iters: 692700/[16], loss: 3.2187, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:30:24 Iters: 692800/[16], loss: 2.8360, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:32:27 Iters: 692900/[16], loss: 3.9544, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:34:31 Iters: 693000/[16], loss: 3.3527, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:36:34 Iters: 693100/[16], loss: 3.0591, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:38:37 Iters: 693200/[16], loss: 3.4312, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:40:40 Iters: 693300/[16], loss: 3.6218, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:42:44 Iters: 693400/[16], loss: 3.2824, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:44:47 Iters: 693500/[16], loss: 3.2129, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:46:50 Iters: 693600/[16], loss: 2.4604, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:48:53 Iters: 693700/[16], loss: 2.9989, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:50:56 Iters: 693800/[16], loss: 3.2507, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:52:59 Iters: 693900/[16], loss: 2.7075, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:55:03 Iters: 694000/[16], loss: 2.8959, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:57:06 Iters: 694100/[16], loss: 3.0794, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-18:59:09 Iters: 694200/[16], loss: 3.1101, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:01:12 Iters: 694300/[16], loss: 3.0883, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:03:15 Iters: 694400/[16], loss: 2.6882, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:05:19 Iters: 694500/[16], loss: 2.9724, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:07:22 Iters: 694600/[16], loss: 4.0557, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:09:25 Iters: 694700/[16], loss: 2.8393, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:11:29 Iters: 694800/[16], loss: 2.8100, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:13:32 Iters: 694900/[16], loss: 2.5912, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:15:35 Iters: 695000/[16], loss: 2.5900, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:17:39 Iters: 695100/[16], loss: 2.9386, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:19:42 Iters: 695200/[16], loss: 2.3948, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:21:45 Iters: 695300/[16], loss: 2.8553, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:23:48 Iters: 695400/[16], loss: 3.9454, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:25:52 Iters: 695500/[16], loss: 3.0326, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:27:55 Iters: 695600/[16], loss: 3.1601, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:29:58 Iters: 695700/[16], loss: 3.4916, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:32:01 Iters: 695800/[16], loss: 2.7554, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:34:05 Iters: 695900/[16], loss: 3.5359, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:36:08 Iters: 696000/[16], loss: 3.7036, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:38:12 Iters: 696100/[16], loss: 3.4194, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:40:15 Iters: 696200/[16], loss: 2.2899, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:42:18 Iters: 696300/[16], loss: 3.4181, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:44:22 Iters: 696400/[16], loss: 3.0149, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:46:25 Iters: 696500/[16], loss: 2.9311, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:48:28 Iters: 696600/[16], loss: 3.3166, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:50:32 Iters: 696700/[16], loss: 2.6449, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:52:35 Iters: 696800/[16], loss: 3.4040, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:54:38 Iters: 696900/[16], loss: 3.0117, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:56:42 Iters: 697000/[16], loss: 3.1772, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-19:58:45 Iters: 697100/[16], loss: 2.7061, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:00:48 Iters: 697200/[16], loss: 2.9252, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:02:52 Iters: 697300/[16], loss: 3.0984, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:04:55 Iters: 697400/[16], loss: 2.7035, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:06:58 Iters: 697500/[16], loss: 2.6726, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:09:01 Iters: 697600/[16], loss: 3.0990, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:11:05 Iters: 697700/[16], loss: 2.6559, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:13:08 Iters: 697800/[16], loss: 2.9615, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:15:11 Iters: 697900/[16], loss: 3.3416, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:17:14 Iters: 698000/[16], loss: 3.2806, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:19:17 Iters: 698100/[16], loss: 3.5887, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:21:21 Iters: 698200/[16], loss: 2.9521, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:23:24 Iters: 698300/[16], loss: 3.2224, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:25:27 Iters: 698400/[16], loss: 2.9136, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:27:30 Iters: 698500/[16], loss: 3.6196, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:29:33 Iters: 698600/[16], loss: 3.0962, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:31:36 Iters: 698700/[16], loss: 3.3416, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:33:39 Iters: 698800/[16], loss: 3.5148, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:35:42 Iters: 698900/[16], loss: 2.8496, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:37:45 Iters: 699000/[16], loss: 2.8830, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:39:48 Iters: 699100/[16], loss: 2.7676, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:41:51 Iters: 699200/[16], loss: 3.5135, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:43:55 Iters: 699300/[16], loss: 2.8563, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:45:58 Iters: 699400/[16], loss: 2.9519, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:48:01 Iters: 699500/[16], loss: 3.3364, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:50:04 Iters: 699600/[16], loss: 3.0066, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:52:07 Iters: 699700/[16], loss: 2.8937, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:54:10 Iters: 699800/[16], loss: 2.9625, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:56:13 Iters: 699900/[16], loss: 2.9332, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:58:16 Iters: 700000/[16], loss: 3.9583, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-20:58:16 Saving checkpoint: 700000
-20220710-20:59:33 LFW Ave Accuracy: 99.6166
-20220710-21:00:48 AgeDB-30 Ave Accuracy: 97.2667
-20220710-21:02:15 CFP-FP Ave Accuracy: 95.1286
-20220710-21:02:15 Current Best Accuracy: LFW: 99.7000 in iters: 690000, AgeDB-30: 97.3333 in iters: 680000 and CFP-FP: 95.2571 in iters: 680000
-20220710-21:04:18 Iters: 700100/[16], loss: 3.4950, train_accuracy: 0.5156, time: 3.62 s/iter, learning rate: 0.0005000000000000001
-20220710-21:06:21 Iters: 700200/[16], loss: 2.7377, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:08:24 Iters: 700300/[16], loss: 3.5238, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:10:27 Iters: 700400/[16], loss: 2.7167, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:12:30 Iters: 700500/[16], loss: 3.1055, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:14:32 Iters: 700600/[16], loss: 3.1513, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:16:35 Iters: 700700/[16], loss: 3.5451, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:18:38 Iters: 700800/[16], loss: 3.7323, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:20:41 Iters: 700900/[16], loss: 3.4097, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:22:44 Iters: 701000/[16], loss: 3.4348, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:24:47 Iters: 701100/[16], loss: 3.4230, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:26:50 Iters: 701200/[16], loss: 2.9943, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:28:53 Iters: 701300/[16], loss: 2.8651, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:30:56 Iters: 701400/[16], loss: 3.3163, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:32:59 Iters: 701500/[16], loss: 2.6782, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:35:02 Iters: 701600/[16], loss: 3.7666, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:37:05 Iters: 701700/[16], loss: 2.8525, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:39:08 Iters: 701800/[16], loss: 3.2673, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:41:11 Iters: 701900/[16], loss: 2.5001, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:43:14 Iters: 702000/[16], loss: 2.8561, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:45:17 Iters: 702100/[16], loss: 2.8431, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:47:20 Iters: 702200/[16], loss: 3.3348, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:49:23 Iters: 702300/[16], loss: 3.3791, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:51:26 Iters: 702400/[16], loss: 3.3828, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:53:29 Iters: 702500/[16], loss: 2.9646, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:55:32 Iters: 702600/[16], loss: 3.0793, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:57:35 Iters: 702700/[16], loss: 2.7754, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-21:59:38 Iters: 702800/[16], loss: 3.4089, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:01:41 Iters: 702900/[16], loss: 3.9797, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:03:44 Iters: 703000/[16], loss: 3.2074, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:05:47 Iters: 703100/[16], loss: 3.2266, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:07:49 Iters: 703200/[16], loss: 3.2323, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:09:52 Iters: 703300/[16], loss: 3.7890, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:11:55 Iters: 703400/[16], loss: 3.1525, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:13:58 Iters: 703500/[16], loss: 2.5188, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:16:01 Iters: 703600/[16], loss: 3.2067, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:18:04 Iters: 703700/[16], loss: 3.4833, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:20:07 Iters: 703800/[16], loss: 2.3656, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:22:10 Iters: 703900/[16], loss: 3.1661, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:24:13 Iters: 704000/[16], loss: 2.8968, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:26:16 Iters: 704100/[16], loss: 2.4238, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:28:19 Iters: 704200/[16], loss: 3.0643, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:30:22 Iters: 704300/[16], loss: 3.6926, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:32:25 Iters: 704400/[16], loss: 2.7514, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:34:27 Iters: 704500/[16], loss: 3.2682, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:36:30 Iters: 704600/[16], loss: 3.3148, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:38:33 Iters: 704700/[16], loss: 2.9632, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:40:36 Iters: 704800/[16], loss: 2.9191, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:42:39 Iters: 704900/[16], loss: 3.6481, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:44:42 Iters: 705000/[16], loss: 3.2820, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:46:45 Iters: 705100/[16], loss: 2.7316, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:48:48 Iters: 705200/[16], loss: 3.1914, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:50:51 Iters: 705300/[16], loss: 3.0300, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:52:54 Iters: 705400/[16], loss: 2.9541, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:54:57 Iters: 705500/[16], loss: 2.9045, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:57:00 Iters: 705600/[16], loss: 3.4059, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-22:59:03 Iters: 705700/[16], loss: 2.4885, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:01:06 Iters: 705800/[16], loss: 2.8930, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:03:09 Iters: 705900/[16], loss: 3.2914, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:05:11 Iters: 706000/[16], loss: 3.0132, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:07:14 Iters: 706100/[16], loss: 3.3321, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:09:17 Iters: 706200/[16], loss: 3.4236, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:11:20 Iters: 706300/[16], loss: 2.7539, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:13:23 Iters: 706400/[16], loss: 3.5814, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:15:26 Iters: 706500/[16], loss: 3.3640, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:17:29 Iters: 706600/[16], loss: 2.9646, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:19:32 Iters: 706700/[16], loss: 3.1134, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:21:35 Iters: 706800/[16], loss: 3.6356, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:23:38 Iters: 706900/[16], loss: 3.6075, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:25:41 Iters: 707000/[16], loss: 3.2501, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:27:44 Iters: 707100/[16], loss: 3.2860, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:29:47 Iters: 707200/[16], loss: 2.8973, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:31:50 Iters: 707300/[16], loss: 3.2678, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:33:52 Iters: 707400/[16], loss: 3.6798, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:35:55 Iters: 707500/[16], loss: 3.4164, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:37:58 Iters: 707600/[16], loss: 3.7051, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:40:01 Iters: 707700/[16], loss: 3.5561, train_accuracy: 0.4375, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:42:04 Iters: 707800/[16], loss: 3.3320, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:44:07 Iters: 707900/[16], loss: 3.4380, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:46:10 Iters: 708000/[16], loss: 3.4407, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:48:13 Iters: 708100/[16], loss: 3.5403, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:50:16 Iters: 708200/[16], loss: 3.3328, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:52:19 Iters: 708300/[16], loss: 3.4899, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:54:22 Iters: 708400/[16], loss: 2.8661, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:56:24 Iters: 708500/[16], loss: 3.1781, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220710-23:58:27 Iters: 708600/[16], loss: 3.0923, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:00:30 Iters: 708700/[16], loss: 2.9496, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:02:33 Iters: 708800/[16], loss: 3.4234, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:04:36 Iters: 708900/[16], loss: 3.1878, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:06:39 Iters: 709000/[16], loss: 2.7046, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:08:42 Iters: 709100/[16], loss: 4.0455, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:10:45 Iters: 709200/[16], loss: 2.9139, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:12:48 Iters: 709300/[16], loss: 3.1613, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:14:51 Iters: 709400/[16], loss: 3.7895, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:16:54 Iters: 709500/[16], loss: 3.0589, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:18:57 Iters: 709600/[16], loss: 3.8428, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:20:59 Iters: 709700/[16], loss: 3.0023, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:23:02 Iters: 709800/[16], loss: 2.8136, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:25:05 Iters: 709900/[16], loss: 3.0171, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:27:08 Iters: 710000/[16], loss: 3.0092, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:27:08 Saving checkpoint: 710000
-20220711-00:28:25 LFW Ave Accuracy: 99.7166
-20220711-00:29:40 AgeDB-30 Ave Accuracy: 97.1667
-20220711-00:31:07 CFP-FP Ave Accuracy: 95.1429
-20220711-00:31:07 Current Best Accuracy: LFW: 99.7166 in iters: 710000, AgeDB-30: 97.3333 in iters: 680000 and CFP-FP: 95.2571 in iters: 680000
-20220711-00:33:10 Iters: 710100/[16], loss: 3.1424, train_accuracy: 0.5625, time: 3.61 s/iter, learning rate: 0.0005000000000000001
-20220711-00:35:13 Iters: 710200/[16], loss: 2.6797, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:37:16 Iters: 710300/[16], loss: 3.2263, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:39:18 Iters: 710400/[16], loss: 2.9390, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:41:21 Iters: 710500/[16], loss: 2.8591, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:43:24 Iters: 710600/[16], loss: 2.5997, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:45:27 Iters: 710700/[16], loss: 3.3307, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:47:30 Iters: 710800/[16], loss: 3.3007, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:49:33 Iters: 710900/[16], loss: 3.0090, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:51:36 Iters: 711000/[16], loss: 3.6353, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:53:39 Iters: 711100/[16], loss: 2.8884, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:55:42 Iters: 711200/[16], loss: 3.1405, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:57:45 Iters: 711300/[16], loss: 3.7035, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-00:59:48 Iters: 711400/[16], loss: 3.4586, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:01:51 Iters: 711500/[16], loss: 2.6624, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:03:54 Iters: 711600/[16], loss: 3.2190, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:05:57 Iters: 711700/[16], loss: 3.4571, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:08:00 Iters: 711800/[16], loss: 2.9286, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:10:03 Iters: 711900/[16], loss: 3.5764, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:12:06 Iters: 712000/[16], loss: 3.0634, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:14:09 Iters: 712100/[16], loss: 3.7994, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:16:12 Iters: 712200/[16], loss: 2.7027, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:18:14 Iters: 712300/[16], loss: 3.3756, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:20:17 Iters: 712400/[16], loss: 3.0357, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:22:20 Iters: 712500/[16], loss: 3.2060, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:24:23 Iters: 712600/[16], loss: 2.8972, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:26:26 Iters: 712700/[16], loss: 2.8917, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:28:29 Iters: 712800/[16], loss: 3.1338, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:30:32 Iters: 712900/[16], loss: 2.8542, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:32:35 Iters: 713000/[16], loss: 3.7481, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:34:38 Iters: 713100/[16], loss: 3.9797, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:36:41 Iters: 713200/[16], loss: 2.9722, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:38:44 Iters: 713300/[16], loss: 3.9309, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:40:47 Iters: 713400/[16], loss: 3.3608, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:42:50 Iters: 713500/[16], loss: 3.2910, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:44:53 Iters: 713600/[16], loss: 2.6197, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:46:56 Iters: 713700/[16], loss: 3.0128, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:48:59 Iters: 713800/[16], loss: 2.9390, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:51:02 Iters: 713900/[16], loss: 3.4724, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:53:05 Iters: 714000/[16], loss: 3.2775, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:55:08 Iters: 714100/[16], loss: 4.1425, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:57:11 Iters: 714200/[16], loss: 3.5841, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-01:59:14 Iters: 714300/[16], loss: 2.5357, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:01:17 Iters: 714400/[16], loss: 3.4820, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:03:20 Iters: 714500/[16], loss: 3.5196, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:05:23 Iters: 714600/[16], loss: 3.1931, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:07:26 Iters: 714700/[16], loss: 3.0306, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:09:29 Iters: 714800/[16], loss: 3.3448, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:11:32 Iters: 714900/[16], loss: 3.4030, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:13:36 Iters: 715000/[16], loss: 2.9575, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:15:39 Iters: 715100/[16], loss: 2.9355, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:17:42 Iters: 715200/[16], loss: 3.5185, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:19:45 Iters: 715300/[16], loss: 3.6707, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:21:48 Iters: 715400/[16], loss: 3.0359, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:23:51 Iters: 715500/[16], loss: 3.6695, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:25:55 Iters: 715600/[16], loss: 2.6424, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:27:58 Iters: 715700/[16], loss: 3.3281, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:30:01 Iters: 715800/[16], loss: 3.4725, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:32:05 Iters: 715900/[16], loss: 3.5073, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:34:08 Iters: 716000/[16], loss: 3.2059, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:36:11 Iters: 716100/[16], loss: 2.4395, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:38:14 Iters: 716200/[16], loss: 2.9080, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:40:18 Iters: 716300/[16], loss: 3.8937, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:42:21 Iters: 716400/[16], loss: 3.2887, train_accuracy: 0.4922, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:44:24 Iters: 716500/[16], loss: 3.1057, train_accuracy: 0.5547, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220711-02:46:28 Iters: 716600/[16], loss: 3.2835, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:48:31 Iters: 716700/[16], loss: 3.6186, train_accuracy: 0.5234, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220711-02:50:35 Iters: 716800/[16], loss: 3.6730, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:52:39 Iters: 716900/[16], loss: 3.3659, train_accuracy: 0.5000, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220711-02:54:42 Iters: 717000/[16], loss: 3.1570, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:56:45 Iters: 717100/[16], loss: 2.6900, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-02:58:49 Iters: 717200/[16], loss: 2.8518, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:00:52 Iters: 717300/[16], loss: 3.6993, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:02:55 Iters: 717400/[16], loss: 2.7264, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:04:59 Iters: 717500/[16], loss: 3.1016, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:07:02 Iters: 717600/[16], loss: 2.9811, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:09:05 Iters: 717700/[16], loss: 3.1438, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:11:09 Iters: 717800/[16], loss: 2.9942, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:13:12 Iters: 717900/[16], loss: 3.4486, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:15:15 Iters: 718000/[16], loss: 2.9018, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:17:19 Iters: 718100/[16], loss: 3.4513, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:19:22 Iters: 718200/[16], loss: 2.9258, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:21:25 Iters: 718300/[16], loss: 3.1753, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:23:29 Iters: 718400/[16], loss: 2.8069, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220711-03:25:32 Iters: 718500/[16], loss: 3.5456, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:27:35 Iters: 718600/[16], loss: 2.8924, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:29:39 Iters: 718700/[16], loss: 3.4896, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:31:42 Iters: 718800/[16], loss: 3.1490, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:33:45 Iters: 718900/[16], loss: 3.1091, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:35:48 Iters: 719000/[16], loss: 2.3254, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:37:52 Iters: 719100/[16], loss: 3.0192, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:39:55 Iters: 719200/[16], loss: 3.0371, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:41:58 Iters: 719300/[16], loss: 3.4066, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:44:02 Iters: 719400/[16], loss: 3.1059, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:46:05 Iters: 719500/[16], loss: 2.9315, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:48:09 Iters: 719600/[16], loss: 3.3305, train_accuracy: 0.5469, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220711-03:50:12 Iters: 719700/[16], loss: 4.1641, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:52:15 Iters: 719800/[16], loss: 3.4594, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:54:19 Iters: 719900/[16], loss: 3.2565, train_accuracy: 0.6016, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220711-03:56:22 Iters: 720000/[16], loss: 2.8587, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-03:56:22 Saving checkpoint: 720000
-20220711-03:57:40 LFW Ave Accuracy: 99.6333
-20220711-03:58:59 AgeDB-30 Ave Accuracy: 97.1333
-20220711-04:00:29 CFP-FP Ave Accuracy: 94.9571
-20220711-04:00:29 Current Best Accuracy: LFW: 99.7166 in iters: 710000, AgeDB-30: 97.3333 in iters: 680000 and CFP-FP: 95.2571 in iters: 680000
-20220711-04:02:32 Iters: 720100/[16], loss: 3.6217, train_accuracy: 0.5391, time: 3.70 s/iter, learning rate: 0.0005000000000000001
-20220711-04:04:35 Iters: 720200/[16], loss: 3.6993, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:06:38 Iters: 720300/[16], loss: 3.3762, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:08:42 Iters: 720400/[16], loss: 3.0997, train_accuracy: 0.5000, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220711-04:10:46 Iters: 720500/[16], loss: 2.9160, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 0.0005000000000000001
-20220711-04:12:49 Iters: 720600/[16], loss: 2.9169, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:14:52 Iters: 720700/[16], loss: 3.1918, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:16:56 Iters: 720800/[16], loss: 2.7522, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:18:59 Iters: 720900/[16], loss: 3.2319, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:21:02 Iters: 721000/[16], loss: 3.3527, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:23:05 Iters: 721100/[16], loss: 3.1033, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:25:08 Iters: 721200/[16], loss: 2.7512, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:27:12 Iters: 721300/[16], loss: 2.9996, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:29:15 Iters: 721400/[16], loss: 3.2974, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:31:18 Iters: 721500/[16], loss: 2.6937, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:33:21 Iters: 721600/[16], loss: 3.6691, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:35:24 Iters: 721700/[16], loss: 2.8210, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:37:27 Iters: 721800/[16], loss: 3.2595, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:39:31 Iters: 721900/[16], loss: 2.8647, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:41:34 Iters: 722000/[16], loss: 3.0117, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:43:37 Iters: 722100/[16], loss: 3.2525, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:45:40 Iters: 722200/[16], loss: 3.5464, train_accuracy: 0.5078, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:47:43 Iters: 722300/[16], loss: 3.3948, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:49:46 Iters: 722400/[16], loss: 2.7509, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:51:49 Iters: 722500/[16], loss: 3.0608, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:53:52 Iters: 722600/[16], loss: 3.6648, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:55:55 Iters: 722700/[16], loss: 2.3930, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-04:57:58 Iters: 722800/[16], loss: 3.2431, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:00:01 Iters: 722900/[16], loss: 2.9766, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:02:04 Iters: 723000/[16], loss: 2.8206, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:04:07 Iters: 723100/[16], loss: 3.1874, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:06:10 Iters: 723200/[16], loss: 3.5668, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:08:13 Iters: 723300/[16], loss: 2.8610, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:10:15 Iters: 723400/[16], loss: 2.9548, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:12:18 Iters: 723500/[16], loss: 2.6494, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:14:21 Iters: 723600/[16], loss: 3.3500, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:16:24 Iters: 723700/[16], loss: 3.0846, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:18:27 Iters: 723800/[16], loss: 2.9213, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:20:30 Iters: 723900/[16], loss: 2.8600, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:22:33 Iters: 724000/[16], loss: 3.0050, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:24:36 Iters: 724100/[16], loss: 2.9292, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:26:39 Iters: 724200/[16], loss: 3.5839, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:28:42 Iters: 724300/[16], loss: 3.9643, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:30:45 Iters: 724400/[16], loss: 2.8482, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:32:48 Iters: 724500/[16], loss: 3.7340, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:34:51 Iters: 724600/[16], loss: 2.6115, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:36:54 Iters: 724700/[16], loss: 3.3123, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:38:57 Iters: 724800/[16], loss: 2.9777, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:41:00 Iters: 724900/[16], loss: 2.8698, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:43:03 Iters: 725000/[16], loss: 3.9968, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:45:06 Iters: 725100/[16], loss: 2.9701, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:47:09 Iters: 725200/[16], loss: 3.7822, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:49:12 Iters: 725300/[16], loss: 2.7515, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:51:15 Iters: 725400/[16], loss: 3.0667, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:53:18 Iters: 725500/[16], loss: 3.6583, train_accuracy: 0.4688, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:55:21 Iters: 725600/[16], loss: 3.0936, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:57:24 Iters: 725700/[16], loss: 2.7186, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-05:59:27 Iters: 725800/[16], loss: 3.1251, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:01:30 Iters: 725900/[16], loss: 3.1774, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:03:33 Iters: 726000/[16], loss: 3.7343, train_accuracy: 0.4844, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:05:36 Iters: 726100/[16], loss: 3.0757, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:07:39 Iters: 726200/[16], loss: 4.0419, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:09:42 Iters: 726300/[16], loss: 2.9265, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:11:45 Iters: 726400/[16], loss: 3.0894, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:13:48 Iters: 726500/[16], loss: 2.9963, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:15:51 Iters: 726600/[16], loss: 3.2804, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:17:54 Iters: 726700/[16], loss: 3.4473, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:19:57 Iters: 726800/[16], loss: 2.9914, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:22:00 Iters: 726900/[16], loss: 3.1281, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:24:03 Iters: 727000/[16], loss: 3.3112, train_accuracy: 0.4531, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:26:06 Iters: 727100/[16], loss: 3.5331, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:28:09 Iters: 727200/[16], loss: 3.6456, train_accuracy: 0.4766, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:30:12 Iters: 727300/[16], loss: 3.4604, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:32:15 Iters: 727400/[16], loss: 3.4778, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:34:18 Iters: 727500/[16], loss: 3.8425, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:36:21 Iters: 727600/[16], loss: 2.7236, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:38:24 Iters: 727700/[16], loss: 2.6103, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:40:27 Iters: 727800/[16], loss: 2.7759, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 0.0005000000000000001
-20220711-06:41:16 Train Epoch: 17/18 ...
-20220711-06:42:31 Iters: 727900/[17], loss: 3.2690, train_accuracy: 0.5000, time: 0.74 s/iter, learning rate: 5.000000000000002e-06
-20220711-06:44:34 Iters: 728000/[17], loss: 2.6418, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-06:46:37 Iters: 728100/[17], loss: 2.9927, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-06:48:40 Iters: 728200/[17], loss: 3.0187, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-06:50:44 Iters: 728300/[17], loss: 3.2666, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-06:52:47 Iters: 728400/[17], loss: 2.8236, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-06:54:51 Iters: 728500/[17], loss: 2.9781, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-06:56:54 Iters: 728600/[17], loss: 3.2324, train_accuracy: 0.5000, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-06:58:57 Iters: 728700/[17], loss: 3.1669, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:01:00 Iters: 728800/[17], loss: 2.6054, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:03:03 Iters: 728900/[17], loss: 3.3539, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:05:06 Iters: 729000/[17], loss: 2.2065, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:07:09 Iters: 729100/[17], loss: 2.0266, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:09:12 Iters: 729200/[17], loss: 3.4417, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:11:15 Iters: 729300/[17], loss: 2.3797, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:13:18 Iters: 729400/[17], loss: 2.8833, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:15:21 Iters: 729500/[17], loss: 3.3240, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:17:24 Iters: 729600/[17], loss: 2.8271, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:19:27 Iters: 729700/[17], loss: 2.7073, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:21:30 Iters: 729800/[17], loss: 3.1092, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:23:33 Iters: 729900/[17], loss: 2.1319, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:25:36 Iters: 730000/[17], loss: 2.9404, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:25:36 Saving checkpoint: 730000
-20220711-07:26:52 LFW Ave Accuracy: 99.6666
-20220711-07:28:07 AgeDB-30 Ave Accuracy: 97.3500
-20220711-07:29:33 CFP-FP Ave Accuracy: 95.3429
-20220711-07:29:33 Current Best Accuracy: LFW: 99.7166 in iters: 710000, AgeDB-30: 97.3500 in iters: 730000 and CFP-FP: 95.3429 in iters: 730000
-20220711-07:31:36 Iters: 730100/[17], loss: 3.4295, train_accuracy: 0.5781, time: 3.60 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:33:39 Iters: 730200/[17], loss: 2.4830, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:35:42 Iters: 730300/[17], loss: 3.3630, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:37:45 Iters: 730400/[17], loss: 2.7323, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:39:48 Iters: 730500/[17], loss: 2.7555, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:41:51 Iters: 730600/[17], loss: 2.5275, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:43:54 Iters: 730700/[17], loss: 2.4747, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:45:57 Iters: 730800/[17], loss: 2.5876, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:48:00 Iters: 730900/[17], loss: 2.5949, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:50:03 Iters: 731000/[17], loss: 2.3175, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:52:06 Iters: 731100/[17], loss: 3.2775, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:54:09 Iters: 731200/[17], loss: 3.0138, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:56:12 Iters: 731300/[17], loss: 2.5317, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-07:58:15 Iters: 731400/[17], loss: 2.6807, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:00:18 Iters: 731500/[17], loss: 2.5098, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:02:21 Iters: 731600/[17], loss: 2.8521, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:04:24 Iters: 731700/[17], loss: 3.7340, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:06:27 Iters: 731800/[17], loss: 2.3405, train_accuracy: 0.7109, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:08:30 Iters: 731900/[17], loss: 2.3937, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:10:33 Iters: 732000/[17], loss: 2.4258, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:12:37 Iters: 732100/[17], loss: 2.7034, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:14:40 Iters: 732200/[17], loss: 2.4925, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:16:43 Iters: 732300/[17], loss: 2.9853, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:18:46 Iters: 732400/[17], loss: 3.1425, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:20:49 Iters: 732500/[17], loss: 2.9315, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:22:53 Iters: 732600/[17], loss: 3.1044, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:24:56 Iters: 732700/[17], loss: 2.9521, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:26:59 Iters: 732800/[17], loss: 3.0075, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:29:02 Iters: 732900/[17], loss: 2.4999, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:31:06 Iters: 733000/[17], loss: 3.2586, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:33:09 Iters: 733100/[17], loss: 2.5606, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:35:12 Iters: 733200/[17], loss: 3.0659, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:37:15 Iters: 733300/[17], loss: 2.0554, train_accuracy: 0.7188, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:39:18 Iters: 733400/[17], loss: 2.2353, train_accuracy: 0.7188, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:41:22 Iters: 733500/[17], loss: 2.4868, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:43:25 Iters: 733600/[17], loss: 3.0602, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:45:28 Iters: 733700/[17], loss: 2.9513, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:47:31 Iters: 733800/[17], loss: 2.3516, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:49:34 Iters: 733900/[17], loss: 2.7151, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:51:37 Iters: 734000/[17], loss: 3.0088, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:53:40 Iters: 734100/[17], loss: 2.4302, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:55:44 Iters: 734200/[17], loss: 3.3631, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:57:47 Iters: 734300/[17], loss: 2.2405, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-08:59:50 Iters: 734400/[17], loss: 2.3564, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:01:53 Iters: 734500/[17], loss: 2.3368, train_accuracy: 0.7422, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:03:56 Iters: 734600/[17], loss: 3.0353, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:05:59 Iters: 734700/[17], loss: 2.8386, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:08:02 Iters: 734800/[17], loss: 2.5901, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:10:05 Iters: 734900/[17], loss: 2.5011, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:12:08 Iters: 735000/[17], loss: 3.0110, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:14:11 Iters: 735100/[17], loss: 3.0860, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:16:14 Iters: 735200/[17], loss: 2.9678, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:18:17 Iters: 735300/[17], loss: 2.8352, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:20:20 Iters: 735400/[17], loss: 3.0171, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:22:23 Iters: 735500/[17], loss: 2.4248, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:24:27 Iters: 735600/[17], loss: 2.4435, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:26:30 Iters: 735700/[17], loss: 2.5905, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:28:33 Iters: 735800/[17], loss: 2.3268, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:30:36 Iters: 735900/[17], loss: 2.9254, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:32:40 Iters: 736000/[17], loss: 2.4324, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:34:43 Iters: 736100/[17], loss: 2.4983, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:36:46 Iters: 736200/[17], loss: 3.6317, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:38:49 Iters: 736300/[17], loss: 2.8309, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:40:52 Iters: 736400/[17], loss: 3.7482, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:42:55 Iters: 736500/[17], loss: 2.4473, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:44:58 Iters: 736600/[17], loss: 2.9905, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:47:01 Iters: 736700/[17], loss: 3.1050, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:49:05 Iters: 736800/[17], loss: 2.8678, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:51:08 Iters: 736900/[17], loss: 2.2224, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:53:11 Iters: 737000/[17], loss: 3.0374, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:55:14 Iters: 737100/[17], loss: 2.5690, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:57:17 Iters: 737200/[17], loss: 2.4990, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-09:59:20 Iters: 737300/[17], loss: 2.4150, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:01:24 Iters: 737400/[17], loss: 2.5627, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:03:27 Iters: 737500/[17], loss: 2.6382, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:05:30 Iters: 737600/[17], loss: 2.7167, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:07:33 Iters: 737700/[17], loss: 2.4063, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:09:36 Iters: 737800/[17], loss: 2.9214, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:11:39 Iters: 737900/[17], loss: 2.5347, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:13:42 Iters: 738000/[17], loss: 2.0738, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:15:45 Iters: 738100/[17], loss: 3.1964, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:17:48 Iters: 738200/[17], loss: 2.2653, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:19:51 Iters: 738300/[17], loss: 2.3152, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:21:54 Iters: 738400/[17], loss: 2.2302, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:23:57 Iters: 738500/[17], loss: 2.7853, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:26:01 Iters: 738600/[17], loss: 2.7654, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:28:04 Iters: 738700/[17], loss: 2.2561, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:30:07 Iters: 738800/[17], loss: 3.1415, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:32:10 Iters: 738900/[17], loss: 2.9061, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:34:13 Iters: 739000/[17], loss: 2.4799, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:36:16 Iters: 739100/[17], loss: 2.5313, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:38:20 Iters: 739200/[17], loss: 2.4674, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:40:23 Iters: 739300/[17], loss: 2.6397, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:42:26 Iters: 739400/[17], loss: 2.2482, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:44:29 Iters: 739500/[17], loss: 2.9587, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:46:33 Iters: 739600/[17], loss: 2.4749, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:48:36 Iters: 739700/[17], loss: 2.7810, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:50:39 Iters: 739800/[17], loss: 2.9109, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:52:42 Iters: 739900/[17], loss: 2.6459, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:54:46 Iters: 740000/[17], loss: 2.7614, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-10:54:46 Saving checkpoint: 740000
-20220711-10:56:03 LFW Ave Accuracy: 99.7333
-20220711-10:57:18 AgeDB-30 Ave Accuracy: 97.4500
-20220711-10:58:46 CFP-FP Ave Accuracy: 95.4143
-20220711-10:58:46 Current Best Accuracy: LFW: 99.7333 in iters: 740000, AgeDB-30: 97.4500 in iters: 740000 and CFP-FP: 95.4143 in iters: 740000
-20220711-11:00:49 Iters: 740100/[17], loss: 2.6820, train_accuracy: 0.6406, time: 3.63 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:02:52 Iters: 740200/[17], loss: 3.1927, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:04:56 Iters: 740300/[17], loss: 3.0487, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:06:59 Iters: 740400/[17], loss: 1.8992, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:09:02 Iters: 740500/[17], loss: 2.4030, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:11:05 Iters: 740600/[17], loss: 2.1179, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:13:08 Iters: 740700/[17], loss: 2.9770, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:15:12 Iters: 740800/[17], loss: 2.7247, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:17:15 Iters: 740900/[17], loss: 2.9432, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:19:18 Iters: 741000/[17], loss: 2.3416, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:21:22 Iters: 741100/[17], loss: 2.6403, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:23:25 Iters: 741200/[17], loss: 2.2465, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:25:28 Iters: 741300/[17], loss: 3.0495, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:27:31 Iters: 741400/[17], loss: 3.1531, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:29:34 Iters: 741500/[17], loss: 3.0751, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:31:37 Iters: 741600/[17], loss: 2.1185, train_accuracy: 0.7422, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:33:40 Iters: 741700/[17], loss: 3.5330, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:35:43 Iters: 741800/[17], loss: 2.6247, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:37:46 Iters: 741900/[17], loss: 3.0747, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:39:49 Iters: 742000/[17], loss: 2.4493, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:41:53 Iters: 742100/[17], loss: 2.5713, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:43:56 Iters: 742200/[17], loss: 2.8115, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:45:59 Iters: 742300/[17], loss: 2.7893, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:48:02 Iters: 742400/[17], loss: 2.8194, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:50:05 Iters: 742500/[17], loss: 3.1952, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:52:09 Iters: 742600/[17], loss: 2.5470, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:54:12 Iters: 742700/[17], loss: 3.4220, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:56:15 Iters: 742800/[17], loss: 2.9292, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-11:58:18 Iters: 742900/[17], loss: 3.0587, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:00:22 Iters: 743000/[17], loss: 2.1979, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:02:25 Iters: 743100/[17], loss: 2.4799, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:04:28 Iters: 743200/[17], loss: 2.7785, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:06:31 Iters: 743300/[17], loss: 2.5883, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:08:34 Iters: 743400/[17], loss: 3.0491, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:10:37 Iters: 743500/[17], loss: 2.6242, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:12:40 Iters: 743600/[17], loss: 3.0593, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:14:43 Iters: 743700/[17], loss: 2.4828, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:16:46 Iters: 743800/[17], loss: 2.7171, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:18:49 Iters: 743900/[17], loss: 3.0783, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:20:53 Iters: 744000/[17], loss: 2.6555, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:22:56 Iters: 744100/[17], loss: 3.1378, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:25:00 Iters: 744200/[17], loss: 3.2526, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:27:03 Iters: 744300/[17], loss: 2.8991, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:29:07 Iters: 744400/[17], loss: 2.5862, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:31:10 Iters: 744500/[17], loss: 2.3062, train_accuracy: 0.7188, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:33:14 Iters: 744600/[17], loss: 2.9273, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:35:17 Iters: 744700/[17], loss: 2.5588, train_accuracy: 0.6562, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:37:21 Iters: 744800/[17], loss: 2.2707, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:39:24 Iters: 744900/[17], loss: 2.9097, train_accuracy: 0.5703, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:41:28 Iters: 745000/[17], loss: 2.6870, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:43:31 Iters: 745100/[17], loss: 2.9357, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:45:34 Iters: 745200/[17], loss: 2.6063, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:47:37 Iters: 745300/[17], loss: 2.4911, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:49:41 Iters: 745400/[17], loss: 2.2897, train_accuracy: 0.6953, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:51:44 Iters: 745500/[17], loss: 2.4005, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:53:48 Iters: 745600/[17], loss: 2.2967, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:55:51 Iters: 745700/[17], loss: 2.3990, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:57:54 Iters: 745800/[17], loss: 2.7938, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-12:59:57 Iters: 745900/[17], loss: 3.1868, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:02:00 Iters: 746000/[17], loss: 2.0176, train_accuracy: 0.7109, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:04:03 Iters: 746100/[17], loss: 2.4308, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:06:06 Iters: 746200/[17], loss: 2.8707, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:08:10 Iters: 746300/[17], loss: 2.1776, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:10:13 Iters: 746400/[17], loss: 2.6591, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:12:16 Iters: 746500/[17], loss: 2.8637, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:14:19 Iters: 746600/[17], loss: 2.8575, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:16:22 Iters: 746700/[17], loss: 2.0643, train_accuracy: 0.7188, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:18:25 Iters: 746800/[17], loss: 2.8390, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:20:28 Iters: 746900/[17], loss: 2.5629, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:22:32 Iters: 747000/[17], loss: 2.5730, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:24:35 Iters: 747100/[17], loss: 2.4717, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:26:38 Iters: 747200/[17], loss: 2.2915, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:28:42 Iters: 747300/[17], loss: 3.1243, train_accuracy: 0.5781, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:30:45 Iters: 747400/[17], loss: 3.2985, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:32:49 Iters: 747500/[17], loss: 2.4576, train_accuracy: 0.6562, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:34:52 Iters: 747600/[17], loss: 2.3545, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:36:55 Iters: 747700/[17], loss: 2.2964, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:38:58 Iters: 747800/[17], loss: 2.3473, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:41:02 Iters: 747900/[17], loss: 3.0951, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:43:05 Iters: 748000/[17], loss: 3.2954, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:45:08 Iters: 748100/[17], loss: 2.4153, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:47:11 Iters: 748200/[17], loss: 2.9510, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:49:14 Iters: 748300/[17], loss: 2.4933, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:51:17 Iters: 748400/[17], loss: 2.6721, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:53:20 Iters: 748500/[17], loss: 2.7967, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:55:23 Iters: 748600/[17], loss: 2.9556, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:57:27 Iters: 748700/[17], loss: 2.3341, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-13:59:30 Iters: 748800/[17], loss: 2.4444, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:01:33 Iters: 748900/[17], loss: 2.9177, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:03:36 Iters: 749000/[17], loss: 2.6252, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:05:39 Iters: 749100/[17], loss: 2.1245, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:07:42 Iters: 749200/[17], loss: 2.7271, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:09:46 Iters: 749300/[17], loss: 2.3756, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:11:49 Iters: 749400/[17], loss: 2.5979, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:13:52 Iters: 749500/[17], loss: 2.0201, train_accuracy: 0.7344, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:15:55 Iters: 749600/[17], loss: 2.5731, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:17:58 Iters: 749700/[17], loss: 2.4154, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:20:01 Iters: 749800/[17], loss: 2.3637, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:22:04 Iters: 749900/[17], loss: 2.6283, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:24:08 Iters: 750000/[17], loss: 2.7085, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:24:08 Saving checkpoint: 750000
-20220711-14:25:26 LFW Ave Accuracy: 99.7000
-20220711-14:26:43 AgeDB-30 Ave Accuracy: 97.3000
-20220711-14:28:12 CFP-FP Ave Accuracy: 95.3143
-20220711-14:28:12 Current Best Accuracy: LFW: 99.7333 in iters: 740000, AgeDB-30: 97.4500 in iters: 740000 and CFP-FP: 95.4143 in iters: 740000
-20220711-14:30:15 Iters: 750100/[17], loss: 2.1122, train_accuracy: 0.7109, time: 3.67 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:32:18 Iters: 750200/[17], loss: 2.7060, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:34:21 Iters: 750300/[17], loss: 2.8239, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:36:24 Iters: 750400/[17], loss: 2.6361, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:38:27 Iters: 750500/[17], loss: 2.0563, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:40:31 Iters: 750600/[17], loss: 1.9620, train_accuracy: 0.7188, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:42:34 Iters: 750700/[17], loss: 2.4592, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:44:37 Iters: 750800/[17], loss: 2.1366, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:46:40 Iters: 750900/[17], loss: 2.4395, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:48:43 Iters: 751000/[17], loss: 2.7008, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:50:46 Iters: 751100/[17], loss: 2.8965, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:52:49 Iters: 751200/[17], loss: 2.9480, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:54:52 Iters: 751300/[17], loss: 3.4041, train_accuracy: 0.5156, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:56:55 Iters: 751400/[17], loss: 3.1158, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-14:58:58 Iters: 751500/[17], loss: 3.4827, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:01:01 Iters: 751600/[17], loss: 2.4723, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:03:04 Iters: 751700/[17], loss: 2.8412, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:05:07 Iters: 751800/[17], loss: 2.6203, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:07:10 Iters: 751900/[17], loss: 2.9273, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:09:13 Iters: 752000/[17], loss: 2.8107, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:11:16 Iters: 752100/[17], loss: 3.2888, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:13:19 Iters: 752200/[17], loss: 2.2701, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:15:22 Iters: 752300/[17], loss: 2.2060, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:17:25 Iters: 752400/[17], loss: 3.0411, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:19:28 Iters: 752500/[17], loss: 2.7500, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:21:31 Iters: 752600/[17], loss: 3.6339, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:23:34 Iters: 752700/[17], loss: 2.2376, train_accuracy: 0.7188, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:25:37 Iters: 752800/[17], loss: 3.0094, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:27:40 Iters: 752900/[17], loss: 3.0834, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:29:43 Iters: 753000/[17], loss: 2.7433, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:31:46 Iters: 753100/[17], loss: 2.4460, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:33:49 Iters: 753200/[17], loss: 2.6229, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:35:52 Iters: 753300/[17], loss: 2.8301, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:37:55 Iters: 753400/[17], loss: 2.2365, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:39:58 Iters: 753500/[17], loss: 2.1757, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:42:01 Iters: 753600/[17], loss: 2.2408, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:44:04 Iters: 753700/[17], loss: 2.9418, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:46:07 Iters: 753800/[17], loss: 2.6153, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:48:10 Iters: 753900/[17], loss: 2.4580, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:50:13 Iters: 754000/[17], loss: 1.9752, train_accuracy: 0.7109, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:52:16 Iters: 754100/[17], loss: 2.5511, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:54:19 Iters: 754200/[17], loss: 2.5353, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:56:22 Iters: 754300/[17], loss: 2.2230, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-15:58:25 Iters: 754400/[17], loss: 2.3769, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:00:28 Iters: 754500/[17], loss: 2.8392, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:02:31 Iters: 754600/[17], loss: 2.6214, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:04:34 Iters: 754700/[17], loss: 2.5856, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:06:37 Iters: 754800/[17], loss: 2.6406, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:08:40 Iters: 754900/[17], loss: 2.9253, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:10:43 Iters: 755000/[17], loss: 2.5476, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:12:46 Iters: 755100/[17], loss: 3.1928, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:14:49 Iters: 755200/[17], loss: 2.5428, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:16:52 Iters: 755300/[17], loss: 2.9424, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:18:56 Iters: 755400/[17], loss: 2.6536, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:20:59 Iters: 755500/[17], loss: 2.4412, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:23:02 Iters: 755600/[17], loss: 2.7395, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:25:04 Iters: 755700/[17], loss: 2.5901, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:27:07 Iters: 755800/[17], loss: 2.0102, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:29:10 Iters: 755900/[17], loss: 2.4991, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:31:13 Iters: 756000/[17], loss: 2.5750, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:33:16 Iters: 756100/[17], loss: 2.6408, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:35:19 Iters: 756200/[17], loss: 2.7091, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:37:22 Iters: 756300/[17], loss: 2.7861, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:39:25 Iters: 756400/[17], loss: 2.4819, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:41:28 Iters: 756500/[17], loss: 2.2446, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:43:31 Iters: 756600/[17], loss: 2.5601, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:45:34 Iters: 756700/[17], loss: 2.4097, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:47:37 Iters: 756800/[17], loss: 2.9232, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:49:40 Iters: 756900/[17], loss: 3.3427, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:51:43 Iters: 757000/[17], loss: 2.2374, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:53:46 Iters: 757100/[17], loss: 2.8841, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:55:49 Iters: 757200/[17], loss: 3.1921, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:57:52 Iters: 757300/[17], loss: 3.1882, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-16:59:55 Iters: 757400/[17], loss: 2.4288, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:01:58 Iters: 757500/[17], loss: 3.3814, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:04:01 Iters: 757600/[17], loss: 2.0481, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:06:04 Iters: 757700/[17], loss: 2.5485, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:08:07 Iters: 757800/[17], loss: 2.1709, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:10:09 Iters: 757900/[17], loss: 2.7287, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:12:13 Iters: 758000/[17], loss: 2.7542, train_accuracy: 0.5234, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:14:16 Iters: 758100/[17], loss: 2.7783, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:16:19 Iters: 758200/[17], loss: 2.6402, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:18:22 Iters: 758300/[17], loss: 3.2044, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:20:26 Iters: 758400/[17], loss: 3.4020, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:22:29 Iters: 758500/[17], loss: 2.7434, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:24:33 Iters: 758600/[17], loss: 2.4020, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:26:36 Iters: 758700/[17], loss: 2.8839, train_accuracy: 0.5156, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:28:39 Iters: 758800/[17], loss: 2.6510, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:30:43 Iters: 758900/[17], loss: 2.3704, train_accuracy: 0.6953, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:32:47 Iters: 759000/[17], loss: 2.8913, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:34:50 Iters: 759100/[17], loss: 2.4601, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:36:54 Iters: 759200/[17], loss: 2.5718, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:38:57 Iters: 759300/[17], loss: 2.6228, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:41:01 Iters: 759400/[17], loss: 2.7397, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:43:04 Iters: 759500/[17], loss: 2.6314, train_accuracy: 0.5625, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:45:07 Iters: 759600/[17], loss: 2.5986, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:47:11 Iters: 759700/[17], loss: 2.9701, train_accuracy: 0.5469, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:49:15 Iters: 759800/[17], loss: 2.2963, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:51:18 Iters: 759900/[17], loss: 2.2383, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:53:22 Iters: 760000/[17], loss: 2.4210, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-17:53:22 Saving checkpoint: 760000
-20220711-17:54:38 LFW Ave Accuracy: 99.6666
-20220711-17:55:55 AgeDB-30 Ave Accuracy: 97.3167
-20220711-17:57:25 CFP-FP Ave Accuracy: 95.4000
-20220711-17:57:25 Current Best Accuracy: LFW: 99.7333 in iters: 740000, AgeDB-30: 97.4500 in iters: 740000 and CFP-FP: 95.4143 in iters: 740000
-20220711-17:59:28 Iters: 760100/[17], loss: 2.2367, train_accuracy: 0.6875, time: 3.66 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:01:31 Iters: 760200/[17], loss: 3.1496, train_accuracy: 0.6016, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:03:35 Iters: 760300/[17], loss: 2.1695, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:05:38 Iters: 760400/[17], loss: 3.1232, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:07:42 Iters: 760500/[17], loss: 3.2416, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:09:45 Iters: 760600/[17], loss: 2.4285, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:11:49 Iters: 760700/[17], loss: 2.7107, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:13:52 Iters: 760800/[17], loss: 2.4969, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:15:55 Iters: 760900/[17], loss: 3.1641, train_accuracy: 0.5391, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:17:59 Iters: 761000/[17], loss: 1.9997, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:20:03 Iters: 761100/[17], loss: 2.7646, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:22:06 Iters: 761200/[17], loss: 2.3304, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:24:09 Iters: 761300/[17], loss: 2.1461, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:26:13 Iters: 761400/[17], loss: 2.1850, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:28:16 Iters: 761500/[17], loss: 2.8477, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:30:20 Iters: 761600/[17], loss: 2.7526, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:32:23 Iters: 761700/[17], loss: 2.4264, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:34:26 Iters: 761800/[17], loss: 2.9456, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:36:30 Iters: 761900/[17], loss: 2.8521, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:38:33 Iters: 762000/[17], loss: 2.6268, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:40:36 Iters: 762100/[17], loss: 2.7986, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:42:39 Iters: 762200/[17], loss: 2.7097, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:44:42 Iters: 762300/[17], loss: 2.3206, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:46:46 Iters: 762400/[17], loss: 2.4940, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:48:49 Iters: 762500/[17], loss: 3.5917, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:50:52 Iters: 762600/[17], loss: 2.7724, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:52:55 Iters: 762700/[17], loss: 2.9039, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:54:59 Iters: 762800/[17], loss: 2.9517, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:57:02 Iters: 762900/[17], loss: 2.4485, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-18:59:05 Iters: 763000/[17], loss: 2.4441, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:01:08 Iters: 763100/[17], loss: 2.8360, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:03:11 Iters: 763200/[17], loss: 2.9196, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:05:14 Iters: 763300/[17], loss: 2.5783, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:07:17 Iters: 763400/[17], loss: 2.8014, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:09:21 Iters: 763500/[17], loss: 2.9450, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:11:24 Iters: 763600/[17], loss: 2.0623, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:13:27 Iters: 763700/[17], loss: 2.6637, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:15:30 Iters: 763800/[17], loss: 3.2063, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:17:33 Iters: 763900/[17], loss: 2.2711, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:19:36 Iters: 764000/[17], loss: 2.2645, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:21:39 Iters: 764100/[17], loss: 3.0605, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:23:42 Iters: 764200/[17], loss: 2.7033, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:25:45 Iters: 764300/[17], loss: 2.9293, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:27:48 Iters: 764400/[17], loss: 3.0285, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:29:51 Iters: 764500/[17], loss: 2.6620, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:31:54 Iters: 764600/[17], loss: 2.3282, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:33:57 Iters: 764700/[17], loss: 2.9650, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:36:00 Iters: 764800/[17], loss: 2.6716, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:38:03 Iters: 764900/[17], loss: 2.8096, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:40:06 Iters: 765000/[17], loss: 1.8379, train_accuracy: 0.7109, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:42:09 Iters: 765100/[17], loss: 2.5645, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:44:12 Iters: 765200/[17], loss: 2.2856, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:46:15 Iters: 765300/[17], loss: 3.0514, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:48:18 Iters: 765400/[17], loss: 2.5883, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:50:21 Iters: 765500/[17], loss: 2.5532, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:52:24 Iters: 765600/[17], loss: 2.6848, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:54:27 Iters: 765700/[17], loss: 2.2645, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:56:30 Iters: 765800/[17], loss: 2.8375, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-19:58:33 Iters: 765900/[17], loss: 3.2789, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:00:36 Iters: 766000/[17], loss: 2.3703, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:02:40 Iters: 766100/[17], loss: 3.2141, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:04:43 Iters: 766200/[17], loss: 2.3976, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:06:46 Iters: 766300/[17], loss: 2.1299, train_accuracy: 0.7344, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:08:50 Iters: 766400/[17], loss: 2.6684, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:10:53 Iters: 766500/[17], loss: 2.5613, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:12:56 Iters: 766600/[17], loss: 2.5739, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:14:59 Iters: 766700/[17], loss: 2.8895, train_accuracy: 0.5391, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:17:03 Iters: 766800/[17], loss: 2.5511, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:19:06 Iters: 766900/[17], loss: 2.1315, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:21:09 Iters: 767000/[17], loss: 2.9829, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:23:13 Iters: 767100/[17], loss: 2.3842, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:25:16 Iters: 767200/[17], loss: 2.8382, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:27:19 Iters: 767300/[17], loss: 3.4322, train_accuracy: 0.5781, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:29:23 Iters: 767400/[17], loss: 2.8242, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:31:26 Iters: 767500/[17], loss: 2.6041, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:33:29 Iters: 767600/[17], loss: 2.3825, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:35:33 Iters: 767700/[17], loss: 3.1523, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:37:36 Iters: 767800/[17], loss: 3.3168, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:39:39 Iters: 767900/[17], loss: 2.5309, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:41:43 Iters: 768000/[17], loss: 2.4738, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:43:46 Iters: 768100/[17], loss: 2.6851, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:45:49 Iters: 768200/[17], loss: 3.1238, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:47:52 Iters: 768300/[17], loss: 2.8168, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:49:55 Iters: 768400/[17], loss: 2.2489, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:51:59 Iters: 768500/[17], loss: 2.5334, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:54:03 Iters: 768600/[17], loss: 2.9289, train_accuracy: 0.5469, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:56:06 Iters: 768700/[17], loss: 3.2173, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-20:58:10 Iters: 768800/[17], loss: 2.6229, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:00:13 Iters: 768900/[17], loss: 2.5278, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:02:16 Iters: 769000/[17], loss: 2.8458, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:04:20 Iters: 769100/[17], loss: 2.8865, train_accuracy: 0.5703, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:06:23 Iters: 769200/[17], loss: 1.8985, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:08:27 Iters: 769300/[17], loss: 2.6479, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:10:30 Iters: 769400/[17], loss: 2.8645, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:12:33 Iters: 769500/[17], loss: 2.6036, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:14:36 Iters: 769600/[17], loss: 2.9669, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:16:40 Iters: 769700/[17], loss: 2.8332, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:18:43 Iters: 769800/[17], loss: 2.5719, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:20:46 Iters: 769900/[17], loss: 2.8854, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:22:50 Iters: 770000/[17], loss: 2.7828, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:22:50 Saving checkpoint: 770000
-20220711-21:24:09 LFW Ave Accuracy: 99.6833
-20220711-21:25:26 AgeDB-30 Ave Accuracy: 97.3833
-20220711-21:26:57 CFP-FP Ave Accuracy: 95.3714
-20220711-21:26:57 Current Best Accuracy: LFW: 99.7333 in iters: 740000, AgeDB-30: 97.4500 in iters: 740000 and CFP-FP: 95.4143 in iters: 740000
-20220711-21:29:00 Iters: 770100/[17], loss: 2.4442, train_accuracy: 0.6406, time: 3.70 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:31:03 Iters: 770200/[17], loss: 2.2874, train_accuracy: 0.7188, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:33:07 Iters: 770300/[17], loss: 2.6227, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:35:10 Iters: 770400/[17], loss: 2.8160, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:37:13 Iters: 770500/[17], loss: 2.4745, train_accuracy: 0.6562, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:39:17 Iters: 770600/[17], loss: 2.6451, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:41:20 Iters: 770700/[17], loss: 2.7719, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:43:24 Iters: 770800/[17], loss: 2.7878, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:45:27 Iters: 770900/[17], loss: 2.7160, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:47:31 Iters: 771000/[17], loss: 2.9438, train_accuracy: 0.5703, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:49:34 Iters: 771100/[17], loss: 2.9120, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:51:37 Iters: 771200/[17], loss: 2.4275, train_accuracy: 0.6875, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:53:41 Iters: 771300/[17], loss: 2.7344, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:55:44 Iters: 771400/[17], loss: 2.3126, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:57:47 Iters: 771500/[17], loss: 2.2763, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-21:59:51 Iters: 771600/[17], loss: 2.7522, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:01:54 Iters: 771700/[17], loss: 2.9951, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:03:58 Iters: 771800/[17], loss: 3.0275, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:06:01 Iters: 771900/[17], loss: 2.6267, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:08:04 Iters: 772000/[17], loss: 2.1920, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:10:07 Iters: 772100/[17], loss: 3.1847, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:12:11 Iters: 772200/[17], loss: 2.2697, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:14:13 Iters: 772300/[17], loss: 2.5992, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:16:17 Iters: 772400/[17], loss: 2.7836, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:18:20 Iters: 772500/[17], loss: 2.8318, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:20:23 Iters: 772600/[17], loss: 2.5738, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:22:27 Iters: 772700/[17], loss: 3.3550, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:24:30 Iters: 772800/[17], loss: 2.6734, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:26:33 Iters: 772900/[17], loss: 2.2645, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:28:37 Iters: 773000/[17], loss: 2.5827, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:30:40 Iters: 773100/[17], loss: 2.7672, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:32:43 Iters: 773200/[17], loss: 2.2053, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:34:47 Iters: 773300/[17], loss: 2.9084, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.000000000000002e-06
-20220711-22:35:23 Train Epoch: 18/18 ...
-20220711-22:36:50 Iters: 773400/[18], loss: 2.4624, train_accuracy: 0.6641, time: 0.87 s/iter, learning rate: 5.0000000000000016e-05
-20220711-22:38:54 Iters: 773500/[18], loss: 3.0891, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-22:40:57 Iters: 773600/[18], loss: 2.5619, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-22:43:01 Iters: 773700/[18], loss: 2.1583, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-22:45:05 Iters: 773800/[18], loss: 3.1960, train_accuracy: 0.5703, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-22:47:08 Iters: 773900/[18], loss: 3.1922, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-22:49:11 Iters: 774000/[18], loss: 2.6370, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-22:51:15 Iters: 774100/[18], loss: 2.7603, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-22:53:19 Iters: 774200/[18], loss: 3.0467, train_accuracy: 0.5547, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-22:55:22 Iters: 774300/[18], loss: 2.8392, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-22:57:26 Iters: 774400/[18], loss: 2.7453, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-22:59:29 Iters: 774500/[18], loss: 2.8572, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:01:32 Iters: 774600/[18], loss: 2.7591, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:03:35 Iters: 774700/[18], loss: 3.1308, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:05:38 Iters: 774800/[18], loss: 2.7602, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:07:41 Iters: 774900/[18], loss: 2.3658, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:09:44 Iters: 775000/[18], loss: 2.3242, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:11:47 Iters: 775100/[18], loss: 2.8495, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:13:50 Iters: 775200/[18], loss: 2.4857, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:15:53 Iters: 775300/[18], loss: 3.4163, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:17:57 Iters: 775400/[18], loss: 2.6981, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:20:00 Iters: 775500/[18], loss: 2.6682, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:22:04 Iters: 775600/[18], loss: 2.9609, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:24:07 Iters: 775700/[18], loss: 2.6974, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:26:11 Iters: 775800/[18], loss: 2.7654, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:28:14 Iters: 775900/[18], loss: 2.7827, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:30:18 Iters: 776000/[18], loss: 2.4830, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:32:21 Iters: 776100/[18], loss: 3.0311, train_accuracy: 0.5859, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:34:24 Iters: 776200/[18], loss: 2.3848, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:36:28 Iters: 776300/[18], loss: 2.2990, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:38:32 Iters: 776400/[18], loss: 2.4043, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:40:35 Iters: 776500/[18], loss: 2.7383, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:42:39 Iters: 776600/[18], loss: 3.3184, train_accuracy: 0.5703, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:44:42 Iters: 776700/[18], loss: 2.6377, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:46:46 Iters: 776800/[18], loss: 2.6630, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:48:49 Iters: 776900/[18], loss: 2.1062, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:50:53 Iters: 777000/[18], loss: 2.4695, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:52:56 Iters: 777100/[18], loss: 2.4223, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:54:59 Iters: 777200/[18], loss: 2.7963, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:57:03 Iters: 777300/[18], loss: 2.2598, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220711-23:59:06 Iters: 777400/[18], loss: 2.5295, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:01:09 Iters: 777500/[18], loss: 2.8030, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:03:13 Iters: 777600/[18], loss: 2.1964, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:05:16 Iters: 777700/[18], loss: 3.2218, train_accuracy: 0.5547, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:07:19 Iters: 777800/[18], loss: 2.4784, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:09:23 Iters: 777900/[18], loss: 2.8614, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:11:26 Iters: 778000/[18], loss: 2.7092, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:13:30 Iters: 778100/[18], loss: 2.4625, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:15:33 Iters: 778200/[18], loss: 2.7424, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:17:37 Iters: 778300/[18], loss: 2.4594, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:19:40 Iters: 778400/[18], loss: 2.8588, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:21:44 Iters: 778500/[18], loss: 2.3978, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:23:47 Iters: 778600/[18], loss: 2.6034, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:25:51 Iters: 778700/[18], loss: 2.4374, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:27:55 Iters: 778800/[18], loss: 2.5439, train_accuracy: 0.6562, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:29:58 Iters: 778900/[18], loss: 2.6517, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:32:02 Iters: 779000/[18], loss: 2.4658, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:34:05 Iters: 779100/[18], loss: 2.5798, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:36:08 Iters: 779200/[18], loss: 2.2513, train_accuracy: 0.7109, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:38:12 Iters: 779300/[18], loss: 2.8330, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:40:16 Iters: 779400/[18], loss: 2.7288, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:42:19 Iters: 779500/[18], loss: 2.6524, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:44:23 Iters: 779600/[18], loss: 3.2246, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:46:27 Iters: 779700/[18], loss: 2.7191, train_accuracy: 0.5703, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:48:30 Iters: 779800/[18], loss: 2.7039, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:50:34 Iters: 779900/[18], loss: 2.3702, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:52:37 Iters: 780000/[18], loss: 2.6173, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-00:52:37 Saving checkpoint: 780000
-20220712-00:53:54 LFW Ave Accuracy: 99.7000
-20220712-00:55:10 AgeDB-30 Ave Accuracy: 97.2500
-20220712-00:56:37 CFP-FP Ave Accuracy: 95.5286
-20220712-00:56:37 Current Best Accuracy: LFW: 99.7333 in iters: 740000, AgeDB-30: 97.4500 in iters: 740000 and CFP-FP: 95.5286 in iters: 780000
-20220712-00:58:40 Iters: 780100/[18], loss: 2.6303, train_accuracy: 0.5625, time: 3.63 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:00:43 Iters: 780200/[18], loss: 2.1093, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:02:47 Iters: 780300/[18], loss: 2.3617, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:04:50 Iters: 780400/[18], loss: 3.1781, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:06:54 Iters: 780500/[18], loss: 2.9701, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:08:57 Iters: 780600/[18], loss: 2.7265, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:11:00 Iters: 780700/[18], loss: 2.8163, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:13:03 Iters: 780800/[18], loss: 2.6920, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:15:07 Iters: 780900/[18], loss: 2.4192, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:17:10 Iters: 781000/[18], loss: 2.5936, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:19:13 Iters: 781100/[18], loss: 2.8660, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:21:17 Iters: 781200/[18], loss: 2.4267, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:23:20 Iters: 781300/[18], loss: 2.4566, train_accuracy: 0.7109, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:25:24 Iters: 781400/[18], loss: 2.5033, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:27:27 Iters: 781500/[18], loss: 2.5797, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:29:31 Iters: 781600/[18], loss: 2.4838, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:31:34 Iters: 781700/[18], loss: 2.2925, train_accuracy: 0.6953, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:33:38 Iters: 781800/[18], loss: 2.4713, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:35:41 Iters: 781900/[18], loss: 2.4260, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:37:45 Iters: 782000/[18], loss: 2.7595, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:39:48 Iters: 782100/[18], loss: 2.8600, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:41:52 Iters: 782200/[18], loss: 2.5178, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:43:55 Iters: 782300/[18], loss: 2.2054, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:45:58 Iters: 782400/[18], loss: 2.2853, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:48:02 Iters: 782500/[18], loss: 2.6852, train_accuracy: 0.6562, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:50:05 Iters: 782600/[18], loss: 2.5572, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:52:09 Iters: 782700/[18], loss: 2.7281, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:54:12 Iters: 782800/[18], loss: 2.5693, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:56:16 Iters: 782900/[18], loss: 2.6422, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-01:58:19 Iters: 783000/[18], loss: 2.7501, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:00:23 Iters: 783100/[18], loss: 2.8480, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:02:26 Iters: 783200/[18], loss: 2.4204, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:04:29 Iters: 783300/[18], loss: 2.3184, train_accuracy: 0.7109, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:06:33 Iters: 783400/[18], loss: 3.0337, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:08:36 Iters: 783500/[18], loss: 2.3943, train_accuracy: 0.6562, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:10:39 Iters: 783600/[18], loss: 2.2098, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:12:43 Iters: 783700/[18], loss: 2.2950, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:14:46 Iters: 783800/[18], loss: 2.7274, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:16:49 Iters: 783900/[18], loss: 2.5131, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:18:53 Iters: 784000/[18], loss: 2.9701, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:20:56 Iters: 784100/[18], loss: 2.6690, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:22:59 Iters: 784200/[18], loss: 2.6196, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:25:03 Iters: 784300/[18], loss: 3.1644, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:27:06 Iters: 784400/[18], loss: 2.1949, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:29:10 Iters: 784500/[18], loss: 2.1968, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:31:14 Iters: 784600/[18], loss: 3.1035, train_accuracy: 0.5703, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:33:17 Iters: 784700/[18], loss: 2.2071, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:35:20 Iters: 784800/[18], loss: 2.7197, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:37:24 Iters: 784900/[18], loss: 2.5290, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:39:27 Iters: 785000/[18], loss: 2.0251, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:41:31 Iters: 785100/[18], loss: 2.2947, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:43:34 Iters: 785200/[18], loss: 2.7480, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:45:38 Iters: 785300/[18], loss: 2.8163, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:47:41 Iters: 785400/[18], loss: 3.0999, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:49:44 Iters: 785500/[18], loss: 2.4729, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:51:48 Iters: 785600/[18], loss: 2.4261, train_accuracy: 0.7266, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:53:51 Iters: 785700/[18], loss: 2.6606, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:55:55 Iters: 785800/[18], loss: 2.4921, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-02:57:58 Iters: 785900/[18], loss: 2.0439, train_accuracy: 0.7109, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:00:02 Iters: 786000/[18], loss: 2.7710, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:02:05 Iters: 786100/[18], loss: 2.7090, train_accuracy: 0.6016, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:04:09 Iters: 786200/[18], loss: 2.2757, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:06:12 Iters: 786300/[18], loss: 2.2759, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:08:16 Iters: 786400/[18], loss: 2.8967, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:10:20 Iters: 786500/[18], loss: 2.5152, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:12:23 Iters: 786600/[18], loss: 2.3426, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:14:26 Iters: 786700/[18], loss: 3.1434, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:16:30 Iters: 786800/[18], loss: 2.6500, train_accuracy: 0.6016, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:18:33 Iters: 786900/[18], loss: 2.2805, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:20:37 Iters: 787000/[18], loss: 2.8092, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:22:40 Iters: 787100/[18], loss: 2.7336, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:24:43 Iters: 787200/[18], loss: 2.6309, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:26:47 Iters: 787300/[18], loss: 2.6598, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:28:50 Iters: 787400/[18], loss: 2.9972, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:30:54 Iters: 787500/[18], loss: 3.1289, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:32:57 Iters: 787600/[18], loss: 3.1276, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:35:00 Iters: 787700/[18], loss: 2.0934, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:37:04 Iters: 787800/[18], loss: 3.2553, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:39:07 Iters: 787900/[18], loss: 2.0900, train_accuracy: 0.7344, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:41:11 Iters: 788000/[18], loss: 2.2067, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:43:14 Iters: 788100/[18], loss: 2.9988, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:45:17 Iters: 788200/[18], loss: 2.1425, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:47:20 Iters: 788300/[18], loss: 2.2018, train_accuracy: 0.7109, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:49:23 Iters: 788400/[18], loss: 3.3070, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:51:27 Iters: 788500/[18], loss: 3.5315, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:53:30 Iters: 788600/[18], loss: 2.3985, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:55:33 Iters: 788700/[18], loss: 2.6088, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:57:37 Iters: 788800/[18], loss: 2.5145, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-03:59:40 Iters: 788900/[18], loss: 2.3261, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:01:43 Iters: 789000/[18], loss: 2.2863, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:03:47 Iters: 789100/[18], loss: 2.3696, train_accuracy: 0.7344, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:05:50 Iters: 789200/[18], loss: 3.4801, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:07:53 Iters: 789300/[18], loss: 2.3215, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:09:57 Iters: 789400/[18], loss: 2.9293, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:12:00 Iters: 789500/[18], loss: 2.8384, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:14:04 Iters: 789600/[18], loss: 2.5933, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:16:07 Iters: 789700/[18], loss: 2.2876, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:18:10 Iters: 789800/[18], loss: 2.3056, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:20:14 Iters: 789900/[18], loss: 2.7682, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:22:17 Iters: 790000/[18], loss: 1.9631, train_accuracy: 0.7500, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:22:17 Saving checkpoint: 790000
-20220712-04:23:34 LFW Ave Accuracy: 99.6833
-20220712-04:24:49 AgeDB-30 Ave Accuracy: 97.3000
-20220712-04:26:16 CFP-FP Ave Accuracy: 95.5429
-20220712-04:26:16 Current Best Accuracy: LFW: 99.7333 in iters: 740000, AgeDB-30: 97.4500 in iters: 740000 and CFP-FP: 95.5429 in iters: 790000
-20220712-04:28:19 Iters: 790100/[18], loss: 2.3770, train_accuracy: 0.6875, time: 3.62 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:30:22 Iters: 790200/[18], loss: 2.1778, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:32:25 Iters: 790300/[18], loss: 2.6280, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:34:29 Iters: 790400/[18], loss: 2.5379, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:36:32 Iters: 790500/[18], loss: 2.3643, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:38:35 Iters: 790600/[18], loss: 3.7178, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:40:38 Iters: 790700/[18], loss: 2.8458, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:42:42 Iters: 790800/[18], loss: 2.8736, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:44:45 Iters: 790900/[18], loss: 2.8658, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:46:48 Iters: 791000/[18], loss: 2.6625, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:48:52 Iters: 791100/[18], loss: 2.1132, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:50:55 Iters: 791200/[18], loss: 2.8153, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:52:58 Iters: 791300/[18], loss: 2.7407, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:55:02 Iters: 791400/[18], loss: 3.1021, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:57:05 Iters: 791500/[18], loss: 2.5993, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-04:59:08 Iters: 791600/[18], loss: 1.9336, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:01:12 Iters: 791700/[18], loss: 2.6007, train_accuracy: 0.5312, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:03:15 Iters: 791800/[18], loss: 2.9015, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:05:18 Iters: 791900/[18], loss: 2.4160, train_accuracy: 0.7188, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:07:22 Iters: 792000/[18], loss: 2.6337, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:09:25 Iters: 792100/[18], loss: 3.1111, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:11:28 Iters: 792200/[18], loss: 2.7884, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:13:32 Iters: 792300/[18], loss: 2.8264, train_accuracy: 0.5703, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:15:35 Iters: 792400/[18], loss: 2.8112, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:17:38 Iters: 792500/[18], loss: 2.2600, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:19:42 Iters: 792600/[18], loss: 2.4451, train_accuracy: 0.7109, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:21:45 Iters: 792700/[18], loss: 2.8747, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:23:48 Iters: 792800/[18], loss: 2.2423, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:25:52 Iters: 792900/[18], loss: 2.5528, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:27:55 Iters: 793000/[18], loss: 2.6203, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:29:58 Iters: 793100/[18], loss: 2.5671, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:32:02 Iters: 793200/[18], loss: 2.4426, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:34:05 Iters: 793300/[18], loss: 2.9977, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:36:09 Iters: 793400/[18], loss: 2.4363, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:38:12 Iters: 793500/[18], loss: 3.2400, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:40:15 Iters: 793600/[18], loss: 2.4007, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:42:19 Iters: 793700/[18], loss: 2.1647, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:44:22 Iters: 793800/[18], loss: 3.0213, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:46:25 Iters: 793900/[18], loss: 2.8064, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:48:29 Iters: 794000/[18], loss: 3.1835, train_accuracy: 0.5625, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:50:32 Iters: 794100/[18], loss: 2.0115, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:52:36 Iters: 794200/[18], loss: 2.0286, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:54:39 Iters: 794300/[18], loss: 2.5942, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:56:42 Iters: 794400/[18], loss: 3.2195, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-05:58:46 Iters: 794500/[18], loss: 2.4087, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:00:49 Iters: 794600/[18], loss: 2.1469, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:02:53 Iters: 794700/[18], loss: 2.7389, train_accuracy: 0.5938, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:04:56 Iters: 794800/[18], loss: 2.2550, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:07:00 Iters: 794900/[18], loss: 2.8935, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:09:03 Iters: 795000/[18], loss: 2.7127, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:11:06 Iters: 795100/[18], loss: 2.9050, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:13:10 Iters: 795200/[18], loss: 2.4791, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:15:13 Iters: 795300/[18], loss: 3.3729, train_accuracy: 0.5547, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:17:17 Iters: 795400/[18], loss: 2.1671, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:19:20 Iters: 795500/[18], loss: 2.5682, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:21:23 Iters: 795600/[18], loss: 2.2068, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:23:27 Iters: 795700/[18], loss: 2.9494, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:25:30 Iters: 795800/[18], loss: 2.7923, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:27:34 Iters: 795900/[18], loss: 2.7033, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:29:37 Iters: 796000/[18], loss: 2.5307, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:31:41 Iters: 796100/[18], loss: 2.5171, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:33:44 Iters: 796200/[18], loss: 1.9404, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:35:48 Iters: 796300/[18], loss: 2.3375, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:37:51 Iters: 796400/[18], loss: 2.4671, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:39:54 Iters: 796500/[18], loss: 2.1439, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:41:58 Iters: 796600/[18], loss: 2.4363, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:44:01 Iters: 796700/[18], loss: 3.1074, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:46:05 Iters: 796800/[18], loss: 2.3327, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:48:08 Iters: 796900/[18], loss: 2.7311, train_accuracy: 0.5859, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:50:11 Iters: 797000/[18], loss: 2.8182, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:52:15 Iters: 797100/[18], loss: 3.0006, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:54:18 Iters: 797200/[18], loss: 2.8119, train_accuracy: 0.6016, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:56:21 Iters: 797300/[18], loss: 2.3350, train_accuracy: 0.7188, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-06:58:25 Iters: 797400/[18], loss: 2.3050, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:00:28 Iters: 797500/[18], loss: 2.8323, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:02:32 Iters: 797600/[18], loss: 2.0280, train_accuracy: 0.7031, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:04:35 Iters: 797700/[18], loss: 2.4888, train_accuracy: 0.6250, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:06:38 Iters: 797800/[18], loss: 2.8394, train_accuracy: 0.6094, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:08:42 Iters: 797900/[18], loss: 2.4488, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:10:45 Iters: 798000/[18], loss: 2.6765, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:12:48 Iters: 798100/[18], loss: 2.5420, train_accuracy: 0.5469, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:14:52 Iters: 798200/[18], loss: 2.2580, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:16:55 Iters: 798300/[18], loss: 2.0557, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:18:59 Iters: 798400/[18], loss: 2.4942, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:21:02 Iters: 798500/[18], loss: 2.4112, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:23:06 Iters: 798600/[18], loss: 2.5309, train_accuracy: 0.6953, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:25:10 Iters: 798700/[18], loss: 1.9215, train_accuracy: 0.7031, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:27:14 Iters: 798800/[18], loss: 2.7420, train_accuracy: 0.5859, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:29:18 Iters: 798900/[18], loss: 3.1675, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:31:22 Iters: 799000/[18], loss: 2.4571, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:33:26 Iters: 799100/[18], loss: 2.9601, train_accuracy: 0.5312, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:35:29 Iters: 799200/[18], loss: 2.6033, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:37:33 Iters: 799300/[18], loss: 3.2010, train_accuracy: 0.6016, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:39:37 Iters: 799400/[18], loss: 2.5956, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:41:41 Iters: 799500/[18], loss: 2.5548, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:43:45 Iters: 799600/[18], loss: 2.7940, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:45:49 Iters: 799700/[18], loss: 1.8502, train_accuracy: 0.7344, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:47:53 Iters: 799800/[18], loss: 2.5296, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:49:57 Iters: 799900/[18], loss: 2.5833, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:52:01 Iters: 800000/[18], loss: 1.8052, train_accuracy: 0.7266, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-07:52:01 Saving checkpoint: 800000
-20220712-07:53:20 LFW Ave Accuracy: 99.6833
-20220712-07:54:37 AgeDB-30 Ave Accuracy: 97.3000
-20220712-07:56:07 CFP-FP Ave Accuracy: 95.4286
-20220712-07:56:07 Current Best Accuracy: LFW: 99.7333 in iters: 740000, AgeDB-30: 97.4500 in iters: 740000 and CFP-FP: 95.5429 in iters: 790000
-20220712-07:58:09 Iters: 800100/[18], loss: 2.6897, train_accuracy: 0.6172, time: 3.68 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:00:13 Iters: 800200/[18], loss: 3.4410, train_accuracy: 0.5312, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:02:17 Iters: 800300/[18], loss: 2.9094, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:04:21 Iters: 800400/[18], loss: 2.4332, train_accuracy: 0.7109, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:06:25 Iters: 800500/[18], loss: 2.6029, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:08:29 Iters: 800600/[18], loss: 2.1809, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:10:33 Iters: 800700/[18], loss: 3.2927, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:12:37 Iters: 800800/[18], loss: 2.5784, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:14:41 Iters: 800900/[18], loss: 2.4748, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:16:45 Iters: 801000/[18], loss: 2.7761, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:18:49 Iters: 801100/[18], loss: 2.4963, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:20:53 Iters: 801200/[18], loss: 2.2328, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:22:57 Iters: 801300/[18], loss: 2.5711, train_accuracy: 0.5859, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:25:01 Iters: 801400/[18], loss: 2.0025, train_accuracy: 0.7344, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:27:05 Iters: 801500/[18], loss: 2.6858, train_accuracy: 0.5859, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:29:09 Iters: 801600/[18], loss: 3.1025, train_accuracy: 0.5703, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:31:13 Iters: 801700/[18], loss: 3.3070, train_accuracy: 0.5391, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:33:17 Iters: 801800/[18], loss: 2.8446, train_accuracy: 0.5859, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:35:21 Iters: 801900/[18], loss: 2.8810, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:37:25 Iters: 802000/[18], loss: 2.5076, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:39:29 Iters: 802100/[18], loss: 2.3578, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:41:33 Iters: 802200/[18], loss: 2.6246, train_accuracy: 0.7109, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:43:37 Iters: 802300/[18], loss: 2.7740, train_accuracy: 0.5703, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:45:41 Iters: 802400/[18], loss: 3.1395, train_accuracy: 0.5625, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:47:45 Iters: 802500/[18], loss: 2.4523, train_accuracy: 0.6562, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:49:49 Iters: 802600/[18], loss: 2.2266, train_accuracy: 0.7031, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:51:53 Iters: 802700/[18], loss: 2.9749, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:53:57 Iters: 802800/[18], loss: 2.5856, train_accuracy: 0.6016, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:56:01 Iters: 802900/[18], loss: 3.3095, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-08:58:05 Iters: 803000/[18], loss: 2.2645, train_accuracy: 0.7031, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:00:09 Iters: 803100/[18], loss: 2.8955, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:02:13 Iters: 803200/[18], loss: 2.1793, train_accuracy: 0.7031, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:04:17 Iters: 803300/[18], loss: 2.5629, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:06:21 Iters: 803400/[18], loss: 2.3878, train_accuracy: 0.6875, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:08:25 Iters: 803500/[18], loss: 2.9923, train_accuracy: 0.5781, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:10:29 Iters: 803600/[18], loss: 2.7546, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:12:33 Iters: 803700/[18], loss: 3.1250, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:14:37 Iters: 803800/[18], loss: 2.9788, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:16:42 Iters: 803900/[18], loss: 2.5251, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:18:46 Iters: 804000/[18], loss: 2.7892, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:20:50 Iters: 804100/[18], loss: 2.9605, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:22:54 Iters: 804200/[18], loss: 2.7379, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:24:58 Iters: 804300/[18], loss: 2.4122, train_accuracy: 0.6562, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:27:02 Iters: 804400/[18], loss: 2.1605, train_accuracy: 0.7344, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:29:07 Iters: 804500/[18], loss: 2.9212, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:31:11 Iters: 804600/[18], loss: 2.1428, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:33:15 Iters: 804700/[18], loss: 2.5603, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:35:19 Iters: 804800/[18], loss: 2.3940, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:37:23 Iters: 804900/[18], loss: 2.7061, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:39:27 Iters: 805000/[18], loss: 2.3006, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:41:31 Iters: 805100/[18], loss: 2.2709, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:43:36 Iters: 805200/[18], loss: 3.6630, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:45:40 Iters: 805300/[18], loss: 2.5356, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:47:44 Iters: 805400/[18], loss: 2.1683, train_accuracy: 0.6953, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:49:48 Iters: 805500/[18], loss: 2.3910, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:51:53 Iters: 805600/[18], loss: 2.8481, train_accuracy: 0.6016, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:53:57 Iters: 805700/[18], loss: 2.3404, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:56:01 Iters: 805800/[18], loss: 2.5464, train_accuracy: 0.6562, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-09:58:05 Iters: 805900/[18], loss: 2.4490, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:00:10 Iters: 806000/[18], loss: 3.0163, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:02:14 Iters: 806100/[18], loss: 2.5990, train_accuracy: 0.6562, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:04:18 Iters: 806200/[18], loss: 2.5441, train_accuracy: 0.6953, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:06:23 Iters: 806300/[18], loss: 2.6321, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:08:27 Iters: 806400/[18], loss: 2.0428, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:10:31 Iters: 806500/[18], loss: 2.7192, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:12:36 Iters: 806600/[18], loss: 2.7292, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:14:40 Iters: 806700/[18], loss: 2.2871, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:16:44 Iters: 806800/[18], loss: 2.2816, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:18:49 Iters: 806900/[18], loss: 3.3862, train_accuracy: 0.5547, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:20:53 Iters: 807000/[18], loss: 2.4179, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:22:57 Iters: 807100/[18], loss: 2.2945, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:25:01 Iters: 807200/[18], loss: 3.0617, train_accuracy: 0.5156, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:27:06 Iters: 807300/[18], loss: 2.7359, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:29:10 Iters: 807400/[18], loss: 2.6312, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:31:14 Iters: 807500/[18], loss: 2.3852, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:33:19 Iters: 807600/[18], loss: 2.2878, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:35:23 Iters: 807700/[18], loss: 2.2161, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:37:27 Iters: 807800/[18], loss: 2.1981, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:39:31 Iters: 807900/[18], loss: 2.4204, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:41:35 Iters: 808000/[18], loss: 2.6841, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:43:39 Iters: 808100/[18], loss: 2.5992, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:45:43 Iters: 808200/[18], loss: 2.7165, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:47:46 Iters: 808300/[18], loss: 2.1057, train_accuracy: 0.6875, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:49:50 Iters: 808400/[18], loss: 2.6630, train_accuracy: 0.5625, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:51:54 Iters: 808500/[18], loss: 3.5853, train_accuracy: 0.5469, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:53:58 Iters: 808600/[18], loss: 2.4249, train_accuracy: 0.6875, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:56:02 Iters: 808700/[18], loss: 2.4295, train_accuracy: 0.7031, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-10:58:06 Iters: 808800/[18], loss: 2.4436, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:00:10 Iters: 808900/[18], loss: 2.7767, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:02:14 Iters: 809000/[18], loss: 2.1014, train_accuracy: 0.7109, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:04:18 Iters: 809100/[18], loss: 2.7062, train_accuracy: 0.7031, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:06:22 Iters: 809200/[18], loss: 2.3179, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:08:26 Iters: 809300/[18], loss: 2.7864, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:10:30 Iters: 809400/[18], loss: 2.1572, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:12:34 Iters: 809500/[18], loss: 2.2839, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:14:38 Iters: 809600/[18], loss: 2.8277, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:16:41 Iters: 809700/[18], loss: 2.2813, train_accuracy: 0.7031, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:18:45 Iters: 809800/[18], loss: 2.0044, train_accuracy: 0.7344, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:20:49 Iters: 809900/[18], loss: 3.0037, train_accuracy: 0.6016, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:22:53 Iters: 810000/[18], loss: 2.6301, train_accuracy: 0.7109, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:22:53 Saving checkpoint: 810000
-20220712-11:24:11 LFW Ave Accuracy: 99.6833
-20220712-11:25:27 AgeDB-30 Ave Accuracy: 97.2667
-20220712-11:26:55 CFP-FP Ave Accuracy: 95.4857
-20220712-11:26:55 Current Best Accuracy: LFW: 99.7333 in iters: 740000, AgeDB-30: 97.4500 in iters: 740000 and CFP-FP: 95.5429 in iters: 790000
-20220712-11:28:58 Iters: 810100/[18], loss: 2.9213, train_accuracy: 0.5859, time: 3.65 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:31:02 Iters: 810200/[18], loss: 3.6431, train_accuracy: 0.5781, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:33:06 Iters: 810300/[18], loss: 2.5250, train_accuracy: 0.6016, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:35:10 Iters: 810400/[18], loss: 2.3825, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:37:15 Iters: 810500/[18], loss: 2.4931, train_accuracy: 0.6484, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:39:19 Iters: 810600/[18], loss: 2.4724, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:41:23 Iters: 810700/[18], loss: 2.7633, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:43:27 Iters: 810800/[18], loss: 2.3282, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:45:31 Iters: 810900/[18], loss: 1.9732, train_accuracy: 0.6953, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:47:34 Iters: 811000/[18], loss: 2.5287, train_accuracy: 0.6875, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:49:38 Iters: 811100/[18], loss: 2.9482, train_accuracy: 0.5312, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:51:42 Iters: 811200/[18], loss: 2.2542, train_accuracy: 0.6875, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:53:46 Iters: 811300/[18], loss: 2.5218, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:55:49 Iters: 811400/[18], loss: 2.5169, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:57:52 Iters: 811500/[18], loss: 2.3466, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-11:59:56 Iters: 811600/[18], loss: 2.4951, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:01:59 Iters: 811700/[18], loss: 2.9744, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:04:02 Iters: 811800/[18], loss: 2.9252, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:06:06 Iters: 811900/[18], loss: 3.0788, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:08:09 Iters: 812000/[18], loss: 2.4658, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:10:13 Iters: 812100/[18], loss: 2.2254, train_accuracy: 0.6953, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:12:16 Iters: 812200/[18], loss: 2.3572, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:14:20 Iters: 812300/[18], loss: 2.7639, train_accuracy: 0.6328, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:16:23 Iters: 812400/[18], loss: 2.4106, train_accuracy: 0.6875, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:18:26 Iters: 812500/[18], loss: 2.3333, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:20:30 Iters: 812600/[18], loss: 2.7849, train_accuracy: 0.6172, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:22:33 Iters: 812700/[18], loss: 2.5128, train_accuracy: 0.6406, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:24:36 Iters: 812800/[18], loss: 2.2723, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:26:40 Iters: 812900/[18], loss: 2.3952, train_accuracy: 0.6484, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:28:43 Iters: 813000/[18], loss: 2.4947, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:30:46 Iters: 813100/[18], loss: 2.5426, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:32:50 Iters: 813200/[18], loss: 2.5677, train_accuracy: 0.6562, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:34:53 Iters: 813300/[18], loss: 2.1349, train_accuracy: 0.6719, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:36:56 Iters: 813400/[18], loss: 2.3191, train_accuracy: 0.6797, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:38:59 Iters: 813500/[18], loss: 2.1100, train_accuracy: 0.7188, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:41:03 Iters: 813600/[18], loss: 2.0921, train_accuracy: 0.6641, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:43:06 Iters: 813700/[18], loss: 3.1079, train_accuracy: 0.5781, time: 1.23 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:45:10 Iters: 813800/[18], loss: 2.9711, train_accuracy: 0.6016, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:47:14 Iters: 813900/[18], loss: 3.2989, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:49:17 Iters: 814000/[18], loss: 2.8416, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:51:21 Iters: 814100/[18], loss: 2.6307, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:53:25 Iters: 814200/[18], loss: 2.6848, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:55:29 Iters: 814300/[18], loss: 2.8084, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:57:33 Iters: 814400/[18], loss: 2.4204, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-12:59:37 Iters: 814500/[18], loss: 2.6864, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:01:41 Iters: 814600/[18], loss: 2.6261, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:03:45 Iters: 814700/[18], loss: 2.7919, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:05:49 Iters: 814800/[18], loss: 2.4948, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:07:53 Iters: 814900/[18], loss: 2.8486, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:09:57 Iters: 815000/[18], loss: 2.2534, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:12:01 Iters: 815100/[18], loss: 2.8924, train_accuracy: 0.6016, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:14:05 Iters: 815200/[18], loss: 2.4401, train_accuracy: 0.6953, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:16:09 Iters: 815300/[18], loss: 2.3432, train_accuracy: 0.6797, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:18:13 Iters: 815400/[18], loss: 2.6636, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:20:17 Iters: 815500/[18], loss: 2.5878, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:22:21 Iters: 815600/[18], loss: 2.7376, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:24:25 Iters: 815700/[18], loss: 3.5374, train_accuracy: 0.5547, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:26:29 Iters: 815800/[18], loss: 2.6592, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:28:33 Iters: 815900/[18], loss: 3.1617, train_accuracy: 0.5859, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:30:37 Iters: 816000/[18], loss: 3.3937, train_accuracy: 0.5625, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:32:41 Iters: 816100/[18], loss: 2.6399, train_accuracy: 0.6328, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:34:45 Iters: 816200/[18], loss: 2.7626, train_accuracy: 0.6016, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:36:49 Iters: 816300/[18], loss: 3.3653, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:38:53 Iters: 816400/[18], loss: 2.6692, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:40:57 Iters: 816500/[18], loss: 2.7190, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:43:01 Iters: 816600/[18], loss: 3.0781, train_accuracy: 0.5625, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:45:05 Iters: 816700/[18], loss: 2.0519, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:47:09 Iters: 816800/[18], loss: 3.0697, train_accuracy: 0.5859, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:49:13 Iters: 816900/[18], loss: 2.7007, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:51:17 Iters: 817000/[18], loss: 2.3398, train_accuracy: 0.5781, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:53:20 Iters: 817100/[18], loss: 2.6645, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:55:24 Iters: 817200/[18], loss: 3.2126, train_accuracy: 0.5859, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:57:28 Iters: 817300/[18], loss: 2.7856, train_accuracy: 0.5859, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-13:59:32 Iters: 817400/[18], loss: 2.7876, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:01:36 Iters: 817500/[18], loss: 2.2583, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:03:40 Iters: 817600/[18], loss: 2.8464, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:05:44 Iters: 817700/[18], loss: 2.5264, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:07:48 Iters: 817800/[18], loss: 2.6201, train_accuracy: 0.6094, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:09:52 Iters: 817900/[18], loss: 2.6171, train_accuracy: 0.6562, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:11:56 Iters: 818000/[18], loss: 2.3217, train_accuracy: 0.5938, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:14:00 Iters: 818100/[18], loss: 2.9086, train_accuracy: 0.5859, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:16:04 Iters: 818200/[18], loss: 2.5418, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:18:08 Iters: 818300/[18], loss: 2.3113, train_accuracy: 0.6719, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:20:12 Iters: 818400/[18], loss: 2.5145, train_accuracy: 0.6406, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:22:16 Iters: 818500/[18], loss: 3.0111, train_accuracy: 0.6250, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:24:20 Iters: 818600/[18], loss: 2.0768, train_accuracy: 0.7109, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:26:23 Iters: 818700/[18], loss: 2.4021, train_accuracy: 0.6641, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:28:27 Iters: 818800/[18], loss: 2.2452, train_accuracy: 0.6172, time: 1.24 s/iter, learning rate: 5.0000000000000016e-05
-20220712-14:28:52 Finally Best Accuracy: LFW: 99.7333 in iters: 740000, AgeDB-30: 97.4500 in iters: 740000 and CFP-FP: 95.5429 in iters: 790000