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add shufflenetv2 model  
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2023-06-27 23:48:12.676545  cuda
2023-06-27 23:48:26.762922  Train: epoch: 1 batch: 0/4, loss: 0.692719
2023-06-27 23:49:11.360796  Validation: avg loss: 0.6874, avg acc: 59.0476%
2023-06-27 23:49:25.567329  Train: epoch: 2 batch: 0/4, loss: 0.694902
2023-06-27 23:50:10.296476  Validation: avg loss: 0.6767, avg acc: 59.0476%
2023-06-27 23:50:24.635274  Train: epoch: 3 batch: 0/4, loss: 0.748423
2023-06-27 23:51:09.163575  Validation: avg loss: 0.6772, avg acc: 59.0476%
2023-06-27 23:51:23.470784  Train: epoch: 4 batch: 0/4, loss: 0.668663
2023-06-27 23:52:07.813810  Validation: avg loss: 0.6772, avg acc: 59.0476%
2023-06-27 23:52:21.899027  Train: epoch: 5 batch: 0/4, loss: 0.706061
2023-06-27 23:53:07.070617  Validation: avg loss: 0.6767, avg acc: 59.0476%
2023-06-27 23:53:21.447441  Train: epoch: 6 batch: 0/4, loss: 0.642712
2023-06-27 23:54:06.452342  Validation: avg loss: 0.6765, avg acc: 59.0476%
2023-06-27 23:54:20.942774  Train: epoch: 7 batch: 0/4, loss: 0.599767
2023-06-27 23:55:06.229018  Validation: avg loss: 0.6817, avg acc: 59.0476%
2023-06-27 23:55:20.888484  Train: epoch: 8 batch: 0/4, loss: 0.571694
2023-06-27 23:56:06.082262  Validation: avg loss: 0.7155, avg acc: 59.0476%
2023-06-27 23:56:20.563207  Train: epoch: 9 batch: 0/4, loss: 0.544862
2023-06-27 23:57:05.395680  Validation: avg loss: 0.6855, avg acc: 59.0476%
2023-06-27 23:57:19.790148  Train: epoch: 10 batch: 0/4, loss: 0.440930
2023-06-27 23:58:04.789425  Validation: avg loss: 0.6718, avg acc: 59.0476%
2023-06-27 23:58:19.325372  Train: epoch: 11 batch: 0/4, loss: 0.377782
2023-06-27 23:59:04.998068  Validation: avg loss: 0.6241, avg acc: 59.0476%
2023-06-27 23:59:19.486408  Train: epoch: 12 batch: 0/4, loss: 0.324883
2023-06-28 00:00:04.747729  Validation: avg loss: 0.6436, avg acc: 60.0000%
2023-06-28 00:00:19.341636  Train: epoch: 13 batch: 0/4, loss: 0.278890
2023-06-28 00:01:04.832568  Validation: avg loss: 0.5584, avg acc: 66.6667%
2023-06-28 00:01:19.377407  Train: epoch: 14 batch: 0/4, loss: 0.166610
2023-06-28 00:02:04.890031  Validation: avg loss: 0.5498, avg acc: 71.4286%
2023-06-28 00:02:19.093233  Train: epoch: 15 batch: 0/4, loss: 0.137812
2023-06-28 00:03:03.813998  Validation: avg loss: 0.5520, avg acc: 72.3810%
2023-06-28 00:03:17.912250  Train: epoch: 16 batch: 0/4, loss: 0.082624
2023-06-28 00:04:02.792006  Validation: avg loss: 0.5491, avg acc: 77.1429%
2023-06-28 00:04:17.202393  Train: epoch: 17 batch: 0/4, loss: 0.068832
2023-06-28 00:05:02.498994  Validation: avg loss: 0.5363, avg acc: 80.9524%
2023-06-28 00:05:16.720201  Train: epoch: 18 batch: 0/4, loss: 0.040624
2023-06-28 00:06:01.866280  Validation: avg loss: 0.5907, avg acc: 76.1905%
2023-06-28 00:06:16.861147  Train: epoch: 19 batch: 0/4, loss: 0.026078
2023-06-28 00:07:01.990593  Validation: avg loss: 0.7162, avg acc: 72.3810%
2023-06-28 00:07:16.178375  Train: epoch: 20 batch: 0/4, loss: 0.032948
2023-06-28 00:08:01.200749  Validation: avg loss: 0.8366, avg acc: 69.5238%
2023-06-28 00:08:15.625394  Train: epoch: 21 batch: 0/4, loss: 0.019867
2023-06-28 00:09:00.661095  Validation: avg loss: 0.8742, avg acc: 69.5238%
2023-06-28 00:09:14.899593  Train: epoch: 22 batch: 0/4, loss: 0.019119
2023-06-28 00:09:59.624718  Validation: avg loss: 0.8526, avg acc: 72.3810%
2023-06-28 00:10:13.976701  Train: epoch: 23 batch: 0/4, loss: 0.013461
2023-06-28 00:10:59.149665  Validation: avg loss: 0.8197, avg acc: 74.2857%
2023-06-28 00:11:13.582789  Train: epoch: 24 batch: 0/4, loss: 0.011778
2023-06-28 00:11:58.923315  Validation: avg loss: 0.8129, avg acc: 73.3333%
2023-06-28 00:12:13.196466  Train: epoch: 25 batch: 0/4, loss: 0.008090
2023-06-28 00:12:58.233841  Validation: avg loss: 0.8435, avg acc: 74.2857%
2023-06-28 00:13:12.497409  Train: epoch: 26 batch: 0/4, loss: 0.007485
2023-06-28 00:13:58.058072  Validation: avg loss: 0.8873, avg acc: 73.3333%
2023-06-28 00:14:12.557283  Train: epoch: 27 batch: 0/4, loss: 0.012831
2023-06-28 00:14:57.797601  Validation: avg loss: 0.9106, avg acc: 77.1429%
2023-06-28 00:15:12.113567  Train: epoch: 28 batch: 0/4, loss: 0.008352
2023-06-28 00:15:57.620574  Validation: avg loss: 0.8554, avg acc: 75.2381%
2023-06-28 00:16:11.834754  Train: epoch: 29 batch: 0/4, loss: 0.003746
2023-06-28 00:16:57.506909  Validation: avg loss: 0.8870, avg acc: 74.2857%
2023-06-28 00:17:11.843754  Train: epoch: 30 batch: 0/4, loss: 0.006036
2023-06-28 00:17:56.766811  Validation: avg loss: 0.7923, avg acc: 76.1905%
2023-06-28 00:18:10.978173  Train: epoch: 31 batch: 0/4, loss: 0.003501
2023-06-28 00:18:55.909253  Validation: avg loss: 0.7713, avg acc: 78.0952%
2023-06-28 00:19:10.124389  Train: epoch: 32 batch: 0/4, loss: 0.003706
2023-06-28 00:19:55.141533  Validation: avg loss: 0.7787, avg acc: 77.1429%
2023-06-28 00:20:09.240360  Train: epoch: 33 batch: 0/4, loss: 0.002162
2023-06-28 00:20:54.349713  Validation: avg loss: 0.7848, avg acc: 78.0952%
2023-06-28 00:21:08.623282  Train: epoch: 34 batch: 0/4, loss: 0.002487
2023-06-28 00:21:53.630017  Validation: avg loss: 0.7555, avg acc: 77.1429%
2023-06-28 00:22:07.706382  Train: epoch: 35 batch: 0/4, loss: 0.002501
2023-06-28 00:22:52.953638  Validation: avg loss: 0.7596, avg acc: 76.1905%
2023-06-28 00:23:07.442662  Train: epoch: 36 batch: 0/4, loss: 0.001733
2023-06-28 00:23:52.886656  Validation: avg loss: 0.7571, avg acc: 77.1429%
2023-06-28 00:24:07.114257  Train: epoch: 37 batch: 0/4, loss: 0.001552
2023-06-28 00:24:52.605825  Validation: avg loss: 0.7792, avg acc: 77.1429%
2023-06-28 00:25:06.795100  Train: epoch: 38 batch: 0/4, loss: 0.001730
2023-06-28 00:25:52.150007  Validation: avg loss: 0.8097, avg acc: 77.1429%
2023-06-28 00:26:06.181996  Train: epoch: 39 batch: 0/4, loss: 0.001733
2023-06-28 00:26:50.730113  Validation: avg loss: 0.8571, avg acc: 80.0000%
2023-06-28 00:27:04.861965  Train: epoch: 40 batch: 0/4, loss: 0.002980
2023-06-28 00:27:50.246158  Validation: avg loss: 0.9048, avg acc: 75.2381%
2023-06-28 00:28:05.091455  Train: epoch: 41 batch: 0/4, loss: 0.001818
2023-06-28 00:28:50.104469  Validation: avg loss: 0.9164, avg acc: 76.1905%
2023-06-28 00:29:04.291436  Train: epoch: 42 batch: 0/4, loss: 0.002385
2023-06-28 00:29:49.425707  Validation: avg loss: 0.9623, avg acc: 74.2857%
2023-06-28 00:30:03.823110  Train: epoch: 43 batch: 0/4, loss: 0.000947
2023-06-28 00:30:48.510228  Validation: avg loss: 0.9341, avg acc: 75.2381%
2023-06-28 00:31:02.610482  Train: epoch: 44 batch: 0/4, loss: 0.001757
2023-06-28 00:31:47.867238  Validation: avg loss: 0.9372, avg acc: 76.1905%
2023-06-28 00:32:02.121239  Train: epoch: 45 batch: 0/4, loss: 0.001092
2023-06-28 00:32:53.404032  Validation: avg loss: 0.9359, avg acc: 76.1905%
2023-06-28 00:33:07.542866  Train: epoch: 46 batch: 0/4, loss: 0.000694
2023-06-28 00:33:53.051343  Validation: avg loss: 0.9180, avg acc: 75.2381%
2023-06-28 00:34:07.450520  Train: epoch: 47 batch: 0/4, loss: 0.000740
2023-06-28 00:34:52.014761  Validation: avg loss: 0.9117, avg acc: 74.2857%
2023-06-28 00:35:06.127767  Train: epoch: 48 batch: 0/4, loss: 0.000983
2023-06-28 00:35:50.953911  Validation: avg loss: 0.9229, avg acc: 74.2857%
2023-06-28 00:36:05.429212  Train: epoch: 49 batch: 0/4, loss: 0.000797
2023-06-28 00:36:50.611333  Validation: avg loss: 0.9111, avg acc: 75.2381%
2023-06-28 00:37:04.781826  Train: epoch: 50 batch: 0/4, loss: 0.000721
2023-06-28 00:37:49.944599  Validation: avg loss: 0.8921, avg acc: 75.2381%