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Scheduled epochs: 150 (epochs + cooldown_epochs). Warmup within epochs when warmup_prefix=False. LR stepped per epoch.
Train: 0 [ 0/156 ( 1%)] Loss: 6.96 (6.96) Time: 4.444s, 230.40/s (4.444s, 230.40/s) LR: 1.000e-05 Data: 1.824 (1.824)
Train: 0 [ 50/156 ( 33%)] Loss: 6.94 (6.94) Time: 0.396s, 2588.32/s (0.472s, 2171.74/s) LR: 1.000e-05 Data: 0.027 (0.062)
Train: 0 [ 100/156 ( 65%)] Loss: 6.94 (6.94) Time: 0.400s, 2562.40/s (0.434s, 2358.67/s) LR: 1.000e-05 Data: 0.030 (0.045)
Train: 0 [ 150/156 ( 97%)] Loss: 6.93 (6.94) Time: 0.400s, 2559.79/s (0.422s, 2424.30/s) LR: 1.000e-05 Data: 0.026 (0.039)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.687 (1.687) Loss: 6.946 ( 6.946) Acc@1: 0.098 ( 0.098) Acc@5: 0.586 ( 0.586)
Test: [ 48/48] Time: 0.701 (0.352) Loss: 6.940 ( 6.939) Acc@1: 0.118 ( 0.076) Acc@5: 0.354 ( 0.490)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-0.pth.tar', 0.07599999996662139)
Train: 1 [ 0/156 ( 1%)] Loss: 6.95 (6.95) Time: 1.540s, 664.93/s (1.540s, 664.93/s) LR: 8.001e-02 Data: 1.031 (1.031)
Train: 1 [ 50/156 ( 33%)] Loss: 6.93 (6.93) Time: 0.408s, 2508.83/s (0.431s, 2378.13/s) LR: 8.001e-02 Data: 0.027 (0.046)
Train: 1 [ 100/156 ( 65%)] Loss: 6.92 (6.92) Time: 0.409s, 2501.28/s (0.421s, 2434.58/s) LR: 8.001e-02 Data: 0.027 (0.037)
Train: 1 [ 150/156 ( 97%)] Loss: 6.92 (6.92) Time: 0.406s, 2522.21/s (0.417s, 2456.31/s) LR: 8.001e-02 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.442 (1.442) Loss: 6.898 ( 6.898) Acc@1: 0.000 ( 0.000) Acc@5: 0.391 ( 0.391)
Test: [ 48/48] Time: 0.092 (0.331) Loss: 6.885 ( 6.899) Acc@1: 0.590 ( 0.144) Acc@5: 1.061 ( 0.690)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-1.pth.tar', 0.14399999958038331)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-0.pth.tar', 0.07599999996662139)
Train: 2 [ 0/156 ( 1%)] Loss: 6.91 (6.91) Time: 1.701s, 601.97/s (1.701s, 601.97/s) LR: 1.600e-01 Data: 1.327 (1.327)
Train: 2 [ 50/156 ( 33%)] Loss: 6.93 (6.92) Time: 0.410s, 2494.85/s (0.435s, 2353.39/s) LR: 1.600e-01 Data: 0.027 (0.052)
Train: 2 [ 100/156 ( 65%)] Loss: 6.93 (6.92) Time: 0.414s, 2474.25/s (0.422s, 2423.82/s) LR: 1.600e-01 Data: 0.028 (0.040)
Train: 2 [ 150/156 ( 97%)] Loss: 6.92 (6.92) Time: 0.410s, 2497.37/s (0.418s, 2450.06/s) LR: 1.600e-01 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.441 (1.441) Loss: 6.862 ( 6.862) Acc@1: 0.195 ( 0.195) Acc@5: 1.465 ( 1.465)
Test: [ 48/48] Time: 0.092 (0.330) Loss: 6.861 ( 6.863) Acc@1: 0.472 ( 0.442) Acc@5: 2.005 ( 1.758)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-2.pth.tar', 0.4419999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-1.pth.tar', 0.14399999958038331)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-0.pth.tar', 0.07599999996662139)
Train: 3 [ 0/156 ( 1%)] Loss: 6.90 (6.90) Time: 1.894s, 540.79/s (1.894s, 540.79/s) LR: 2.400e-01 Data: 1.519 (1.519)
Train: 3 [ 50/156 ( 33%)] Loss: 6.92 (6.91) Time: 0.408s, 2509.12/s (0.437s, 2344.41/s) LR: 2.400e-01 Data: 0.027 (0.056)
Train: 3 [ 100/156 ( 65%)] Loss: 6.91 (6.91) Time: 0.406s, 2523.46/s (0.423s, 2421.28/s) LR: 2.400e-01 Data: 0.027 (0.042)
Train: 3 [ 150/156 ( 97%)] Loss: 6.91 (6.91) Time: 0.401s, 2550.95/s (0.417s, 2454.26/s) LR: 2.400e-01 Data: 0.026 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.495 (1.495) Loss: 6.841 ( 6.841) Acc@1: 0.684 ( 0.684) Acc@5: 1.953 ( 1.953)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.826 ( 6.835) Acc@1: 0.354 ( 0.604) Acc@5: 2.712 ( 2.266)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-3.pth.tar', 0.6040000001525879)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-2.pth.tar', 0.4419999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-1.pth.tar', 0.14399999958038331)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-0.pth.tar', 0.07599999996662139)
Train: 4 [ 0/156 ( 1%)] Loss: 6.91 (6.91) Time: 1.645s, 622.46/s (1.645s, 622.46/s) LR: 3.200e-01 Data: 1.275 (1.275)
Train: 4 [ 50/156 ( 33%)] Loss: 6.89 (6.90) Time: 0.408s, 2508.65/s (0.429s, 2384.99/s) LR: 3.200e-01 Data: 0.026 (0.051)
Train: 4 [ 100/156 ( 65%)] Loss: 6.90 (6.90) Time: 0.409s, 2503.66/s (0.418s, 2448.27/s) LR: 3.200e-01 Data: 0.028 (0.039)
Train: 4 [ 150/156 ( 97%)] Loss: 6.91 (6.90) Time: 0.405s, 2531.31/s (0.416s, 2464.05/s) LR: 3.200e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.460 (1.460) Loss: 6.816 ( 6.816) Acc@1: 0.781 ( 0.781) Acc@5: 2.246 ( 2.246)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.796 ( 6.816) Acc@1: 0.472 ( 0.610) Acc@5: 3.774 ( 2.592)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-4.pth.tar', 0.6099999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-3.pth.tar', 0.6040000001525879)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-2.pth.tar', 0.4419999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-1.pth.tar', 0.14399999958038331)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-0.pth.tar', 0.07599999996662139)
Train: 5 [ 0/156 ( 1%)] Loss: 6.90 (6.90) Time: 1.594s, 642.44/s (1.594s, 642.44/s) LR: 3.989e-01 Data: 1.221 (1.221)
Train: 5 [ 50/156 ( 33%)] Loss: 6.90 (6.89) Time: 0.411s, 2492.72/s (0.429s, 2384.24/s) LR: 3.989e-01 Data: 0.027 (0.050)
Train: 5 [ 100/156 ( 65%)] Loss: 6.89 (6.89) Time: 0.413s, 2478.90/s (0.420s, 2440.73/s) LR: 3.989e-01 Data: 0.027 (0.039)
Train: 5 [ 150/156 ( 97%)] Loss: 6.89 (6.89) Time: 0.413s, 2477.50/s (0.417s, 2457.90/s) LR: 3.989e-01 Data: 0.027 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.475 (1.475) Loss: 6.795 ( 6.795) Acc@1: 0.879 ( 0.879) Acc@5: 3.418 ( 3.418)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 6.778 ( 6.795) Acc@1: 0.708 ( 0.852) Acc@5: 3.656 ( 3.102)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-5.pth.tar', 0.8520000003051758)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-4.pth.tar', 0.6099999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-3.pth.tar', 0.6040000001525879)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-2.pth.tar', 0.4419999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-1.pth.tar', 0.14399999958038331)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-0.pth.tar', 0.07599999996662139)
Train: 6 [ 0/156 ( 1%)] Loss: 6.88 (6.88) Time: 1.529s, 669.67/s (1.529s, 669.67/s) LR: 3.984e-01 Data: 1.141 (1.141)
Train: 6 [ 50/156 ( 33%)] Loss: 6.89 (6.88) Time: 0.410s, 2496.54/s (0.432s, 2369.04/s) LR: 3.984e-01 Data: 0.028 (0.049)
Train: 6 [ 100/156 ( 65%)] Loss: 6.89 (6.89) Time: 0.407s, 2514.68/s (0.421s, 2430.64/s) LR: 3.984e-01 Data: 0.026 (0.038)
Train: 6 [ 150/156 ( 97%)] Loss: 6.89 (6.89) Time: 0.408s, 2512.16/s (0.417s, 2454.22/s) LR: 3.984e-01 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.487 (1.487) Loss: 6.770 ( 6.770) Acc@1: 1.270 ( 1.270) Acc@5: 3.613 ( 3.613)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 6.751 ( 6.772) Acc@1: 0.825 ( 1.030) Acc@5: 4.009 ( 3.584)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-6.pth.tar', 1.0299999990081787)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-5.pth.tar', 0.8520000003051758)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-4.pth.tar', 0.6099999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-3.pth.tar', 0.6040000001525879)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-2.pth.tar', 0.4419999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-1.pth.tar', 0.14399999958038331)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-0.pth.tar', 0.07599999996662139)
Train: 7 [ 0/156 ( 1%)] Loss: 6.88 (6.88) Time: 1.756s, 583.19/s (1.756s, 583.19/s) LR: 3.979e-01 Data: 1.382 (1.382)
Train: 7 [ 50/156 ( 33%)] Loss: 6.88 (6.88) Time: 0.406s, 2521.25/s (0.433s, 2363.23/s) LR: 3.979e-01 Data: 0.027 (0.053)
Train: 7 [ 100/156 ( 65%)] Loss: 6.89 (6.88) Time: 0.410s, 2495.37/s (0.421s, 2429.58/s) LR: 3.979e-01 Data: 0.027 (0.040)
Train: 7 [ 150/156 ( 97%)] Loss: 6.89 (6.88) Time: 0.402s, 2549.53/s (0.417s, 2456.90/s) LR: 3.979e-01 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.436 (1.436) Loss: 6.752 ( 6.752) Acc@1: 1.074 ( 1.074) Acc@5: 4.297 ( 4.297)
Test: [ 48/48] Time: 0.090 (0.329) Loss: 6.726 ( 6.749) Acc@1: 0.943 ( 1.072) Acc@5: 3.774 ( 3.896)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-7.pth.tar', 1.0719999997329712)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-6.pth.tar', 1.0299999990081787)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-5.pth.tar', 0.8520000003051758)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-4.pth.tar', 0.6099999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-3.pth.tar', 0.6040000001525879)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-2.pth.tar', 0.4419999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-1.pth.tar', 0.14399999958038331)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-0.pth.tar', 0.07599999996662139)
Train: 8 [ 0/156 ( 1%)] Loss: 6.88 (6.88) Time: 1.572s, 651.57/s (1.572s, 651.57/s) LR: 3.972e-01 Data: 1.159 (1.159)
Train: 8 [ 50/156 ( 33%)] Loss: 6.87 (6.87) Time: 0.402s, 2545.18/s (0.425s, 2412.22/s) LR: 3.972e-01 Data: 0.028 (0.049)
Train: 8 [ 100/156 ( 65%)] Loss: 6.88 (6.88) Time: 0.403s, 2542.36/s (0.414s, 2474.21/s) LR: 3.972e-01 Data: 0.026 (0.038)
Train: 8 [ 150/156 ( 97%)] Loss: 6.87 (6.88) Time: 0.405s, 2529.21/s (0.411s, 2491.28/s) LR: 3.972e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.431 (1.431) Loss: 6.734 ( 6.734) Acc@1: 0.977 ( 0.977) Acc@5: 4.785 ( 4.785)
Test: [ 48/48] Time: 0.092 (0.327) Loss: 6.716 ( 6.735) Acc@1: 1.179 ( 1.216) Acc@5: 5.189 ( 4.512)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-8.pth.tar', 1.2159999991607666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-7.pth.tar', 1.0719999997329712)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-6.pth.tar', 1.0299999990081787)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-5.pth.tar', 0.8520000003051758)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-4.pth.tar', 0.6099999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-3.pth.tar', 0.6040000001525879)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-2.pth.tar', 0.4419999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-1.pth.tar', 0.14399999958038331)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-0.pth.tar', 0.07599999996662139)
Train: 9 [ 0/156 ( 1%)] Loss: 6.87 (6.87) Time: 1.745s, 586.69/s (1.745s, 586.69/s) LR: 3.965e-01 Data: 1.372 (1.372)
Train: 9 [ 50/156 ( 33%)] Loss: 6.87 (6.87) Time: 0.411s, 2491.04/s (0.436s, 2350.94/s) LR: 3.965e-01 Data: 0.026 (0.054)
Train: 9 [ 100/156 ( 65%)] Loss: 6.88 (6.87) Time: 0.413s, 2480.64/s (0.422s, 2425.50/s) LR: 3.965e-01 Data: 0.026 (0.041)
Train: 9 [ 150/156 ( 97%)] Loss: 6.89 (6.87) Time: 0.403s, 2538.58/s (0.417s, 2457.55/s) LR: 3.965e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.489 (1.489) Loss: 6.715 ( 6.715) Acc@1: 1.270 ( 1.270) Acc@5: 4.688 ( 4.688)
Test: [ 48/48] Time: 0.091 (0.328) Loss: 6.693 ( 6.715) Acc@1: 1.769 ( 1.292) Acc@5: 5.307 ( 4.400)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-9.pth.tar', 1.2920000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-8.pth.tar', 1.2159999991607666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-7.pth.tar', 1.0719999997329712)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-6.pth.tar', 1.0299999990081787)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-5.pth.tar', 0.8520000003051758)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-4.pth.tar', 0.6099999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-3.pth.tar', 0.6040000001525879)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-2.pth.tar', 0.4419999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-1.pth.tar', 0.14399999958038331)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-0.pth.tar', 0.07599999996662139)
Train: 10 [ 0/156 ( 1%)] Loss: 6.86 (6.86) Time: 1.619s, 632.56/s (1.619s, 632.56/s) LR: 3.956e-01 Data: 1.165 (1.165)
Train: 10 [ 50/156 ( 33%)] Loss: 6.87 (6.87) Time: 0.407s, 2518.77/s (0.431s, 2378.45/s) LR: 3.956e-01 Data: 0.027 (0.050)
Train: 10 [ 100/156 ( 65%)] Loss: 6.87 (6.87) Time: 0.418s, 2449.82/s (0.420s, 2436.14/s) LR: 3.956e-01 Data: 0.035 (0.039)
Train: 10 [ 150/156 ( 97%)] Loss: 6.87 (6.87) Time: 0.408s, 2507.98/s (0.417s, 2455.33/s) LR: 3.956e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.501 (1.501) Loss: 6.688 ( 6.688) Acc@1: 0.977 ( 0.977) Acc@5: 5.566 ( 5.566)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.664 ( 6.688) Acc@1: 1.415 ( 1.418) Acc@5: 5.189 ( 4.940)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-10.pth.tar', 1.4180000006103515)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-9.pth.tar', 1.2920000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-8.pth.tar', 1.2159999991607666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-7.pth.tar', 1.0719999997329712)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-6.pth.tar', 1.0299999990081787)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-5.pth.tar', 0.8520000003051758)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-4.pth.tar', 0.6099999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-3.pth.tar', 0.6040000001525879)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-2.pth.tar', 0.4419999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-1.pth.tar', 0.14399999958038331)
Train: 11 [ 0/156 ( 1%)] Loss: 6.87 (6.87) Time: 1.578s, 648.80/s (1.578s, 648.80/s) LR: 3.947e-01 Data: 1.204 (1.204)
Train: 11 [ 50/156 ( 33%)] Loss: 6.88 (6.86) Time: 0.416s, 2463.61/s (0.432s, 2370.60/s) LR: 3.947e-01 Data: 0.033 (0.050)
Train: 11 [ 100/156 ( 65%)] Loss: 6.87 (6.86) Time: 0.413s, 2478.52/s (0.422s, 2428.76/s) LR: 3.947e-01 Data: 0.027 (0.039)
Train: 11 [ 150/156 ( 97%)] Loss: 6.87 (6.87) Time: 0.406s, 2522.87/s (0.418s, 2449.53/s) LR: 3.947e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.409 (1.409) Loss: 6.689 ( 6.689) Acc@1: 1.855 ( 1.855) Acc@5: 5.469 ( 5.469)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.672 ( 6.692) Acc@1: 1.769 ( 1.686) Acc@5: 5.660 ( 5.476)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-11.pth.tar', 1.6860000007629394)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-10.pth.tar', 1.4180000006103515)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-9.pth.tar', 1.2920000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-8.pth.tar', 1.2159999991607666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-7.pth.tar', 1.0719999997329712)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-6.pth.tar', 1.0299999990081787)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-5.pth.tar', 0.8520000003051758)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-4.pth.tar', 0.6099999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-3.pth.tar', 0.6040000001525879)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-2.pth.tar', 0.4419999998664856)
Train: 12 [ 0/156 ( 1%)] Loss: 6.84 (6.84) Time: 1.684s, 608.25/s (1.684s, 608.25/s) LR: 3.937e-01 Data: 1.311 (1.311)
Train: 12 [ 50/156 ( 33%)] Loss: 6.87 (6.86) Time: 0.411s, 2488.55/s (0.432s, 2370.26/s) LR: 3.937e-01 Data: 0.034 (0.052)
Train: 12 [ 100/156 ( 65%)] Loss: 6.87 (6.86) Time: 0.411s, 2489.95/s (0.422s, 2428.88/s) LR: 3.937e-01 Data: 0.026 (0.040)
Train: 12 [ 150/156 ( 97%)] Loss: 6.86 (6.86) Time: 0.410s, 2497.92/s (0.418s, 2446.94/s) LR: 3.937e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.436 (1.436) Loss: 6.661 ( 6.661) Acc@1: 1.270 ( 1.270) Acc@5: 4.395 ( 4.395)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.638 ( 6.657) Acc@1: 1.533 ( 1.540) Acc@5: 5.542 ( 5.192)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-11.pth.tar', 1.6860000007629394)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-12.pth.tar', 1.5399999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-10.pth.tar', 1.4180000006103515)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-9.pth.tar', 1.2920000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-8.pth.tar', 1.2159999991607666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-7.pth.tar', 1.0719999997329712)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-6.pth.tar', 1.0299999990081787)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-5.pth.tar', 0.8520000003051758)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-4.pth.tar', 0.6099999998664856)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-3.pth.tar', 0.6040000001525879)
Train: 13 [ 0/156 ( 1%)] Loss: 6.84 (6.84) Time: 1.819s, 562.99/s (1.819s, 562.99/s) LR: 3.926e-01 Data: 1.445 (1.445)
Train: 13 [ 50/156 ( 33%)] Loss: 6.85 (6.85) Time: 0.408s, 2508.58/s (0.434s, 2358.33/s) LR: 3.926e-01 Data: 0.027 (0.055)
Train: 13 [ 100/156 ( 65%)] Loss: 6.85 (6.85) Time: 0.410s, 2496.35/s (0.422s, 2426.85/s) LR: 3.926e-01 Data: 0.029 (0.041)
Train: 13 [ 150/156 ( 97%)] Loss: 6.87 (6.86) Time: 0.407s, 2513.56/s (0.419s, 2444.20/s) LR: 3.926e-01 Data: 0.026 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.473 (1.473) Loss: 6.654 ( 6.654) Acc@1: 1.270 ( 1.270) Acc@5: 6.152 ( 6.152)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.629 ( 6.649) Acc@1: 1.651 ( 1.626) Acc@5: 5.660 ( 5.668)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-11.pth.tar', 1.6860000007629394)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-13.pth.tar', 1.6260000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-12.pth.tar', 1.5399999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-10.pth.tar', 1.4180000006103515)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-9.pth.tar', 1.2920000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-8.pth.tar', 1.2159999991607666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-7.pth.tar', 1.0719999997329712)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-6.pth.tar', 1.0299999990081787)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-5.pth.tar', 0.8520000003051758)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-4.pth.tar', 0.6099999998664856)
Train: 14 [ 0/156 ( 1%)] Loss: 6.84 (6.84) Time: 1.570s, 652.06/s (1.570s, 652.06/s) LR: 3.915e-01 Data: 1.142 (1.142)
Train: 14 [ 50/156 ( 33%)] Loss: 6.84 (6.85) Time: 0.405s, 2527.22/s (0.428s, 2391.38/s) LR: 3.915e-01 Data: 0.028 (0.049)
Train: 14 [ 100/156 ( 65%)] Loss: 6.83 (6.85) Time: 0.407s, 2518.76/s (0.417s, 2453.06/s) LR: 3.915e-01 Data: 0.026 (0.038)
Train: 14 [ 150/156 ( 97%)] Loss: 6.84 (6.85) Time: 0.409s, 2503.44/s (0.415s, 2469.50/s) LR: 3.915e-01 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.439 (1.439) Loss: 6.633 ( 6.633) Acc@1: 1.660 ( 1.660) Acc@5: 6.934 ( 6.934)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 6.623 ( 6.641) Acc@1: 1.651 ( 1.946) Acc@5: 6.486 ( 6.250)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-14.pth.tar', 1.9460000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-11.pth.tar', 1.6860000007629394)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-13.pth.tar', 1.6260000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-12.pth.tar', 1.5399999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-10.pth.tar', 1.4180000006103515)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-9.pth.tar', 1.2920000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-8.pth.tar', 1.2159999991607666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-7.pth.tar', 1.0719999997329712)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-6.pth.tar', 1.0299999990081787)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-5.pth.tar', 0.8520000003051758)
Train: 15 [ 0/156 ( 1%)] Loss: 6.84 (6.84) Time: 1.720s, 595.39/s (1.720s, 595.39/s) LR: 3.902e-01 Data: 1.282 (1.282)
Train: 15 [ 50/156 ( 33%)] Loss: 6.85 (6.84) Time: 0.414s, 2475.68/s (0.439s, 2332.48/s) LR: 3.902e-01 Data: 0.027 (0.052)
Train: 15 [ 100/156 ( 65%)] Loss: 6.87 (6.85) Time: 0.413s, 2479.15/s (0.426s, 2404.87/s) LR: 3.902e-01 Data: 0.027 (0.039)
Train: 15 [ 150/156 ( 97%)] Loss: 6.85 (6.85) Time: 0.414s, 2473.03/s (0.421s, 2429.53/s) LR: 3.902e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.495 (1.495) Loss: 6.625 ( 6.625) Acc@1: 1.660 ( 1.660) Acc@5: 5.859 ( 5.859)
Test: [ 48/48] Time: 0.092 (0.331) Loss: 6.614 ( 6.630) Acc@1: 1.887 ( 1.924) Acc@5: 5.425 ( 6.318)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-14.pth.tar', 1.9460000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-15.pth.tar', 1.9240000035095215)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-11.pth.tar', 1.6860000007629394)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-13.pth.tar', 1.6260000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-12.pth.tar', 1.5399999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-10.pth.tar', 1.4180000006103515)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-9.pth.tar', 1.2920000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-8.pth.tar', 1.2159999991607666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-7.pth.tar', 1.0719999997329712)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-6.pth.tar', 1.0299999990081787)
Train: 16 [ 0/156 ( 1%)] Loss: 6.83 (6.83) Time: 1.649s, 620.94/s (1.649s, 620.94/s) LR: 3.889e-01 Data: 1.190 (1.190)
Train: 16 [ 50/156 ( 33%)] Loss: 6.83 (6.84) Time: 0.408s, 2508.07/s (0.436s, 2346.67/s) LR: 3.889e-01 Data: 0.027 (0.050)
Train: 16 [ 100/156 ( 65%)] Loss: 6.85 (6.84) Time: 0.414s, 2475.08/s (0.423s, 2423.14/s) LR: 3.889e-01 Data: 0.029 (0.039)
Train: 16 [ 150/156 ( 97%)] Loss: 6.86 (6.84) Time: 0.405s, 2525.91/s (0.418s, 2452.12/s) LR: 3.889e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.674 (1.674) Loss: 6.622 ( 6.622) Acc@1: 1.758 ( 1.758) Acc@5: 6.348 ( 6.348)
Test: [ 48/48] Time: 0.093 (0.330) Loss: 6.610 ( 6.623) Acc@1: 2.241 ( 2.062) Acc@5: 6.368 ( 6.502)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-16.pth.tar', 2.0619999996185303)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-14.pth.tar', 1.9460000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-15.pth.tar', 1.9240000035095215)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-11.pth.tar', 1.6860000007629394)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-13.pth.tar', 1.6260000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-12.pth.tar', 1.5399999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-10.pth.tar', 1.4180000006103515)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-9.pth.tar', 1.2920000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-8.pth.tar', 1.2159999991607666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-7.pth.tar', 1.0719999997329712)
Train: 17 [ 0/156 ( 1%)] Loss: 6.85 (6.85) Time: 1.542s, 663.90/s (1.542s, 663.90/s) LR: 3.875e-01 Data: 1.164 (1.164)
Train: 17 [ 50/156 ( 33%)] Loss: 6.85 (6.83) Time: 0.409s, 2502.85/s (0.434s, 2358.38/s) LR: 3.875e-01 Data: 0.026 (0.050)
Train: 17 [ 100/156 ( 65%)] Loss: 6.85 (6.84) Time: 0.406s, 2522.03/s (0.421s, 2433.81/s) LR: 3.875e-01 Data: 0.028 (0.039)
Train: 17 [ 150/156 ( 97%)] Loss: 6.86 (6.84) Time: 0.404s, 2532.96/s (0.416s, 2460.99/s) LR: 3.875e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.420 (1.420) Loss: 6.608 ( 6.608) Acc@1: 1.465 ( 1.465) Acc@5: 6.445 ( 6.445)
Test: [ 48/48] Time: 0.092 (0.330) Loss: 6.597 ( 6.610) Acc@1: 2.005 ( 1.936) Acc@5: 6.722 ( 6.720)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-16.pth.tar', 2.0619999996185303)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-14.pth.tar', 1.9460000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-17.pth.tar', 1.9360000022125243)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-15.pth.tar', 1.9240000035095215)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-11.pth.tar', 1.6860000007629394)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-13.pth.tar', 1.6260000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-12.pth.tar', 1.5399999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-10.pth.tar', 1.4180000006103515)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-9.pth.tar', 1.2920000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-8.pth.tar', 1.2159999991607666)
Train: 18 [ 0/156 ( 1%)] Loss: 6.83 (6.83) Time: 1.674s, 611.59/s (1.674s, 611.59/s) LR: 3.860e-01 Data: 1.106 (1.106)
Train: 18 [ 50/156 ( 33%)] Loss: 6.83 (6.83) Time: 0.416s, 2460.60/s (0.437s, 2342.84/s) LR: 3.860e-01 Data: 0.027 (0.048)
Train: 18 [ 100/156 ( 65%)] Loss: 6.85 (6.83) Time: 0.410s, 2498.05/s (0.425s, 2409.82/s) LR: 3.860e-01 Data: 0.026 (0.038)
Train: 18 [ 150/156 ( 97%)] Loss: 6.85 (6.83) Time: 0.403s, 2539.79/s (0.419s, 2443.00/s) LR: 3.860e-01 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.430 (1.430) Loss: 6.600 ( 6.600) Acc@1: 1.953 ( 1.953) Acc@5: 6.836 ( 6.836)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.599 ( 6.603) Acc@1: 1.415 ( 2.060) Acc@5: 5.896 ( 6.710)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-16.pth.tar', 2.0619999996185303)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-18.pth.tar', 2.0600000006103514)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-14.pth.tar', 1.9460000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-17.pth.tar', 1.9360000022125243)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-15.pth.tar', 1.9240000035095215)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-11.pth.tar', 1.6860000007629394)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-13.pth.tar', 1.6260000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-12.pth.tar', 1.5399999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-10.pth.tar', 1.4180000006103515)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-9.pth.tar', 1.2920000007629395)
Train: 19 [ 0/156 ( 1%)] Loss: 6.82 (6.82) Time: 1.844s, 555.25/s (1.844s, 555.25/s) LR: 3.844e-01 Data: 1.150 (1.150)
Train: 19 [ 50/156 ( 33%)] Loss: 6.84 (6.82) Time: 0.408s, 2511.74/s (0.434s, 2358.11/s) LR: 3.844e-01 Data: 0.027 (0.049)
Train: 19 [ 100/156 ( 65%)] Loss: 6.83 (6.83) Time: 0.411s, 2493.06/s (0.422s, 2429.32/s) LR: 3.844e-01 Data: 0.026 (0.038)
Train: 19 [ 150/156 ( 97%)] Loss: 6.83 (6.83) Time: 0.409s, 2501.58/s (0.419s, 2446.15/s) LR: 3.844e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.430 (1.430) Loss: 6.561 ( 6.561) Acc@1: 2.246 ( 2.246) Acc@5: 6.348 ( 6.348)
Test: [ 48/48] Time: 0.092 (0.328) Loss: 6.559 ( 6.570) Acc@1: 2.123 ( 2.418) Acc@5: 7.311 ( 7.300)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-19.pth.tar', 2.4180000009155274)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-16.pth.tar', 2.0619999996185303)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-18.pth.tar', 2.0600000006103514)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-14.pth.tar', 1.9460000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-17.pth.tar', 1.9360000022125243)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-15.pth.tar', 1.9240000035095215)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-11.pth.tar', 1.6860000007629394)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-13.pth.tar', 1.6260000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-12.pth.tar', 1.5399999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-10.pth.tar', 1.4180000006103515)
Train: 20 [ 0/156 ( 1%)] Loss: 6.81 (6.81) Time: 2.059s, 497.27/s (2.059s, 497.27/s) LR: 3.827e-01 Data: 1.416 (1.416)
Train: 20 [ 50/156 ( 33%)] Loss: 6.82 (6.82) Time: 0.410s, 2498.45/s (0.445s, 2299.33/s) LR: 3.827e-01 Data: 0.027 (0.054)
Train: 20 [ 100/156 ( 65%)] Loss: 6.82 (6.82) Time: 0.413s, 2478.66/s (0.429s, 2385.55/s) LR: 3.827e-01 Data: 0.027 (0.041)
Train: 20 [ 150/156 ( 97%)] Loss: 6.87 (6.82) Time: 0.411s, 2493.49/s (0.424s, 2417.28/s) LR: 3.827e-01 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.436 (1.436) Loss: 6.566 ( 6.566) Acc@1: 1.953 ( 1.953) Acc@5: 6.250 ( 6.250)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.559 ( 6.572) Acc@1: 2.594 ( 2.376) Acc@5: 6.958 ( 7.254)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-19.pth.tar', 2.4180000009155274)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-20.pth.tar', 2.375999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-16.pth.tar', 2.0619999996185303)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-18.pth.tar', 2.0600000006103514)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-14.pth.tar', 1.9460000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-17.pth.tar', 1.9360000022125243)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-15.pth.tar', 1.9240000035095215)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-11.pth.tar', 1.6860000007629394)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-13.pth.tar', 1.6260000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-12.pth.tar', 1.5399999993133544)
Train: 21 [ 0/156 ( 1%)] Loss: 6.83 (6.83) Time: 1.671s, 612.84/s (1.671s, 612.84/s) LR: 3.810e-01 Data: 1.294 (1.294)
Train: 21 [ 50/156 ( 33%)] Loss: 6.82 (6.81) Time: 0.412s, 2483.36/s (0.435s, 2352.70/s) LR: 3.810e-01 Data: 0.028 (0.052)
Train: 21 [ 100/156 ( 65%)] Loss: 6.82 (6.82) Time: 0.410s, 2496.05/s (0.425s, 2411.91/s) LR: 3.810e-01 Data: 0.028 (0.040)
Train: 21 [ 150/156 ( 97%)] Loss: 6.81 (6.82) Time: 0.403s, 2539.55/s (0.419s, 2443.59/s) LR: 3.810e-01 Data: 0.024 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.417 (1.417) Loss: 6.542 ( 6.542) Acc@1: 2.441 ( 2.441) Acc@5: 7.422 ( 7.422)
Test: [ 48/48] Time: 0.090 (0.329) Loss: 6.536 ( 6.555) Acc@1: 1.533 ( 2.482) Acc@5: 8.137 ( 7.656)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-21.pth.tar', 2.4819999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-19.pth.tar', 2.4180000009155274)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-20.pth.tar', 2.375999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-16.pth.tar', 2.0619999996185303)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-18.pth.tar', 2.0600000006103514)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-14.pth.tar', 1.9460000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-17.pth.tar', 1.9360000022125243)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-15.pth.tar', 1.9240000035095215)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-11.pth.tar', 1.6860000007629394)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-13.pth.tar', 1.6260000020599366)
Train: 22 [ 0/156 ( 1%)] Loss: 6.80 (6.80) Time: 1.856s, 551.64/s (1.856s, 551.64/s) LR: 3.791e-01 Data: 1.485 (1.485)
Train: 22 [ 50/156 ( 33%)] Loss: 6.80 (6.80) Time: 0.401s, 2551.13/s (0.432s, 2371.84/s) LR: 3.791e-01 Data: 0.026 (0.056)
Train: 22 [ 100/156 ( 65%)] Loss: 6.81 (6.81) Time: 0.408s, 2507.00/s (0.419s, 2444.86/s) LR: 3.791e-01 Data: 0.028 (0.042)
Train: 22 [ 150/156 ( 97%)] Loss: 6.84 (6.81) Time: 0.408s, 2509.34/s (0.415s, 2466.40/s) LR: 3.791e-01 Data: 0.025 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.444 (1.444) Loss: 6.542 ( 6.542) Acc@1: 2.344 ( 2.344) Acc@5: 7.422 ( 7.422)
Test: [ 48/48] Time: 0.092 (0.327) Loss: 6.533 ( 6.543) Acc@1: 2.358 ( 2.416) Acc@5: 8.019 ( 7.594)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-21.pth.tar', 2.4819999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-19.pth.tar', 2.4180000009155274)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-22.pth.tar', 2.4160000023651125)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-20.pth.tar', 2.375999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-16.pth.tar', 2.0619999996185303)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-18.pth.tar', 2.0600000006103514)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-14.pth.tar', 1.9460000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-17.pth.tar', 1.9360000022125243)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-15.pth.tar', 1.9240000035095215)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-11.pth.tar', 1.6860000007629394)
Train: 23 [ 0/156 ( 1%)] Loss: 6.78 (6.78) Time: 1.648s, 621.42/s (1.648s, 621.42/s) LR: 3.772e-01 Data: 1.271 (1.271)
Train: 23 [ 50/156 ( 33%)] Loss: 6.79 (6.80) Time: 0.414s, 2471.02/s (0.439s, 2333.28/s) LR: 3.772e-01 Data: 0.028 (0.053)
Train: 23 [ 100/156 ( 65%)] Loss: 6.82 (6.80) Time: 0.411s, 2491.71/s (0.426s, 2405.27/s) LR: 3.772e-01 Data: 0.026 (0.040)
Train: 23 [ 150/156 ( 97%)] Loss: 6.82 (6.81) Time: 0.409s, 2505.16/s (0.421s, 2429.89/s) LR: 3.772e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.448 (1.448) Loss: 6.523 ( 6.523) Acc@1: 2.344 ( 2.344) Acc@5: 8.301 ( 8.301)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 6.516 ( 6.529) Acc@1: 2.476 ( 2.632) Acc@5: 8.373 ( 8.196)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-23.pth.tar', 2.632000001068115)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-21.pth.tar', 2.4819999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-19.pth.tar', 2.4180000009155274)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-22.pth.tar', 2.4160000023651125)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-20.pth.tar', 2.375999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-16.pth.tar', 2.0619999996185303)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-18.pth.tar', 2.0600000006103514)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-14.pth.tar', 1.9460000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-17.pth.tar', 1.9360000022125243)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-15.pth.tar', 1.9240000035095215)
Train: 24 [ 0/156 ( 1%)] Loss: 6.80 (6.80) Time: 1.654s, 619.04/s (1.654s, 619.04/s) LR: 3.753e-01 Data: 1.277 (1.277)
Train: 24 [ 50/156 ( 33%)] Loss: 6.78 (6.79) Time: 0.412s, 2484.53/s (0.436s, 2346.02/s) LR: 3.753e-01 Data: 0.029 (0.052)
Train: 24 [ 100/156 ( 65%)] Loss: 6.83 (6.80) Time: 0.415s, 2470.16/s (0.425s, 2407.24/s) LR: 3.753e-01 Data: 0.027 (0.040)
Train: 24 [ 150/156 ( 97%)] Loss: 6.80 (6.80) Time: 0.411s, 2489.97/s (0.421s, 2433.49/s) LR: 3.753e-01 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.429 (1.429) Loss: 6.525 ( 6.525) Acc@1: 2.246 ( 2.246) Acc@5: 7.031 ( 7.031)
Test: [ 48/48] Time: 0.092 (0.328) Loss: 6.513 ( 6.521) Acc@1: 2.712 ( 2.680) Acc@5: 7.547 ( 8.072)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-24.pth.tar', 2.679999998474121)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-23.pth.tar', 2.632000001068115)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-21.pth.tar', 2.4819999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-19.pth.tar', 2.4180000009155274)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-22.pth.tar', 2.4160000023651125)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-20.pth.tar', 2.375999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-16.pth.tar', 2.0619999996185303)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-18.pth.tar', 2.0600000006103514)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-14.pth.tar', 1.9460000020599366)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-17.pth.tar', 1.9360000022125243)
Train: 25 [ 0/156 ( 1%)] Loss: 6.81 (6.81) Time: 1.526s, 670.86/s (1.526s, 670.86/s) LR: 3.732e-01 Data: 1.149 (1.149)
Train: 25 [ 50/156 ( 33%)] Loss: 6.79 (6.79) Time: 0.411s, 2491.70/s (0.434s, 2358.98/s) LR: 3.732e-01 Data: 0.028 (0.050)
Train: 25 [ 100/156 ( 65%)] Loss: 6.78 (6.79) Time: 0.414s, 2473.99/s (0.423s, 2420.69/s) LR: 3.732e-01 Data: 0.027 (0.039)
Train: 25 [ 150/156 ( 97%)] Loss: 6.82 (6.80) Time: 0.410s, 2497.29/s (0.420s, 2438.48/s) LR: 3.732e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.437 (1.437) Loss: 6.493 ( 6.493) Acc@1: 2.148 ( 2.148) Acc@5: 7.617 ( 7.617)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.474 ( 6.494) Acc@1: 4.009 ( 2.690) Acc@5: 9.080 ( 8.356)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-25.pth.tar', 2.6900000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-24.pth.tar', 2.679999998474121)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-23.pth.tar', 2.632000001068115)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-21.pth.tar', 2.4819999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-19.pth.tar', 2.4180000009155274)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-22.pth.tar', 2.4160000023651125)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-20.pth.tar', 2.375999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-16.pth.tar', 2.0619999996185303)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-18.pth.tar', 2.0600000006103514)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-14.pth.tar', 1.9460000020599366)
Train: 26 [ 0/156 ( 1%)] Loss: 6.77 (6.77) Time: 1.832s, 559.02/s (1.832s, 559.02/s) LR: 3.711e-01 Data: 1.276 (1.276)
Train: 26 [ 50/156 ( 33%)] Loss: 6.80 (6.78) Time: 0.414s, 2471.29/s (0.440s, 2328.87/s) LR: 3.711e-01 Data: 0.027 (0.052)
Train: 26 [ 100/156 ( 65%)] Loss: 6.78 (6.78) Time: 0.411s, 2491.13/s (0.426s, 2403.09/s) LR: 3.711e-01 Data: 0.027 (0.040)
Train: 26 [ 150/156 ( 97%)] Loss: 6.82 (6.79) Time: 0.414s, 2475.04/s (0.422s, 2428.52/s) LR: 3.711e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.435 (1.435) Loss: 6.494 ( 6.494) Acc@1: 3.320 ( 3.320) Acc@5: 8.887 ( 8.887)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.483 ( 6.495) Acc@1: 3.184 ( 2.966) Acc@5: 8.608 ( 8.542)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-26.pth.tar', 2.9660000054168703)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-25.pth.tar', 2.6900000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-24.pth.tar', 2.679999998474121)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-23.pth.tar', 2.632000001068115)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-21.pth.tar', 2.4819999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-19.pth.tar', 2.4180000009155274)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-22.pth.tar', 2.4160000023651125)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-20.pth.tar', 2.375999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-16.pth.tar', 2.0619999996185303)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-18.pth.tar', 2.0600000006103514)
Train: 27 [ 0/156 ( 1%)] Loss: 6.77 (6.77) Time: 1.558s, 657.42/s (1.558s, 657.42/s) LR: 3.689e-01 Data: 1.183 (1.183)
Train: 27 [ 50/156 ( 33%)] Loss: 6.75 (6.77) Time: 0.410s, 2496.82/s (0.432s, 2371.53/s) LR: 3.689e-01 Data: 0.028 (0.050)
Train: 27 [ 100/156 ( 65%)] Loss: 6.79 (6.78) Time: 0.412s, 2482.64/s (0.423s, 2422.01/s) LR: 3.689e-01 Data: 0.028 (0.039)
Train: 27 [ 150/156 ( 97%)] Loss: 6.79 (6.78) Time: 0.411s, 2491.06/s (0.420s, 2440.95/s) LR: 3.689e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.463 (1.463) Loss: 6.469 ( 6.469) Acc@1: 2.539 ( 2.539) Acc@5: 7.910 ( 7.910)
Test: [ 48/48] Time: 0.092 (0.330) Loss: 6.435 ( 6.456) Acc@1: 2.594 ( 3.142) Acc@5: 8.726 ( 9.014)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-27.pth.tar', 3.141999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-26.pth.tar', 2.9660000054168703)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-25.pth.tar', 2.6900000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-24.pth.tar', 2.679999998474121)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-23.pth.tar', 2.632000001068115)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-21.pth.tar', 2.4819999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-19.pth.tar', 2.4180000009155274)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-22.pth.tar', 2.4160000023651125)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-20.pth.tar', 2.375999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-16.pth.tar', 2.0619999996185303)
Train: 28 [ 0/156 ( 1%)] Loss: 6.77 (6.77) Time: 1.794s, 570.79/s (1.794s, 570.79/s) LR: 3.666e-01 Data: 1.233 (1.233)
Train: 28 [ 50/156 ( 33%)] Loss: 6.76 (6.76) Time: 0.409s, 2503.65/s (0.441s, 2321.59/s) LR: 3.666e-01 Data: 0.027 (0.051)
Train: 28 [ 100/156 ( 65%)] Loss: 6.79 (6.77) Time: 0.405s, 2531.41/s (0.425s, 2406.67/s) LR: 3.666e-01 Data: 0.027 (0.039)
Train: 28 [ 150/156 ( 97%)] Loss: 6.78 (6.78) Time: 0.401s, 2553.65/s (0.419s, 2445.38/s) LR: 3.666e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.433 (1.433) Loss: 6.467 ( 6.467) Acc@1: 2.832 ( 2.832) Acc@5: 9.277 ( 9.277)
Test: [ 48/48] Time: 0.090 (0.329) Loss: 6.439 ( 6.462) Acc@1: 3.302 ( 3.178) Acc@5: 9.316 ( 9.046)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-28.pth.tar', 3.1779999960327148)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-27.pth.tar', 3.141999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-26.pth.tar', 2.9660000054168703)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-25.pth.tar', 2.6900000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-24.pth.tar', 2.679999998474121)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-23.pth.tar', 2.632000001068115)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-21.pth.tar', 2.4819999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-19.pth.tar', 2.4180000009155274)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-22.pth.tar', 2.4160000023651125)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-20.pth.tar', 2.375999999771118)
Train: 29 [ 0/156 ( 1%)] Loss: 6.75 (6.75) Time: 1.643s, 623.21/s (1.643s, 623.21/s) LR: 3.642e-01 Data: 1.272 (1.272)
Train: 29 [ 50/156 ( 33%)] Loss: 6.76 (6.75) Time: 0.407s, 2515.73/s (0.430s, 2380.51/s) LR: 3.642e-01 Data: 0.027 (0.051)
Train: 29 [ 100/156 ( 65%)] Loss: 6.77 (6.76) Time: 0.406s, 2519.95/s (0.419s, 2442.33/s) LR: 3.642e-01 Data: 0.026 (0.040)
Train: 29 [ 150/156 ( 97%)] Loss: 6.78 (6.77) Time: 0.410s, 2496.23/s (0.416s, 2459.26/s) LR: 3.642e-01 Data: 0.024 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.451 (1.451) Loss: 6.429 ( 6.429) Acc@1: 2.344 ( 2.344) Acc@5: 9.277 ( 9.277)
Test: [ 48/48] Time: 0.092 (0.330) Loss: 6.420 ( 6.434) Acc@1: 3.892 ( 3.182) Acc@5: 9.434 ( 9.046)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-29.pth.tar', 3.1820000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-28.pth.tar', 3.1779999960327148)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-27.pth.tar', 3.141999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-26.pth.tar', 2.9660000054168703)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-25.pth.tar', 2.6900000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-24.pth.tar', 2.679999998474121)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-23.pth.tar', 2.632000001068115)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-21.pth.tar', 2.4819999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-19.pth.tar', 2.4180000009155274)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-22.pth.tar', 2.4160000023651125)
Train: 30 [ 0/156 ( 1%)] Loss: 6.73 (6.73) Time: 1.897s, 539.68/s (1.897s, 539.68/s) LR: 3.618e-01 Data: 1.519 (1.519)
Train: 30 [ 50/156 ( 33%)] Loss: 6.76 (6.74) Time: 0.405s, 2529.15/s (0.439s, 2330.83/s) LR: 3.618e-01 Data: 0.027 (0.057)
Train: 30 [ 100/156 ( 65%)] Loss: 6.75 (6.75) Time: 0.403s, 2543.46/s (0.422s, 2425.25/s) LR: 3.618e-01 Data: 0.027 (0.042)
Train: 30 [ 150/156 ( 97%)] Loss: 6.79 (6.76) Time: 0.400s, 2562.87/s (0.416s, 2462.25/s) LR: 3.618e-01 Data: 0.025 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.445 (1.445) Loss: 6.446 ( 6.446) Acc@1: 3.809 ( 3.809) Acc@5: 9.863 ( 9.863)
Test: [ 48/48] Time: 0.089 (0.328) Loss: 6.425 ( 6.434) Acc@1: 3.538 ( 3.602) Acc@5: 9.434 ( 9.918)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-30.pth.tar', 3.6019999974822996)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-29.pth.tar', 3.1820000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-28.pth.tar', 3.1779999960327148)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-27.pth.tar', 3.141999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-26.pth.tar', 2.9660000054168703)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-25.pth.tar', 2.6900000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-24.pth.tar', 2.679999998474121)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-23.pth.tar', 2.632000001068115)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-21.pth.tar', 2.4819999993133544)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-19.pth.tar', 2.4180000009155274)
Train: 31 [ 0/156 ( 1%)] Loss: 6.70 (6.70) Time: 2.219s, 461.39/s (2.219s, 461.39/s) LR: 3.593e-01 Data: 1.848 (1.848)
Train: 31 [ 50/156 ( 33%)] Loss: 6.77 (6.73) Time: 0.406s, 2523.48/s (0.440s, 2327.00/s) LR: 3.593e-01 Data: 0.027 (0.063)
Train: 31 [ 100/156 ( 65%)] Loss: 6.76 (6.74) Time: 0.407s, 2515.51/s (0.424s, 2415.00/s) LR: 3.593e-01 Data: 0.027 (0.045)
Train: 31 [ 150/156 ( 97%)] Loss: 6.79 (6.75) Time: 0.416s, 2463.61/s (0.420s, 2439.58/s) LR: 3.593e-01 Data: 0.026 (0.039)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.461 (1.461) Loss: 6.426 ( 6.426) Acc@1: 2.637 ( 2.637) Acc@5: 9.082 ( 9.082)
Test: [ 48/48] Time: 0.091 (0.327) Loss: 6.392 ( 6.413) Acc@1: 4.363 ( 3.484) Acc@5: 9.906 ( 9.730)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-30.pth.tar', 3.6019999974822996)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-31.pth.tar', 3.4839999964904784)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-29.pth.tar', 3.1820000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-28.pth.tar', 3.1779999960327148)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-27.pth.tar', 3.141999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-26.pth.tar', 2.9660000054168703)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-25.pth.tar', 2.6900000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-24.pth.tar', 2.679999998474121)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-23.pth.tar', 2.632000001068115)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-21.pth.tar', 2.4819999993133544)
Train: 32 [ 0/156 ( 1%)] Loss: 6.70 (6.70) Time: 1.987s, 515.37/s (1.987s, 515.37/s) LR: 3.567e-01 Data: 1.231 (1.231)
Train: 32 [ 50/156 ( 33%)] Loss: 6.73 (6.73) Time: 0.412s, 2484.77/s (0.439s, 2335.07/s) LR: 3.567e-01 Data: 0.026 (0.050)
Train: 32 [ 100/156 ( 65%)] Loss: 6.74 (6.74) Time: 0.415s, 2469.72/s (0.424s, 2414.12/s) LR: 3.567e-01 Data: 0.027 (0.039)
Train: 32 [ 150/156 ( 97%)] Loss: 6.72 (6.74) Time: 0.414s, 2475.36/s (0.421s, 2433.31/s) LR: 3.567e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.472 (1.472) Loss: 6.370 ( 6.370) Acc@1: 3.418 ( 3.418) Acc@5: 10.547 ( 10.547)
Test: [ 48/48] Time: 0.090 (0.328) Loss: 6.354 ( 6.363) Acc@1: 3.774 ( 3.674) Acc@5: 10.024 ( 10.554)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-32.pth.tar', 3.674000002975464)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-30.pth.tar', 3.6019999974822996)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-31.pth.tar', 3.4839999964904784)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-29.pth.tar', 3.1820000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-28.pth.tar', 3.1779999960327148)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-27.pth.tar', 3.141999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-26.pth.tar', 2.9660000054168703)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-25.pth.tar', 2.6900000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-24.pth.tar', 2.679999998474121)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-23.pth.tar', 2.632000001068115)
Train: 33 [ 0/156 ( 1%)] Loss: 6.71 (6.71) Time: 1.815s, 564.06/s (1.815s, 564.06/s) LR: 3.541e-01 Data: 1.443 (1.443)
Train: 33 [ 50/156 ( 33%)] Loss: 6.73 (6.72) Time: 0.404s, 2537.38/s (0.433s, 2364.45/s) LR: 3.541e-01 Data: 0.026 (0.055)
Train: 33 [ 100/156 ( 65%)] Loss: 6.73 (6.72) Time: 0.412s, 2486.63/s (0.420s, 2437.14/s) LR: 3.541e-01 Data: 0.027 (0.041)
Train: 33 [ 150/156 ( 97%)] Loss: 6.73 (6.73) Time: 0.410s, 2500.56/s (0.417s, 2456.61/s) LR: 3.541e-01 Data: 0.026 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.451 (1.451) Loss: 6.395 ( 6.395) Acc@1: 3.320 ( 3.320) Acc@5: 9.375 ( 9.375)
Test: [ 48/48] Time: 0.093 (0.330) Loss: 6.337 ( 6.371) Acc@1: 3.892 ( 3.560) Acc@5: 11.321 ( 10.138)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-32.pth.tar', 3.674000002975464)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-30.pth.tar', 3.6019999974822996)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-33.pth.tar', 3.5600000016784668)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-31.pth.tar', 3.4839999964904784)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-29.pth.tar', 3.1820000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-28.pth.tar', 3.1779999960327148)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-27.pth.tar', 3.141999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-26.pth.tar', 2.9660000054168703)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-25.pth.tar', 2.6900000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-24.pth.tar', 2.679999998474121)
Train: 34 [ 0/156 ( 1%)] Loss: 6.70 (6.70) Time: 1.742s, 587.86/s (1.742s, 587.86/s) LR: 3.514e-01 Data: 1.151 (1.151)
Train: 34 [ 50/156 ( 33%)] Loss: 6.69 (6.70) Time: 0.414s, 2472.22/s (0.438s, 2335.39/s) LR: 3.514e-01 Data: 0.029 (0.049)
Train: 34 [ 100/156 ( 65%)] Loss: 6.72 (6.71) Time: 0.411s, 2489.36/s (0.423s, 2418.53/s) LR: 3.514e-01 Data: 0.028 (0.038)
Train: 34 [ 150/156 ( 97%)] Loss: 6.74 (6.72) Time: 0.406s, 2520.50/s (0.418s, 2449.01/s) LR: 3.514e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.438 (1.438) Loss: 6.369 ( 6.369) Acc@1: 3.516 ( 3.516) Acc@5: 11.133 ( 11.133)
Test: [ 48/48] Time: 0.090 (0.328) Loss: 6.322 ( 6.360) Acc@1: 3.892 ( 3.686) Acc@5: 10.024 ( 10.340)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-34.pth.tar', 3.6860000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-32.pth.tar', 3.674000002975464)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-30.pth.tar', 3.6019999974822996)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-33.pth.tar', 3.5600000016784668)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-31.pth.tar', 3.4839999964904784)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-29.pth.tar', 3.1820000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-28.pth.tar', 3.1779999960327148)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-27.pth.tar', 3.141999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-26.pth.tar', 2.9660000054168703)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-25.pth.tar', 2.6900000044250487)
Train: 35 [ 0/156 ( 1%)] Loss: 6.69 (6.69) Time: 2.171s, 471.70/s (2.171s, 471.70/s) LR: 3.486e-01 Data: 1.797 (1.797)
Train: 35 [ 50/156 ( 33%)] Loss: 6.72 (6.69) Time: 0.410s, 2498.20/s (0.444s, 2306.53/s) LR: 3.486e-01 Data: 0.027 (0.062)
Train: 35 [ 100/156 ( 65%)] Loss: 6.72 (6.70) Time: 0.419s, 2446.15/s (0.429s, 2386.45/s) LR: 3.486e-01 Data: 0.028 (0.045)
Train: 35 [ 150/156 ( 97%)] Loss: 6.77 (6.71) Time: 0.406s, 2521.43/s (0.423s, 2418.61/s) LR: 3.486e-01 Data: 0.025 (0.039)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.420 (1.420) Loss: 6.354 ( 6.354) Acc@1: 3.809 ( 3.809) Acc@5: 10.742 ( 10.742)
Test: [ 48/48] Time: 0.090 (0.328) Loss: 6.319 ( 6.355) Acc@1: 3.420 ( 3.820) Acc@5: 10.967 ( 10.650)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-35.pth.tar', 3.8199999987792967)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-34.pth.tar', 3.6860000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-32.pth.tar', 3.674000002975464)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-30.pth.tar', 3.6019999974822996)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-33.pth.tar', 3.5600000016784668)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-31.pth.tar', 3.4839999964904784)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-29.pth.tar', 3.1820000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-28.pth.tar', 3.1779999960327148)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-27.pth.tar', 3.141999999771118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-26.pth.tar', 2.9660000054168703)
Train: 36 [ 0/156 ( 1%)] Loss: 6.66 (6.66) Time: 1.512s, 677.05/s (1.512s, 677.05/s) LR: 3.458e-01 Data: 1.139 (1.139)
Train: 36 [ 50/156 ( 33%)] Loss: 6.68 (6.68) Time: 0.406s, 2522.10/s (0.427s, 2396.44/s) LR: 3.458e-01 Data: 0.028 (0.049)
Train: 36 [ 100/156 ( 65%)] Loss: 6.68 (6.69) Time: 0.411s, 2494.41/s (0.418s, 2451.89/s) LR: 3.458e-01 Data: 0.027 (0.038)
Train: 36 [ 150/156 ( 97%)] Loss: 6.71 (6.70) Time: 0.409s, 2502.37/s (0.416s, 2464.06/s) LR: 3.458e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.418 (1.418) Loss: 6.323 ( 6.323) Acc@1: 4.004 ( 4.004) Acc@5: 12.207 ( 12.207)
Test: [ 48/48] Time: 0.091 (0.328) Loss: 6.302 ( 6.342) Acc@1: 4.835 ( 4.232) Acc@5: 11.085 ( 11.252)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-36.pth.tar', 4.231999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-35.pth.tar', 3.8199999987792967)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-34.pth.tar', 3.6860000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-32.pth.tar', 3.674000002975464)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-30.pth.tar', 3.6019999974822996)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-33.pth.tar', 3.5600000016784668)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-31.pth.tar', 3.4839999964904784)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-29.pth.tar', 3.1820000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-28.pth.tar', 3.1779999960327148)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-27.pth.tar', 3.141999999771118)
Train: 37 [ 0/156 ( 1%)] Loss: 6.67 (6.67) Time: 1.427s, 717.51/s (1.427s, 717.51/s) LR: 3.429e-01 Data: 1.050 (1.050)
Train: 37 [ 50/156 ( 33%)] Loss: 6.71 (6.67) Time: 0.418s, 2452.38/s (0.433s, 2362.36/s) LR: 3.429e-01 Data: 0.027 (0.047)
Train: 37 [ 100/156 ( 65%)] Loss: 6.73 (6.68) Time: 0.408s, 2510.48/s (0.423s, 2420.45/s) LR: 3.429e-01 Data: 0.028 (0.037)
Train: 37 [ 150/156 ( 97%)] Loss: 6.72 (6.69) Time: 0.407s, 2514.95/s (0.418s, 2447.59/s) LR: 3.429e-01 Data: 0.025 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.462 (1.462) Loss: 6.366 ( 6.366) Acc@1: 3.809 ( 3.809) Acc@5: 11.035 ( 11.035)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.332 ( 6.365) Acc@1: 4.363 ( 4.254) Acc@5: 12.264 ( 11.340)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-37.pth.tar', 4.2540000045776365)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-36.pth.tar', 4.231999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-35.pth.tar', 3.8199999987792967)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-34.pth.tar', 3.6860000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-32.pth.tar', 3.674000002975464)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-30.pth.tar', 3.6019999974822996)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-33.pth.tar', 3.5600000016784668)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-31.pth.tar', 3.4839999964904784)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-29.pth.tar', 3.1820000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-28.pth.tar', 3.1779999960327148)
Train: 38 [ 0/156 ( 1%)] Loss: 6.67 (6.67) Time: 1.883s, 543.87/s (1.883s, 543.87/s) LR: 3.399e-01 Data: 1.507 (1.507)
Train: 38 [ 50/156 ( 33%)] Loss: 6.69 (6.66) Time: 0.414s, 2475.85/s (0.441s, 2322.36/s) LR: 3.399e-01 Data: 0.027 (0.056)
Train: 38 [ 100/156 ( 65%)] Loss: 6.68 (6.67) Time: 0.417s, 2456.06/s (0.428s, 2393.18/s) LR: 3.399e-01 Data: 0.028 (0.042)
Train: 38 [ 150/156 ( 97%)] Loss: 6.74 (6.68) Time: 0.413s, 2476.82/s (0.423s, 2418.40/s) LR: 3.399e-01 Data: 0.026 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.457 (1.457) Loss: 6.337 ( 6.337) Acc@1: 4.297 ( 4.297) Acc@5: 11.621 ( 11.621)
Test: [ 48/48] Time: 0.092 (0.331) Loss: 6.317 ( 6.335) Acc@1: 4.599 ( 3.984) Acc@5: 11.321 ( 11.126)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-37.pth.tar', 4.2540000045776365)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-36.pth.tar', 4.231999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-38.pth.tar', 3.9839999938964845)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-35.pth.tar', 3.8199999987792967)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-34.pth.tar', 3.6860000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-32.pth.tar', 3.674000002975464)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-30.pth.tar', 3.6019999974822996)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-33.pth.tar', 3.5600000016784668)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-31.pth.tar', 3.4839999964904784)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-29.pth.tar', 3.1820000016784666)
Train: 39 [ 0/156 ( 1%)] Loss: 6.63 (6.63) Time: 1.753s, 584.11/s (1.753s, 584.11/s) LR: 3.369e-01 Data: 1.377 (1.377)
Train: 39 [ 50/156 ( 33%)] Loss: 6.69 (6.64) Time: 0.416s, 2464.31/s (0.442s, 2317.48/s) LR: 3.369e-01 Data: 0.028 (0.053)
Train: 39 [ 100/156 ( 65%)] Loss: 6.68 (6.66) Time: 0.413s, 2480.49/s (0.428s, 2394.88/s) LR: 3.369e-01 Data: 0.027 (0.040)
Train: 39 [ 150/156 ( 97%)] Loss: 6.70 (6.67) Time: 0.415s, 2468.76/s (0.423s, 2420.95/s) LR: 3.369e-01 Data: 0.027 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.418 (1.418) Loss: 6.322 ( 6.322) Acc@1: 4.297 ( 4.297) Acc@5: 12.207 ( 12.207)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.274 ( 6.315) Acc@1: 5.189 ( 4.436) Acc@5: 12.736 ( 11.636)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-39.pth.tar', 4.436000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-37.pth.tar', 4.2540000045776365)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-36.pth.tar', 4.231999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-38.pth.tar', 3.9839999938964845)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-35.pth.tar', 3.8199999987792967)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-34.pth.tar', 3.6860000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-32.pth.tar', 3.674000002975464)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-30.pth.tar', 3.6019999974822996)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-33.pth.tar', 3.5600000016784668)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-31.pth.tar', 3.4839999964904784)
Train: 40 [ 0/156 ( 1%)] Loss: 6.64 (6.64) Time: 1.599s, 640.21/s (1.599s, 640.21/s) LR: 3.338e-01 Data: 1.159 (1.159)
Train: 40 [ 50/156 ( 33%)] Loss: 6.64 (6.63) Time: 0.409s, 2502.77/s (0.433s, 2363.06/s) LR: 3.338e-01 Data: 0.026 (0.050)
Train: 40 [ 100/156 ( 65%)] Loss: 6.68 (6.64) Time: 0.412s, 2487.70/s (0.424s, 2415.10/s) LR: 3.338e-01 Data: 0.026 (0.039)
Train: 40 [ 150/156 ( 97%)] Loss: 6.66 (6.65) Time: 0.409s, 2505.40/s (0.420s, 2436.53/s) LR: 3.338e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.458 (1.458) Loss: 6.329 ( 6.329) Acc@1: 4.395 ( 4.395) Acc@5: 12.500 ( 12.500)
Test: [ 48/48] Time: 0.092 (0.330) Loss: 6.301 ( 6.345) Acc@1: 4.009 ( 3.868) Acc@5: 11.910 ( 10.654)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-39.pth.tar', 4.436000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-37.pth.tar', 4.2540000045776365)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-36.pth.tar', 4.231999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-38.pth.tar', 3.9839999938964845)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-40.pth.tar', 3.8680000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-35.pth.tar', 3.8199999987792967)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-34.pth.tar', 3.6860000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-32.pth.tar', 3.674000002975464)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-30.pth.tar', 3.6019999974822996)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-33.pth.tar', 3.5600000016784668)
Train: 41 [ 0/156 ( 1%)] Loss: 6.58 (6.58) Time: 1.598s, 640.71/s (1.598s, 640.71/s) LR: 3.307e-01 Data: 1.221 (1.221)
Train: 41 [ 50/156 ( 33%)] Loss: 6.66 (6.61) Time: 0.417s, 2455.60/s (0.443s, 2313.96/s) LR: 3.307e-01 Data: 0.027 (0.051)
Train: 41 [ 100/156 ( 65%)] Loss: 6.64 (6.63) Time: 0.413s, 2480.86/s (0.427s, 2396.40/s) LR: 3.307e-01 Data: 0.028 (0.039)
Train: 41 [ 150/156 ( 97%)] Loss: 6.68 (6.64) Time: 0.414s, 2476.18/s (0.423s, 2422.49/s) LR: 3.307e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.429 (1.429) Loss: 6.292 ( 6.292) Acc@1: 4.883 ( 4.883) Acc@5: 11.719 ( 11.719)
Test: [ 48/48] Time: 0.092 (0.331) Loss: 6.276 ( 6.297) Acc@1: 5.896 ( 4.546) Acc@5: 12.028 ( 11.798)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-41.pth.tar', 4.546000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-39.pth.tar', 4.436000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-37.pth.tar', 4.2540000045776365)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-36.pth.tar', 4.231999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-38.pth.tar', 3.9839999938964845)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-40.pth.tar', 3.8680000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-35.pth.tar', 3.8199999987792967)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-34.pth.tar', 3.6860000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-32.pth.tar', 3.674000002975464)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-30.pth.tar', 3.6019999974822996)
Train: 42 [ 0/156 ( 1%)] Loss: 6.58 (6.58) Time: 1.730s, 591.78/s (1.730s, 591.78/s) LR: 3.275e-01 Data: 1.352 (1.352)
Train: 42 [ 50/156 ( 33%)] Loss: 6.61 (6.60) Time: 0.411s, 2490.25/s (0.439s, 2331.23/s) LR: 3.275e-01 Data: 0.027 (0.053)
Train: 42 [ 100/156 ( 65%)] Loss: 6.63 (6.62) Time: 0.415s, 2466.70/s (0.426s, 2404.50/s) LR: 3.275e-01 Data: 0.027 (0.041)
Train: 42 [ 150/156 ( 97%)] Loss: 6.66 (6.63) Time: 0.415s, 2467.16/s (0.422s, 2429.34/s) LR: 3.275e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.448 (1.448) Loss: 6.271 ( 6.271) Acc@1: 3.809 ( 3.809) Acc@5: 12.305 ( 12.305)
Test: [ 48/48] Time: 0.092 (0.330) Loss: 6.269 ( 6.299) Acc@1: 4.363 ( 4.672) Acc@5: 12.854 ( 11.968)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-42.pth.tar', 4.672000004577637)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-41.pth.tar', 4.546000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-39.pth.tar', 4.436000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-37.pth.tar', 4.2540000045776365)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-36.pth.tar', 4.231999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-38.pth.tar', 3.9839999938964845)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-40.pth.tar', 3.8680000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-35.pth.tar', 3.8199999987792967)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-34.pth.tar', 3.6860000016784666)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-32.pth.tar', 3.674000002975464)
Train: 43 [ 0/156 ( 1%)] Loss: 6.57 (6.57) Time: 1.584s, 646.27/s (1.584s, 646.27/s) LR: 3.242e-01 Data: 1.173 (1.173)
Train: 43 [ 50/156 ( 33%)] Loss: 6.61 (6.59) Time: 0.414s, 2473.78/s (0.435s, 2351.87/s) LR: 3.242e-01 Data: 0.028 (0.050)
Train: 43 [ 100/156 ( 65%)] Loss: 6.63 (6.60) Time: 0.407s, 2516.96/s (0.424s, 2415.39/s) LR: 3.242e-01 Data: 0.027 (0.039)
Train: 43 [ 150/156 ( 97%)] Loss: 6.67 (6.62) Time: 0.406s, 2525.14/s (0.419s, 2442.58/s) LR: 3.242e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.420 (1.420) Loss: 6.259 ( 6.259) Acc@1: 4.297 ( 4.297) Acc@5: 12.988 ( 12.988)
Test: [ 48/48] Time: 0.092 (0.331) Loss: 6.242 ( 6.292) Acc@1: 5.307 ( 4.536) Acc@5: 12.500 ( 11.976)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-42.pth.tar', 4.672000004577637)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-41.pth.tar', 4.546000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-43.pth.tar', 4.5360000022888185)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-39.pth.tar', 4.436000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-37.pth.tar', 4.2540000045776365)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-36.pth.tar', 4.231999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-38.pth.tar', 3.9839999938964845)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-40.pth.tar', 3.8680000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-35.pth.tar', 3.8199999987792967)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-34.pth.tar', 3.6860000016784666)
Train: 44 [ 0/156 ( 1%)] Loss: 6.55 (6.55) Time: 1.668s, 614.00/s (1.668s, 614.00/s) LR: 3.209e-01 Data: 1.129 (1.129)
Train: 44 [ 50/156 ( 33%)] Loss: 6.58 (6.57) Time: 0.411s, 2489.16/s (0.437s, 2343.56/s) LR: 3.209e-01 Data: 0.026 (0.049)
Train: 44 [ 100/156 ( 65%)] Loss: 6.61 (6.59) Time: 0.411s, 2490.90/s (0.424s, 2415.12/s) LR: 3.209e-01 Data: 0.027 (0.038)
Train: 44 [ 150/156 ( 97%)] Loss: 6.61 (6.60) Time: 0.411s, 2493.17/s (0.421s, 2434.86/s) LR: 3.209e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.577 (1.577) Loss: 6.237 ( 6.237) Acc@1: 4.590 ( 4.590) Acc@5: 13.086 ( 13.086)
Test: [ 48/48] Time: 0.092 (0.328) Loss: 6.257 ( 6.276) Acc@1: 5.307 ( 4.670) Acc@5: 12.972 ( 11.926)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-42.pth.tar', 4.672000004577637)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-44.pth.tar', 4.670000002288818)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-41.pth.tar', 4.546000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-43.pth.tar', 4.5360000022888185)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-39.pth.tar', 4.436000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-37.pth.tar', 4.2540000045776365)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-36.pth.tar', 4.231999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-38.pth.tar', 3.9839999938964845)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-40.pth.tar', 3.8680000044250487)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-35.pth.tar', 3.8199999987792967)
Train: 45 [ 0/156 ( 1%)] Loss: 6.55 (6.55) Time: 1.678s, 610.14/s (1.678s, 610.14/s) LR: 3.176e-01 Data: 1.303 (1.303)
Train: 45 [ 50/156 ( 33%)] Loss: 6.59 (6.55) Time: 0.413s, 2480.61/s (0.434s, 2357.57/s) LR: 3.176e-01 Data: 0.028 (0.052)
Train: 45 [ 100/156 ( 65%)] Loss: 6.63 (6.57) Time: 0.416s, 2460.70/s (0.424s, 2413.22/s) LR: 3.176e-01 Data: 0.027 (0.040)
Train: 45 [ 150/156 ( 97%)] Loss: 6.65 (6.59) Time: 0.408s, 2509.03/s (0.420s, 2438.72/s) LR: 3.176e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.440 (1.440) Loss: 6.238 ( 6.238) Acc@1: 4.395 ( 4.395) Acc@5: 13.867 ( 13.867)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.207 ( 6.279) Acc@1: 5.778 ( 4.708) Acc@5: 13.561 ( 12.208)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-45.pth.tar', 4.708000005187988)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-42.pth.tar', 4.672000004577637)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-44.pth.tar', 4.670000002288818)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-41.pth.tar', 4.546000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-43.pth.tar', 4.5360000022888185)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-39.pth.tar', 4.436000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-37.pth.tar', 4.2540000045776365)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-36.pth.tar', 4.231999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-38.pth.tar', 3.9839999938964845)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-40.pth.tar', 3.8680000044250487)
Train: 46 [ 0/156 ( 1%)] Loss: 6.53 (6.53) Time: 1.727s, 592.85/s (1.727s, 592.85/s) LR: 3.141e-01 Data: 1.350 (1.350)
Train: 46 [ 50/156 ( 33%)] Loss: 6.58 (6.54) Time: 0.416s, 2461.14/s (0.437s, 2341.21/s) LR: 3.141e-01 Data: 0.026 (0.053)
Train: 46 [ 100/156 ( 65%)] Loss: 6.59 (6.56) Time: 0.410s, 2495.27/s (0.425s, 2410.27/s) LR: 3.141e-01 Data: 0.027 (0.040)
Train: 46 [ 150/156 ( 97%)] Loss: 6.58 (6.57) Time: 0.415s, 2468.02/s (0.421s, 2431.46/s) LR: 3.141e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.443 (1.443) Loss: 6.234 ( 6.234) Acc@1: 4.883 ( 4.883) Acc@5: 13.770 ( 13.770)
Test: [ 48/48] Time: 0.091 (0.332) Loss: 6.195 ( 6.262) Acc@1: 5.778 ( 5.116) Acc@5: 12.972 ( 12.648)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-45.pth.tar', 4.708000005187988)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-42.pth.tar', 4.672000004577637)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-44.pth.tar', 4.670000002288818)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-41.pth.tar', 4.546000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-43.pth.tar', 4.5360000022888185)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-39.pth.tar', 4.436000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-37.pth.tar', 4.2540000045776365)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-36.pth.tar', 4.231999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-38.pth.tar', 3.9839999938964845)
Train: 47 [ 0/156 ( 1%)] Loss: 6.49 (6.49) Time: 1.584s, 646.27/s (1.584s, 646.27/s) LR: 3.107e-01 Data: 1.208 (1.208)
Train: 47 [ 50/156 ( 33%)] Loss: 6.56 (6.52) Time: 0.419s, 2442.80/s (0.436s, 2349.25/s) LR: 3.107e-01 Data: 0.033 (0.051)
Train: 47 [ 100/156 ( 65%)] Loss: 6.57 (6.54) Time: 0.407s, 2515.97/s (0.424s, 2414.24/s) LR: 3.107e-01 Data: 0.026 (0.039)
Train: 47 [ 150/156 ( 97%)] Loss: 6.62 (6.56) Time: 0.402s, 2547.58/s (0.418s, 2448.86/s) LR: 3.107e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.453 (1.453) Loss: 6.257 ( 6.257) Acc@1: 5.371 ( 5.371) Acc@5: 14.062 ( 14.062)
Test: [ 48/48] Time: 0.090 (0.331) Loss: 6.245 ( 6.284) Acc@1: 4.835 ( 4.870) Acc@5: 13.090 ( 12.236)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-47.pth.tar', 4.870000007476807)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-45.pth.tar', 4.708000005187988)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-42.pth.tar', 4.672000004577637)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-44.pth.tar', 4.670000002288818)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-41.pth.tar', 4.546000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-43.pth.tar', 4.5360000022888185)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-39.pth.tar', 4.436000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-37.pth.tar', 4.2540000045776365)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-36.pth.tar', 4.231999999389648)
Train: 48 [ 0/156 ( 1%)] Loss: 6.48 (6.48) Time: 1.734s, 590.54/s (1.734s, 590.54/s) LR: 3.072e-01 Data: 1.361 (1.361)
Train: 48 [ 50/156 ( 33%)] Loss: 6.51 (6.51) Time: 0.405s, 2525.72/s (0.431s, 2373.64/s) LR: 3.072e-01 Data: 0.025 (0.053)
Train: 48 [ 100/156 ( 65%)] Loss: 6.51 (6.52) Time: 0.411s, 2489.47/s (0.420s, 2435.93/s) LR: 3.072e-01 Data: 0.028 (0.040)
Train: 48 [ 150/156 ( 97%)] Loss: 6.57 (6.54) Time: 0.414s, 2474.67/s (0.418s, 2451.71/s) LR: 3.072e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.455 (1.455) Loss: 6.209 ( 6.209) Acc@1: 4.297 ( 4.297) Acc@5: 13.770 ( 13.770)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.186 ( 6.238) Acc@1: 6.250 ( 5.118) Acc@5: 12.972 ( 12.608)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-47.pth.tar', 4.870000007476807)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-45.pth.tar', 4.708000005187988)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-42.pth.tar', 4.672000004577637)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-44.pth.tar', 4.670000002288818)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-41.pth.tar', 4.546000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-43.pth.tar', 4.5360000022888185)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-39.pth.tar', 4.436000007629395)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-37.pth.tar', 4.2540000045776365)
Train: 49 [ 0/156 ( 1%)] Loss: 6.45 (6.45) Time: 1.722s, 594.81/s (1.722s, 594.81/s) LR: 3.036e-01 Data: 1.160 (1.160)
Train: 49 [ 50/156 ( 33%)] Loss: 6.52 (6.49) Time: 0.408s, 2509.32/s (0.433s, 2364.69/s) LR: 3.036e-01 Data: 0.027 (0.049)
Train: 49 [ 100/156 ( 65%)] Loss: 6.59 (6.51) Time: 0.417s, 2456.84/s (0.422s, 2427.38/s) LR: 3.036e-01 Data: 0.028 (0.038)
Train: 49 [ 150/156 ( 97%)] Loss: 6.51 (6.52) Time: 0.408s, 2512.46/s (0.419s, 2443.93/s) LR: 3.036e-01 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.445 (1.445) Loss: 6.283 ( 6.283) Acc@1: 5.176 ( 5.176) Acc@5: 12.500 ( 12.500)
Test: [ 48/48] Time: 0.090 (0.332) Loss: 6.220 ( 6.293) Acc@1: 5.542 ( 5.008) Acc@5: 15.094 ( 12.258)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-49.pth.tar', 5.007999999694825)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-47.pth.tar', 4.870000007476807)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-45.pth.tar', 4.708000005187988)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-42.pth.tar', 4.672000004577637)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-44.pth.tar', 4.670000002288818)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-41.pth.tar', 4.546000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-43.pth.tar', 4.5360000022888185)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-39.pth.tar', 4.436000007629395)
Train: 50 [ 0/156 ( 1%)] Loss: 6.45 (6.45) Time: 1.714s, 597.54/s (1.714s, 597.54/s) LR: 3.000e-01 Data: 1.184 (1.184)
Train: 50 [ 50/156 ( 33%)] Loss: 6.50 (6.47) Time: 0.405s, 2526.62/s (0.432s, 2371.54/s) LR: 3.000e-01 Data: 0.027 (0.050)
Train: 50 [ 100/156 ( 65%)] Loss: 6.51 (6.49) Time: 0.409s, 2504.20/s (0.420s, 2438.29/s) LR: 3.000e-01 Data: 0.026 (0.039)
Train: 50 [ 150/156 ( 97%)] Loss: 6.54 (6.51) Time: 0.414s, 2473.51/s (0.417s, 2453.86/s) LR: 3.000e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.426 (1.426) Loss: 6.260 ( 6.260) Acc@1: 4.395 ( 4.395) Acc@5: 12.793 ( 12.793)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.251 ( 6.275) Acc@1: 4.835 ( 5.138) Acc@5: 12.854 ( 12.606)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-50.pth.tar', 5.137999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-49.pth.tar', 5.007999999694825)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-47.pth.tar', 4.870000007476807)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-45.pth.tar', 4.708000005187988)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-42.pth.tar', 4.672000004577637)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-44.pth.tar', 4.670000002288818)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-41.pth.tar', 4.546000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-43.pth.tar', 4.5360000022888185)
Train: 51 [ 0/156 ( 1%)] Loss: 6.45 (6.45) Time: 1.756s, 583.01/s (1.756s, 583.01/s) LR: 2.964e-01 Data: 1.379 (1.379)
Train: 51 [ 50/156 ( 33%)] Loss: 6.42 (6.44) Time: 0.412s, 2483.69/s (0.440s, 2329.65/s) LR: 2.964e-01 Data: 0.026 (0.054)
Train: 51 [ 100/156 ( 65%)] Loss: 6.53 (6.47) Time: 0.413s, 2481.03/s (0.427s, 2400.12/s) LR: 2.964e-01 Data: 0.027 (0.041)
Train: 51 [ 150/156 ( 97%)] Loss: 6.55 (6.49) Time: 0.415s, 2465.39/s (0.422s, 2424.77/s) LR: 2.964e-01 Data: 0.027 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.425 (1.425) Loss: 6.281 ( 6.281) Acc@1: 5.469 ( 5.469) Acc@5: 13.672 ( 13.672)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.231 ( 6.271) Acc@1: 4.599 ( 5.026) Acc@5: 13.325 ( 12.458)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-50.pth.tar', 5.137999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-51.pth.tar', 5.026000001983642)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-49.pth.tar', 5.007999999694825)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-47.pth.tar', 4.870000007476807)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-45.pth.tar', 4.708000005187988)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-42.pth.tar', 4.672000004577637)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-44.pth.tar', 4.670000002288818)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-41.pth.tar', 4.546000007934571)
Train: 52 [ 0/156 ( 1%)] Loss: 6.38 (6.38) Time: 1.536s, 666.81/s (1.536s, 666.81/s) LR: 2.927e-01 Data: 1.160 (1.160)
Train: 52 [ 50/156 ( 33%)] Loss: 6.46 (6.43) Time: 0.412s, 2486.43/s (0.433s, 2362.49/s) LR: 2.927e-01 Data: 0.028 (0.050)
Train: 52 [ 100/156 ( 65%)] Loss: 6.48 (6.45) Time: 0.409s, 2505.41/s (0.423s, 2418.32/s) LR: 2.927e-01 Data: 0.027 (0.039)
Train: 52 [ 150/156 ( 97%)] Loss: 6.53 (6.47) Time: 0.410s, 2495.35/s (0.419s, 2441.42/s) LR: 2.927e-01 Data: 0.023 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.461 (1.461) Loss: 6.236 ( 6.236) Acc@1: 5.859 ( 5.859) Acc@5: 13.281 ( 13.281)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.235 ( 6.274) Acc@1: 4.245 ( 5.038) Acc@5: 13.325 ( 12.286)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-50.pth.tar', 5.137999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-52.pth.tar', 5.038000001831055)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-51.pth.tar', 5.026000001983642)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-49.pth.tar', 5.007999999694825)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-47.pth.tar', 4.870000007476807)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-45.pth.tar', 4.708000005187988)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-42.pth.tar', 4.672000004577637)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-44.pth.tar', 4.670000002288818)
Train: 53 [ 0/156 ( 1%)] Loss: 6.38 (6.38) Time: 1.719s, 595.80/s (1.719s, 595.80/s) LR: 2.889e-01 Data: 1.342 (1.342)
Train: 53 [ 50/156 ( 33%)] Loss: 6.48 (6.41) Time: 0.413s, 2479.77/s (0.437s, 2341.59/s) LR: 2.889e-01 Data: 0.027 (0.053)
Train: 53 [ 100/156 ( 65%)] Loss: 6.53 (6.44) Time: 0.414s, 2474.85/s (0.425s, 2408.54/s) LR: 2.889e-01 Data: 0.027 (0.040)
Train: 53 [ 150/156 ( 97%)] Loss: 6.54 (6.45) Time: 0.415s, 2465.70/s (0.421s, 2431.73/s) LR: 2.889e-01 Data: 0.027 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.444 (1.444) Loss: 6.186 ( 6.186) Acc@1: 6.934 ( 6.934) Acc@5: 15.625 ( 15.625)
Test: [ 48/48] Time: 0.092 (0.332) Loss: 6.186 ( 6.213) Acc@1: 4.953 ( 5.474) Acc@5: 13.915 ( 13.238)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-53.pth.tar', 5.4739999940490724)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-50.pth.tar', 5.137999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-52.pth.tar', 5.038000001831055)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-51.pth.tar', 5.026000001983642)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-49.pth.tar', 5.007999999694825)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-47.pth.tar', 4.870000007476807)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-45.pth.tar', 4.708000005187988)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-42.pth.tar', 4.672000004577637)
Train: 54 [ 0/156 ( 1%)] Loss: 6.40 (6.40) Time: 1.570s, 652.35/s (1.570s, 652.35/s) LR: 2.852e-01 Data: 1.193 (1.193)
Train: 54 [ 50/156 ( 33%)] Loss: 6.38 (6.38) Time: 0.413s, 2476.64/s (0.435s, 2355.76/s) LR: 2.852e-01 Data: 0.032 (0.050)
Train: 54 [ 100/156 ( 65%)] Loss: 6.45 (6.41) Time: 0.415s, 2468.00/s (0.424s, 2416.28/s) LR: 2.852e-01 Data: 0.028 (0.039)
Train: 54 [ 150/156 ( 97%)] Loss: 6.50 (6.43) Time: 0.408s, 2509.27/s (0.420s, 2437.24/s) LR: 2.852e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.471 (1.471) Loss: 6.204 ( 6.204) Acc@1: 5.762 ( 5.762) Acc@5: 14.062 ( 14.062)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.175 ( 6.244) Acc@1: 6.368 ( 5.446) Acc@5: 14.623 ( 12.990)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-53.pth.tar', 5.4739999940490724)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-54.pth.tar', 5.446000002746582)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-50.pth.tar', 5.137999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-52.pth.tar', 5.038000001831055)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-51.pth.tar', 5.026000001983642)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-49.pth.tar', 5.007999999694825)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-47.pth.tar', 4.870000007476807)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-45.pth.tar', 4.708000005187988)
Train: 55 [ 0/156 ( 1%)] Loss: 6.31 (6.31) Time: 2.203s, 464.78/s (2.203s, 464.78/s) LR: 2.813e-01 Data: 1.827 (1.827)
Train: 55 [ 50/156 ( 33%)] Loss: 6.34 (6.36) Time: 0.412s, 2487.94/s (0.446s, 2297.56/s) LR: 2.813e-01 Data: 0.026 (0.062)
Train: 55 [ 100/156 ( 65%)] Loss: 6.43 (6.39) Time: 0.409s, 2502.87/s (0.429s, 2385.37/s) LR: 2.813e-01 Data: 0.027 (0.045)
Train: 55 [ 150/156 ( 97%)] Loss: 6.46 (6.41) Time: 0.409s, 2502.59/s (0.424s, 2416.30/s) LR: 2.813e-01 Data: 0.025 (0.039)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.462 (1.462) Loss: 6.182 ( 6.182) Acc@1: 5.762 ( 5.762) Acc@5: 14.941 ( 14.941)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 6.181 ( 6.233) Acc@1: 5.542 ( 5.590) Acc@5: 14.976 ( 13.178)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-55.pth.tar', 5.589999999694824)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-53.pth.tar', 5.4739999940490724)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-54.pth.tar', 5.446000002746582)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-50.pth.tar', 5.137999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-52.pth.tar', 5.038000001831055)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-51.pth.tar', 5.026000001983642)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-49.pth.tar', 5.007999999694825)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-47.pth.tar', 4.870000007476807)
Train: 56 [ 0/156 ( 1%)] Loss: 6.33 (6.33) Time: 1.616s, 633.72/s (1.616s, 633.72/s) LR: 2.775e-01 Data: 1.238 (1.238)
Train: 56 [ 50/156 ( 33%)] Loss: 6.41 (6.34) Time: 0.417s, 2454.96/s (0.436s, 2350.72/s) LR: 2.775e-01 Data: 0.028 (0.051)
Train: 56 [ 100/156 ( 65%)] Loss: 6.44 (6.37) Time: 0.406s, 2520.15/s (0.423s, 2421.14/s) LR: 2.775e-01 Data: 0.026 (0.039)
Train: 56 [ 150/156 ( 97%)] Loss: 6.46 (6.39) Time: 0.411s, 2491.53/s (0.418s, 2447.55/s) LR: 2.775e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.458 (1.458) Loss: 6.140 ( 6.140) Acc@1: 6.348 ( 6.348) Acc@5: 14.844 ( 14.844)
Test: [ 48/48] Time: 0.092 (0.330) Loss: 6.169 ( 6.212) Acc@1: 5.896 ( 5.562) Acc@5: 13.208 ( 13.226)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-55.pth.tar', 5.589999999694824)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-56.pth.tar', 5.562000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-53.pth.tar', 5.4739999940490724)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-54.pth.tar', 5.446000002746582)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-50.pth.tar', 5.137999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-52.pth.tar', 5.038000001831055)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-51.pth.tar', 5.026000001983642)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-49.pth.tar', 5.007999999694825)
Train: 57 [ 0/156 ( 1%)] Loss: 6.30 (6.30) Time: 1.750s, 585.01/s (1.750s, 585.01/s) LR: 2.736e-01 Data: 1.373 (1.373)
Train: 57 [ 50/156 ( 33%)] Loss: 6.37 (6.31) Time: 0.417s, 2457.90/s (0.440s, 2328.66/s) LR: 2.736e-01 Data: 0.028 (0.053)
Train: 57 [ 100/156 ( 65%)] Loss: 6.35 (6.34) Time: 0.411s, 2490.39/s (0.425s, 2406.73/s) LR: 2.736e-01 Data: 0.029 (0.041)
Train: 57 [ 150/156 ( 97%)] Loss: 6.44 (6.36) Time: 0.408s, 2508.91/s (0.420s, 2435.84/s) LR: 2.736e-01 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.454 (1.454) Loss: 6.223 ( 6.223) Acc@1: 5.762 ( 5.762) Acc@5: 13.770 ( 13.770)
Test: [ 48/48] Time: 0.091 (0.332) Loss: 6.224 ( 6.272) Acc@1: 5.896 ( 5.148) Acc@5: 12.736 ( 12.336)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-55.pth.tar', 5.589999999694824)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-56.pth.tar', 5.562000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-53.pth.tar', 5.4739999940490724)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-54.pth.tar', 5.446000002746582)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-57.pth.tar', 5.147999999847412)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-50.pth.tar', 5.137999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-52.pth.tar', 5.038000001831055)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-51.pth.tar', 5.026000001983642)
Train: 58 [ 0/156 ( 1%)] Loss: 6.26 (6.26) Time: 1.734s, 590.41/s (1.734s, 590.41/s) LR: 2.697e-01 Data: 1.358 (1.358)
Train: 58 [ 50/156 ( 33%)] Loss: 6.35 (6.29) Time: 0.413s, 2477.92/s (0.437s, 2342.93/s) LR: 2.697e-01 Data: 0.028 (0.053)
Train: 58 [ 100/156 ( 65%)] Loss: 6.38 (6.33) Time: 0.408s, 2512.70/s (0.424s, 2412.29/s) LR: 2.697e-01 Data: 0.027 (0.041)
Train: 58 [ 150/156 ( 97%)] Loss: 6.41 (6.35) Time: 0.407s, 2518.23/s (0.419s, 2442.19/s) LR: 2.697e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.449 (1.449) Loss: 6.278 ( 6.278) Acc@1: 5.176 ( 5.176) Acc@5: 13.086 ( 13.086)
Test: [ 48/48] Time: 0.091 (0.331) Loss: 6.224 ( 6.294) Acc@1: 6.014 ( 5.280) Acc@5: 13.208 ( 11.980)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-55.pth.tar', 5.589999999694824)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-56.pth.tar', 5.562000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-53.pth.tar', 5.4739999940490724)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-54.pth.tar', 5.446000002746582)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-58.pth.tar', 5.280000002593994)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-57.pth.tar', 5.147999999847412)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-50.pth.tar', 5.137999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-52.pth.tar', 5.038000001831055)
Train: 59 [ 0/156 ( 1%)] Loss: 6.28 (6.28) Time: 2.314s, 442.51/s (2.314s, 442.51/s) LR: 2.658e-01 Data: 1.142 (1.142)
Train: 59 [ 50/156 ( 33%)] Loss: 6.27 (6.27) Time: 0.412s, 2486.43/s (0.449s, 2279.44/s) LR: 2.658e-01 Data: 0.027 (0.049)
Train: 59 [ 100/156 ( 65%)] Loss: 6.29 (6.30) Time: 0.407s, 2513.15/s (0.429s, 2385.22/s) LR: 2.658e-01 Data: 0.027 (0.038)
Train: 59 [ 150/156 ( 97%)] Loss: 6.39 (6.32) Time: 0.411s, 2494.23/s (0.423s, 2422.21/s) LR: 2.658e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.449 (1.449) Loss: 6.209 ( 6.209) Acc@1: 6.055 ( 6.055) Acc@5: 14.258 ( 14.258)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 6.197 ( 6.242) Acc@1: 5.071 ( 5.536) Acc@5: 13.561 ( 12.928)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-55.pth.tar', 5.589999999694824)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-56.pth.tar', 5.562000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-59.pth.tar', 5.536000004882813)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-53.pth.tar', 5.4739999940490724)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-54.pth.tar', 5.446000002746582)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-58.pth.tar', 5.280000002593994)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-57.pth.tar', 5.147999999847412)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-50.pth.tar', 5.137999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-46.pth.tar', 5.11599999710083)
Train: 60 [ 0/156 ( 1%)] Loss: 6.21 (6.21) Time: 1.734s, 590.57/s (1.734s, 590.57/s) LR: 2.618e-01 Data: 1.125 (1.125)
Train: 60 [ 50/156 ( 33%)] Loss: 6.24 (6.23) Time: 0.409s, 2506.69/s (0.438s, 2338.59/s) LR: 2.618e-01 Data: 0.027 (0.049)
Train: 60 [ 100/156 ( 65%)] Loss: 6.33 (6.27) Time: 0.410s, 2497.36/s (0.424s, 2416.84/s) LR: 2.618e-01 Data: 0.028 (0.038)
Train: 60 [ 150/156 ( 97%)] Loss: 6.40 (6.30) Time: 0.414s, 2474.84/s (0.420s, 2439.81/s) LR: 2.618e-01 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.422 (1.422) Loss: 6.228 ( 6.228) Acc@1: 6.152 ( 6.152) Acc@5: 14.062 ( 14.062)
Test: [ 48/48] Time: 0.092 (0.331) Loss: 6.223 ( 6.257) Acc@1: 5.425 ( 5.348) Acc@5: 12.972 ( 13.058)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-55.pth.tar', 5.589999999694824)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-56.pth.tar', 5.562000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-59.pth.tar', 5.536000004882813)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-53.pth.tar', 5.4739999940490724)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-54.pth.tar', 5.446000002746582)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-60.pth.tar', 5.347999996948242)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-58.pth.tar', 5.280000002593994)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-57.pth.tar', 5.147999999847412)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-50.pth.tar', 5.137999999389648)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-48.pth.tar', 5.118)
Train: 61 [ 0/156 ( 1%)] Loss: 6.20 (6.20) Time: 2.073s, 494.06/s (2.073s, 494.06/s) LR: 2.578e-01 Data: 1.695 (1.695)
Train: 61 [ 50/156 ( 33%)] Loss: 6.24 (6.21) Time: 0.411s, 2491.31/s (0.444s, 2305.58/s) LR: 2.578e-01 Data: 0.026 (0.060)
Train: 61 [ 100/156 ( 65%)] Loss: 6.30 (6.25) Time: 0.410s, 2497.37/s (0.428s, 2391.19/s) LR: 2.578e-01 Data: 0.026 (0.044)
Train: 61 [ 150/156 ( 97%)] Loss: 6.36 (6.27) Time: 0.411s, 2490.73/s (0.423s, 2420.67/s) LR: 2.578e-01 Data: 0.026 (0.038)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.430 (1.430) Loss: 6.199 ( 6.199) Acc@1: 6.934 ( 6.934) Acc@5: 13.965 ( 13.965)
Test: [ 48/48] Time: 0.091 (0.328) Loss: 6.160 ( 6.256) Acc@1: 6.250 ( 5.596) Acc@5: 13.797 ( 12.852)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-61.pth.tar', 5.596)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-55.pth.tar', 5.589999999694824)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-56.pth.tar', 5.562000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-59.pth.tar', 5.536000004882813)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-53.pth.tar', 5.4739999940490724)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-54.pth.tar', 5.446000002746582)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-60.pth.tar', 5.347999996948242)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-58.pth.tar', 5.280000002593994)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-57.pth.tar', 5.147999999847412)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-50.pth.tar', 5.137999999389648)
Train: 62 [ 0/156 ( 1%)] Loss: 6.18 (6.18) Time: 1.509s, 678.37/s (1.509s, 678.37/s) LR: 2.538e-01 Data: 1.134 (1.134)
Train: 62 [ 50/156 ( 33%)] Loss: 6.27 (6.18) Time: 0.413s, 2477.17/s (0.436s, 2350.81/s) LR: 2.538e-01 Data: 0.027 (0.049)
Train: 62 [ 100/156 ( 65%)] Loss: 6.28 (6.22) Time: 0.414s, 2475.71/s (0.424s, 2413.53/s) LR: 2.538e-01 Data: 0.028 (0.039)
Train: 62 [ 150/156 ( 97%)] Loss: 6.31 (6.25) Time: 0.411s, 2490.82/s (0.421s, 2434.97/s) LR: 2.538e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.425 (1.425) Loss: 6.168 ( 6.168) Acc@1: 6.543 ( 6.543) Acc@5: 14.355 ( 14.355)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.189 ( 6.251) Acc@1: 5.778 ( 5.614) Acc@5: 13.208 ( 12.980)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-62.pth.tar', 5.61399999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-61.pth.tar', 5.596)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-55.pth.tar', 5.589999999694824)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-56.pth.tar', 5.562000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-59.pth.tar', 5.536000004882813)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-53.pth.tar', 5.4739999940490724)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-54.pth.tar', 5.446000002746582)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-60.pth.tar', 5.347999996948242)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-58.pth.tar', 5.280000002593994)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-57.pth.tar', 5.147999999847412)
Train: 63 [ 0/156 ( 1%)] Loss: 6.10 (6.10) Time: 1.513s, 676.85/s (1.513s, 676.85/s) LR: 2.497e-01 Data: 1.076 (1.076)
Train: 63 [ 50/156 ( 33%)] Loss: 6.24 (6.16) Time: 0.410s, 2499.81/s (0.430s, 2379.33/s) LR: 2.497e-01 Data: 0.028 (0.048)
Train: 63 [ 100/156 ( 65%)] Loss: 6.25 (6.19) Time: 0.412s, 2483.22/s (0.421s, 2432.50/s) LR: 2.497e-01 Data: 0.028 (0.038)
Train: 63 [ 150/156 ( 97%)] Loss: 6.28 (6.22) Time: 0.411s, 2488.48/s (0.418s, 2447.12/s) LR: 2.497e-01 Data: 0.024 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.426 (1.426) Loss: 6.259 ( 6.259) Acc@1: 5.371 ( 5.371) Acc@5: 12.793 ( 12.793)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.335 ( 6.344) Acc@1: 5.307 ( 5.072) Acc@5: 11.792 ( 11.760)
Train: 64 [ 0/156 ( 1%)] Loss: 6.07 (6.07) Time: 1.718s, 595.87/s (1.718s, 595.87/s) LR: 2.457e-01 Data: 1.110 (1.110)
Train: 64 [ 50/156 ( 33%)] Loss: 6.21 (6.12) Time: 0.412s, 2487.93/s (0.439s, 2331.06/s) LR: 2.457e-01 Data: 0.027 (0.048)
Train: 64 [ 100/156 ( 65%)] Loss: 6.27 (6.16) Time: 0.412s, 2482.86/s (0.426s, 2404.88/s) LR: 2.457e-01 Data: 0.027 (0.038)
Train: 64 [ 150/156 ( 97%)] Loss: 6.26 (6.19) Time: 0.411s, 2491.64/s (0.422s, 2427.47/s) LR: 2.457e-01 Data: 0.024 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.450 (1.450) Loss: 6.236 ( 6.236) Acc@1: 6.445 ( 6.445) Acc@5: 13.477 ( 13.477)
Test: [ 48/48] Time: 0.092 (0.330) Loss: 6.244 ( 6.313) Acc@1: 5.071 ( 5.400) Acc@5: 13.208 ( 12.266)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-62.pth.tar', 5.61399999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-61.pth.tar', 5.596)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-55.pth.tar', 5.589999999694824)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-56.pth.tar', 5.562000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-59.pth.tar', 5.536000004882813)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-53.pth.tar', 5.4739999940490724)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-54.pth.tar', 5.446000002746582)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-64.pth.tar', 5.400000004882813)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-60.pth.tar', 5.347999996948242)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-58.pth.tar', 5.280000002593994)
Train: 65 [ 0/156 ( 1%)] Loss: 6.11 (6.11) Time: 1.694s, 604.51/s (1.694s, 604.51/s) LR: 2.416e-01 Data: 1.112 (1.112)
Train: 65 [ 50/156 ( 33%)] Loss: 6.15 (6.10) Time: 0.414s, 2472.42/s (0.444s, 2307.18/s) LR: 2.416e-01 Data: 0.027 (0.055)
Train: 65 [ 100/156 ( 65%)] Loss: 6.25 (6.14) Time: 0.412s, 2487.95/s (0.428s, 2393.91/s) LR: 2.416e-01 Data: 0.027 (0.041)
Train: 65 [ 150/156 ( 97%)] Loss: 6.20 (6.16) Time: 0.410s, 2498.16/s (0.423s, 2421.98/s) LR: 2.416e-01 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.558 (1.558) Loss: 6.212 ( 6.212) Acc@1: 7.227 ( 7.227) Acc@5: 13.574 ( 13.574)
Test: [ 48/48] Time: 0.092 (0.331) Loss: 6.214 ( 6.294) Acc@1: 5.542 ( 5.462) Acc@5: 13.090 ( 12.334)
Current checkpoints:
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-62.pth.tar', 5.61399999710083)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-61.pth.tar', 5.596)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-55.pth.tar', 5.589999999694824)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-56.pth.tar', 5.562000007934571)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-59.pth.tar', 5.536000004882813)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-53.pth.tar', 5.4739999940490724)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-65.pth.tar', 5.461999999694824)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-54.pth.tar', 5.446000002746582)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-64.pth.tar', 5.400000004882813)
('./output/train/ImageNetTraining80.0-frac-1over8/checkpoint-60.pth.tar', 5.347999996948242)
Train: 66 [ 0/156 ( 1%)] Loss: 6.10 (6.10) Time: 1.606s, 637.72/s (1.606s, 637.72/s) LR: 2.375e-01 Data: 1.096 (1.096)
Train: 66 [ 50/156 ( 33%)] Loss: 6.09 (6.06) Time: 0.409s, 2504.84/s (0.435s, 2354.14/s) LR: 2.375e-01 Data: 0.027 (0.048)
Train: 66 [ 100/156 ( 65%)] Loss: 6.20 (6.11) Time: 0.408s, 2507.47/s (0.423s, 2423.41/s) LR: 2.375e-01 Data: 0.027 (0.038)
Train: 66 [ 150/156 ( 97%)] Loss: 6.19 (6.14) Time: 0.409s, 2502.76/s (0.419s, 2443.46/s) LR: 2.375e-01 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.416 (1.416) Loss: 6.242 ( 6.242) Acc@1: 5.957 ( 5.957) Acc@5: 13.770 ( 13.770)
Test: [ 48/48] Time: 0.092 (0.328) Loss: 6.245 ( 6.332) Acc@1: 5.778 ( 5.244) Acc@5: 12.146 ( 11.950)
Train: 67 [ 0/156 ( 1%)] Loss: 6.02 (6.02) Time: 1.493s, 685.65/s (1.493s, 685.65/s) LR: 2.334e-01 Data: 1.043 (1.043)
Train: 67 [ 50/156 ( 33%)] Loss: 6.11 (6.04) Time: 0.422s, 2427.15/s (0.433s, 2365.36/s) LR: 2.334e-01 Data: 0.025 (0.047)
Train: 67 [ 100/156 ( 65%)] Loss: 6.06 (6.08) Time: 0.408s, 2510.51/s (0.422s, 2423.95/s) LR: 2.334e-01 Data: 0.026 (0.037)
Train: 67 [ 150/156 ( 97%)] Loss: 6.17 (6.11) Time: 0.407s, 2518.53/s (0.418s, 2449.59/s) LR: 2.334e-01 Data: 0.024 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.429 (1.429) Loss: 6.194 ( 6.194) Acc@1: 5.859 ( 5.859) Acc@5: 13.965 ( 13.965)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.222 ( 6.330) Acc@1: 5.896 ( 5.070) Acc@5: 12.736 ( 12.068)
Train: 68 [ 0/156 ( 1%)] Loss: 6.02 (6.02) Time: 1.736s, 589.95/s (1.736s, 589.95/s) LR: 2.292e-01 Data: 1.349 (1.349)
Train: 68 [ 50/156 ( 33%)] Loss: 6.11 (6.01) Time: 0.415s, 2469.25/s (0.436s, 2346.36/s) LR: 2.292e-01 Data: 0.027 (0.053)
Train: 68 [ 100/156 ( 65%)] Loss: 6.09 (6.05) Time: 0.413s, 2479.93/s (0.425s, 2410.92/s) LR: 2.292e-01 Data: 0.026 (0.040)
Train: 68 [ 150/156 ( 97%)] Loss: 6.16 (6.08) Time: 0.410s, 2498.46/s (0.421s, 2433.73/s) LR: 2.292e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.417 (1.417) Loss: 6.277 ( 6.277) Acc@1: 5.371 ( 5.371) Acc@5: 13.086 ( 13.086)
Test: [ 48/48] Time: 0.091 (0.328) Loss: 6.318 ( 6.354) Acc@1: 4.953 ( 5.276) Acc@5: 11.675 ( 12.132)
Train: 69 [ 0/156 ( 1%)] Loss: 5.97 (5.97) Time: 1.537s, 666.37/s (1.537s, 666.37/s) LR: 2.251e-01 Data: 1.136 (1.136)
Train: 69 [ 50/156 ( 33%)] Loss: 5.98 (5.98) Time: 0.413s, 2481.45/s (0.435s, 2356.59/s) LR: 2.251e-01 Data: 0.027 (0.049)
Train: 69 [ 100/156 ( 65%)] Loss: 6.14 (6.02) Time: 0.416s, 2458.63/s (0.424s, 2415.47/s) LR: 2.251e-01 Data: 0.028 (0.038)
Train: 69 [ 150/156 ( 97%)] Loss: 6.22 (6.05) Time: 0.412s, 2484.40/s (0.420s, 2438.37/s) LR: 2.251e-01 Data: 0.024 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.445 (1.445) Loss: 6.258 ( 6.258) Acc@1: 5.566 ( 5.566) Acc@5: 13.965 ( 13.965)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 6.273 ( 6.363) Acc@1: 5.660 ( 5.234) Acc@5: 13.443 ( 12.050)
Train: 70 [ 0/156 ( 1%)] Loss: 5.90 (5.90) Time: 1.487s, 688.54/s (1.487s, 688.54/s) LR: 2.209e-01 Data: 1.110 (1.110)
Train: 70 [ 50/156 ( 33%)] Loss: 6.01 (5.94) Time: 0.408s, 2507.17/s (0.434s, 2357.68/s) LR: 2.209e-01 Data: 0.026 (0.049)
Train: 70 [ 100/156 ( 65%)] Loss: 6.08 (5.98) Time: 0.411s, 2493.02/s (0.423s, 2421.84/s) LR: 2.209e-01 Data: 0.027 (0.038)
Train: 70 [ 150/156 ( 97%)] Loss: 6.12 (6.02) Time: 0.407s, 2513.74/s (0.420s, 2440.55/s) LR: 2.209e-01 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.464 (1.464) Loss: 6.299 ( 6.299) Acc@1: 5.176 ( 5.176) Acc@5: 12.305 ( 12.305)
Test: [ 48/48] Time: 0.091 (0.333) Loss: 6.328 ( 6.397) Acc@1: 4.481 ( 5.048) Acc@5: 11.321 ( 11.450)
Train: 71 [ 0/156 ( 1%)] Loss: 5.92 (5.92) Time: 1.960s, 522.48/s (1.960s, 522.48/s) LR: 2.167e-01 Data: 1.584 (1.584)
Train: 71 [ 50/156 ( 33%)] Loss: 6.01 (5.91) Time: 0.411s, 2493.75/s (0.440s, 2328.74/s) LR: 2.167e-01 Data: 0.027 (0.058)
Train: 71 [ 100/156 ( 65%)] Loss: 6.03 (5.95) Time: 0.406s, 2523.22/s (0.426s, 2406.17/s) LR: 2.167e-01 Data: 0.027 (0.043)
Train: 71 [ 150/156 ( 97%)] Loss: 6.12 (5.99) Time: 0.405s, 2527.36/s (0.419s, 2441.39/s) LR: 2.167e-01 Data: 0.026 (0.038)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.421 (1.421) Loss: 6.391 ( 6.391) Acc@1: 4.980 ( 4.980) Acc@5: 13.086 ( 13.086)
Test: [ 48/48] Time: 0.091 (0.327) Loss: 6.357 ( 6.444) Acc@1: 6.840 ( 5.200) Acc@5: 13.915 ( 11.624)
Train: 72 [ 0/156 ( 1%)] Loss: 5.83 (5.83) Time: 1.638s, 624.99/s (1.638s, 624.99/s) LR: 2.126e-01 Data: 1.263 (1.263)
Train: 72 [ 50/156 ( 33%)] Loss: 5.90 (5.87) Time: 0.409s, 2501.77/s (0.432s, 2368.59/s) LR: 2.126e-01 Data: 0.027 (0.051)
Train: 72 [ 100/156 ( 65%)] Loss: 6.08 (5.91) Time: 0.412s, 2484.40/s (0.422s, 2426.37/s) LR: 2.126e-01 Data: 0.027 (0.039)
Train: 72 [ 150/156 ( 97%)] Loss: 6.08 (5.95) Time: 0.408s, 2510.06/s (0.419s, 2442.96/s) LR: 2.126e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.425 (1.425) Loss: 6.346 ( 6.346) Acc@1: 5.762 ( 5.762) Acc@5: 12.402 ( 12.402)
Test: [ 48/48] Time: 0.090 (0.329) Loss: 6.303 ( 6.430) Acc@1: 4.835 ( 4.878) Acc@5: 12.382 ( 11.388)
Train: 73 [ 0/156 ( 1%)] Loss: 5.80 (5.80) Time: 1.923s, 532.41/s (1.923s, 532.41/s) LR: 2.084e-01 Data: 1.419 (1.419)
Train: 73 [ 50/156 ( 33%)] Loss: 5.93 (5.84) Time: 0.402s, 2546.26/s (0.432s, 2368.79/s) LR: 2.084e-01 Data: 0.026 (0.054)
Train: 73 [ 100/156 ( 65%)] Loss: 5.99 (5.88) Time: 0.403s, 2543.78/s (0.417s, 2454.06/s) LR: 2.084e-01 Data: 0.027 (0.041)
Train: 73 [ 150/156 ( 97%)] Loss: 6.06 (5.92) Time: 0.401s, 2550.61/s (0.412s, 2482.56/s) LR: 2.084e-01 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.424 (1.424) Loss: 6.347 ( 6.347) Acc@1: 5.273 ( 5.273) Acc@5: 11.719 ( 11.719)
Test: [ 48/48] Time: 0.090 (0.331) Loss: 6.370 ( 6.443) Acc@1: 5.896 ( 4.864) Acc@5: 12.736 ( 11.266)
Train: 74 [ 0/156 ( 1%)] Loss: 5.73 (5.73) Time: 1.653s, 619.52/s (1.653s, 619.52/s) LR: 2.042e-01 Data: 1.280 (1.280)
Train: 74 [ 50/156 ( 33%)] Loss: 5.86 (5.80) Time: 0.406s, 2523.59/s (0.431s, 2376.80/s) LR: 2.042e-01 Data: 0.027 (0.052)
Train: 74 [ 100/156 ( 65%)] Loss: 5.99 (5.85) Time: 0.414s, 2472.20/s (0.420s, 2439.08/s) LR: 2.042e-01 Data: 0.030 (0.040)
Train: 74 [ 150/156 ( 97%)] Loss: 5.96 (5.89) Time: 0.410s, 2497.34/s (0.417s, 2453.80/s) LR: 2.042e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.431 (1.431) Loss: 6.377 ( 6.377) Acc@1: 5.273 ( 5.273) Acc@5: 11.719 ( 11.719)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 6.420 ( 6.504) Acc@1: 5.071 ( 4.808) Acc@5: 11.439 ( 11.074)
Train: 75 [ 0/156 ( 1%)] Loss: 5.67 (5.67) Time: 1.616s, 633.72/s (1.616s, 633.72/s) LR: 2.000e-01 Data: 1.239 (1.239)
Train: 75 [ 50/156 ( 33%)] Loss: 5.83 (5.76) Time: 0.410s, 2497.47/s (0.441s, 2320.22/s) LR: 2.000e-01 Data: 0.027 (0.056)
Train: 75 [ 100/156 ( 65%)] Loss: 5.91 (5.81) Time: 0.405s, 2530.98/s (0.425s, 2410.66/s) LR: 2.000e-01 Data: 0.027 (0.042)
Train: 75 [ 150/156 ( 97%)] Loss: 5.97 (5.85) Time: 0.405s, 2527.44/s (0.418s, 2447.23/s) LR: 2.000e-01 Data: 0.026 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.455 (1.455) Loss: 6.348 ( 6.348) Acc@1: 5.762 ( 5.762) Acc@5: 11.719 ( 11.719)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.425 ( 6.483) Acc@1: 4.835 ( 4.816) Acc@5: 11.557 ( 11.136)
Train: 76 [ 0/156 ( 1%)] Loss: 5.71 (5.71) Time: 1.892s, 541.25/s (1.892s, 541.25/s) LR: 1.958e-01 Data: 1.078 (1.078)
Train: 76 [ 50/156 ( 33%)] Loss: 5.77 (5.70) Time: 0.408s, 2511.21/s (0.436s, 2346.11/s) LR: 1.958e-01 Data: 0.027 (0.047)
Train: 76 [ 100/156 ( 65%)] Loss: 5.97 (5.77) Time: 0.411s, 2494.18/s (0.423s, 2418.12/s) LR: 1.958e-01 Data: 0.027 (0.037)
Train: 76 [ 150/156 ( 97%)] Loss: 5.98 (5.81) Time: 0.411s, 2494.42/s (0.420s, 2438.81/s) LR: 1.958e-01 Data: 0.025 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.437 (1.437) Loss: 6.452 ( 6.452) Acc@1: 5.078 ( 5.078) Acc@5: 12.012 ( 12.012)
Test: [ 48/48] Time: 0.091 (0.328) Loss: 6.552 ( 6.602) Acc@1: 4.717 ( 4.350) Acc@5: 10.967 ( 10.494)
Train: 77 [ 0/156 ( 1%)] Loss: 5.73 (5.73) Time: 1.807s, 566.81/s (1.807s, 566.81/s) LR: 1.916e-01 Data: 1.433 (1.433)
Train: 77 [ 50/156 ( 33%)] Loss: 5.65 (5.68) Time: 0.409s, 2500.77/s (0.435s, 2355.53/s) LR: 1.916e-01 Data: 0.027 (0.055)
Train: 77 [ 100/156 ( 65%)] Loss: 5.85 (5.73) Time: 0.409s, 2503.12/s (0.423s, 2422.12/s) LR: 1.916e-01 Data: 0.027 (0.041)
Train: 77 [ 150/156 ( 97%)] Loss: 5.89 (5.77) Time: 0.409s, 2502.04/s (0.419s, 2441.90/s) LR: 1.916e-01 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.478 (1.478) Loss: 6.404 ( 6.404) Acc@1: 5.371 ( 5.371) Acc@5: 12.500 ( 12.500)
Test: [ 48/48] Time: 0.092 (0.333) Loss: 6.413 ( 6.528) Acc@1: 4.717 ( 4.746) Acc@5: 12.028 ( 10.898)
Train: 78 [ 0/156 ( 1%)] Loss: 5.67 (5.67) Time: 1.624s, 630.39/s (1.624s, 630.39/s) LR: 1.874e-01 Data: 1.247 (1.247)
Train: 78 [ 50/156 ( 33%)] Loss: 5.69 (5.64) Time: 0.410s, 2499.80/s (0.439s, 2332.79/s) LR: 1.874e-01 Data: 0.028 (0.051)
Train: 78 [ 100/156 ( 65%)] Loss: 5.77 (5.70) Time: 0.405s, 2528.26/s (0.423s, 2419.73/s) LR: 1.874e-01 Data: 0.027 (0.039)
Train: 78 [ 150/156 ( 97%)] Loss: 5.79 (5.74) Time: 0.403s, 2541.44/s (0.417s, 2454.36/s) LR: 1.874e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.452 (1.452) Loss: 6.517 ( 6.517) Acc@1: 4.980 ( 4.980) Acc@5: 10.938 ( 10.938)
Test: [ 48/48] Time: 0.090 (0.328) Loss: 6.554 ( 6.622) Acc@1: 4.953 ( 4.544) Acc@5: 12.028 ( 10.576)
Train: 79 [ 0/156 ( 1%)] Loss: 5.54 (5.54) Time: 1.715s, 597.05/s (1.715s, 597.05/s) LR: 1.833e-01 Data: 1.342 (1.342)
Train: 79 [ 50/156 ( 33%)] Loss: 5.69 (5.61) Time: 0.406s, 2521.56/s (0.433s, 2366.96/s) LR: 1.833e-01 Data: 0.026 (0.053)
Train: 79 [ 100/156 ( 65%)] Loss: 5.79 (5.66) Time: 0.414s, 2476.01/s (0.421s, 2430.79/s) LR: 1.833e-01 Data: 0.032 (0.040)
Train: 79 [ 150/156 ( 97%)] Loss: 5.91 (5.70) Time: 0.411s, 2492.11/s (0.419s, 2444.39/s) LR: 1.833e-01 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.438 (1.438) Loss: 6.545 ( 6.545) Acc@1: 4.492 ( 4.492) Acc@5: 10.254 ( 10.254)
Test: [ 48/48] Time: 0.091 (0.333) Loss: 6.566 ( 6.615) Acc@1: 5.071 ( 4.456) Acc@5: 11.910 ( 10.592)
Train: 80 [ 0/156 ( 1%)] Loss: 5.55 (5.55) Time: 1.655s, 618.59/s (1.655s, 618.59/s) LR: 1.791e-01 Data: 1.279 (1.279)
Train: 80 [ 50/156 ( 33%)] Loss: 5.68 (5.57) Time: 0.417s, 2454.37/s (0.437s, 2343.04/s) LR: 1.791e-01 Data: 0.026 (0.051)
Train: 80 [ 100/156 ( 65%)] Loss: 5.77 (5.62) Time: 0.406s, 2524.09/s (0.424s, 2417.22/s) LR: 1.791e-01 Data: 0.025 (0.039)
Train: 80 [ 150/156 ( 97%)] Loss: 5.80 (5.66) Time: 0.401s, 2553.02/s (0.418s, 2452.29/s) LR: 1.791e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.420 (1.420) Loss: 6.595 ( 6.595) Acc@1: 4.688 ( 4.688) Acc@5: 11.133 ( 11.133)
Test: [ 48/48] Time: 0.090 (0.328) Loss: 6.619 ( 6.681) Acc@1: 4.127 ( 4.576) Acc@5: 10.377 ( 10.664)
Train: 81 [ 0/156 ( 1%)] Loss: 5.47 (5.47) Time: 1.645s, 622.40/s (1.645s, 622.40/s) LR: 1.749e-01 Data: 1.274 (1.274)
Train: 81 [ 50/156 ( 33%)] Loss: 5.59 (5.53) Time: 0.404s, 2537.14/s (0.429s, 2387.79/s) LR: 1.749e-01 Data: 0.025 (0.051)
Train: 81 [ 100/156 ( 65%)] Loss: 5.66 (5.58) Time: 0.409s, 2504.41/s (0.418s, 2447.22/s) LR: 1.749e-01 Data: 0.027 (0.039)
Train: 81 [ 150/156 ( 97%)] Loss: 5.77 (5.62) Time: 0.411s, 2490.25/s (0.416s, 2461.03/s) LR: 1.749e-01 Data: 0.024 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.438 (1.438) Loss: 6.674 ( 6.674) Acc@1: 3.613 ( 3.613) Acc@5: 10.352 ( 10.352)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 6.740 ( 6.766) Acc@1: 3.302 ( 4.028) Acc@5: 10.142 ( 9.572)
Train: 82 [ 0/156 ( 1%)] Loss: 5.47 (5.47) Time: 1.702s, 601.81/s (1.702s, 601.81/s) LR: 1.708e-01 Data: 1.325 (1.325)
Train: 82 [ 50/156 ( 33%)] Loss: 5.62 (5.49) Time: 0.411s, 2490.22/s (0.438s, 2337.65/s) LR: 1.708e-01 Data: 0.027 (0.053)
Train: 82 [ 100/156 ( 65%)] Loss: 5.58 (5.54) Time: 0.407s, 2515.99/s (0.423s, 2420.57/s) LR: 1.708e-01 Data: 0.026 (0.040)
Train: 82 [ 150/156 ( 97%)] Loss: 5.69 (5.58) Time: 0.405s, 2530.02/s (0.417s, 2453.56/s) LR: 1.708e-01 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.426 (1.426) Loss: 6.627 ( 6.627) Acc@1: 4.590 ( 4.590) Acc@5: 11.133 ( 11.133)
Test: [ 48/48] Time: 0.091 (0.331) Loss: 6.657 ( 6.741) Acc@1: 3.892 ( 4.244) Acc@5: 11.321 ( 10.150)
Train: 83 [ 0/156 ( 1%)] Loss: 5.31 (5.31) Time: 1.623s, 630.81/s (1.623s, 630.81/s) LR: 1.666e-01 Data: 1.223 (1.223)
Train: 83 [ 50/156 ( 33%)] Loss: 5.57 (5.44) Time: 0.412s, 2482.69/s (0.433s, 2366.10/s) LR: 1.666e-01 Data: 0.029 (0.051)
Train: 83 [ 100/156 ( 65%)] Loss: 5.59 (5.50) Time: 0.418s, 2448.70/s (0.423s, 2422.85/s) LR: 1.666e-01 Data: 0.028 (0.039)
Train: 83 [ 150/156 ( 97%)] Loss: 5.65 (5.54) Time: 0.408s, 2511.18/s (0.419s, 2441.31/s) LR: 1.666e-01 Data: 0.027 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.441 (1.441) Loss: 6.712 ( 6.712) Acc@1: 4.492 ( 4.492) Acc@5: 10.352 ( 10.352)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.744 ( 6.823) Acc@1: 4.835 ( 4.122) Acc@5: 10.377 ( 10.032)
Train: 84 [ 0/156 ( 1%)] Loss: 5.36 (5.36) Time: 1.800s, 568.83/s (1.800s, 568.83/s) LR: 1.625e-01 Data: 1.423 (1.423)
Train: 84 [ 50/156 ( 33%)] Loss: 5.57 (5.39) Time: 0.411s, 2490.48/s (0.440s, 2327.52/s) LR: 1.625e-01 Data: 0.026 (0.054)
Train: 84 [ 100/156 ( 65%)] Loss: 5.56 (5.45) Time: 0.410s, 2494.64/s (0.426s, 2401.82/s) LR: 1.625e-01 Data: 0.026 (0.041)
Train: 84 [ 150/156 ( 97%)] Loss: 5.69 (5.50) Time: 0.408s, 2510.45/s (0.422s, 2429.23/s) LR: 1.625e-01 Data: 0.026 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.505 (1.505) Loss: 6.674 ( 6.674) Acc@1: 3.711 ( 3.711) Acc@5: 10.449 ( 10.449)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 6.728 ( 6.824) Acc@1: 4.009 ( 4.130) Acc@5: 9.316 ( 9.796)
Train: 85 [ 0/156 ( 1%)] Loss: 5.28 (5.28) Time: 1.919s, 533.72/s (1.919s, 533.72/s) LR: 1.584e-01 Data: 1.099 (1.099)
Train: 85 [ 50/156 ( 33%)] Loss: 5.45 (5.36) Time: 0.408s, 2507.99/s (0.437s, 2345.62/s) LR: 1.584e-01 Data: 0.028 (0.049)
Train: 85 [ 100/156 ( 65%)] Loss: 5.50 (5.42) Time: 0.407s, 2513.48/s (0.422s, 2428.20/s) LR: 1.584e-01 Data: 0.027 (0.038)
Train: 85 [ 150/156 ( 97%)] Loss: 5.65 (5.46) Time: 0.409s, 2503.89/s (0.418s, 2450.03/s) LR: 1.584e-01 Data: 0.025 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.431 (1.431) Loss: 6.706 ( 6.706) Acc@1: 4.199 ( 4.199) Acc@5: 9.961 ( 9.961)
Test: [ 48/48] Time: 0.092 (0.328) Loss: 6.802 ( 6.838) Acc@1: 3.892 ( 4.000) Acc@5: 8.491 ( 9.350)
Train: 86 [ 0/156 ( 1%)] Loss: 5.26 (5.26) Time: 1.666s, 614.83/s (1.666s, 614.83/s) LR: 1.543e-01 Data: 1.066 (1.066)
Train: 86 [ 50/156 ( 33%)] Loss: 5.46 (5.31) Time: 0.412s, 2484.06/s (0.437s, 2344.52/s) LR: 1.543e-01 Data: 0.028 (0.048)
Train: 86 [ 100/156 ( 65%)] Loss: 5.57 (5.37) Time: 0.414s, 2475.48/s (0.425s, 2409.73/s) LR: 1.543e-01 Data: 0.027 (0.038)
Train: 86 [ 150/156 ( 97%)] Loss: 5.64 (5.42) Time: 0.409s, 2503.93/s (0.421s, 2433.31/s) LR: 1.543e-01 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.430 (1.430) Loss: 6.676 ( 6.676) Acc@1: 5.273 ( 5.273) Acc@5: 10.938 ( 10.938)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.885 ( 6.874) Acc@1: 4.009 ( 4.344) Acc@5: 10.024 ( 9.868)
Train: 87 [ 0/156 ( 1%)] Loss: 5.32 (5.32) Time: 1.627s, 629.43/s (1.627s, 629.43/s) LR: 1.503e-01 Data: 1.140 (1.140)
Train: 87 [ 50/156 ( 33%)] Loss: 5.36 (5.27) Time: 0.412s, 2487.89/s (0.436s, 2351.16/s) LR: 1.503e-01 Data: 0.027 (0.049)
Train: 87 [ 100/156 ( 65%)] Loss: 5.41 (5.33) Time: 0.413s, 2479.41/s (0.424s, 2417.65/s) LR: 1.503e-01 Data: 0.028 (0.038)
Train: 87 [ 150/156 ( 97%)] Loss: 5.53 (5.38) Time: 0.409s, 2502.20/s (0.420s, 2438.65/s) LR: 1.503e-01 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.433 (1.433) Loss: 6.853 ( 6.853) Acc@1: 4.199 ( 4.199) Acc@5: 10.156 ( 10.156)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 7.009 ( 6.985) Acc@1: 3.892 ( 4.014) Acc@5: 9.316 ( 9.266)
Train: 88 [ 0/156 ( 1%)] Loss: 5.17 (5.17) Time: 1.607s, 637.16/s (1.607s, 637.16/s) LR: 1.462e-01 Data: 1.058 (1.058)
Train: 88 [ 50/156 ( 33%)] Loss: 5.33 (5.23) Time: 0.415s, 2465.79/s (0.435s, 2353.47/s) LR: 1.462e-01 Data: 0.029 (0.048)
Train: 88 [ 100/156 ( 65%)] Loss: 5.45 (5.29) Time: 0.408s, 2508.28/s (0.424s, 2415.60/s) LR: 1.462e-01 Data: 0.026 (0.038)
Train: 88 [ 150/156 ( 97%)] Loss: 5.54 (5.34) Time: 0.407s, 2513.15/s (0.419s, 2442.75/s) LR: 1.462e-01 Data: 0.027 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.514 (1.514) Loss: 6.810 ( 6.810) Acc@1: 4.102 ( 4.102) Acc@5: 9.668 ( 9.668)
Test: [ 48/48] Time: 0.091 (0.331) Loss: 6.931 ( 6.995) Acc@1: 3.184 ( 3.900) Acc@5: 10.259 ( 9.268)
Train: 89 [ 0/156 ( 1%)] Loss: 5.17 (5.17) Time: 1.616s, 633.78/s (1.616s, 633.78/s) LR: 1.422e-01 Data: 1.240 (1.240)
Train: 89 [ 50/156 ( 33%)] Loss: 5.24 (5.21) Time: 0.411s, 2490.81/s (0.436s, 2349.78/s) LR: 1.422e-01 Data: 0.027 (0.051)
Train: 89 [ 100/156 ( 65%)] Loss: 5.36 (5.25) Time: 0.407s, 2515.17/s (0.423s, 2419.40/s) LR: 1.422e-01 Data: 0.028 (0.039)
Train: 89 [ 150/156 ( 97%)] Loss: 5.38 (5.29) Time: 0.405s, 2527.62/s (0.418s, 2450.83/s) LR: 1.422e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.434 (1.434) Loss: 6.971 ( 6.971) Acc@1: 3.223 ( 3.223) Acc@5: 8.301 ( 8.301)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 6.968 ( 7.086) Acc@1: 3.420 ( 3.644) Acc@5: 9.906 ( 8.914)
Train: 90 [ 0/156 ( 1%)] Loss: 5.16 (5.16) Time: 1.433s, 714.41/s (1.433s, 714.41/s) LR: 1.382e-01 Data: 1.044 (1.044)
Train: 90 [ 50/156 ( 33%)] Loss: 5.22 (5.14) Time: 0.411s, 2490.41/s (0.428s, 2389.77/s) LR: 1.382e-01 Data: 0.027 (0.048)
Train: 90 [ 100/156 ( 65%)] Loss: 5.28 (5.19) Time: 0.413s, 2480.82/s (0.420s, 2438.28/s) LR: 1.382e-01 Data: 0.026 (0.037)
Train: 90 [ 150/156 ( 97%)] Loss: 5.23 (5.25) Time: 0.410s, 2499.44/s (0.418s, 2451.94/s) LR: 1.382e-01 Data: 0.025 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.454 (1.454) Loss: 6.983 ( 6.983) Acc@1: 3.418 ( 3.418) Acc@5: 8.984 ( 8.984)
Test: [ 48/48] Time: 0.092 (0.331) Loss: 6.916 ( 7.064) Acc@1: 4.599 ( 3.982) Acc@5: 9.788 ( 9.348)
Train: 91 [ 0/156 ( 1%)] Loss: 5.04 (5.04) Time: 1.651s, 620.16/s (1.651s, 620.16/s) LR: 1.342e-01 Data: 1.276 (1.276)
Train: 91 [ 50/156 ( 33%)] Loss: 5.06 (5.10) Time: 0.413s, 2477.93/s (0.436s, 2347.24/s) LR: 1.342e-01 Data: 0.028 (0.052)
Train: 91 [ 100/156 ( 65%)] Loss: 5.40 (5.16) Time: 0.402s, 2547.95/s (0.421s, 2431.29/s) LR: 1.342e-01 Data: 0.027 (0.039)
Train: 91 [ 150/156 ( 97%)] Loss: 5.36 (5.21) Time: 0.398s, 2570.49/s (0.414s, 2471.09/s) LR: 1.342e-01 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.430 (1.430) Loss: 6.971 ( 6.971) Acc@1: 3.809 ( 3.809) Acc@5: 10.352 ( 10.352)
Test: [ 48/48] Time: 0.089 (0.330) Loss: 7.119 ( 7.174) Acc@1: 3.774 ( 3.786) Acc@5: 9.080 ( 9.094)
Train: 92 [ 0/156 ( 1%)] Loss: 5.04 (5.04) Time: 1.733s, 590.79/s (1.733s, 590.79/s) LR: 1.303e-01 Data: 1.368 (1.368)
Train: 92 [ 50/156 ( 33%)] Loss: 5.07 (5.06) Time: 0.398s, 2571.71/s (0.425s, 2412.19/s) LR: 1.303e-01 Data: 0.026 (0.053)
Train: 92 [ 100/156 ( 65%)] Loss: 5.19 (5.11) Time: 0.402s, 2549.60/s (0.412s, 2482.67/s) LR: 1.303e-01 Data: 0.028 (0.041)
Train: 92 [ 150/156 ( 97%)] Loss: 5.28 (5.16) Time: 0.400s, 2562.15/s (0.409s, 2505.09/s) LR: 1.303e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.444 (1.444) Loss: 7.055 ( 7.055) Acc@1: 3.809 ( 3.809) Acc@5: 9.180 ( 9.180)
Test: [ 48/48] Time: 0.090 (0.331) Loss: 7.176 ( 7.217) Acc@1: 3.774 ( 3.572) Acc@5: 8.726 ( 8.814)
Train: 93 [ 0/156 ( 1%)] Loss: 4.87 (4.87) Time: 1.562s, 655.61/s (1.562s, 655.61/s) LR: 1.264e-01 Data: 1.193 (1.193)
Train: 93 [ 50/156 ( 33%)] Loss: 5.07 (5.02) Time: 0.406s, 2522.26/s (0.427s, 2396.52/s) LR: 1.264e-01 Data: 0.026 (0.050)
Train: 93 [ 100/156 ( 65%)] Loss: 5.13 (5.07) Time: 0.407s, 2516.72/s (0.417s, 2455.26/s) LR: 1.264e-01 Data: 0.027 (0.039)
Train: 93 [ 150/156 ( 97%)] Loss: 5.31 (5.12) Time: 0.406s, 2521.07/s (0.415s, 2470.30/s) LR: 1.264e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.447 (1.447) Loss: 7.049 ( 7.049) Acc@1: 3.906 ( 3.906) Acc@5: 9.863 ( 9.863)
Test: [ 48/48] Time: 0.092 (0.330) Loss: 7.159 ( 7.251) Acc@1: 4.009 ( 3.692) Acc@5: 10.142 ( 8.898)
Train: 94 [ 0/156 ( 1%)] Loss: 4.91 (4.91) Time: 1.679s, 609.78/s (1.679s, 609.78/s) LR: 1.225e-01 Data: 1.303 (1.303)
Train: 94 [ 50/156 ( 33%)] Loss: 5.08 (4.98) Time: 0.406s, 2523.87/s (0.434s, 2358.56/s) LR: 1.225e-01 Data: 0.027 (0.052)
Train: 94 [ 100/156 ( 65%)] Loss: 5.06 (5.03) Time: 0.408s, 2508.40/s (0.421s, 2429.85/s) LR: 1.225e-01 Data: 0.027 (0.040)
Train: 94 [ 150/156 ( 97%)] Loss: 5.23 (5.08) Time: 0.412s, 2483.55/s (0.418s, 2448.76/s) LR: 1.225e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.435 (1.435) Loss: 6.992 ( 6.992) Acc@1: 5.273 ( 5.273) Acc@5: 11.133 ( 11.133)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 7.020 ( 7.183) Acc@1: 4.009 ( 3.702) Acc@5: 9.788 ( 8.880)
Train: 95 [ 0/156 ( 1%)] Loss: 4.91 (4.91) Time: 1.691s, 605.68/s (1.691s, 605.68/s) LR: 1.187e-01 Data: 1.315 (1.315)
Train: 95 [ 50/156 ( 33%)] Loss: 5.08 (4.93) Time: 0.411s, 2491.78/s (0.437s, 2343.62/s) LR: 1.187e-01 Data: 0.027 (0.052)
Train: 95 [ 100/156 ( 65%)] Loss: 5.07 (4.99) Time: 0.409s, 2501.37/s (0.424s, 2417.59/s) LR: 1.187e-01 Data: 0.026 (0.040)
Train: 95 [ 150/156 ( 97%)] Loss: 5.12 (5.03) Time: 0.410s, 2496.88/s (0.419s, 2444.43/s) LR: 1.187e-01 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.454 (1.454) Loss: 7.191 ( 7.191) Acc@1: 3.418 ( 3.418) Acc@5: 8.789 ( 8.789)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 7.188 ( 7.353) Acc@1: 3.420 ( 3.686) Acc@5: 10.377 ( 8.836)
Train: 96 [ 0/156 ( 1%)] Loss: 4.90 (4.90) Time: 1.682s, 608.85/s (1.682s, 608.85/s) LR: 1.148e-01 Data: 1.306 (1.306)
Train: 96 [ 50/156 ( 33%)] Loss: 4.96 (4.88) Time: 0.409s, 2502.07/s (0.437s, 2342.70/s) LR: 1.148e-01 Data: 0.028 (0.053)
Train: 96 [ 100/156 ( 65%)] Loss: 5.17 (4.94) Time: 0.404s, 2536.13/s (0.422s, 2427.17/s) LR: 1.148e-01 Data: 0.027 (0.040)
Train: 96 [ 150/156 ( 97%)] Loss: 5.22 (4.99) Time: 0.403s, 2538.06/s (0.416s, 2459.30/s) LR: 1.148e-01 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.435 (1.435) Loss: 7.166 ( 7.166) Acc@1: 3.320 ( 3.320) Acc@5: 9.082 ( 9.082)
Test: [ 48/48] Time: 0.090 (0.328) Loss: 7.195 ( 7.336) Acc@1: 3.892 ( 3.622) Acc@5: 9.552 ( 8.524)
Train: 97 [ 0/156 ( 1%)] Loss: 4.80 (4.80) Time: 1.658s, 617.77/s (1.658s, 617.77/s) LR: 1.111e-01 Data: 1.284 (1.284)
Train: 97 [ 50/156 ( 33%)] Loss: 4.81 (4.85) Time: 0.407s, 2514.52/s (0.432s, 2369.12/s) LR: 1.111e-01 Data: 0.026 (0.052)
Train: 97 [ 100/156 ( 65%)] Loss: 5.02 (4.90) Time: 0.412s, 2488.04/s (0.422s, 2426.99/s) LR: 1.111e-01 Data: 0.026 (0.040)
Train: 97 [ 150/156 ( 97%)] Loss: 5.13 (4.95) Time: 0.409s, 2502.26/s (0.419s, 2446.06/s) LR: 1.111e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.548 (1.548) Loss: 7.199 ( 7.199) Acc@1: 4.297 ( 4.297) Acc@5: 9.375 ( 9.375)
Test: [ 48/48] Time: 0.092 (0.333) Loss: 7.240 ( 7.344) Acc@1: 3.656 ( 3.442) Acc@5: 9.434 ( 8.568)
Train: 98 [ 0/156 ( 1%)] Loss: 4.74 (4.74) Time: 1.710s, 598.89/s (1.710s, 598.89/s) LR: 1.073e-01 Data: 1.284 (1.284)
Train: 98 [ 50/156 ( 33%)] Loss: 4.87 (4.80) Time: 0.405s, 2525.90/s (0.434s, 2356.75/s) LR: 1.073e-01 Data: 0.027 (0.051)
Train: 98 [ 100/156 ( 65%)] Loss: 4.97 (4.87) Time: 0.403s, 2542.02/s (0.420s, 2438.61/s) LR: 1.073e-01 Data: 0.027 (0.040)
Train: 98 [ 150/156 ( 97%)] Loss: 5.00 (4.91) Time: 0.402s, 2545.47/s (0.415s, 2469.25/s) LR: 1.073e-01 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.424 (1.424) Loss: 7.183 ( 7.183) Acc@1: 3.711 ( 3.711) Acc@5: 10.254 ( 10.254)
Test: [ 48/48] Time: 0.090 (0.329) Loss: 7.218 ( 7.334) Acc@1: 3.302 ( 3.472) Acc@5: 8.608 ( 8.536)
Train: 99 [ 0/156 ( 1%)] Loss: 4.65 (4.65) Time: 2.024s, 505.99/s (2.024s, 505.99/s) LR: 1.036e-01 Data: 1.651 (1.651)
Train: 99 [ 50/156 ( 33%)] Loss: 4.83 (4.74) Time: 0.407s, 2514.52/s (0.439s, 2333.36/s) LR: 1.036e-01 Data: 0.027 (0.059)
Train: 99 [ 100/156 ( 65%)] Loss: 4.84 (4.80) Time: 0.411s, 2489.01/s (0.425s, 2407.00/s) LR: 1.036e-01 Data: 0.028 (0.043)
Train: 99 [ 150/156 ( 97%)] Loss: 4.96 (4.85) Time: 0.408s, 2510.04/s (0.421s, 2433.22/s) LR: 1.036e-01 Data: 0.025 (0.038)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.461 (1.461) Loss: 7.380 ( 7.380) Acc@1: 3.418 ( 3.418) Acc@5: 8.496 ( 8.496)
Test: [ 48/48] Time: 0.090 (0.330) Loss: 7.423 ( 7.509) Acc@1: 3.656 ( 3.558) Acc@5: 9.316 ( 8.440)
Train: 100 [ 0/156 ( 1%)] Loss: 4.70 (4.70) Time: 1.633s, 627.23/s (1.633s, 627.23/s) LR: 1.000e-01 Data: 1.261 (1.261)
Train: 100 [ 50/156 ( 33%)] Loss: 4.78 (4.72) Time: 0.404s, 2533.23/s (0.429s, 2388.19/s) LR: 1.000e-01 Data: 0.027 (0.052)
Train: 100 [ 100/156 ( 65%)] Loss: 4.85 (4.77) Time: 0.408s, 2512.66/s (0.418s, 2452.37/s) LR: 1.000e-01 Data: 0.027 (0.039)
Train: 100 [ 150/156 ( 97%)] Loss: 4.88 (4.81) Time: 0.408s, 2508.21/s (0.415s, 2467.62/s) LR: 1.000e-01 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.436 (1.436) Loss: 7.263 ( 7.263) Acc@1: 3.613 ( 3.613) Acc@5: 9.961 ( 9.961)
Test: [ 48/48] Time: 0.092 (0.331) Loss: 7.401 ( 7.502) Acc@1: 3.538 ( 3.294) Acc@5: 9.198 ( 8.270)
Train: 101 [ 0/156 ( 1%)] Loss: 4.62 (4.62) Time: 1.580s, 648.06/s (1.580s, 648.06/s) LR: 9.639e-02 Data: 1.060 (1.060)
Train: 101 [ 50/156 ( 33%)] Loss: 4.69 (4.68) Time: 0.409s, 2500.68/s (0.433s, 2365.58/s) LR: 9.639e-02 Data: 0.029 (0.047)
Train: 101 [ 100/156 ( 65%)] Loss: 4.72 (4.72) Time: 0.416s, 2463.89/s (0.421s, 2433.44/s) LR: 9.639e-02 Data: 0.026 (0.037)
Train: 101 [ 150/156 ( 97%)] Loss: 4.89 (4.76) Time: 0.412s, 2486.73/s (0.418s, 2451.81/s) LR: 9.639e-02 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.431 (1.431) Loss: 7.284 ( 7.284) Acc@1: 3.613 ( 3.613) Acc@5: 8.789 ( 8.789)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 7.352 ( 7.522) Acc@1: 4.127 ( 3.372) Acc@5: 9.552 ( 8.220)
Train: 102 [ 0/156 ( 1%)] Loss: 4.45 (4.45) Time: 2.045s, 500.77/s (2.045s, 500.77/s) LR: 9.283e-02 Data: 1.670 (1.670)
Train: 102 [ 50/156 ( 33%)] Loss: 4.74 (4.62) Time: 0.409s, 2502.16/s (0.440s, 2327.84/s) LR: 9.283e-02 Data: 0.028 (0.059)
Train: 102 [ 100/156 ( 65%)] Loss: 4.79 (4.68) Time: 0.412s, 2486.89/s (0.425s, 2407.44/s) LR: 9.283e-02 Data: 0.026 (0.043)
Train: 102 [ 150/156 ( 97%)] Loss: 4.83 (4.73) Time: 0.410s, 2494.97/s (0.421s, 2433.76/s) LR: 9.283e-02 Data: 0.027 (0.038)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.435 (1.435) Loss: 7.353 ( 7.353) Acc@1: 3.613 ( 3.613) Acc@5: 9.082 ( 9.082)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 7.468 ( 7.586) Acc@1: 3.774 ( 3.384) Acc@5: 8.844 ( 8.354)
Train: 103 [ 0/156 ( 1%)] Loss: 4.51 (4.51) Time: 1.665s, 614.92/s (1.665s, 614.92/s) LR: 8.932e-02 Data: 1.266 (1.266)
Train: 103 [ 50/156 ( 33%)] Loss: 4.51 (4.59) Time: 0.411s, 2491.69/s (0.437s, 2342.88/s) LR: 8.932e-02 Data: 0.027 (0.051)
Train: 103 [ 100/156 ( 65%)] Loss: 4.73 (4.64) Time: 0.414s, 2474.64/s (0.425s, 2410.53/s) LR: 8.932e-02 Data: 0.027 (0.039)
Train: 103 [ 150/156 ( 97%)] Loss: 4.85 (4.68) Time: 0.411s, 2494.31/s (0.420s, 2436.28/s) LR: 8.932e-02 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.457 (1.457) Loss: 7.381 ( 7.381) Acc@1: 4.102 ( 4.102) Acc@5: 9.668 ( 9.668)
Test: [ 48/48] Time: 0.092 (0.328) Loss: 7.525 ( 7.631) Acc@1: 3.420 ( 3.354) Acc@5: 8.019 ( 8.334)
Train: 104 [ 0/156 ( 1%)] Loss: 4.55 (4.55) Time: 1.621s, 631.73/s (1.621s, 631.73/s) LR: 8.586e-02 Data: 1.128 (1.128)
Train: 104 [ 50/156 ( 33%)] Loss: 4.72 (4.55) Time: 0.410s, 2497.79/s (0.437s, 2342.59/s) LR: 8.586e-02 Data: 0.027 (0.049)
Train: 104 [ 100/156 ( 65%)] Loss: 4.73 (4.60) Time: 0.413s, 2480.04/s (0.424s, 2415.76/s) LR: 8.586e-02 Data: 0.031 (0.038)
Train: 104 [ 150/156 ( 97%)] Loss: 4.72 (4.64) Time: 0.409s, 2505.97/s (0.420s, 2438.77/s) LR: 8.586e-02 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.467 (1.467) Loss: 7.467 ( 7.467) Acc@1: 4.004 ( 4.004) Acc@5: 9.668 ( 9.668)
Test: [ 48/48] Time: 0.091 (0.327) Loss: 7.468 ( 7.612) Acc@1: 3.302 ( 3.122) Acc@5: 8.962 ( 8.058)
Train: 105 [ 0/156 ( 1%)] Loss: 4.64 (4.64) Time: 1.635s, 626.47/s (1.635s, 626.47/s) LR: 8.244e-02 Data: 1.260 (1.260)
Train: 105 [ 50/156 ( 33%)] Loss: 4.54 (4.50) Time: 0.410s, 2498.47/s (0.433s, 2365.87/s) LR: 8.244e-02 Data: 0.028 (0.051)
Train: 105 [ 100/156 ( 65%)] Loss: 4.67 (4.56) Time: 0.412s, 2484.37/s (0.423s, 2421.89/s) LR: 8.244e-02 Data: 0.026 (0.039)
Train: 105 [ 150/156 ( 97%)] Loss: 4.75 (4.59) Time: 0.410s, 2499.52/s (0.419s, 2441.04/s) LR: 8.244e-02 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.473 (1.473) Loss: 7.571 ( 7.571) Acc@1: 3.418 ( 3.418) Acc@5: 9.473 ( 9.473)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 7.598 ( 7.758) Acc@1: 3.538 ( 3.210) Acc@5: 8.137 ( 7.982)
Train: 106 [ 0/156 ( 1%)] Loss: 4.30 (4.30) Time: 1.774s, 577.35/s (1.774s, 577.35/s) LR: 7.908e-02 Data: 1.397 (1.397)
Train: 106 [ 50/156 ( 33%)] Loss: 4.62 (4.45) Time: 0.413s, 2479.32/s (0.440s, 2327.34/s) LR: 7.908e-02 Data: 0.027 (0.054)
Train: 106 [ 100/156 ( 65%)] Loss: 4.61 (4.50) Time: 0.409s, 2500.70/s (0.426s, 2404.05/s) LR: 7.908e-02 Data: 0.028 (0.041)
Train: 106 [ 150/156 ( 97%)] Loss: 4.68 (4.55) Time: 0.412s, 2488.00/s (0.421s, 2432.95/s) LR: 7.908e-02 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.465 (1.465) Loss: 7.579 ( 7.579) Acc@1: 3.809 ( 3.809) Acc@5: 8.398 ( 8.398)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 7.585 ( 7.729) Acc@1: 3.420 ( 3.324) Acc@5: 8.491 ( 8.072)
Train: 107 [ 0/156 ( 1%)] Loss: 4.32 (4.32) Time: 1.833s, 558.64/s (1.833s, 558.64/s) LR: 7.577e-02 Data: 1.458 (1.458)
Train: 107 [ 50/156 ( 33%)] Loss: 4.47 (4.43) Time: 0.406s, 2519.79/s (0.435s, 2356.54/s) LR: 7.577e-02 Data: 0.027 (0.056)
Train: 107 [ 100/156 ( 65%)] Loss: 4.51 (4.47) Time: 0.408s, 2512.74/s (0.420s, 2436.93/s) LR: 7.577e-02 Data: 0.028 (0.041)
Train: 107 [ 150/156 ( 97%)] Loss: 4.62 (4.51) Time: 0.410s, 2498.03/s (0.416s, 2460.97/s) LR: 7.577e-02 Data: 0.026 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.444 (1.444) Loss: 7.623 ( 7.623) Acc@1: 3.223 ( 3.223) Acc@5: 8.105 ( 8.105)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 7.647 ( 7.820) Acc@1: 2.948 ( 3.124) Acc@5: 8.491 ( 7.950)
Train: 108 [ 0/156 ( 1%)] Loss: 4.40 (4.40) Time: 1.727s, 593.05/s (1.727s, 593.05/s) LR: 7.252e-02 Data: 1.350 (1.350)
Train: 108 [ 50/156 ( 33%)] Loss: 4.38 (4.38) Time: 0.410s, 2496.61/s (0.439s, 2335.22/s) LR: 7.252e-02 Data: 0.027 (0.054)
Train: 108 [ 100/156 ( 65%)] Loss: 4.48 (4.43) Time: 0.404s, 2534.25/s (0.423s, 2422.49/s) LR: 7.252e-02 Data: 0.027 (0.041)
Train: 108 [ 150/156 ( 97%)] Loss: 4.61 (4.47) Time: 0.400s, 2560.24/s (0.416s, 2460.08/s) LR: 7.252e-02 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.459 (1.459) Loss: 7.587 ( 7.587) Acc@1: 3.320 ( 3.320) Acc@5: 8.008 ( 8.008)
Test: [ 48/48] Time: 0.089 (0.330) Loss: 7.584 ( 7.744) Acc@1: 3.184 ( 3.158) Acc@5: 9.670 ( 7.872)
Train: 109 [ 0/156 ( 1%)] Loss: 4.25 (4.25) Time: 2.024s, 505.98/s (2.024s, 505.98/s) LR: 6.932e-02 Data: 1.654 (1.654)
Train: 109 [ 50/156 ( 33%)] Loss: 4.41 (4.34) Time: 0.407s, 2513.42/s (0.435s, 2354.37/s) LR: 6.932e-02 Data: 0.027 (0.059)
Train: 109 [ 100/156 ( 65%)] Loss: 4.45 (4.38) Time: 0.407s, 2518.21/s (0.420s, 2438.78/s) LR: 6.932e-02 Data: 0.029 (0.043)
Train: 109 [ 150/156 ( 97%)] Loss: 4.46 (4.42) Time: 0.407s, 2517.40/s (0.415s, 2464.96/s) LR: 6.932e-02 Data: 0.026 (0.038)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.423 (1.423) Loss: 7.793 ( 7.793) Acc@1: 3.418 ( 3.418) Acc@5: 7.617 ( 7.617)
Test: [ 48/48] Time: 0.091 (0.327) Loss: 7.771 ( 7.943) Acc@1: 2.948 ( 3.042) Acc@5: 8.726 ( 7.850)
Train: 110 [ 0/156 ( 1%)] Loss: 4.20 (4.20) Time: 1.846s, 554.78/s (1.846s, 554.78/s) LR: 6.617e-02 Data: 1.469 (1.469)
Train: 110 [ 50/156 ( 33%)] Loss: 4.38 (4.30) Time: 0.414s, 2475.25/s (0.439s, 2329.97/s) LR: 6.617e-02 Data: 0.027 (0.055)
Train: 110 [ 100/156 ( 65%)] Loss: 4.31 (4.34) Time: 0.412s, 2482.56/s (0.426s, 2405.42/s) LR: 6.617e-02 Data: 0.027 (0.041)
Train: 110 [ 150/156 ( 97%)] Loss: 4.51 (4.38) Time: 0.409s, 2501.29/s (0.421s, 2430.42/s) LR: 6.617e-02 Data: 0.025 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.460 (1.460) Loss: 7.781 ( 7.781) Acc@1: 3.027 ( 3.027) Acc@5: 8.691 ( 8.691)
Test: [ 48/48] Time: 0.090 (0.333) Loss: 7.764 ( 7.942) Acc@1: 3.066 ( 3.034) Acc@5: 7.901 ( 7.638)
Train: 111 [ 0/156 ( 1%)] Loss: 4.23 (4.23) Time: 1.715s, 597.13/s (1.715s, 597.13/s) LR: 6.309e-02 Data: 1.093 (1.093)
Train: 111 [ 50/156 ( 33%)] Loss: 4.23 (4.26) Time: 0.408s, 2507.95/s (0.433s, 2366.39/s) LR: 6.309e-02 Data: 0.027 (0.048)
Train: 111 [ 100/156 ( 65%)] Loss: 4.50 (4.31) Time: 0.410s, 2499.91/s (0.422s, 2428.78/s) LR: 6.309e-02 Data: 0.027 (0.038)
Train: 111 [ 150/156 ( 97%)] Loss: 4.28 (4.35) Time: 0.410s, 2498.32/s (0.418s, 2447.84/s) LR: 6.309e-02 Data: 0.025 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.448 (1.448) Loss: 7.700 ( 7.700) Acc@1: 3.613 ( 3.613) Acc@5: 9.082 ( 9.082)
Test: [ 48/48] Time: 0.091 (0.328) Loss: 7.708 ( 7.903) Acc@1: 3.302 ( 3.120) Acc@5: 8.844 ( 7.888)
Train: 112 [ 0/156 ( 1%)] Loss: 4.17 (4.17) Time: 1.880s, 544.82/s (1.880s, 544.82/s) LR: 6.007e-02 Data: 1.173 (1.173)
Train: 112 [ 50/156 ( 33%)] Loss: 4.29 (4.22) Time: 0.408s, 2507.50/s (0.437s, 2341.21/s) LR: 6.007e-02 Data: 0.025 (0.050)
Train: 112 [ 100/156 ( 65%)] Loss: 4.34 (4.27) Time: 0.412s, 2483.47/s (0.423s, 2418.44/s) LR: 6.007e-02 Data: 0.027 (0.039)
Train: 112 [ 150/156 ( 97%)] Loss: 4.38 (4.30) Time: 0.411s, 2490.38/s (0.420s, 2437.27/s) LR: 6.007e-02 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.453 (1.453) Loss: 7.805 ( 7.805) Acc@1: 3.809 ( 3.809) Acc@5: 8.594 ( 8.594)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 7.816 ( 8.014) Acc@1: 3.420 ( 3.088) Acc@5: 8.726 ( 7.732)
Train: 113 [ 0/156 ( 1%)] Loss: 4.05 (4.05) Time: 1.678s, 610.41/s (1.678s, 610.41/s) LR: 5.711e-02 Data: 1.301 (1.301)
Train: 113 [ 50/156 ( 33%)] Loss: 4.28 (4.19) Time: 0.408s, 2511.33/s (0.436s, 2350.14/s) LR: 5.711e-02 Data: 0.023 (0.052)
Train: 113 [ 100/156 ( 65%)] Loss: 4.20 (4.23) Time: 0.413s, 2481.61/s (0.424s, 2413.88/s) LR: 5.711e-02 Data: 0.027 (0.040)
Train: 113 [ 150/156 ( 97%)] Loss: 4.39 (4.26) Time: 0.409s, 2501.32/s (0.420s, 2435.34/s) LR: 5.711e-02 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.441 (1.441) Loss: 7.670 ( 7.670) Acc@1: 2.832 ( 2.832) Acc@5: 8.789 ( 8.789)
Test: [ 48/48] Time: 0.091 (0.331) Loss: 7.758 ( 7.881) Acc@1: 3.656 ( 2.984) Acc@5: 8.373 ( 7.598)
Train: 114 [ 0/156 ( 1%)] Loss: 4.14 (4.14) Time: 1.871s, 547.28/s (1.871s, 547.28/s) LR: 5.421e-02 Data: 1.171 (1.171)
Train: 114 [ 50/156 ( 33%)] Loss: 4.29 (4.13) Time: 0.413s, 2480.21/s (0.439s, 2333.35/s) LR: 5.421e-02 Data: 0.027 (0.050)
Train: 114 [ 100/156 ( 65%)] Loss: 4.23 (4.18) Time: 0.406s, 2521.80/s (0.425s, 2407.59/s) LR: 5.421e-02 Data: 0.027 (0.038)
Train: 114 [ 150/156 ( 97%)] Loss: 4.36 (4.22) Time: 0.401s, 2552.63/s (0.419s, 2444.73/s) LR: 5.421e-02 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.435 (1.435) Loss: 7.891 ( 7.891) Acc@1: 3.613 ( 3.613) Acc@5: 8.691 ( 8.691)
Test: [ 48/48] Time: 0.090 (0.328) Loss: 7.908 ( 8.092) Acc@1: 3.184 ( 3.100) Acc@5: 8.491 ( 7.548)
Train: 115 [ 0/156 ( 1%)] Loss: 3.95 (3.95) Time: 1.626s, 629.89/s (1.626s, 629.89/s) LR: 5.137e-02 Data: 1.257 (1.257)
Train: 115 [ 50/156 ( 33%)] Loss: 4.04 (4.11) Time: 0.410s, 2496.87/s (0.427s, 2397.22/s) LR: 5.137e-02 Data: 0.030 (0.051)
Train: 115 [ 100/156 ( 65%)] Loss: 4.22 (4.15) Time: 0.406s, 2524.48/s (0.416s, 2458.81/s) LR: 5.137e-02 Data: 0.027 (0.039)
Train: 115 [ 150/156 ( 97%)] Loss: 4.25 (4.18) Time: 0.405s, 2525.86/s (0.414s, 2473.83/s) LR: 5.137e-02 Data: 0.024 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.502 (1.502) Loss: 7.987 ( 7.987) Acc@1: 2.832 ( 2.832) Acc@5: 7.422 ( 7.422)
Test: [ 48/48] Time: 0.092 (0.328) Loss: 7.992 ( 8.143) Acc@1: 2.594 ( 2.946) Acc@5: 7.783 ( 7.406)
Train: 116 [ 0/156 ( 1%)] Loss: 3.99 (3.99) Time: 1.606s, 637.71/s (1.606s, 637.71/s) LR: 4.860e-02 Data: 1.146 (1.146)
Train: 116 [ 50/156 ( 33%)] Loss: 4.11 (4.08) Time: 0.411s, 2489.67/s (0.435s, 2355.00/s) LR: 4.860e-02 Data: 0.027 (0.049)
Train: 116 [ 100/156 ( 65%)] Loss: 4.23 (4.11) Time: 0.412s, 2486.93/s (0.423s, 2418.54/s) LR: 4.860e-02 Data: 0.029 (0.038)
Train: 116 [ 150/156 ( 97%)] Loss: 4.29 (4.15) Time: 0.411s, 2490.08/s (0.420s, 2440.30/s) LR: 4.860e-02 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.429 (1.429) Loss: 8.148 ( 8.148) Acc@1: 2.637 ( 2.637) Acc@5: 8.008 ( 8.008)
Test: [ 48/48] Time: 0.091 (0.331) Loss: 8.154 ( 8.286) Acc@1: 3.066 ( 2.954) Acc@5: 7.193 ( 7.342)
Train: 117 [ 0/156 ( 1%)] Loss: 4.01 (4.01) Time: 1.631s, 627.73/s (1.631s, 627.73/s) LR: 4.590e-02 Data: 1.255 (1.255)
Train: 117 [ 50/156 ( 33%)] Loss: 3.99 (4.03) Time: 0.409s, 2502.16/s (0.434s, 2358.48/s) LR: 4.590e-02 Data: 0.027 (0.051)
Train: 117 [ 100/156 ( 65%)] Loss: 4.08 (4.08) Time: 0.408s, 2512.41/s (0.423s, 2419.32/s) LR: 4.590e-02 Data: 0.028 (0.039)
Train: 117 [ 150/156 ( 97%)] Loss: 4.28 (4.11) Time: 0.407s, 2518.96/s (0.418s, 2448.44/s) LR: 4.590e-02 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.432 (1.432) Loss: 7.796 ( 7.796) Acc@1: 3.418 ( 3.418) Acc@5: 9.082 ( 9.082)
Test: [ 48/48] Time: 0.090 (0.333) Loss: 7.749 ( 7.940) Acc@1: 2.948 ( 2.990) Acc@5: 8.255 ( 7.636)
Train: 118 [ 0/156 ( 1%)] Loss: 3.87 (3.87) Time: 1.750s, 585.11/s (1.750s, 585.11/s) LR: 4.326e-02 Data: 1.220 (1.220)
Train: 118 [ 50/156 ( 33%)] Loss: 4.02 (4.01) Time: 0.407s, 2516.55/s (0.434s, 2361.60/s) LR: 4.326e-02 Data: 0.027 (0.051)
Train: 118 [ 100/156 ( 65%)] Loss: 4.14 (4.05) Time: 0.411s, 2494.06/s (0.422s, 2426.60/s) LR: 4.326e-02 Data: 0.027 (0.039)
Train: 118 [ 150/156 ( 97%)] Loss: 4.19 (4.07) Time: 0.412s, 2487.38/s (0.419s, 2445.66/s) LR: 4.326e-02 Data: 0.027 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.440 (1.440) Loss: 7.851 ( 7.851) Acc@1: 3.223 ( 3.223) Acc@5: 8.398 ( 8.398)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 7.864 ( 8.039) Acc@1: 4.127 ( 3.008) Acc@5: 8.373 ( 7.544)
Train: 119 [ 0/156 ( 1%)] Loss: 3.97 (3.97) Time: 1.823s, 561.64/s (1.823s, 561.64/s) LR: 4.069e-02 Data: 1.430 (1.430)
Train: 119 [ 50/156 ( 33%)] Loss: 4.00 (3.98) Time: 0.411s, 2489.67/s (0.440s, 2326.26/s) LR: 4.069e-02 Data: 0.027 (0.055)
Train: 119 [ 100/156 ( 65%)] Loss: 4.05 (4.01) Time: 0.414s, 2472.84/s (0.427s, 2399.37/s) LR: 4.069e-02 Data: 0.032 (0.041)
Train: 119 [ 150/156 ( 97%)] Loss: 4.05 (4.03) Time: 0.411s, 2488.90/s (0.422s, 2427.54/s) LR: 4.069e-02 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.425 (1.425) Loss: 7.862 ( 7.862) Acc@1: 3.223 ( 3.223) Acc@5: 8.594 ( 8.594)
Test: [ 48/48] Time: 0.090 (0.329) Loss: 7.946 ( 8.041) Acc@1: 3.420 ( 3.134) Acc@5: 8.137 ( 7.600)
Train: 120 [ 0/156 ( 1%)] Loss: 3.78 (3.78) Time: 1.612s, 635.13/s (1.612s, 635.13/s) LR: 3.820e-02 Data: 1.239 (1.239)
Train: 120 [ 50/156 ( 33%)] Loss: 3.94 (3.95) Time: 0.405s, 2530.78/s (0.429s, 2384.41/s) LR: 3.820e-02 Data: 0.027 (0.050)
Train: 120 [ 100/156 ( 65%)] Loss: 4.15 (3.98) Time: 0.405s, 2525.73/s (0.418s, 2452.37/s) LR: 3.820e-02 Data: 0.026 (0.039)
Train: 120 [ 150/156 ( 97%)] Loss: 4.11 (4.00) Time: 0.408s, 2506.99/s (0.415s, 2469.95/s) LR: 3.820e-02 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.422 (1.422) Loss: 7.915 ( 7.915) Acc@1: 3.027 ( 3.027) Acc@5: 8.594 ( 8.594)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 7.899 ( 8.078) Acc@1: 3.892 ( 2.996) Acc@5: 8.137 ( 7.456)
Train: 121 [ 0/156 ( 1%)] Loss: 3.86 (3.86) Time: 1.660s, 616.91/s (1.660s, 616.91/s) LR: 3.577e-02 Data: 1.282 (1.282)
Train: 121 [ 50/156 ( 33%)] Loss: 3.91 (3.90) Time: 0.411s, 2490.33/s (0.437s, 2343.79/s) LR: 3.577e-02 Data: 0.027 (0.052)
Train: 121 [ 100/156 ( 65%)] Loss: 3.93 (3.92) Time: 0.412s, 2482.54/s (0.425s, 2411.49/s) LR: 3.577e-02 Data: 0.026 (0.040)
Train: 121 [ 150/156 ( 97%)] Loss: 4.08 (3.95) Time: 0.410s, 2496.96/s (0.421s, 2434.08/s) LR: 3.577e-02 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.427 (1.427) Loss: 7.938 ( 7.938) Acc@1: 3.613 ( 3.613) Acc@5: 8.496 ( 8.496)
Test: [ 48/48] Time: 0.090 (0.331) Loss: 7.899 ( 8.143) Acc@1: 3.774 ( 3.076) Acc@5: 8.019 ( 7.606)
Train: 122 [ 0/156 ( 1%)] Loss: 3.90 (3.90) Time: 1.640s, 624.28/s (1.640s, 624.28/s) LR: 3.342e-02 Data: 1.267 (1.267)
Train: 122 [ 50/156 ( 33%)] Loss: 3.84 (3.86) Time: 0.404s, 2536.28/s (0.430s, 2379.50/s) LR: 3.342e-02 Data: 0.026 (0.051)
Train: 122 [ 100/156 ( 65%)] Loss: 3.94 (3.89) Time: 0.406s, 2523.90/s (0.418s, 2448.92/s) LR: 3.342e-02 Data: 0.027 (0.039)
Train: 122 [ 150/156 ( 97%)] Loss: 4.03 (3.92) Time: 0.409s, 2505.04/s (0.415s, 2466.57/s) LR: 3.342e-02 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.431 (1.431) Loss: 7.964 ( 7.964) Acc@1: 2.832 ( 2.832) Acc@5: 8.301 ( 8.301)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 8.008 ( 8.131) Acc@1: 3.066 ( 3.004) Acc@5: 7.429 ( 7.420)
Train: 123 [ 0/156 ( 1%)] Loss: 3.73 (3.73) Time: 2.035s, 503.10/s (2.035s, 503.10/s) LR: 3.113e-02 Data: 1.659 (1.659)
Train: 123 [ 50/156 ( 33%)] Loss: 3.91 (3.84) Time: 0.408s, 2508.99/s (0.442s, 2316.37/s) LR: 3.113e-02 Data: 0.027 (0.059)
Train: 123 [ 100/156 ( 65%)] Loss: 3.94 (3.87) Time: 0.408s, 2510.67/s (0.425s, 2407.74/s) LR: 3.113e-02 Data: 0.027 (0.044)
Train: 123 [ 150/156 ( 97%)] Loss: 3.92 (3.89) Time: 0.410s, 2494.75/s (0.421s, 2434.23/s) LR: 3.113e-02 Data: 0.026 (0.038)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.471 (1.471) Loss: 7.870 ( 7.870) Acc@1: 3.809 ( 3.809) Acc@5: 7.812 ( 7.812)
Test: [ 48/48] Time: 0.092 (0.329) Loss: 7.893 ( 8.035) Acc@1: 2.476 ( 3.006) Acc@5: 8.137 ( 7.594)
Train: 124 [ 0/156 ( 1%)] Loss: 3.76 (3.76) Time: 1.802s, 568.22/s (1.802s, 568.22/s) LR: 2.893e-02 Data: 1.425 (1.425)
Train: 124 [ 50/156 ( 33%)] Loss: 3.73 (3.81) Time: 0.417s, 2458.27/s (0.440s, 2329.25/s) LR: 2.893e-02 Data: 0.026 (0.055)
Train: 124 [ 100/156 ( 65%)] Loss: 3.86 (3.83) Time: 0.410s, 2498.21/s (0.426s, 2406.16/s) LR: 2.893e-02 Data: 0.029 (0.041)
Train: 124 [ 150/156 ( 97%)] Loss: 3.85 (3.85) Time: 0.402s, 2544.56/s (0.419s, 2441.36/s) LR: 2.893e-02 Data: 0.026 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.457 (1.457) Loss: 8.062 ( 8.062) Acc@1: 2.832 ( 2.832) Acc@5: 8.008 ( 8.008)
Test: [ 48/48] Time: 0.091 (0.332) Loss: 8.053 ( 8.216) Acc@1: 2.948 ( 2.946) Acc@5: 6.722 ( 7.500)
Train: 125 [ 0/156 ( 1%)] Loss: 3.81 (3.81) Time: 1.877s, 545.65/s (1.877s, 545.65/s) LR: 2.679e-02 Data: 1.504 (1.504)
Train: 125 [ 50/156 ( 33%)] Loss: 3.81 (3.80) Time: 0.406s, 2521.98/s (0.435s, 2354.15/s) LR: 2.679e-02 Data: 0.027 (0.056)
Train: 125 [ 100/156 ( 65%)] Loss: 3.81 (3.81) Time: 0.408s, 2510.40/s (0.422s, 2427.91/s) LR: 2.679e-02 Data: 0.027 (0.042)
Train: 125 [ 150/156 ( 97%)] Loss: 3.90 (3.83) Time: 0.407s, 2514.93/s (0.418s, 2447.62/s) LR: 2.679e-02 Data: 0.026 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.426 (1.426) Loss: 7.988 ( 7.988) Acc@1: 2.539 ( 2.539) Acc@5: 8.105 ( 8.105)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 7.950 ( 8.160) Acc@1: 3.656 ( 2.916) Acc@5: 7.783 ( 7.516)
Train: 126 [ 0/156 ( 1%)] Loss: 3.62 (3.62) Time: 1.648s, 621.38/s (1.648s, 621.38/s) LR: 2.474e-02 Data: 1.074 (1.074)
Train: 126 [ 50/156 ( 33%)] Loss: 3.78 (3.74) Time: 0.413s, 2479.01/s (0.436s, 2349.94/s) LR: 2.474e-02 Data: 0.026 (0.048)
Train: 126 [ 100/156 ( 65%)] Loss: 3.83 (3.76) Time: 0.415s, 2468.14/s (0.425s, 2409.90/s) LR: 2.474e-02 Data: 0.027 (0.038)
Train: 126 [ 150/156 ( 97%)] Loss: 3.79 (3.79) Time: 0.404s, 2532.02/s (0.420s, 2435.61/s) LR: 2.474e-02 Data: 0.025 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.434 (1.434) Loss: 7.890 ( 7.890) Acc@1: 3.125 ( 3.125) Acc@5: 8.203 ( 8.203)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 7.947 ( 8.058) Acc@1: 3.066 ( 3.050) Acc@5: 6.486 ( 7.606)
Train: 127 [ 0/156 ( 1%)] Loss: 3.65 (3.65) Time: 1.754s, 583.76/s (1.754s, 583.76/s) LR: 2.276e-02 Data: 1.358 (1.358)
Train: 127 [ 50/156 ( 33%)] Loss: 3.68 (3.73) Time: 0.414s, 2475.03/s (0.440s, 2329.54/s) LR: 2.276e-02 Data: 0.027 (0.056)
Train: 127 [ 100/156 ( 65%)] Loss: 3.81 (3.75) Time: 0.410s, 2499.85/s (0.426s, 2406.09/s) LR: 2.276e-02 Data: 0.032 (0.042)
Train: 127 [ 150/156 ( 97%)] Loss: 3.81 (3.76) Time: 0.402s, 2544.67/s (0.419s, 2443.88/s) LR: 2.276e-02 Data: 0.026 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.436 (1.436) Loss: 7.886 ( 7.886) Acc@1: 2.930 ( 2.930) Acc@5: 8.398 ( 8.398)
Test: [ 48/48] Time: 0.090 (0.328) Loss: 7.912 ( 8.090) Acc@1: 2.948 ( 3.002) Acc@5: 7.547 ( 7.592)
Train: 128 [ 0/156 ( 1%)] Loss: 3.77 (3.77) Time: 1.844s, 555.38/s (1.844s, 555.38/s) LR: 2.086e-02 Data: 1.474 (1.474)
Train: 128 [ 50/156 ( 33%)] Loss: 3.75 (3.70) Time: 0.405s, 2530.86/s (0.433s, 2362.97/s) LR: 2.086e-02 Data: 0.024 (0.056)
Train: 128 [ 100/156 ( 65%)] Loss: 3.71 (3.71) Time: 0.409s, 2502.22/s (0.421s, 2429.48/s) LR: 2.086e-02 Data: 0.026 (0.042)
Train: 128 [ 150/156 ( 97%)] Loss: 3.83 (3.74) Time: 0.410s, 2499.72/s (0.419s, 2445.90/s) LR: 2.086e-02 Data: 0.025 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.427 (1.427) Loss: 7.959 ( 7.959) Acc@1: 3.320 ( 3.320) Acc@5: 8.008 ( 8.008)
Test: [ 48/48] Time: 0.090 (0.331) Loss: 7.938 ( 8.078) Acc@1: 3.066 ( 2.950) Acc@5: 7.783 ( 7.370)
Train: 129 [ 0/156 ( 1%)] Loss: 3.59 (3.59) Time: 1.637s, 625.53/s (1.637s, 625.53/s) LR: 1.903e-02 Data: 1.192 (1.192)
Train: 129 [ 50/156 ( 33%)] Loss: 3.72 (3.66) Time: 0.407s, 2518.09/s (0.431s, 2373.27/s) LR: 1.903e-02 Data: 0.029 (0.050)
Train: 129 [ 100/156 ( 65%)] Loss: 3.80 (3.69) Time: 0.406s, 2520.91/s (0.419s, 2441.92/s) LR: 1.903e-02 Data: 0.026 (0.039)
Train: 129 [ 150/156 ( 97%)] Loss: 3.80 (3.71) Time: 0.410s, 2494.76/s (0.416s, 2459.69/s) LR: 1.903e-02 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.443 (1.443) Loss: 8.093 ( 8.093) Acc@1: 2.441 ( 2.441) Acc@5: 7.715 ( 7.715)
Test: [ 48/48] Time: 0.091 (0.328) Loss: 8.043 ( 8.268) Acc@1: 3.420 ( 2.966) Acc@5: 7.429 ( 7.276)
Train: 130 [ 0/156 ( 1%)] Loss: 3.65 (3.65) Time: 2.018s, 507.56/s (2.018s, 507.56/s) LR: 1.729e-02 Data: 1.644 (1.644)
Train: 130 [ 50/156 ( 33%)] Loss: 3.74 (3.66) Time: 0.406s, 2522.54/s (0.439s, 2330.63/s) LR: 1.729e-02 Data: 0.026 (0.059)
Train: 130 [ 100/156 ( 65%)] Loss: 3.64 (3.67) Time: 0.413s, 2480.44/s (0.425s, 2411.65/s) LR: 1.729e-02 Data: 0.027 (0.043)
Train: 130 [ 150/156 ( 97%)] Loss: 3.72 (3.68) Time: 0.409s, 2502.27/s (0.421s, 2434.20/s) LR: 1.729e-02 Data: 0.025 (0.038)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.429 (1.429) Loss: 8.093 ( 8.093) Acc@1: 2.344 ( 2.344) Acc@5: 7.031 ( 7.031)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 8.051 ( 8.257) Acc@1: 2.476 ( 2.876) Acc@5: 7.075 ( 7.284)
Train: 131 [ 0/156 ( 1%)] Loss: 3.70 (3.70) Time: 1.822s, 561.97/s (1.822s, 561.97/s) LR: 1.563e-02 Data: 1.445 (1.445)
Train: 131 [ 50/156 ( 33%)] Loss: 3.66 (3.64) Time: 0.410s, 2496.93/s (0.440s, 2326.93/s) LR: 1.563e-02 Data: 0.026 (0.055)
Train: 131 [ 100/156 ( 65%)] Loss: 3.67 (3.64) Time: 0.419s, 2441.43/s (0.426s, 2403.03/s) LR: 1.563e-02 Data: 0.028 (0.041)
Train: 131 [ 150/156 ( 97%)] Loss: 3.68 (3.66) Time: 0.409s, 2504.64/s (0.421s, 2431.78/s) LR: 1.563e-02 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.441 (1.441) Loss: 8.053 ( 8.053) Acc@1: 3.320 ( 3.320) Acc@5: 7.324 ( 7.324)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 8.002 ( 8.204) Acc@1: 2.948 ( 2.988) Acc@5: 7.547 ( 7.322)
Train: 132 [ 0/156 ( 1%)] Loss: 3.52 (3.52) Time: 1.674s, 611.59/s (1.674s, 611.59/s) LR: 1.404e-02 Data: 1.301 (1.301)
Train: 132 [ 50/156 ( 33%)] Loss: 3.63 (3.60) Time: 0.406s, 2524.41/s (0.430s, 2380.79/s) LR: 1.404e-02 Data: 0.027 (0.052)
Train: 132 [ 100/156 ( 65%)] Loss: 3.56 (3.62) Time: 0.407s, 2513.99/s (0.418s, 2447.71/s) LR: 1.404e-02 Data: 0.027 (0.040)
Train: 132 [ 150/156 ( 97%)] Loss: 3.70 (3.64) Time: 0.407s, 2515.14/s (0.415s, 2468.34/s) LR: 1.404e-02 Data: 0.024 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.422 (1.422) Loss: 8.077 ( 8.077) Acc@1: 3.223 ( 3.223) Acc@5: 7.520 ( 7.520)
Test: [ 48/48] Time: 0.092 (0.328) Loss: 8.055 ( 8.223) Acc@1: 2.830 ( 2.940) Acc@5: 7.193 ( 7.362)
Train: 133 [ 0/156 ( 1%)] Loss: 3.64 (3.64) Time: 1.939s, 528.20/s (1.939s, 528.20/s) LR: 1.254e-02 Data: 1.136 (1.136)
Train: 133 [ 50/156 ( 33%)] Loss: 3.48 (3.58) Time: 0.412s, 2485.10/s (0.441s, 2322.12/s) LR: 1.254e-02 Data: 0.028 (0.049)
Train: 133 [ 100/156 ( 65%)] Loss: 3.71 (3.60) Time: 0.415s, 2470.03/s (0.427s, 2399.86/s) LR: 1.254e-02 Data: 0.028 (0.038)
Train: 133 [ 150/156 ( 97%)] Loss: 3.69 (3.62) Time: 0.409s, 2504.55/s (0.422s, 2429.05/s) LR: 1.254e-02 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.448 (1.448) Loss: 8.055 ( 8.055) Acc@1: 2.832 ( 2.832) Acc@5: 8.105 ( 8.105)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 8.003 ( 8.203) Acc@1: 2.830 ( 2.962) Acc@5: 8.491 ( 7.482)
Train: 134 [ 0/156 ( 1%)] Loss: 3.54 (3.54) Time: 1.801s, 568.52/s (1.801s, 568.52/s) LR: 1.112e-02 Data: 1.295 (1.295)
Train: 134 [ 50/156 ( 33%)] Loss: 3.59 (3.57) Time: 0.403s, 2539.07/s (0.437s, 2344.43/s) LR: 1.112e-02 Data: 0.026 (0.052)
Train: 134 [ 100/156 ( 65%)] Loss: 3.57 (3.58) Time: 0.402s, 2549.31/s (0.420s, 2437.48/s) LR: 1.112e-02 Data: 0.027 (0.039)
Train: 134 [ 150/156 ( 97%)] Loss: 3.57 (3.59) Time: 0.398s, 2570.02/s (0.414s, 2473.43/s) LR: 1.112e-02 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.442 (1.442) Loss: 8.136 ( 8.136) Acc@1: 2.930 ( 2.930) Acc@5: 7.617 ( 7.617)
Test: [ 48/48] Time: 0.090 (0.329) Loss: 8.066 ( 8.282) Acc@1: 3.184 ( 2.954) Acc@5: 7.901 ( 7.338)
Train: 135 [ 0/156 ( 1%)] Loss: 3.52 (3.52) Time: 1.710s, 598.69/s (1.710s, 598.69/s) LR: 9.789e-03 Data: 1.343 (1.343)
Train: 135 [ 50/156 ( 33%)] Loss: 3.67 (3.55) Time: 0.401s, 2551.69/s (0.427s, 2398.41/s) LR: 9.789e-03 Data: 0.028 (0.053)
Train: 135 [ 100/156 ( 65%)] Loss: 3.62 (3.57) Time: 0.404s, 2533.31/s (0.416s, 2463.86/s) LR: 9.789e-03 Data: 0.027 (0.040)
Train: 135 [ 150/156 ( 97%)] Loss: 3.65 (3.58) Time: 0.406s, 2522.66/s (0.412s, 2483.43/s) LR: 9.789e-03 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.468 (1.468) Loss: 8.029 ( 8.029) Acc@1: 3.418 ( 3.418) Acc@5: 8.301 ( 8.301)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 8.027 ( 8.193) Acc@1: 3.302 ( 2.956) Acc@5: 7.429 ( 7.424)
Train: 136 [ 0/156 ( 1%)] Loss: 3.50 (3.50) Time: 1.642s, 623.68/s (1.642s, 623.68/s) LR: 8.536e-03 Data: 1.265 (1.265)
Train: 136 [ 50/156 ( 33%)] Loss: 3.48 (3.53) Time: 0.416s, 2459.96/s (0.436s, 2346.83/s) LR: 8.536e-03 Data: 0.029 (0.053)
Train: 136 [ 100/156 ( 65%)] Loss: 3.54 (3.55) Time: 0.411s, 2488.93/s (0.424s, 2413.09/s) LR: 8.536e-03 Data: 0.027 (0.040)
Train: 136 [ 150/156 ( 97%)] Loss: 3.64 (3.56) Time: 0.413s, 2478.80/s (0.420s, 2436.82/s) LR: 8.536e-03 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.432 (1.432) Loss: 8.087 ( 8.087) Acc@1: 3.613 ( 3.613) Acc@5: 8.203 ( 8.203)
Test: [ 48/48] Time: 0.091 (0.328) Loss: 8.035 ( 8.246) Acc@1: 3.184 ( 2.950) Acc@5: 7.429 ( 7.244)
Train: 137 [ 0/156 ( 1%)] Loss: 3.49 (3.49) Time: 1.737s, 589.55/s (1.737s, 589.55/s) LR: 7.367e-03 Data: 1.361 (1.361)
Train: 137 [ 50/156 ( 33%)] Loss: 3.59 (3.53) Time: 0.415s, 2468.39/s (0.439s, 2332.49/s) LR: 7.367e-03 Data: 0.027 (0.056)
Train: 137 [ 100/156 ( 65%)] Loss: 3.47 (3.53) Time: 0.411s, 2493.22/s (0.426s, 2404.72/s) LR: 7.367e-03 Data: 0.028 (0.042)
Train: 137 [ 150/156 ( 97%)] Loss: 3.46 (3.54) Time: 0.409s, 2503.23/s (0.421s, 2430.21/s) LR: 7.367e-03 Data: 0.025 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.541 (1.541) Loss: 8.080 ( 8.080) Acc@1: 2.930 ( 2.930) Acc@5: 8.496 ( 8.496)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 8.038 ( 8.248) Acc@1: 3.066 ( 2.844) Acc@5: 7.311 ( 7.328)
Train: 138 [ 0/156 ( 1%)] Loss: 3.52 (3.52) Time: 1.718s, 596.00/s (1.718s, 596.00/s) LR: 6.283e-03 Data: 1.165 (1.165)
Train: 138 [ 50/156 ( 33%)] Loss: 3.59 (3.50) Time: 0.408s, 2512.44/s (0.436s, 2349.91/s) LR: 6.283e-03 Data: 0.027 (0.051)
Train: 138 [ 100/156 ( 65%)] Loss: 3.67 (3.52) Time: 0.412s, 2488.21/s (0.424s, 2413.61/s) LR: 6.283e-03 Data: 0.027 (0.039)
Train: 138 [ 150/156 ( 97%)] Loss: 3.57 (3.52) Time: 0.408s, 2511.66/s (0.420s, 2435.78/s) LR: 6.283e-03 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.434 (1.434) Loss: 8.072 ( 8.072) Acc@1: 2.832 ( 2.832) Acc@5: 8.398 ( 8.398)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 8.060 ( 8.257) Acc@1: 3.656 ( 2.904) Acc@5: 8.019 ( 7.316)
Train: 139 [ 0/156 ( 1%)] Loss: 3.45 (3.45) Time: 1.664s, 615.44/s (1.664s, 615.44/s) LR: 5.284e-03 Data: 1.287 (1.287)
Train: 139 [ 50/156 ( 33%)] Loss: 3.48 (3.51) Time: 0.415s, 2467.51/s (0.436s, 2348.74/s) LR: 5.284e-03 Data: 0.028 (0.052)
Train: 139 [ 100/156 ( 65%)] Loss: 3.51 (3.50) Time: 0.410s, 2494.95/s (0.424s, 2416.87/s) LR: 5.284e-03 Data: 0.027 (0.040)
Train: 139 [ 150/156 ( 97%)] Loss: 3.54 (3.51) Time: 0.409s, 2505.61/s (0.419s, 2442.25/s) LR: 5.284e-03 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.416 (1.416) Loss: 8.096 ( 8.096) Acc@1: 3.125 ( 3.125) Acc@5: 8.105 ( 8.105)
Test: [ 48/48] Time: 0.092 (0.327) Loss: 8.059 ( 8.263) Acc@1: 3.538 ( 2.980) Acc@5: 7.547 ( 7.314)
Train: 140 [ 0/156 ( 1%)] Loss: 3.50 (3.50) Time: 1.576s, 649.75/s (1.576s, 649.75/s) LR: 4.370e-03 Data: 1.198 (1.198)
Train: 140 [ 50/156 ( 33%)] Loss: 3.45 (3.49) Time: 0.413s, 2479.90/s (0.434s, 2359.58/s) LR: 4.370e-03 Data: 0.028 (0.051)
Train: 140 [ 100/156 ( 65%)] Loss: 3.53 (3.49) Time: 0.409s, 2506.70/s (0.422s, 2428.89/s) LR: 4.370e-03 Data: 0.025 (0.039)
Train: 140 [ 150/156 ( 97%)] Loss: 3.57 (3.50) Time: 0.410s, 2500.07/s (0.418s, 2451.10/s) LR: 4.370e-03 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.471 (1.471) Loss: 8.063 ( 8.063) Acc@1: 3.223 ( 3.223) Acc@5: 7.715 ( 7.715)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 8.049 ( 8.238) Acc@1: 3.538 ( 2.960) Acc@5: 8.019 ( 7.368)
Train: 141 [ 0/156 ( 1%)] Loss: 3.44 (3.44) Time: 1.566s, 653.73/s (1.566s, 653.73/s) LR: 3.543e-03 Data: 1.189 (1.189)
Train: 141 [ 50/156 ( 33%)] Loss: 3.49 (3.48) Time: 0.410s, 2498.73/s (0.435s, 2353.94/s) LR: 3.543e-03 Data: 0.028 (0.050)
Train: 141 [ 100/156 ( 65%)] Loss: 3.51 (3.48) Time: 0.407s, 2513.48/s (0.423s, 2423.56/s) LR: 3.543e-03 Data: 0.028 (0.039)
Train: 141 [ 150/156 ( 97%)] Loss: 3.47 (3.49) Time: 0.410s, 2497.29/s (0.418s, 2450.35/s) LR: 3.543e-03 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.489 (1.489) Loss: 8.056 ( 8.056) Acc@1: 3.223 ( 3.223) Acc@5: 7.617 ( 7.617)
Test: [ 48/48] Time: 0.092 (0.330) Loss: 8.041 ( 8.245) Acc@1: 2.948 ( 2.950) Acc@5: 8.255 ( 7.460)
Train: 142 [ 0/156 ( 1%)] Loss: 3.57 (3.57) Time: 1.810s, 565.81/s (1.810s, 565.81/s) LR: 2.801e-03 Data: 1.433 (1.433)
Train: 142 [ 50/156 ( 33%)] Loss: 3.33 (3.48) Time: 0.407s, 2514.33/s (0.437s, 2340.83/s) LR: 2.801e-03 Data: 0.027 (0.054)
Train: 142 [ 100/156 ( 65%)] Loss: 3.48 (3.48) Time: 0.409s, 2504.46/s (0.423s, 2418.24/s) LR: 2.801e-03 Data: 0.027 (0.041)
Train: 142 [ 150/156 ( 97%)] Loss: 3.46 (3.48) Time: 0.407s, 2513.33/s (0.419s, 2441.91/s) LR: 2.801e-03 Data: 0.026 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.457 (1.457) Loss: 8.050 ( 8.050) Acc@1: 3.125 ( 3.125) Acc@5: 7.227 ( 7.227)
Test: [ 48/48] Time: 0.091 (0.330) Loss: 8.012 ( 8.217) Acc@1: 3.302 ( 2.968) Acc@5: 7.429 ( 7.274)
Train: 143 [ 0/156 ( 1%)] Loss: 3.41 (3.41) Time: 1.597s, 641.28/s (1.597s, 641.28/s) LR: 2.146e-03 Data: 1.220 (1.220)
Train: 143 [ 50/156 ( 33%)] Loss: 3.43 (3.46) Time: 0.411s, 2492.20/s (0.434s, 2361.39/s) LR: 2.146e-03 Data: 0.027 (0.051)
Train: 143 [ 100/156 ( 65%)] Loss: 3.45 (3.46) Time: 0.408s, 2507.07/s (0.422s, 2426.22/s) LR: 2.146e-03 Data: 0.027 (0.039)
Train: 143 [ 150/156 ( 97%)] Loss: 3.42 (3.47) Time: 0.408s, 2509.99/s (0.417s, 2453.14/s) LR: 2.146e-03 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.471 (1.471) Loss: 8.080 ( 8.080) Acc@1: 3.027 ( 3.027) Acc@5: 7.812 ( 7.812)
Test: [ 48/48] Time: 0.091 (0.331) Loss: 8.038 ( 8.237) Acc@1: 3.066 ( 2.952) Acc@5: 7.665 ( 7.342)
Train: 144 [ 0/156 ( 1%)] Loss: 3.48 (3.48) Time: 1.564s, 654.69/s (1.564s, 654.69/s) LR: 1.577e-03 Data: 1.188 (1.188)
Train: 144 [ 50/156 ( 33%)] Loss: 3.41 (3.46) Time: 0.408s, 2511.52/s (0.435s, 2354.07/s) LR: 1.577e-03 Data: 0.026 (0.050)
Train: 144 [ 100/156 ( 65%)] Loss: 3.48 (3.46) Time: 0.404s, 2536.29/s (0.421s, 2429.94/s) LR: 1.577e-03 Data: 0.027 (0.039)
Train: 144 [ 150/156 ( 97%)] Loss: 3.40 (3.46) Time: 0.401s, 2552.82/s (0.416s, 2463.24/s) LR: 1.577e-03 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.456 (1.456) Loss: 8.061 ( 8.061) Acc@1: 3.223 ( 3.223) Acc@5: 8.008 ( 8.008)
Test: [ 48/48] Time: 0.090 (0.330) Loss: 7.999 ( 8.222) Acc@1: 2.830 ( 2.990) Acc@5: 7.901 ( 7.454)
Train: 145 [ 0/156 ( 1%)] Loss: 3.42 (3.42) Time: 1.530s, 669.07/s (1.530s, 669.07/s) LR: 1.096e-03 Data: 1.160 (1.160)
Train: 145 [ 50/156 ( 33%)] Loss: 3.35 (3.45) Time: 0.403s, 2540.02/s (0.427s, 2400.47/s) LR: 1.096e-03 Data: 0.026 (0.050)
Train: 145 [ 100/156 ( 65%)] Loss: 3.45 (3.45) Time: 0.407s, 2513.03/s (0.417s, 2455.42/s) LR: 1.096e-03 Data: 0.026 (0.039)
Train: 145 [ 150/156 ( 97%)] Loss: 3.43 (3.46) Time: 0.409s, 2504.85/s (0.415s, 2467.50/s) LR: 1.096e-03 Data: 0.025 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.439 (1.439) Loss: 8.080 ( 8.080) Acc@1: 3.223 ( 3.223) Acc@5: 8.008 ( 8.008)
Test: [ 48/48] Time: 0.092 (0.330) Loss: 8.036 ( 8.255) Acc@1: 3.184 ( 2.976) Acc@5: 7.665 ( 7.340)
Train: 146 [ 0/156 ( 1%)] Loss: 3.43 (3.43) Time: 1.785s, 573.57/s (1.785s, 573.57/s) LR: 7.014e-04 Data: 1.158 (1.158)
Train: 146 [ 50/156 ( 33%)] Loss: 3.40 (3.45) Time: 0.412s, 2485.31/s (0.438s, 2340.02/s) LR: 7.014e-04 Data: 0.027 (0.049)
Train: 146 [ 100/156 ( 65%)] Loss: 3.39 (3.46) Time: 0.414s, 2472.51/s (0.425s, 2411.12/s) LR: 7.014e-04 Data: 0.028 (0.038)
Train: 146 [ 150/156 ( 97%)] Loss: 3.48 (3.45) Time: 0.408s, 2506.82/s (0.421s, 2435.01/s) LR: 7.014e-04 Data: 0.026 (0.034)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.448 (1.448) Loss: 8.051 ( 8.051) Acc@1: 3.125 ( 3.125) Acc@5: 8.105 ( 8.105)
Test: [ 48/48] Time: 0.091 (0.329) Loss: 8.009 ( 8.217) Acc@1: 3.066 ( 2.968) Acc@5: 7.783 ( 7.376)
Train: 147 [ 0/156 ( 1%)] Loss: 3.47 (3.47) Time: 1.715s, 597.15/s (1.715s, 597.15/s) LR: 3.947e-04 Data: 1.338 (1.338)
Train: 147 [ 50/156 ( 33%)] Loss: 3.44 (3.46) Time: 0.414s, 2472.94/s (0.437s, 2345.49/s) LR: 3.947e-04 Data: 0.028 (0.054)
Train: 147 [ 100/156 ( 65%)] Loss: 3.48 (3.44) Time: 0.411s, 2493.42/s (0.424s, 2413.61/s) LR: 3.947e-04 Data: 0.029 (0.041)
Train: 147 [ 150/156 ( 97%)] Loss: 3.43 (3.44) Time: 0.407s, 2518.64/s (0.420s, 2436.49/s) LR: 3.947e-04 Data: 0.025 (0.036)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.437 (1.437) Loss: 8.046 ( 8.046) Acc@1: 3.223 ( 3.223) Acc@5: 8.008 ( 8.008)
Test: [ 48/48] Time: 0.089 (0.330) Loss: 8.012 ( 8.216) Acc@1: 3.184 ( 2.974) Acc@5: 8.019 ( 7.370)
Train: 148 [ 0/156 ( 1%)] Loss: 3.44 (3.44) Time: 1.643s, 623.18/s (1.643s, 623.18/s) LR: 1.754e-04 Data: 1.274 (1.274)
Train: 148 [ 50/156 ( 33%)] Loss: 3.44 (3.44) Time: 0.401s, 2555.65/s (0.429s, 2387.54/s) LR: 1.754e-04 Data: 0.027 (0.055)
Train: 148 [ 100/156 ( 65%)] Loss: 3.43 (3.44) Time: 0.400s, 2561.35/s (0.415s, 2466.25/s) LR: 1.754e-04 Data: 0.026 (0.042)
Train: 148 [ 150/156 ( 97%)] Loss: 3.50 (3.44) Time: 0.399s, 2567.03/s (0.411s, 2492.50/s) LR: 1.754e-04 Data: 0.024 (0.037)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.444 (1.444) Loss: 8.054 ( 8.054) Acc@1: 3.125 ( 3.125) Acc@5: 8.105 ( 8.105)
Test: [ 48/48] Time: 0.091 (0.328) Loss: 8.025 ( 8.221) Acc@1: 2.830 ( 2.946) Acc@5: 7.665 ( 7.356)
Train: 149 [ 0/156 ( 1%)] Loss: 3.32 (3.32) Time: 1.928s, 531.21/s (1.928s, 531.21/s) LR: 4.386e-05 Data: 1.265 (1.265)
Train: 149 [ 50/156 ( 33%)] Loss: 3.55 (3.45) Time: 0.409s, 2506.05/s (0.437s, 2345.88/s) LR: 4.386e-05 Data: 0.026 (0.051)
Train: 149 [ 100/156 ( 65%)] Loss: 3.53 (3.45) Time: 0.411s, 2492.83/s (0.423s, 2420.46/s) LR: 4.386e-05 Data: 0.026 (0.039)
Train: 149 [ 150/156 ( 97%)] Loss: 3.47 (3.45) Time: 0.411s, 2492.45/s (0.419s, 2442.40/s) LR: 4.386e-05 Data: 0.026 (0.035)
Distributing BatchNorm running means and vars
Test: [ 0/48] Time: 1.433 (1.433) Loss: 8.067 ( 8.067) Acc@1: 3.223 ( 3.223) Acc@5: 8.105 ( 8.105)
Test: [ 48/48] Time: 0.092 (0.330) Loss: 8.038 ( 8.238) Acc@1: 3.066 ( 2.964) Acc@5: 7.665 ( 7.356)
*** Best metric: 5.61399999710083 (epoch 62)
--result
[
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{
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{
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{
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}
]
|