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log_93_05.txt
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Files already downloaded and verified
Loading Cifar 10 Dataset...
Files already downloaded and verified
Files already downloaded and verified
Loaded Cifar 10!
========================================
Epoch [1/150] | Train iter [50/586] | acc1 = 10.547 | acc5 = 53.906 | loss = 2.30063 | lr = 0.01000
Epoch [1/150] | Train iter [100/586] | acc1 = 14.062 | acc5 = 64.062 | loss = 2.29102 | lr = 0.01000
Epoch [1/150] | Train iter [150/586] | acc1 = 19.922 | acc5 = 81.250 | loss = 2.25033 | lr = 0.01000
Epoch [1/150] | Train iter [200/586] | acc1 = 25.000 | acc5 = 80.859 | loss = 2.20143 | lr = 0.01000
Epoch [1/150] | Train iter [250/586] | acc1 = 26.953 | acc5 = 79.688 | loss = 2.14651 | lr = 0.01000
Epoch [1/150] | Train iter [300/586] | acc1 = 27.344 | acc5 = 86.719 | loss = 2.09575 | lr = 0.01000
Epoch [1/150] | Train iter [350/586] | acc1 = 33.984 | acc5 = 88.281 | loss = 2.05306 | lr = 0.01000
Epoch [1/150] | Train iter [400/586] | acc1 = 32.031 | acc5 = 86.719 | loss = 2.01537 | lr = 0.01000
Epoch [1/150] | Train iter [450/586] | acc1 = 29.688 | acc5 = 84.375 | loss = 1.97745 | lr = 0.01000
Epoch [1/150] | Train iter [500/586] | acc1 = 35.547 | acc5 = 90.234 | loss = 1.94069 | lr = 0.01000
Epoch [1/150] | Train iter [550/586] | acc1 = 37.109 | acc5 = 89.453 | loss = 1.90826 | lr = 0.01000
Epoch [1/150] | Train iter [586/586] | acc1 = 37.500 | acc5 = 85.000 | loss = 1.88762 | lr = 0.01000
Epoch [1/150] | Validation | acc1 = 46.445 | acc5 = 91.768 | loss = 1.46997
Save best model at acc: 46.4453, loss: 1.4700!
========================================
Epoch [2/150] | Train iter [50/586] | acc1 = 49.219 | acc5 = 93.359 | loss = 1.52503 | lr = 0.01000
Epoch [2/150] | Train iter [100/586] | acc1 = 42.188 | acc5 = 90.625 | loss = 1.50735 | lr = 0.01000
Epoch [2/150] | Train iter [150/586] | acc1 = 41.406 | acc5 = 87.500 | loss = 1.49147 | lr = 0.01000
Epoch [2/150] | Train iter [200/586] | acc1 = 43.359 | acc5 = 92.188 | loss = 1.46865 | lr = 0.01000
Epoch [2/150] | Train iter [250/586] | acc1 = 51.562 | acc5 = 92.969 | loss = 1.44562 | lr = 0.01000
Epoch [2/150] | Train iter [300/586] | acc1 = 52.734 | acc5 = 90.234 | loss = 1.42290 | lr = 0.01000
Epoch [2/150] | Train iter [350/586] | acc1 = 55.078 | acc5 = 92.188 | loss = 1.40297 | lr = 0.01000
Epoch [2/150] | Train iter [400/586] | acc1 = 61.328 | acc5 = 93.750 | loss = 1.38028 | lr = 0.01000
Epoch [2/150] | Train iter [450/586] | acc1 = 58.984 | acc5 = 96.094 | loss = 1.36299 | lr = 0.01000
Epoch [2/150] | Train iter [500/586] | acc1 = 56.641 | acc5 = 93.359 | loss = 1.34344 | lr = 0.01000
Epoch [2/150] | Train iter [550/586] | acc1 = 62.891 | acc5 = 94.531 | loss = 1.32816 | lr = 0.01000
Epoch [2/150] | Train iter [586/586] | acc1 = 60.417 | acc5 = 91.667 | loss = 1.31857 | lr = 0.01000
Epoch [2/150] | Validation | acc1 = 61.953 | acc5 = 94.463 | loss = 1.11010
Save best model at acc: 61.9531, loss: 1.1101!
========================================
Epoch [3/150] | Train iter [50/586] | acc1 = 66.016 | acc5 = 96.094 | loss = 1.09184 | lr = 0.01000
Epoch [3/150] | Train iter [100/586] | acc1 = 62.109 | acc5 = 96.094 | loss = 1.09273 | lr = 0.01000
Epoch [3/150] | Train iter [150/586] | acc1 = 64.453 | acc5 = 94.531 | loss = 1.08334 | lr = 0.01000
Epoch [3/150] | Train iter [200/586] | acc1 = 60.938 | acc5 = 95.312 | loss = 1.07546 | lr = 0.01000
Epoch [3/150] | Train iter [250/586] | acc1 = 62.109 | acc5 = 92.578 | loss = 1.07392 | lr = 0.01000
Epoch [3/150] | Train iter [300/586] | acc1 = 66.406 | acc5 = 95.312 | loss = 1.06770 | lr = 0.01000
Epoch [3/150] | Train iter [350/586] | acc1 = 61.328 | acc5 = 94.531 | loss = 1.06338 | lr = 0.01000
Epoch [3/150] | Train iter [400/586] | acc1 = 66.797 | acc5 = 95.312 | loss = 1.05876 | lr = 0.01000
Epoch [3/150] | Train iter [450/586] | acc1 = 63.281 | acc5 = 92.188 | loss = 1.05188 | lr = 0.01000
Epoch [3/150] | Train iter [500/586] | acc1 = 64.062 | acc5 = 95.312 | loss = 1.04466 | lr = 0.01000
Epoch [3/150] | Train iter [550/586] | acc1 = 67.969 | acc5 = 94.922 | loss = 1.03925 | lr = 0.01000
Epoch [3/150] | Train iter [586/586] | acc1 = 66.250 | acc5 = 96.250 | loss = 1.03484 | lr = 0.01000
Epoch [3/150] | Validation | acc1 = 73.359 | acc5 = 97.197 | loss = 0.82775
Save best model at acc: 73.3594, loss: 0.8278!
========================================
Epoch [4/150] | Train iter [50/586] | acc1 = 65.234 | acc5 = 95.703 | loss = 0.92567 | lr = 0.01000
Epoch [4/150] | Train iter [100/586] | acc1 = 67.578 | acc5 = 94.141 | loss = 0.91226 | lr = 0.01000
Epoch [4/150] | Train iter [150/586] | acc1 = 66.016 | acc5 = 95.703 | loss = 0.91174 | lr = 0.01000
Epoch [4/150] | Train iter [200/586] | acc1 = 68.750 | acc5 = 95.703 | loss = 0.90917 | lr = 0.01000
Epoch [4/150] | Train iter [250/586] | acc1 = 73.828 | acc5 = 96.875 | loss = 0.90754 | lr = 0.01000
Epoch [4/150] | Train iter [300/586] | acc1 = 69.531 | acc5 = 94.531 | loss = 0.90420 | lr = 0.01000
Epoch [4/150] | Train iter [350/586] | acc1 = 68.359 | acc5 = 94.531 | loss = 0.90237 | lr = 0.01000
Epoch [4/150] | Train iter [400/586] | acc1 = 73.438 | acc5 = 96.094 | loss = 0.90283 | lr = 0.01000
Epoch [4/150] | Train iter [450/586] | acc1 = 70.312 | acc5 = 96.094 | loss = 0.90077 | lr = 0.01000
Epoch [4/150] | Train iter [500/586] | acc1 = 68.750 | acc5 = 96.094 | loss = 0.89596 | lr = 0.01000
Epoch [4/150] | Train iter [550/586] | acc1 = 70.312 | acc5 = 95.703 | loss = 0.89362 | lr = 0.01000
Epoch [4/150] | Train iter [586/586] | acc1 = 69.167 | acc5 = 94.167 | loss = 0.89095 | lr = 0.01000
Epoch [4/150] | Validation | acc1 = 78.154 | acc5 = 97.979 | loss = 0.67537
Save best model at acc: 78.1543, loss: 0.6754!
========================================
Epoch [5/150] | Train iter [50/586] | acc1 = 73.438 | acc5 = 94.922 | loss = 0.82470 | lr = 0.01000
Epoch [5/150] | Train iter [100/586] | acc1 = 69.531 | acc5 = 96.094 | loss = 0.80991 | lr = 0.01000
Epoch [5/150] | Train iter [150/586] | acc1 = 76.562 | acc5 = 95.703 | loss = 0.80570 | lr = 0.01000
Epoch [5/150] | Train iter [200/586] | acc1 = 74.219 | acc5 = 96.875 | loss = 0.80581 | lr = 0.01000
Epoch [5/150] | Train iter [250/586] | acc1 = 71.484 | acc5 = 94.922 | loss = 0.81052 | lr = 0.01000
Epoch [5/150] | Train iter [300/586] | acc1 = 70.703 | acc5 = 97.656 | loss = 0.80716 | lr = 0.01000
Epoch [5/150] | Train iter [350/586] | acc1 = 71.875 | acc5 = 95.312 | loss = 0.80834 | lr = 0.01000
Epoch [5/150] | Train iter [400/586] | acc1 = 78.516 | acc5 = 98.828 | loss = 0.80670 | lr = 0.01000
Epoch [5/150] | Train iter [450/586] | acc1 = 76.172 | acc5 = 96.875 | loss = 0.80571 | lr = 0.01000
Epoch [5/150] | Train iter [500/586] | acc1 = 71.094 | acc5 = 96.484 | loss = 0.80109 | lr = 0.01000
Epoch [5/150] | Train iter [550/586] | acc1 = 78.516 | acc5 = 96.875 | loss = 0.80107 | lr = 0.01000
Epoch [5/150] | Train iter [586/586] | acc1 = 76.250 | acc5 = 97.083 | loss = 0.79794 | lr = 0.01000
Epoch [5/150] | Validation | acc1 = 69.463 | acc5 = 96.172 | loss = 0.98424
========================================
Epoch [6/150] | Train iter [50/586] | acc1 = 70.703 | acc5 = 96.094 | loss = 0.74780 | lr = 0.01000
Epoch [6/150] | Train iter [100/586] | acc1 = 74.219 | acc5 = 96.484 | loss = 0.74578 | lr = 0.01000
Epoch [6/150] | Train iter [150/586] | acc1 = 81.250 | acc5 = 98.438 | loss = 0.74154 | lr = 0.01000
Epoch [6/150] | Train iter [200/586] | acc1 = 76.953 | acc5 = 97.266 | loss = 0.73919 | lr = 0.01000
Epoch [6/150] | Train iter [250/586] | acc1 = 76.172 | acc5 = 95.703 | loss = 0.74190 | lr = 0.01000
Epoch [6/150] | Train iter [300/586] | acc1 = 72.266 | acc5 = 98.828 | loss = 0.74371 | lr = 0.01000
Epoch [6/150] | Train iter [350/586] | acc1 = 75.000 | acc5 = 96.484 | loss = 0.74271 | lr = 0.01000
Epoch [6/150] | Train iter [400/586] | acc1 = 72.656 | acc5 = 96.094 | loss = 0.74323 | lr = 0.01000
Epoch [6/150] | Train iter [450/586] | acc1 = 80.078 | acc5 = 96.875 | loss = 0.74300 | lr = 0.01000
Epoch [6/150] | Train iter [500/586] | acc1 = 77.734 | acc5 = 98.047 | loss = 0.74289 | lr = 0.01000
Epoch [6/150] | Train iter [550/586] | acc1 = 71.484 | acc5 = 95.312 | loss = 0.74284 | lr = 0.01000
Epoch [6/150] | Train iter [586/586] | acc1 = 80.417 | acc5 = 97.917 | loss = 0.74084 | lr = 0.01000
Epoch [6/150] | Validation | acc1 = 79.814 | acc5 = 97.119 | loss = 0.65038
Save best model at acc: 79.8145, loss: 0.6504!
========================================
Epoch [7/150] | Train iter [50/586] | acc1 = 75.781 | acc5 = 98.438 | loss = 0.69444 | lr = 0.01000
Epoch [7/150] | Train iter [100/586] | acc1 = 77.734 | acc5 = 98.438 | loss = 0.70139 | lr = 0.01000
Epoch [7/150] | Train iter [150/586] | acc1 = 79.688 | acc5 = 98.047 | loss = 0.70811 | lr = 0.01000
Epoch [7/150] | Train iter [200/586] | acc1 = 77.734 | acc5 = 98.438 | loss = 0.70521 | lr = 0.01000
Epoch [7/150] | Train iter [250/586] | acc1 = 75.391 | acc5 = 93.750 | loss = 0.70722 | lr = 0.01000
Epoch [7/150] | Train iter [300/586] | acc1 = 73.047 | acc5 = 94.922 | loss = 0.70608 | lr = 0.01000
Epoch [7/150] | Train iter [350/586] | acc1 = 80.859 | acc5 = 96.484 | loss = 0.70774 | lr = 0.01000
Epoch [7/150] | Train iter [400/586] | acc1 = 76.562 | acc5 = 95.703 | loss = 0.70806 | lr = 0.01000
Epoch [7/150] | Train iter [450/586] | acc1 = 73.438 | acc5 = 94.141 | loss = 0.70640 | lr = 0.01000
Epoch [7/150] | Train iter [500/586] | acc1 = 79.688 | acc5 = 97.656 | loss = 0.70534 | lr = 0.01000
Epoch [7/150] | Train iter [550/586] | acc1 = 78.906 | acc5 = 97.656 | loss = 0.70608 | lr = 0.01000
Epoch [7/150] | Train iter [586/586] | acc1 = 84.167 | acc5 = 97.500 | loss = 0.70582 | lr = 0.01000
Epoch [7/150] | Validation | acc1 = 79.941 | acc5 = 98.291 | loss = 0.59625
Save best model at acc: 79.9414, loss: 0.5963!
========================================
Epoch [8/150] | Train iter [50/586] | acc1 = 75.781 | acc5 = 96.094 | loss = 0.67334 | lr = 0.01000
Epoch [8/150] | Train iter [100/586] | acc1 = 84.375 | acc5 = 98.828 | loss = 0.67800 | lr = 0.01000
Epoch [8/150] | Train iter [150/586] | acc1 = 75.391 | acc5 = 96.875 | loss = 0.67658 | lr = 0.01000
Epoch [8/150] | Train iter [200/586] | acc1 = 80.859 | acc5 = 95.703 | loss = 0.67936 | lr = 0.01000
Epoch [8/150] | Train iter [250/586] | acc1 = 81.641 | acc5 = 98.438 | loss = 0.67548 | lr = 0.01000
Epoch [8/150] | Train iter [300/586] | acc1 = 78.516 | acc5 = 98.047 | loss = 0.67221 | lr = 0.01000
Epoch [8/150] | Train iter [350/586] | acc1 = 80.078 | acc5 = 98.438 | loss = 0.67291 | lr = 0.01000
Epoch [8/150] | Train iter [400/586] | acc1 = 78.516 | acc5 = 98.438 | loss = 0.67284 | lr = 0.01000
Epoch [8/150] | Train iter [450/586] | acc1 = 78.516 | acc5 = 96.875 | loss = 0.67337 | lr = 0.01000
Epoch [8/150] | Train iter [500/586] | acc1 = 79.297 | acc5 = 95.703 | loss = 0.67421 | lr = 0.01000
Epoch [8/150] | Train iter [550/586] | acc1 = 75.781 | acc5 = 94.922 | loss = 0.67333 | lr = 0.01000
Epoch [8/150] | Train iter [586/586] | acc1 = 80.833 | acc5 = 96.250 | loss = 0.67301 | lr = 0.01000
Epoch [8/150] | Validation | acc1 = 79.756 | acc5 = 97.959 | loss = 0.64683
========================================
Epoch [9/150] | Train iter [50/586] | acc1 = 76.953 | acc5 = 98.438 | loss = 0.64953 | lr = 0.01000
Epoch [9/150] | Train iter [100/586] | acc1 = 78.906 | acc5 = 97.656 | loss = 0.66268 | lr = 0.01000
Epoch [9/150] | Train iter [150/586] | acc1 = 79.297 | acc5 = 96.875 | loss = 0.65876 | lr = 0.01000
Epoch [9/150] | Train iter [200/586] | acc1 = 79.297 | acc5 = 96.094 | loss = 0.66151 | lr = 0.01000
Epoch [9/150] | Train iter [250/586] | acc1 = 80.469 | acc5 = 98.047 | loss = 0.65920 | lr = 0.01000
Epoch [9/150] | Train iter [300/586] | acc1 = 78.125 | acc5 = 98.438 | loss = 0.65992 | lr = 0.01000
Epoch [9/150] | Train iter [350/586] | acc1 = 76.953 | acc5 = 95.703 | loss = 0.66009 | lr = 0.01000
Epoch [9/150] | Train iter [400/586] | acc1 = 76.953 | acc5 = 96.875 | loss = 0.66132 | lr = 0.01000
Epoch [9/150] | Train iter [450/586] | acc1 = 80.469 | acc5 = 96.875 | loss = 0.66246 | lr = 0.01000
Epoch [9/150] | Train iter [500/586] | acc1 = 78.125 | acc5 = 96.875 | loss = 0.66083 | lr = 0.01000
Epoch [9/150] | Train iter [550/586] | acc1 = 78.906 | acc5 = 96.484 | loss = 0.66135 | lr = 0.01000
Epoch [9/150] | Train iter [586/586] | acc1 = 78.333 | acc5 = 97.083 | loss = 0.66131 | lr = 0.01000
Epoch [9/150] | Validation | acc1 = 83.340 | acc5 = 98.584 | loss = 0.55431
Save best model at acc: 83.3398, loss: 0.5543!
========================================
Epoch [10/150] | Train iter [50/586] | acc1 = 76.953 | acc5 = 94.922 | loss = 0.65715 | lr = 0.01000
Epoch [10/150] | Train iter [100/586] | acc1 = 73.828 | acc5 = 97.266 | loss = 0.65043 | lr = 0.01000
Epoch [10/150] | Train iter [150/586] | acc1 = 78.516 | acc5 = 97.266 | loss = 0.64967 | lr = 0.01000
Epoch [10/150] | Train iter [200/586] | acc1 = 80.469 | acc5 = 96.875 | loss = 0.64225 | lr = 0.01000
Epoch [10/150] | Train iter [250/586] | acc1 = 80.078 | acc5 = 95.703 | loss = 0.64599 | lr = 0.01000
Epoch [10/150] | Train iter [300/586] | acc1 = 78.125 | acc5 = 98.438 | loss = 0.64551 | lr = 0.01000
Epoch [10/150] | Train iter [350/586] | acc1 = 78.516 | acc5 = 97.656 | loss = 0.64946 | lr = 0.01000
Epoch [10/150] | Train iter [400/586] | acc1 = 81.641 | acc5 = 97.266 | loss = 0.64732 | lr = 0.01000
Epoch [10/150] | Train iter [450/586] | acc1 = 78.125 | acc5 = 96.094 | loss = 0.64821 | lr = 0.01000
Epoch [10/150] | Train iter [500/586] | acc1 = 78.125 | acc5 = 94.531 | loss = 0.64781 | lr = 0.01000
Epoch [10/150] | Train iter [550/586] | acc1 = 77.344 | acc5 = 99.609 | loss = 0.64881 | lr = 0.01000
Epoch [10/150] | Train iter [586/586] | acc1 = 77.917 | acc5 = 98.333 | loss = 0.65000 | lr = 0.01000
Epoch [10/150] | Validation | acc1 = 81.797 | acc5 = 97.607 | loss = 0.59996
========================================
Epoch [11/150] | Train iter [50/586] | acc1 = 80.078 | acc5 = 96.094 | loss = 0.60034 | lr = 0.01000
Epoch [11/150] | Train iter [100/586] | acc1 = 81.641 | acc5 = 98.438 | loss = 0.61725 | lr = 0.01000
Epoch [11/150] | Train iter [150/586] | acc1 = 82.031 | acc5 = 97.266 | loss = 0.62071 | lr = 0.01000
Epoch [11/150] | Train iter [200/586] | acc1 = 81.641 | acc5 = 98.047 | loss = 0.62838 | lr = 0.01000
Epoch [11/150] | Train iter [250/586] | acc1 = 82.031 | acc5 = 97.656 | loss = 0.62987 | lr = 0.01000
Epoch [11/150] | Train iter [300/586] | acc1 = 82.031 | acc5 = 96.875 | loss = 0.62756 | lr = 0.01000
Epoch [11/150] | Train iter [350/586] | acc1 = 77.344 | acc5 = 96.094 | loss = 0.62692 | lr = 0.01000
Epoch [11/150] | Train iter [400/586] | acc1 = 75.391 | acc5 = 95.703 | loss = 0.62674 | lr = 0.01000
Epoch [11/150] | Train iter [450/586] | acc1 = 78.125 | acc5 = 96.875 | loss = 0.62913 | lr = 0.01000
Epoch [11/150] | Train iter [500/586] | acc1 = 80.078 | acc5 = 96.484 | loss = 0.62991 | lr = 0.01000
Epoch [11/150] | Train iter [550/586] | acc1 = 82.422 | acc5 = 98.047 | loss = 0.63039 | lr = 0.01000
Epoch [11/150] | Train iter [586/586] | acc1 = 82.917 | acc5 = 99.167 | loss = 0.62908 | lr = 0.01000
Epoch [11/150] | Validation | acc1 = 81.748 | acc5 = 97.656 | loss = 0.61346
========================================
Epoch [12/150] | Train iter [50/586] | acc1 = 76.562 | acc5 = 96.875 | loss = 0.62084 | lr = 0.01000
Epoch [12/150] | Train iter [100/586] | acc1 = 78.516 | acc5 = 95.703 | loss = 0.61619 | lr = 0.01000
Epoch [12/150] | Train iter [150/586] | acc1 = 82.031 | acc5 = 98.047 | loss = 0.61635 | lr = 0.01000
Epoch [12/150] | Train iter [200/586] | acc1 = 79.688 | acc5 = 97.266 | loss = 0.61297 | lr = 0.01000
Epoch [12/150] | Train iter [250/586] | acc1 = 80.469 | acc5 = 98.438 | loss = 0.61448 | lr = 0.01000
Epoch [12/150] | Train iter [300/586] | acc1 = 75.391 | acc5 = 96.484 | loss = 0.61635 | lr = 0.01000
Epoch [12/150] | Train iter [350/586] | acc1 = 84.375 | acc5 = 98.438 | loss = 0.61663 | lr = 0.01000
Epoch [12/150] | Train iter [400/586] | acc1 = 80.078 | acc5 = 98.438 | loss = 0.61779 | lr = 0.01000
Epoch [12/150] | Train iter [450/586] | acc1 = 76.172 | acc5 = 97.656 | loss = 0.61919 | lr = 0.01000
Epoch [12/150] | Train iter [500/586] | acc1 = 81.641 | acc5 = 97.266 | loss = 0.61895 | lr = 0.01000
Epoch [12/150] | Train iter [550/586] | acc1 = 79.297 | acc5 = 98.047 | loss = 0.61970 | lr = 0.01000
Epoch [12/150] | Train iter [586/586] | acc1 = 77.917 | acc5 = 97.917 | loss = 0.62173 | lr = 0.01000
Epoch [12/150] | Validation | acc1 = 81.621 | acc5 = 97.969 | loss = 0.61922
========================================
Epoch [13/150] | Train iter [50/586] | acc1 = 79.297 | acc5 = 97.656 | loss = 0.59805 | lr = 0.01000
Epoch [13/150] | Train iter [100/586] | acc1 = 82.422 | acc5 = 98.438 | loss = 0.59348 | lr = 0.01000
Epoch [13/150] | Train iter [150/586] | acc1 = 78.125 | acc5 = 96.875 | loss = 0.59481 | lr = 0.01000
Epoch [13/150] | Train iter [200/586] | acc1 = 81.641 | acc5 = 98.438 | loss = 0.59733 | lr = 0.01000
Epoch [13/150] | Train iter [250/586] | acc1 = 82.812 | acc5 = 96.094 | loss = 0.60205 | lr = 0.01000
Epoch [13/150] | Train iter [300/586] | acc1 = 83.594 | acc5 = 97.266 | loss = 0.60421 | lr = 0.01000
Epoch [13/150] | Train iter [350/586] | acc1 = 79.297 | acc5 = 98.438 | loss = 0.60604 | lr = 0.01000
Epoch [13/150] | Train iter [400/586] | acc1 = 82.422 | acc5 = 96.875 | loss = 0.60851 | lr = 0.01000
Epoch [13/150] | Train iter [450/586] | acc1 = 80.859 | acc5 = 98.438 | loss = 0.61098 | lr = 0.01000
Epoch [13/150] | Train iter [500/586] | acc1 = 83.984 | acc5 = 98.047 | loss = 0.60893 | lr = 0.01000
Epoch [13/150] | Train iter [550/586] | acc1 = 77.734 | acc5 = 94.922 | loss = 0.60913 | lr = 0.01000
Epoch [13/150] | Train iter [586/586] | acc1 = 78.333 | acc5 = 97.500 | loss = 0.61025 | lr = 0.01000
Epoch [13/150] | Validation | acc1 = 84.062 | acc5 = 98.525 | loss = 0.51185
Save best model at acc: 84.0625, loss: 0.5119!
========================================
Epoch [14/150] | Train iter [50/586] | acc1 = 81.250 | acc5 = 98.047 | loss = 0.56999 | lr = 0.01000
Epoch [14/150] | Train iter [100/586] | acc1 = 82.422 | acc5 = 98.047 | loss = 0.58260 | lr = 0.01000
Epoch [14/150] | Train iter [150/586] | acc1 = 81.641 | acc5 = 96.094 | loss = 0.58602 | lr = 0.01000
Epoch [14/150] | Train iter [200/586] | acc1 = 83.203 | acc5 = 98.438 | loss = 0.58259 | lr = 0.01000
Epoch [14/150] | Train iter [250/586] | acc1 = 81.641 | acc5 = 97.656 | loss = 0.58595 | lr = 0.01000
Epoch [14/150] | Train iter [300/586] | acc1 = 82.812 | acc5 = 97.656 | loss = 0.58874 | lr = 0.01000
Epoch [14/150] | Train iter [350/586] | acc1 = 81.250 | acc5 = 99.219 | loss = 0.59007 | lr = 0.01000
Epoch [14/150] | Train iter [400/586] | acc1 = 78.516 | acc5 = 95.703 | loss = 0.59102 | lr = 0.01000
Epoch [14/150] | Train iter [450/586] | acc1 = 81.641 | acc5 = 98.438 | loss = 0.59416 | lr = 0.01000
Epoch [14/150] | Train iter [500/586] | acc1 = 81.641 | acc5 = 96.094 | loss = 0.59872 | lr = 0.01000
Epoch [14/150] | Train iter [550/586] | acc1 = 79.297 | acc5 = 94.922 | loss = 0.59947 | lr = 0.01000
Epoch [14/150] | Train iter [586/586] | acc1 = 79.583 | acc5 = 96.250 | loss = 0.59876 | lr = 0.01000
Epoch [14/150] | Validation | acc1 = 81.709 | acc5 = 97.773 | loss = 0.62546
========================================
Epoch [15/150] | Train iter [50/586] | acc1 = 81.641 | acc5 = 97.656 | loss = 0.57188 | lr = 0.01000
Epoch [15/150] | Train iter [100/586] | acc1 = 83.594 | acc5 = 97.656 | loss = 0.58088 | lr = 0.01000
Epoch [15/150] | Train iter [150/586] | acc1 = 82.031 | acc5 = 98.047 | loss = 0.58654 | lr = 0.01000
Epoch [15/150] | Train iter [200/586] | acc1 = 79.688 | acc5 = 97.266 | loss = 0.58938 | lr = 0.01000
Epoch [15/150] | Train iter [250/586] | acc1 = 82.031 | acc5 = 98.828 | loss = 0.59034 | lr = 0.01000
Epoch [15/150] | Train iter [300/586] | acc1 = 84.766 | acc5 = 97.266 | loss = 0.59014 | lr = 0.01000
Epoch [15/150] | Train iter [350/586] | acc1 = 78.906 | acc5 = 96.875 | loss = 0.59091 | lr = 0.01000
Epoch [15/150] | Train iter [400/586] | acc1 = 76.953 | acc5 = 98.047 | loss = 0.59114 | lr = 0.01000
Epoch [15/150] | Train iter [450/586] | acc1 = 79.297 | acc5 = 98.438 | loss = 0.59096 | lr = 0.01000
Epoch [15/150] | Train iter [500/586] | acc1 = 78.906 | acc5 = 96.484 | loss = 0.59074 | lr = 0.01000
Epoch [15/150] | Train iter [550/586] | acc1 = 83.203 | acc5 = 98.047 | loss = 0.59059 | lr = 0.01000
Epoch [15/150] | Train iter [586/586] | acc1 = 82.083 | acc5 = 97.083 | loss = 0.59016 | lr = 0.01000
Epoch [15/150] | Validation | acc1 = 83.486 | acc5 = 98.613 | loss = 0.53429
========================================
Epoch [16/150] | Train iter [50/586] | acc1 = 82.422 | acc5 = 98.438 | loss = 0.57425 | lr = 0.01000
Epoch [16/150] | Train iter [100/586] | acc1 = 80.859 | acc5 = 95.703 | loss = 0.57979 | lr = 0.01000
Epoch [16/150] | Train iter [150/586] | acc1 = 81.641 | acc5 = 96.094 | loss = 0.58105 | lr = 0.01000
Epoch [16/150] | Train iter [200/586] | acc1 = 79.297 | acc5 = 97.266 | loss = 0.58037 | lr = 0.01000
Epoch [16/150] | Train iter [250/586] | acc1 = 81.250 | acc5 = 98.438 | loss = 0.58369 | lr = 0.01000
Epoch [16/150] | Train iter [300/586] | acc1 = 85.547 | acc5 = 98.047 | loss = 0.58424 | lr = 0.01000
Epoch [16/150] | Train iter [350/586] | acc1 = 87.500 | acc5 = 98.438 | loss = 0.58655 | lr = 0.01000
Epoch [16/150] | Train iter [400/586] | acc1 = 83.594 | acc5 = 98.828 | loss = 0.58668 | lr = 0.01000
Epoch [16/150] | Train iter [450/586] | acc1 = 79.688 | acc5 = 97.656 | loss = 0.58696 | lr = 0.01000
Epoch [16/150] | Train iter [500/586] | acc1 = 86.328 | acc5 = 99.219 | loss = 0.58582 | lr = 0.01000
Epoch [16/150] | Train iter [550/586] | acc1 = 79.688 | acc5 = 97.656 | loss = 0.58482 | lr = 0.01000
Epoch [16/150] | Train iter [586/586] | acc1 = 80.417 | acc5 = 99.167 | loss = 0.58429 | lr = 0.01000
Epoch [16/150] | Validation | acc1 = 82.275 | acc5 = 98.291 | loss = 0.59813
========================================
Epoch [17/150] | Train iter [50/586] | acc1 = 83.203 | acc5 = 98.438 | loss = 0.55235 | lr = 0.01000
Epoch [17/150] | Train iter [100/586] | acc1 = 80.859 | acc5 = 96.875 | loss = 0.56268 | lr = 0.01000
Epoch [17/150] | Train iter [150/586] | acc1 = 83.594 | acc5 = 98.047 | loss = 0.56721 | lr = 0.01000
Epoch [17/150] | Train iter [200/586] | acc1 = 78.906 | acc5 = 98.047 | loss = 0.57013 | lr = 0.01000
Epoch [17/150] | Train iter [250/586] | acc1 = 80.078 | acc5 = 96.484 | loss = 0.57261 | lr = 0.01000
Epoch [17/150] | Train iter [300/586] | acc1 = 80.859 | acc5 = 98.047 | loss = 0.57282 | lr = 0.01000
Epoch [17/150] | Train iter [350/586] | acc1 = 80.078 | acc5 = 95.703 | loss = 0.57597 | lr = 0.01000
Epoch [17/150] | Train iter [400/586] | acc1 = 81.250 | acc5 = 96.484 | loss = 0.57450 | lr = 0.01000
Epoch [17/150] | Train iter [450/586] | acc1 = 82.422 | acc5 = 96.484 | loss = 0.57566 | lr = 0.01000
Epoch [17/150] | Train iter [500/586] | acc1 = 78.125 | acc5 = 98.438 | loss = 0.57570 | lr = 0.01000
Epoch [17/150] | Train iter [550/586] | acc1 = 78.516 | acc5 = 97.266 | loss = 0.57642 | lr = 0.01000
Epoch [17/150] | Train iter [586/586] | acc1 = 79.583 | acc5 = 97.083 | loss = 0.57674 | lr = 0.01000
Epoch [17/150] | Validation | acc1 = 84.072 | acc5 = 98.691 | loss = 0.51246
Save best model at acc: 84.0723, loss: 0.5125!
========================================
Epoch [18/150] | Train iter [50/586] | acc1 = 83.984 | acc5 = 97.656 | loss = 0.55705 | lr = 0.01000
Epoch [18/150] | Train iter [100/586] | acc1 = 79.688 | acc5 = 96.484 | loss = 0.55842 | lr = 0.01000
Epoch [18/150] | Train iter [150/586] | acc1 = 83.984 | acc5 = 97.266 | loss = 0.56418 | lr = 0.01000
Epoch [18/150] | Train iter [200/586] | acc1 = 82.812 | acc5 = 98.047 | loss = 0.56261 | lr = 0.01000
Epoch [18/150] | Train iter [250/586] | acc1 = 84.766 | acc5 = 98.438 | loss = 0.56728 | lr = 0.01000
Epoch [18/150] | Train iter [300/586] | acc1 = 80.469 | acc5 = 95.312 | loss = 0.56636 | lr = 0.01000
Epoch [18/150] | Train iter [350/586] | acc1 = 81.250 | acc5 = 98.828 | loss = 0.56833 | lr = 0.01000
Epoch [18/150] | Train iter [400/586] | acc1 = 80.859 | acc5 = 99.219 | loss = 0.57199 | lr = 0.01000
Epoch [18/150] | Train iter [450/586] | acc1 = 76.953 | acc5 = 96.094 | loss = 0.57187 | lr = 0.01000
Epoch [18/150] | Train iter [500/586] | acc1 = 83.984 | acc5 = 96.875 | loss = 0.56991 | lr = 0.01000
Epoch [18/150] | Train iter [550/586] | acc1 = 82.812 | acc5 = 98.438 | loss = 0.56918 | lr = 0.01000
Epoch [18/150] | Train iter [586/586] | acc1 = 81.667 | acc5 = 98.750 | loss = 0.56995 | lr = 0.01000
Epoch [18/150] | Validation | acc1 = 83.408 | acc5 = 98.193 | loss = 0.58085
========================================
Epoch [19/150] | Train iter [50/586] | acc1 = 83.594 | acc5 = 96.484 | loss = 0.56764 | lr = 0.01000
Epoch [19/150] | Train iter [100/586] | acc1 = 82.812 | acc5 = 95.703 | loss = 0.55942 | lr = 0.01000
Epoch [19/150] | Train iter [150/586] | acc1 = 82.812 | acc5 = 97.656 | loss = 0.55757 | lr = 0.01000
Epoch [19/150] | Train iter [200/586] | acc1 = 82.812 | acc5 = 97.266 | loss = 0.56251 | lr = 0.01000
Epoch [19/150] | Train iter [250/586] | acc1 = 83.203 | acc5 = 96.875 | loss = 0.56537 | lr = 0.01000
Epoch [19/150] | Train iter [300/586] | acc1 = 85.156 | acc5 = 98.047 | loss = 0.56801 | lr = 0.01000
Epoch [19/150] | Train iter [350/586] | acc1 = 81.250 | acc5 = 97.656 | loss = 0.56652 | lr = 0.01000
Epoch [19/150] | Train iter [400/586] | acc1 = 82.031 | acc5 = 94.922 | loss = 0.56925 | lr = 0.01000
Epoch [19/150] | Train iter [450/586] | acc1 = 83.984 | acc5 = 97.656 | loss = 0.56970 | lr = 0.01000
Epoch [19/150] | Train iter [500/586] | acc1 = 84.375 | acc5 = 97.656 | loss = 0.56947 | lr = 0.01000
Epoch [19/150] | Train iter [550/586] | acc1 = 81.250 | acc5 = 96.875 | loss = 0.56923 | lr = 0.01000
Epoch [19/150] | Train iter [586/586] | acc1 = 80.833 | acc5 = 97.917 | loss = 0.56824 | lr = 0.01000
Epoch [19/150] | Validation | acc1 = 81.240 | acc5 = 98.271 | loss = 0.62612
========================================
Epoch [20/150] | Train iter [50/586] | acc1 = 81.250 | acc5 = 97.266 | loss = 0.52649 | lr = 0.01000
Epoch [20/150] | Train iter [100/586] | acc1 = 82.031 | acc5 = 98.438 | loss = 0.54885 | lr = 0.01000
Epoch [20/150] | Train iter [150/586] | acc1 = 80.859 | acc5 = 98.828 | loss = 0.55566 | lr = 0.01000
Epoch [20/150] | Train iter [200/586] | acc1 = 80.469 | acc5 = 97.266 | loss = 0.55541 | lr = 0.01000
Epoch [20/150] | Train iter [250/586] | acc1 = 80.078 | acc5 = 96.875 | loss = 0.55704 | lr = 0.01000
Epoch [20/150] | Train iter [300/586] | acc1 = 80.078 | acc5 = 98.438 | loss = 0.55886 | lr = 0.01000
Epoch [20/150] | Train iter [350/586] | acc1 = 83.203 | acc5 = 99.219 | loss = 0.55666 | lr = 0.01000
Epoch [20/150] | Train iter [400/586] | acc1 = 82.031 | acc5 = 96.875 | loss = 0.55823 | lr = 0.01000
Epoch [20/150] | Train iter [450/586] | acc1 = 77.344 | acc5 = 98.438 | loss = 0.55962 | lr = 0.01000
Epoch [20/150] | Train iter [500/586] | acc1 = 81.641 | acc5 = 96.875 | loss = 0.56024 | lr = 0.01000
Epoch [20/150] | Train iter [550/586] | acc1 = 80.469 | acc5 = 98.828 | loss = 0.56163 | lr = 0.01000
Epoch [20/150] | Train iter [586/586] | acc1 = 82.917 | acc5 = 97.917 | loss = 0.56327 | lr = 0.01000
Epoch [20/150] | Validation | acc1 = 84.062 | acc5 = 98.154 | loss = 0.52693
========================================
Epoch [21/150] | Train iter [50/586] | acc1 = 80.469 | acc5 = 96.484 | loss = 0.53806 | lr = 0.01000
Epoch [21/150] | Train iter [100/586] | acc1 = 80.078 | acc5 = 94.922 | loss = 0.54571 | lr = 0.01000
Epoch [21/150] | Train iter [150/586] | acc1 = 80.078 | acc5 = 97.266 | loss = 0.55627 | lr = 0.01000
Epoch [21/150] | Train iter [200/586] | acc1 = 79.297 | acc5 = 98.438 | loss = 0.55782 | lr = 0.01000
Epoch [21/150] | Train iter [250/586] | acc1 = 83.594 | acc5 = 97.656 | loss = 0.55254 | lr = 0.01000
Epoch [21/150] | Train iter [300/586] | acc1 = 79.688 | acc5 = 98.047 | loss = 0.55532 | lr = 0.01000
Epoch [21/150] | Train iter [350/586] | acc1 = 80.078 | acc5 = 98.047 | loss = 0.55732 | lr = 0.01000
Epoch [21/150] | Train iter [400/586] | acc1 = 85.547 | acc5 = 96.875 | loss = 0.55757 | lr = 0.01000
Epoch [21/150] | Train iter [450/586] | acc1 = 84.375 | acc5 = 99.219 | loss = 0.55607 | lr = 0.01000
Epoch [21/150] | Train iter [500/586] | acc1 = 82.812 | acc5 = 98.047 | loss = 0.55589 | lr = 0.01000
Epoch [21/150] | Train iter [550/586] | acc1 = 82.031 | acc5 = 96.875 | loss = 0.55549 | lr = 0.01000
Epoch [21/150] | Train iter [586/586] | acc1 = 80.000 | acc5 = 97.500 | loss = 0.55632 | lr = 0.01000
Epoch [21/150] | Validation | acc1 = 82.930 | acc5 = 98.545 | loss = 0.55921
========================================
Epoch [22/150] | Train iter [50/586] | acc1 = 83.203 | acc5 = 96.875 | loss = 0.54045 | lr = 0.01000
Epoch [22/150] | Train iter [100/586] | acc1 = 78.516 | acc5 = 98.828 | loss = 0.55061 | lr = 0.01000
Epoch [22/150] | Train iter [150/586] | acc1 = 82.812 | acc5 = 97.266 | loss = 0.54725 | lr = 0.01000
Epoch [22/150] | Train iter [200/586] | acc1 = 86.328 | acc5 = 97.656 | loss = 0.54765 | lr = 0.01000
Epoch [22/150] | Train iter [250/586] | acc1 = 80.859 | acc5 = 99.219 | loss = 0.54948 | lr = 0.01000
Epoch [22/150] | Train iter [300/586] | acc1 = 76.562 | acc5 = 97.266 | loss = 0.55291 | lr = 0.01000
Epoch [22/150] | Train iter [350/586] | acc1 = 82.031 | acc5 = 97.266 | loss = 0.55527 | lr = 0.01000
Epoch [22/150] | Train iter [400/586] | acc1 = 84.375 | acc5 = 97.656 | loss = 0.55644 | lr = 0.01000
Epoch [22/150] | Train iter [450/586] | acc1 = 81.250 | acc5 = 98.438 | loss = 0.55667 | lr = 0.01000
Epoch [22/150] | Train iter [500/586] | acc1 = 83.594 | acc5 = 98.047 | loss = 0.55603 | lr = 0.01000
Epoch [22/150] | Train iter [550/586] | acc1 = 82.031 | acc5 = 97.656 | loss = 0.55867 | lr = 0.01000
Epoch [22/150] | Train iter [586/586] | acc1 = 79.167 | acc5 = 96.250 | loss = 0.55983 | lr = 0.01000
Epoch [22/150] | Validation | acc1 = 78.662 | acc5 = 97.656 | loss = 0.74738
========================================
Epoch [23/150] | Train iter [50/586] | acc1 = 81.641 | acc5 = 98.438 | loss = 0.53083 | lr = 0.01000
Epoch [23/150] | Train iter [100/586] | acc1 = 85.938 | acc5 = 97.266 | loss = 0.53572 | lr = 0.01000
Epoch [23/150] | Train iter [150/586] | acc1 = 83.594 | acc5 = 98.047 | loss = 0.54273 | lr = 0.01000
Epoch [23/150] | Train iter [200/586] | acc1 = 88.672 | acc5 = 98.047 | loss = 0.54722 | lr = 0.01000
Epoch [23/150] | Train iter [250/586] | acc1 = 84.375 | acc5 = 97.656 | loss = 0.54895 | lr = 0.01000
Epoch [23/150] | Train iter [300/586] | acc1 = 82.812 | acc5 = 97.656 | loss = 0.54544 | lr = 0.01000
Epoch [23/150] | Train iter [350/586] | acc1 = 82.031 | acc5 = 96.094 | loss = 0.54757 | lr = 0.01000
Epoch [23/150] | Train iter [400/586] | acc1 = 84.766 | acc5 = 97.656 | loss = 0.54978 | lr = 0.01000
Epoch [23/150] | Train iter [450/586] | acc1 = 85.547 | acc5 = 98.047 | loss = 0.55042 | lr = 0.01000
Epoch [23/150] | Train iter [500/586] | acc1 = 82.422 | acc5 = 97.656 | loss = 0.55134 | lr = 0.01000
Epoch [23/150] | Train iter [550/586] | acc1 = 85.156 | acc5 = 97.656 | loss = 0.55220 | lr = 0.01000
Epoch [23/150] | Train iter [586/586] | acc1 = 85.000 | acc5 = 98.333 | loss = 0.55314 | lr = 0.01000
Epoch [23/150] | Validation | acc1 = 85.889 | acc5 = 98.564 | loss = 0.46839
Save best model at acc: 85.8887, loss: 0.4684!
========================================
Epoch [24/150] | Train iter [50/586] | acc1 = 80.859 | acc5 = 96.484 | loss = 0.53008 | lr = 0.01000
Epoch [24/150] | Train iter [100/586] | acc1 = 86.328 | acc5 = 98.438 | loss = 0.53762 | lr = 0.01000
Epoch [24/150] | Train iter [150/586] | acc1 = 80.469 | acc5 = 95.703 | loss = 0.53421 | lr = 0.01000
Epoch [24/150] | Train iter [200/586] | acc1 = 82.422 | acc5 = 98.047 | loss = 0.53689 | lr = 0.01000
Epoch [24/150] | Train iter [250/586] | acc1 = 80.859 | acc5 = 97.266 | loss = 0.54480 | lr = 0.01000
Epoch [24/150] | Train iter [300/586] | acc1 = 79.688 | acc5 = 96.484 | loss = 0.54482 | lr = 0.01000
Epoch [24/150] | Train iter [350/586] | acc1 = 83.594 | acc5 = 96.875 | loss = 0.54606 | lr = 0.01000
Epoch [24/150] | Train iter [400/586] | acc1 = 81.250 | acc5 = 98.438 | loss = 0.54738 | lr = 0.01000
Epoch [24/150] | Train iter [450/586] | acc1 = 82.422 | acc5 = 95.703 | loss = 0.54715 | lr = 0.01000
Epoch [24/150] | Train iter [500/586] | acc1 = 80.469 | acc5 = 97.266 | loss = 0.55139 | lr = 0.01000
Epoch [24/150] | Train iter [550/586] | acc1 = 81.641 | acc5 = 98.047 | loss = 0.55051 | lr = 0.01000
Epoch [24/150] | Train iter [586/586] | acc1 = 84.167 | acc5 = 97.083 | loss = 0.55045 | lr = 0.01000
Epoch [24/150] | Validation | acc1 = 82.686 | acc5 = 97.256 | loss = 0.60984
========================================
Epoch [25/150] | Train iter [50/586] | acc1 = 80.469 | acc5 = 96.875 | loss = 0.50955 | lr = 0.01000
Epoch [25/150] | Train iter [100/586] | acc1 = 84.766 | acc5 = 99.219 | loss = 0.52211 | lr = 0.01000
Epoch [25/150] | Train iter [150/586] | acc1 = 83.594 | acc5 = 97.656 | loss = 0.53611 | lr = 0.01000
Epoch [25/150] | Train iter [200/586] | acc1 = 84.766 | acc5 = 96.094 | loss = 0.53836 | lr = 0.01000
Epoch [25/150] | Train iter [250/586] | acc1 = 81.641 | acc5 = 96.875 | loss = 0.53979 | lr = 0.01000
Epoch [25/150] | Train iter [300/586] | acc1 = 82.422 | acc5 = 98.047 | loss = 0.54401 | lr = 0.01000
Epoch [25/150] | Train iter [350/586] | acc1 = 81.250 | acc5 = 96.094 | loss = 0.54571 | lr = 0.01000
Epoch [25/150] | Train iter [400/586] | acc1 = 83.203 | acc5 = 97.656 | loss = 0.54629 | lr = 0.01000
Epoch [25/150] | Train iter [450/586] | acc1 = 88.672 | acc5 = 99.609 | loss = 0.54577 | lr = 0.01000
Epoch [25/150] | Train iter [500/586] | acc1 = 89.844 | acc5 = 98.438 | loss = 0.54389 | lr = 0.01000
Epoch [25/150] | Train iter [550/586] | acc1 = 85.938 | acc5 = 98.828 | loss = 0.54579 | lr = 0.01000
Epoch [25/150] | Train iter [586/586] | acc1 = 83.333 | acc5 = 98.750 | loss = 0.54664 | lr = 0.01000
Epoch [25/150] | Validation | acc1 = 85.400 | acc5 = 98.867 | loss = 0.47611
========================================
Epoch [26/150] | Train iter [50/586] | acc1 = 82.422 | acc5 = 97.656 | loss = 0.50665 | lr = 0.01000
Epoch [26/150] | Train iter [100/586] | acc1 = 78.516 | acc5 = 96.094 | loss = 0.52123 | lr = 0.01000
Epoch [26/150] | Train iter [150/586] | acc1 = 85.156 | acc5 = 98.047 | loss = 0.52723 | lr = 0.01000
Epoch [26/150] | Train iter [200/586] | acc1 = 81.641 | acc5 = 98.438 | loss = 0.52703 | lr = 0.01000
Epoch [26/150] | Train iter [250/586] | acc1 = 79.688 | acc5 = 96.875 | loss = 0.53158 | lr = 0.01000
Epoch [26/150] | Train iter [300/586] | acc1 = 84.766 | acc5 = 98.047 | loss = 0.53336 | lr = 0.01000
Epoch [26/150] | Train iter [350/586] | acc1 = 82.031 | acc5 = 97.266 | loss = 0.53718 | lr = 0.01000
Epoch [26/150] | Train iter [400/586] | acc1 = 80.469 | acc5 = 96.094 | loss = 0.53950 | lr = 0.01000
Epoch [26/150] | Train iter [450/586] | acc1 = 84.766 | acc5 = 98.438 | loss = 0.53977 | lr = 0.01000
Epoch [26/150] | Train iter [500/586] | acc1 = 80.859 | acc5 = 96.484 | loss = 0.54046 | lr = 0.01000
Epoch [26/150] | Train iter [550/586] | acc1 = 82.031 | acc5 = 97.656 | loss = 0.54357 | lr = 0.01000
Epoch [26/150] | Train iter [586/586] | acc1 = 82.917 | acc5 = 97.917 | loss = 0.54452 | lr = 0.01000
Epoch [26/150] | Validation | acc1 = 85.303 | acc5 = 98.613 | loss = 0.50445
========================================
Epoch [27/150] | Train iter [50/586] | acc1 = 86.719 | acc5 = 98.438 | loss = 0.50200 | lr = 0.01000
Epoch [27/150] | Train iter [100/586] | acc1 = 81.641 | acc5 = 98.047 | loss = 0.51847 | lr = 0.01000
Epoch [27/150] | Train iter [150/586] | acc1 = 86.719 | acc5 = 98.047 | loss = 0.52331 | lr = 0.01000
Epoch [27/150] | Train iter [200/586] | acc1 = 80.859 | acc5 = 98.438 | loss = 0.52469 | lr = 0.01000
Epoch [27/150] | Train iter [250/586] | acc1 = 82.812 | acc5 = 97.656 | loss = 0.52743 | lr = 0.01000
Epoch [27/150] | Train iter [300/586] | acc1 = 83.203 | acc5 = 97.656 | loss = 0.52625 | lr = 0.01000
Epoch [27/150] | Train iter [350/586] | acc1 = 82.422 | acc5 = 97.266 | loss = 0.53147 | lr = 0.01000
Epoch [27/150] | Train iter [400/586] | acc1 = 83.203 | acc5 = 98.828 | loss = 0.53453 | lr = 0.01000
Epoch [27/150] | Train iter [450/586] | acc1 = 81.641 | acc5 = 96.875 | loss = 0.53623 | lr = 0.01000
Epoch [27/150] | Train iter [500/586] | acc1 = 82.031 | acc5 = 97.266 | loss = 0.53796 | lr = 0.01000
Epoch [27/150] | Train iter [550/586] | acc1 = 76.953 | acc5 = 97.266 | loss = 0.53907 | lr = 0.01000
Epoch [27/150] | Train iter [586/586] | acc1 = 83.333 | acc5 = 97.500 | loss = 0.53970 | lr = 0.01000
Epoch [27/150] | Validation | acc1 = 83.291 | acc5 = 98.291 | loss = 0.58117
========================================
Epoch [28/150] | Train iter [50/586] | acc1 = 85.547 | acc5 = 99.219 | loss = 0.51798 | lr = 0.01000
Epoch [28/150] | Train iter [100/586] | acc1 = 83.594 | acc5 = 98.828 | loss = 0.52038 | lr = 0.01000
Epoch [28/150] | Train iter [150/586] | acc1 = 84.766 | acc5 = 98.828 | loss = 0.52420 | lr = 0.01000
Epoch [28/150] | Train iter [200/586] | acc1 = 79.297 | acc5 = 96.875 | loss = 0.53223 | lr = 0.01000
Epoch [28/150] | Train iter [250/586] | acc1 = 82.422 | acc5 = 98.438 | loss = 0.53594 | lr = 0.01000
Epoch [28/150] | Train iter [300/586] | acc1 = 81.641 | acc5 = 97.656 | loss = 0.53629 | lr = 0.01000
Epoch [28/150] | Train iter [350/586] | acc1 = 77.734 | acc5 = 94.922 | loss = 0.53735 | lr = 0.01000
Epoch [28/150] | Train iter [400/586] | acc1 = 81.641 | acc5 = 96.094 | loss = 0.54038 | lr = 0.01000
Epoch [28/150] | Train iter [450/586] | acc1 = 87.109 | acc5 = 98.828 | loss = 0.54009 | lr = 0.01000
Epoch [28/150] | Train iter [500/586] | acc1 = 82.422 | acc5 = 97.656 | loss = 0.54143 | lr = 0.01000
Epoch [28/150] | Train iter [550/586] | acc1 = 83.203 | acc5 = 97.656 | loss = 0.54256 | lr = 0.01000
Epoch [28/150] | Train iter [586/586] | acc1 = 80.417 | acc5 = 98.333 | loss = 0.54370 | lr = 0.01000
Epoch [28/150] | Validation | acc1 = 85.088 | acc5 = 98.770 | loss = 0.48815
========================================
Epoch [29/150] | Train iter [50/586] | acc1 = 83.203 | acc5 = 96.484 | loss = 0.52661 | lr = 0.01000
Epoch [29/150] | Train iter [100/586] | acc1 = 84.375 | acc5 = 96.875 | loss = 0.53746 | lr = 0.01000
Epoch [29/150] | Train iter [150/586] | acc1 = 80.469 | acc5 = 98.047 | loss = 0.52797 | lr = 0.01000
Epoch [29/150] | Train iter [200/586] | acc1 = 85.547 | acc5 = 97.266 | loss = 0.53236 | lr = 0.01000
Epoch [29/150] | Train iter [250/586] | acc1 = 84.766 | acc5 = 98.438 | loss = 0.53052 | lr = 0.01000
Epoch [29/150] | Train iter [300/586] | acc1 = 83.203 | acc5 = 97.656 | loss = 0.53307 | lr = 0.01000
Epoch [29/150] | Train iter [350/586] | acc1 = 81.250 | acc5 = 95.703 | loss = 0.53637 | lr = 0.01000
Epoch [29/150] | Train iter [400/586] | acc1 = 81.641 | acc5 = 97.656 | loss = 0.53601 | lr = 0.01000
Epoch [29/150] | Train iter [450/586] | acc1 = 85.938 | acc5 = 99.609 | loss = 0.53761 | lr = 0.01000
Epoch [29/150] | Train iter [500/586] | acc1 = 86.328 | acc5 = 99.219 | loss = 0.53798 | lr = 0.01000
Epoch [29/150] | Train iter [550/586] | acc1 = 82.812 | acc5 = 98.047 | loss = 0.53629 | lr = 0.01000
Epoch [29/150] | Train iter [586/586] | acc1 = 77.083 | acc5 = 97.083 | loss = 0.53682 | lr = 0.01000
Epoch [29/150] | Validation | acc1 = 83.584 | acc5 = 98.691 | loss = 0.53387
========================================
Epoch [30/150] | Train iter [50/586] | acc1 = 85.547 | acc5 = 98.438 | loss = 0.50365 | lr = 0.01000
Epoch [30/150] | Train iter [100/586] | acc1 = 83.984 | acc5 = 96.875 | loss = 0.51689 | lr = 0.01000
Epoch [30/150] | Train iter [150/586] | acc1 = 79.297 | acc5 = 96.875 | loss = 0.52158 | lr = 0.01000
Epoch [30/150] | Train iter [200/586] | acc1 = 82.812 | acc5 = 97.656 | loss = 0.52539 | lr = 0.01000
Epoch [30/150] | Train iter [250/586] | acc1 = 84.375 | acc5 = 98.828 | loss = 0.52737 | lr = 0.01000
Epoch [30/150] | Train iter [300/586] | acc1 = 79.688 | acc5 = 95.703 | loss = 0.53110 | lr = 0.01000
Epoch [30/150] | Train iter [350/586] | acc1 = 80.859 | acc5 = 97.656 | loss = 0.53046 | lr = 0.01000
Epoch [30/150] | Train iter [400/586] | acc1 = 85.156 | acc5 = 99.219 | loss = 0.53092 | lr = 0.01000
Epoch [30/150] | Train iter [450/586] | acc1 = 81.250 | acc5 = 98.047 | loss = 0.53130 | lr = 0.01000
Epoch [30/150] | Train iter [500/586] | acc1 = 80.859 | acc5 = 98.438 | loss = 0.53300 | lr = 0.01000
Epoch [30/150] | Train iter [550/586] | acc1 = 85.938 | acc5 = 98.047 | loss = 0.53340 | lr = 0.01000
Epoch [30/150] | Train iter [586/586] | acc1 = 75.833 | acc5 = 94.583 | loss = 0.53485 | lr = 0.01000
Epoch [30/150] | Validation | acc1 = 82.666 | acc5 = 98.340 | loss = 0.60151
========================================
Epoch [31/150] | Train iter [50/586] | acc1 = 82.812 | acc5 = 97.266 | loss = 0.52523 | lr = 0.01000
Epoch [31/150] | Train iter [100/586] | acc1 = 87.109 | acc5 = 99.219 | loss = 0.51458 | lr = 0.01000
Epoch [31/150] | Train iter [150/586] | acc1 = 86.719 | acc5 = 98.438 | loss = 0.51608 | lr = 0.01000
Epoch [31/150] | Train iter [200/586] | acc1 = 80.469 | acc5 = 98.828 | loss = 0.52016 | lr = 0.01000
Epoch [31/150] | Train iter [250/586] | acc1 = 85.938 | acc5 = 97.266 | loss = 0.52525 | lr = 0.01000
Epoch [31/150] | Train iter [300/586] | acc1 = 82.812 | acc5 = 99.219 | loss = 0.53030 | lr = 0.01000
Epoch [31/150] | Train iter [350/586] | acc1 = 81.641 | acc5 = 98.047 | loss = 0.53124 | lr = 0.01000
Epoch [31/150] | Train iter [400/586] | acc1 = 79.297 | acc5 = 96.875 | loss = 0.53222 | lr = 0.01000
Epoch [31/150] | Train iter [450/586] | acc1 = 85.938 | acc5 = 98.047 | loss = 0.53390 | lr = 0.01000
Epoch [31/150] | Train iter [500/586] | acc1 = 79.688 | acc5 = 97.656 | loss = 0.53486 | lr = 0.01000
Epoch [31/150] | Train iter [550/586] | acc1 = 84.375 | acc5 = 98.047 | loss = 0.53677 | lr = 0.01000
Epoch [31/150] | Train iter [586/586] | acc1 = 85.417 | acc5 = 97.083 | loss = 0.53682 | lr = 0.01000
Epoch [31/150] | Validation | acc1 = 82.891 | acc5 = 98.115 | loss = 0.57001
========================================
Epoch [32/150] | Train iter [50/586] | acc1 = 85.938 | acc5 = 98.438 | loss = 0.50607 | lr = 0.01000
Epoch [32/150] | Train iter [100/586] | acc1 = 81.641 | acc5 = 96.875 | loss = 0.50081 | lr = 0.01000
Epoch [32/150] | Train iter [150/586] | acc1 = 84.375 | acc5 = 98.047 | loss = 0.51916 | lr = 0.01000
Epoch [32/150] | Train iter [200/586] | acc1 = 76.562 | acc5 = 98.047 | loss = 0.52408 | lr = 0.01000
Epoch [32/150] | Train iter [250/586] | acc1 = 84.766 | acc5 = 97.656 | loss = 0.52539 | lr = 0.01000
Epoch [32/150] | Train iter [300/586] | acc1 = 78.125 | acc5 = 97.266 | loss = 0.52929 | lr = 0.01000
Epoch [32/150] | Train iter [350/586] | acc1 = 80.469 | acc5 = 96.094 | loss = 0.53273 | lr = 0.01000
Epoch [32/150] | Train iter [400/586] | acc1 = 81.641 | acc5 = 98.438 | loss = 0.53344 | lr = 0.01000
Epoch [32/150] | Train iter [450/586] | acc1 = 83.203 | acc5 = 97.656 | loss = 0.53372 | lr = 0.01000
Epoch [32/150] | Train iter [500/586] | acc1 = 81.250 | acc5 = 98.047 | loss = 0.53363 | lr = 0.01000
Epoch [32/150] | Train iter [550/586] | acc1 = 83.984 | acc5 = 97.656 | loss = 0.53544 | lr = 0.01000
Epoch [32/150] | Train iter [586/586] | acc1 = 80.000 | acc5 = 97.500 | loss = 0.53606 | lr = 0.01000
Epoch [32/150] | Validation | acc1 = 75.059 | acc5 = 95.586 | loss = 0.87828
========================================
Epoch [33/150] | Train iter [50/586] | acc1 = 84.375 | acc5 = 98.828 | loss = 0.50773 | lr = 0.01000
Epoch [33/150] | Train iter [100/586] | acc1 = 85.156 | acc5 = 98.438 | loss = 0.51149 | lr = 0.01000
Epoch [33/150] | Train iter [150/586] | acc1 = 82.812 | acc5 = 98.047 | loss = 0.51830 | lr = 0.01000
Epoch [33/150] | Train iter [200/586] | acc1 = 83.594 | acc5 = 98.828 | loss = 0.51800 | lr = 0.01000
Epoch [33/150] | Train iter [250/586] | acc1 = 77.344 | acc5 = 96.094 | loss = 0.51978 | lr = 0.01000
Epoch [33/150] | Train iter [300/586] | acc1 = 82.031 | acc5 = 97.656 | loss = 0.52469 | lr = 0.01000
Epoch [33/150] | Train iter [350/586] | acc1 = 83.203 | acc5 = 96.484 | loss = 0.52535 | lr = 0.01000
Epoch [33/150] | Train iter [400/586] | acc1 = 81.250 | acc5 = 98.438 | loss = 0.52984 | lr = 0.01000
Epoch [33/150] | Train iter [450/586] | acc1 = 80.469 | acc5 = 97.656 | loss = 0.53131 | lr = 0.01000
Epoch [33/150] | Train iter [500/586] | acc1 = 85.156 | acc5 = 98.438 | loss = 0.53333 | lr = 0.01000
Epoch [33/150] | Train iter [550/586] | acc1 = 85.156 | acc5 = 98.047 | loss = 0.53572 | lr = 0.01000
Epoch [33/150] | Train iter [586/586] | acc1 = 81.667 | acc5 = 97.917 | loss = 0.53602 | lr = 0.01000
Epoch [33/150] | Validation | acc1 = 82.910 | acc5 = 98.506 | loss = 0.57763
========================================
Epoch [34/150] | Train iter [50/586] | acc1 = 80.859 | acc5 = 98.438 | loss = 0.49629 | lr = 0.01000
Epoch [34/150] | Train iter [100/586] | acc1 = 78.906 | acc5 = 95.703 | loss = 0.50930 | lr = 0.01000
Epoch [34/150] | Train iter [150/586] | acc1 = 79.688 | acc5 = 97.656 | loss = 0.51885 | lr = 0.01000
Epoch [34/150] | Train iter [200/586] | acc1 = 79.297 | acc5 = 96.484 | loss = 0.51909 | lr = 0.01000
Epoch [34/150] | Train iter [250/586] | acc1 = 85.938 | acc5 = 97.266 | loss = 0.52299 | lr = 0.01000
Epoch [34/150] | Train iter [300/586] | acc1 = 82.422 | acc5 = 96.875 | loss = 0.52255 | lr = 0.01000
Epoch [34/150] | Train iter [350/586] | acc1 = 82.422 | acc5 = 97.266 | loss = 0.52618 | lr = 0.01000
Epoch [34/150] | Train iter [400/586] | acc1 = 80.859 | acc5 = 98.047 | loss = 0.52903 | lr = 0.01000
Epoch [34/150] | Train iter [450/586] | acc1 = 84.375 | acc5 = 98.047 | loss = 0.52733 | lr = 0.01000
Epoch [34/150] | Train iter [500/586] | acc1 = 83.203 | acc5 = 97.266 | loss = 0.52935 | lr = 0.01000
Epoch [34/150] | Train iter [550/586] | acc1 = 85.156 | acc5 = 97.266 | loss = 0.53112 | lr = 0.01000
Epoch [34/150] | Train iter [586/586] | acc1 = 80.417 | acc5 = 95.833 | loss = 0.53234 | lr = 0.01000
Epoch [34/150] | Validation | acc1 = 84.609 | acc5 = 98.418 | loss = 0.52841
========================================
Epoch [35/150] | Train iter [50/586] | acc1 = 83.984 | acc5 = 98.047 | loss = 0.48875 | lr = 0.01000
Epoch [35/150] | Train iter [100/586] | acc1 = 82.812 | acc5 = 97.656 | loss = 0.50569 | lr = 0.01000
Epoch [35/150] | Train iter [150/586] | acc1 = 84.375 | acc5 = 98.047 | loss = 0.50281 | lr = 0.01000
Epoch [35/150] | Train iter [200/586] | acc1 = 82.422 | acc5 = 98.047 | loss = 0.50326 | lr = 0.01000
Epoch [35/150] | Train iter [250/586] | acc1 = 84.766 | acc5 = 98.828 | loss = 0.50848 | lr = 0.01000
Epoch [35/150] | Train iter [300/586] | acc1 = 85.156 | acc5 = 100.000 | loss = 0.51225 | lr = 0.01000
Epoch [35/150] | Train iter [350/586] | acc1 = 81.250 | acc5 = 97.656 | loss = 0.51627 | lr = 0.01000
Epoch [35/150] | Train iter [400/586] | acc1 = 79.688 | acc5 = 98.047 | loss = 0.52199 | lr = 0.01000
Epoch [35/150] | Train iter [450/586] | acc1 = 87.109 | acc5 = 99.219 | loss = 0.52371 | lr = 0.01000
Epoch [35/150] | Train iter [500/586] | acc1 = 81.641 | acc5 = 98.438 | loss = 0.52466 | lr = 0.01000
Epoch [35/150] | Train iter [550/586] | acc1 = 85.547 | acc5 = 97.266 | loss = 0.52630 | lr = 0.01000
Epoch [35/150] | Train iter [586/586] | acc1 = 84.583 | acc5 = 98.333 | loss = 0.52669 | lr = 0.01000
Epoch [35/150] | Validation | acc1 = 84.453 | acc5 = 98.486 | loss = 0.52898
========================================
Epoch [36/150] | Train iter [50/586] | acc1 = 85.547 | acc5 = 98.438 | loss = 0.50687 | lr = 0.01000
Epoch [36/150] | Train iter [100/586] | acc1 = 80.078 | acc5 = 97.266 | loss = 0.51674 | lr = 0.01000
Epoch [36/150] | Train iter [150/586] | acc1 = 82.422 | acc5 = 98.047 | loss = 0.51764 | lr = 0.01000
Epoch [36/150] | Train iter [200/586] | acc1 = 88.672 | acc5 = 99.219 | loss = 0.52076 | lr = 0.01000
Epoch [36/150] | Train iter [250/586] | acc1 = 83.203 | acc5 = 97.656 | loss = 0.52065 | lr = 0.01000
Epoch [36/150] | Train iter [300/586] | acc1 = 82.812 | acc5 = 96.484 | loss = 0.52351 | lr = 0.01000
Epoch [36/150] | Train iter [350/586] | acc1 = 81.250 | acc5 = 96.484 | loss = 0.52560 | lr = 0.01000
Epoch [36/150] | Train iter [400/586] | acc1 = 84.766 | acc5 = 97.656 | loss = 0.52471 | lr = 0.01000
Epoch [36/150] | Train iter [450/586] | acc1 = 80.859 | acc5 = 96.094 | loss = 0.52658 | lr = 0.01000
Epoch [36/150] | Train iter [500/586] | acc1 = 85.156 | acc5 = 98.828 | loss = 0.52793 | lr = 0.01000
Epoch [36/150] | Train iter [550/586] | acc1 = 82.031 | acc5 = 96.875 | loss = 0.52886 | lr = 0.01000
Epoch [36/150] | Train iter [586/586] | acc1 = 81.667 | acc5 = 98.333 | loss = 0.53040 | lr = 0.01000
Epoch [36/150] | Validation | acc1 = 83.965 | acc5 = 97.617 | loss = 0.56947
========================================
Epoch [37/150] | Train iter [50/586] | acc1 = 85.938 | acc5 = 98.438 | loss = 0.50005 | lr = 0.01000
Epoch [37/150] | Train iter [100/586] | acc1 = 82.812 | acc5 = 95.703 | loss = 0.51317 | lr = 0.01000
Epoch [37/150] | Train iter [150/586] | acc1 = 82.422 | acc5 = 97.266 | loss = 0.51836 | lr = 0.01000
Epoch [37/150] | Train iter [200/586] | acc1 = 85.547 | acc5 = 96.484 | loss = 0.51900 | lr = 0.01000
Epoch [37/150] | Train iter [250/586] | acc1 = 84.375 | acc5 = 97.656 | loss = 0.52159 | lr = 0.01000
Epoch [37/150] | Train iter [300/586] | acc1 = 84.375 | acc5 = 97.656 | loss = 0.52014 | lr = 0.01000
Epoch [37/150] | Train iter [350/586] | acc1 = 83.203 | acc5 = 98.047 | loss = 0.52413 | lr = 0.01000
Epoch [37/150] | Train iter [400/586] | acc1 = 83.594 | acc5 = 98.828 | loss = 0.52620 | lr = 0.01000
Epoch [37/150] | Train iter [450/586] | acc1 = 80.859 | acc5 = 98.047 | loss = 0.52609 | lr = 0.01000
Epoch [37/150] | Train iter [500/586] | acc1 = 82.812 | acc5 = 95.703 | loss = 0.52601 | lr = 0.01000
Epoch [37/150] | Train iter [550/586] | acc1 = 80.469 | acc5 = 97.266 | loss = 0.52870 | lr = 0.01000
Epoch [37/150] | Train iter [586/586] | acc1 = 78.333 | acc5 = 96.667 | loss = 0.52975 | lr = 0.01000
Epoch [37/150] | Validation | acc1 = 82.031 | acc5 = 98.330 | loss = 0.59978
========================================
Epoch [38/150] | Train iter [50/586] | acc1 = 81.250 | acc5 = 97.656 | loss = 0.49096 | lr = 0.01000
Epoch [38/150] | Train iter [100/586] | acc1 = 82.812 | acc5 = 97.656 | loss = 0.50317 | lr = 0.01000
Epoch [38/150] | Train iter [150/586] | acc1 = 79.297 | acc5 = 98.047 | loss = 0.50900 | lr = 0.01000
Epoch [38/150] | Train iter [200/586] | acc1 = 83.203 | acc5 = 98.438 | loss = 0.51146 | lr = 0.01000
Epoch [38/150] | Train iter [250/586] | acc1 = 83.594 | acc5 = 96.484 | loss = 0.51809 | lr = 0.01000
Epoch [38/150] | Train iter [300/586] | acc1 = 80.469 | acc5 = 98.047 | loss = 0.51967 | lr = 0.01000
Epoch [38/150] | Train iter [350/586] | acc1 = 78.906 | acc5 = 96.094 | loss = 0.52247 | lr = 0.01000
Epoch [38/150] | Train iter [400/586] | acc1 = 80.078 | acc5 = 96.875 | loss = 0.52431 | lr = 0.01000
Epoch [38/150] | Train iter [450/586] | acc1 = 82.812 | acc5 = 97.266 | loss = 0.52550 | lr = 0.01000
Epoch [38/150] | Train iter [500/586] | acc1 = 84.375 | acc5 = 99.609 | loss = 0.52528 | lr = 0.01000
Epoch [38/150] | Train iter [550/586] | acc1 = 83.203 | acc5 = 98.047 | loss = 0.52513 | lr = 0.01000
Epoch [38/150] | Train iter [586/586] | acc1 = 82.083 | acc5 = 98.750 | loss = 0.52693 | lr = 0.01000
Epoch [38/150] | Validation | acc1 = 83.408 | acc5 = 98.047 | loss = 0.58051
========================================
Epoch [39/150] | Train iter [50/586] | acc1 = 85.938 | acc5 = 97.656 | loss = 0.49306 | lr = 0.01000
Epoch [39/150] | Train iter [100/586] | acc1 = 81.250 | acc5 = 96.875 | loss = 0.50859 | lr = 0.01000
Epoch [39/150] | Train iter [150/586] | acc1 = 85.156 | acc5 = 97.266 | loss = 0.51334 | lr = 0.01000
Epoch [39/150] | Train iter [200/586] | acc1 = 81.250 | acc5 = 97.266 | loss = 0.51650 | lr = 0.01000
Epoch [39/150] | Train iter [250/586] | acc1 = 79.688 | acc5 = 99.609 | loss = 0.52115 | lr = 0.01000
Epoch [39/150] | Train iter [300/586] | acc1 = 83.594 | acc5 = 97.656 | loss = 0.52491 | lr = 0.01000
Epoch [39/150] | Train iter [350/586] | acc1 = 84.766 | acc5 = 98.828 | loss = 0.52716 | lr = 0.01000
Epoch [39/150] | Train iter [400/586] | acc1 = 84.375 | acc5 = 98.828 | loss = 0.52777 | lr = 0.01000
Epoch [39/150] | Train iter [450/586] | acc1 = 80.859 | acc5 = 97.266 | loss = 0.52795 | lr = 0.01000
Epoch [39/150] | Train iter [500/586] | acc1 = 81.250 | acc5 = 98.047 | loss = 0.52833 | lr = 0.01000
Epoch [39/150] | Train iter [550/586] | acc1 = 83.203 | acc5 = 98.438 | loss = 0.52732 | lr = 0.01000
Epoch [39/150] | Train iter [586/586] | acc1 = 86.250 | acc5 = 98.333 | loss = 0.52667 | lr = 0.01000
Epoch [39/150] | Validation | acc1 = 83.438 | acc5 = 98.496 | loss = 0.55244
========================================
Epoch [40/150] | Train iter [50/586] | acc1 = 91.016 | acc5 = 99.219 | loss = 0.50588 | lr = 0.01000
Epoch [40/150] | Train iter [100/586] | acc1 = 80.078 | acc5 = 96.875 | loss = 0.50678 | lr = 0.01000
Epoch [40/150] | Train iter [150/586] | acc1 = 83.594 | acc5 = 99.609 | loss = 0.50735 | lr = 0.01000
Epoch [40/150] | Train iter [200/586] | acc1 = 83.594 | acc5 = 98.438 | loss = 0.51129 | lr = 0.01000
Epoch [40/150] | Train iter [250/586] | acc1 = 85.156 | acc5 = 98.438 | loss = 0.51352 | lr = 0.01000
Epoch [40/150] | Train iter [300/586] | acc1 = 86.328 | acc5 = 98.438 | loss = 0.51412 | lr = 0.01000
Epoch [40/150] | Train iter [350/586] | acc1 = 81.641 | acc5 = 96.875 | loss = 0.51871 | lr = 0.01000
Epoch [40/150] | Train iter [400/586] | acc1 = 81.250 | acc5 = 97.656 | loss = 0.51998 | lr = 0.01000
Epoch [40/150] | Train iter [450/586] | acc1 = 83.984 | acc5 = 98.828 | loss = 0.52251 | lr = 0.01000
Epoch [40/150] | Train iter [500/586] | acc1 = 82.422 | acc5 = 97.266 | loss = 0.52502 | lr = 0.01000
Epoch [40/150] | Train iter [550/586] | acc1 = 83.594 | acc5 = 99.219 | loss = 0.52622 | lr = 0.01000
Epoch [40/150] | Train iter [586/586] | acc1 = 82.083 | acc5 = 97.083 | loss = 0.52761 | lr = 0.01000
Epoch [40/150] | Validation | acc1 = 83.545 | acc5 = 97.822 | loss = 0.55936
========================================
Epoch [41/150] | Train iter [50/586] | acc1 = 82.031 | acc5 = 98.047 | loss = 0.50793 | lr = 0.01000
Epoch [41/150] | Train iter [100/586] | acc1 = 87.109 | acc5 = 99.609 | loss = 0.50524 | lr = 0.01000
Epoch [41/150] | Train iter [150/586] | acc1 = 83.984 | acc5 = 97.266 | loss = 0.51087 | lr = 0.01000
Epoch [41/150] | Train iter [200/586] | acc1 = 80.469 | acc5 = 98.828 | loss = 0.51056 | lr = 0.01000
Epoch [41/150] | Train iter [250/586] | acc1 = 80.859 | acc5 = 95.703 | loss = 0.51344 | lr = 0.01000
Epoch [41/150] | Train iter [300/586] | acc1 = 77.344 | acc5 = 96.094 | loss = 0.51641 | lr = 0.01000
Epoch [41/150] | Train iter [350/586] | acc1 = 85.938 | acc5 = 99.219 | loss = 0.52057 | lr = 0.01000
Epoch [41/150] | Train iter [400/586] | acc1 = 78.125 | acc5 = 97.266 | loss = 0.52387 | lr = 0.01000
Epoch [41/150] | Train iter [450/586] | acc1 = 82.812 | acc5 = 97.656 | loss = 0.52418 | lr = 0.01000
Epoch [41/150] | Train iter [500/586] | acc1 = 84.375 | acc5 = 98.828 | loss = 0.52259 | lr = 0.01000
Epoch [41/150] | Train iter [550/586] | acc1 = 83.984 | acc5 = 98.828 | loss = 0.52470 | lr = 0.01000
Epoch [41/150] | Train iter [586/586] | acc1 = 83.333 | acc5 = 96.667 | loss = 0.52524 | lr = 0.01000
Epoch [41/150] | Validation | acc1 = 84.580 | acc5 = 98.457 | loss = 0.52933
========================================
Epoch [42/150] | Train iter [50/586] | acc1 = 86.328 | acc5 = 98.438 | loss = 0.50524 | lr = 0.01000
Epoch [42/150] | Train iter [100/586] | acc1 = 85.547 | acc5 = 99.219 | loss = 0.50039 | lr = 0.01000
Epoch [42/150] | Train iter [150/586] | acc1 = 83.984 | acc5 = 98.438 | loss = 0.49963 | lr = 0.01000
Epoch [42/150] | Train iter [200/586] | acc1 = 86.328 | acc5 = 98.438 | loss = 0.50795 | lr = 0.01000
Epoch [42/150] | Train iter [250/586] | acc1 = 81.250 | acc5 = 96.484 | loss = 0.51125 | lr = 0.01000
Epoch [42/150] | Train iter [300/586] | acc1 = 80.469 | acc5 = 96.094 | loss = 0.51174 | lr = 0.01000
Epoch [42/150] | Train iter [350/586] | acc1 = 86.328 | acc5 = 98.828 | loss = 0.51668 | lr = 0.01000
Epoch [42/150] | Train iter [400/586] | acc1 = 85.938 | acc5 = 99.219 | loss = 0.51693 | lr = 0.01000
Epoch [42/150] | Train iter [450/586] | acc1 = 87.500 | acc5 = 98.828 | loss = 0.51722 | lr = 0.01000
Epoch [42/150] | Train iter [500/586] | acc1 = 82.422 | acc5 = 97.656 | loss = 0.51927 | lr = 0.01000
Epoch [42/150] | Train iter [550/586] | acc1 = 80.078 | acc5 = 97.266 | loss = 0.52071 | lr = 0.01000
Epoch [42/150] | Train iter [586/586] | acc1 = 80.833 | acc5 = 96.667 | loss = 0.52099 | lr = 0.01000
Epoch [42/150] | Validation | acc1 = 85.176 | acc5 = 98.604 | loss = 0.49678
========================================
Epoch [43/150] | Train iter [50/586] | acc1 = 83.984 | acc5 = 98.047 | loss = 0.51334 | lr = 0.01000
Epoch [43/150] | Train iter [100/586] | acc1 = 81.250 | acc5 = 96.875 | loss = 0.50981 | lr = 0.01000
Epoch [43/150] | Train iter [150/586] | acc1 = 86.328 | acc5 = 98.047 | loss = 0.50698 | lr = 0.01000
Epoch [43/150] | Train iter [200/586] | acc1 = 80.859 | acc5 = 97.266 | loss = 0.51108 | lr = 0.01000
Epoch [43/150] | Train iter [250/586] | acc1 = 83.594 | acc5 = 97.266 | loss = 0.51913 | lr = 0.01000
Epoch [43/150] | Train iter [300/586] | acc1 = 85.156 | acc5 = 97.266 | loss = 0.51873 | lr = 0.01000
Epoch [43/150] | Train iter [350/586] | acc1 = 84.375 | acc5 = 98.047 | loss = 0.52152 | lr = 0.01000
Epoch [43/150] | Train iter [400/586] | acc1 = 86.719 | acc5 = 98.438 | loss = 0.52338 | lr = 0.01000
Epoch [43/150] | Train iter [450/586] | acc1 = 85.547 | acc5 = 98.828 | loss = 0.52377 | lr = 0.01000
Epoch [43/150] | Train iter [500/586] | acc1 = 80.859 | acc5 = 96.875 | loss = 0.52384 | lr = 0.01000
Epoch [43/150] | Train iter [550/586] | acc1 = 82.812 | acc5 = 97.266 | loss = 0.52220 | lr = 0.01000
Epoch [43/150] | Train iter [586/586] | acc1 = 85.833 | acc5 = 98.333 | loss = 0.52194 | lr = 0.01000
Epoch [43/150] | Validation | acc1 = 85.693 | acc5 = 98.857 | loss = 0.47804
========================================
Epoch [44/150] | Train iter [50/586] | acc1 = 81.641 | acc5 = 97.266 | loss = 0.50503 | lr = 0.01000
Epoch [44/150] | Train iter [100/586] | acc1 = 82.812 | acc5 = 96.484 | loss = 0.51382 | lr = 0.01000
Epoch [44/150] | Train iter [150/586] | acc1 = 83.203 | acc5 = 98.828 | loss = 0.51513 | lr = 0.01000
Epoch [44/150] | Train iter [200/586] | acc1 = 88.672 | acc5 = 98.828 | loss = 0.51775 | lr = 0.01000
Epoch [44/150] | Train iter [250/586] | acc1 = 86.719 | acc5 = 98.828 | loss = 0.51518 | lr = 0.01000
Epoch [44/150] | Train iter [300/586] | acc1 = 82.812 | acc5 = 98.047 | loss = 0.51498 | lr = 0.01000
Epoch [44/150] | Train iter [350/586] | acc1 = 83.594 | acc5 = 98.438 | loss = 0.51734 | lr = 0.01000
Epoch [44/150] | Train iter [400/586] | acc1 = 83.594 | acc5 = 98.828 | loss = 0.51880 | lr = 0.01000
Epoch [44/150] | Train iter [450/586] | acc1 = 85.938 | acc5 = 97.656 | loss = 0.52005 | lr = 0.01000
Epoch [44/150] | Train iter [500/586] | acc1 = 85.156 | acc5 = 97.656 | loss = 0.52090 | lr = 0.01000
Epoch [44/150] | Train iter [550/586] | acc1 = 81.250 | acc5 = 97.266 | loss = 0.52118 | lr = 0.01000
Epoch [44/150] | Train iter [586/586] | acc1 = 80.000 | acc5 = 97.917 | loss = 0.52404 | lr = 0.01000
Epoch [44/150] | Validation | acc1 = 83.809 | acc5 = 97.998 | loss = 0.55172
========================================
Epoch [45/150] | Train iter [50/586] | acc1 = 87.891 | acc5 = 99.219 | loss = 0.50072 | lr = 0.01000
Epoch [45/150] | Train iter [100/586] | acc1 = 79.297 | acc5 = 96.484 | loss = 0.50004 | lr = 0.01000
Epoch [45/150] | Train iter [150/586] | acc1 = 86.719 | acc5 = 97.266 | loss = 0.50620 | lr = 0.01000
Epoch [45/150] | Train iter [200/586] | acc1 = 83.203 | acc5 = 97.656 | loss = 0.51331 | lr = 0.01000
Epoch [45/150] | Train iter [250/586] | acc1 = 85.547 | acc5 = 96.875 | loss = 0.51658 | lr = 0.01000
Epoch [45/150] | Train iter [300/586] | acc1 = 82.812 | acc5 = 98.828 | loss = 0.51364 | lr = 0.01000
Epoch [45/150] | Train iter [350/586] | acc1 = 86.719 | acc5 = 98.828 | loss = 0.51747 | lr = 0.01000
Epoch [45/150] | Train iter [400/586] | acc1 = 83.594 | acc5 = 98.438 | loss = 0.52106 | lr = 0.01000
Epoch [45/150] | Train iter [450/586] | acc1 = 88.281 | acc5 = 98.438 | loss = 0.52055 | lr = 0.01000
Epoch [45/150] | Train iter [500/586] | acc1 = 81.250 | acc5 = 97.656 | loss = 0.52210 | lr = 0.01000
Epoch [45/150] | Train iter [550/586] | acc1 = 82.031 | acc5 = 98.047 | loss = 0.52292 | lr = 0.01000
Epoch [45/150] | Train iter [586/586] | acc1 = 81.667 | acc5 = 98.750 | loss = 0.52625 | lr = 0.01000
Epoch [45/150] | Validation | acc1 = 86.494 | acc5 = 98.838 | loss = 0.42819
Save best model at acc: 86.4941, loss: 0.4282!
========================================
Epoch [46/150] | Train iter [50/586] | acc1 = 84.375 | acc5 = 98.438 | loss = 0.49844 | lr = 0.01000
Epoch [46/150] | Train iter [100/586] | acc1 = 85.938 | acc5 = 98.047 | loss = 0.49435 | lr = 0.01000
Epoch [46/150] | Train iter [150/586] | acc1 = 79.688 | acc5 = 96.094 | loss = 0.49724 | lr = 0.01000
Epoch [46/150] | Train iter [200/586] | acc1 = 87.500 | acc5 = 98.438 | loss = 0.49887 | lr = 0.01000
Epoch [46/150] | Train iter [250/586] | acc1 = 83.203 | acc5 = 98.047 | loss = 0.50148 | lr = 0.01000
Epoch [46/150] | Train iter [300/586] | acc1 = 81.641 | acc5 = 98.047 | loss = 0.50545 | lr = 0.01000
Epoch [46/150] | Train iter [350/586] | acc1 = 82.812 | acc5 = 96.875 | loss = 0.51178 | lr = 0.01000
Epoch [46/150] | Train iter [400/586] | acc1 = 82.812 | acc5 = 97.266 | loss = 0.51432 | lr = 0.01000
Epoch [46/150] | Train iter [450/586] | acc1 = 83.984 | acc5 = 98.828 | loss = 0.51384 | lr = 0.01000
Epoch [46/150] | Train iter [500/586] | acc1 = 80.469 | acc5 = 98.047 | loss = 0.51542 | lr = 0.01000
Epoch [46/150] | Train iter [550/586] | acc1 = 85.938 | acc5 = 98.438 | loss = 0.51454 | lr = 0.01000
Epoch [46/150] | Train iter [586/586] | acc1 = 85.000 | acc5 = 96.250 | loss = 0.51571 | lr = 0.01000
Epoch [46/150] | Validation | acc1 = 85.088 | acc5 = 98.623 | loss = 0.49644
========================================
Epoch [47/150] | Train iter [50/586] | acc1 = 85.938 | acc5 = 99.609 | loss = 0.48702 | lr = 0.01000
Epoch [47/150] | Train iter [100/586] | acc1 = 83.203 | acc5 = 98.047 | loss = 0.50140 | lr = 0.01000
Epoch [47/150] | Train iter [150/586] | acc1 = 82.812 | acc5 = 99.219 | loss = 0.50813 | lr = 0.01000
Epoch [47/150] | Train iter [200/586] | acc1 = 87.109 | acc5 = 97.656 | loss = 0.51271 | lr = 0.01000
Epoch [47/150] | Train iter [250/586] | acc1 = 83.594 | acc5 = 98.438 | loss = 0.51104 | lr = 0.01000
Epoch [47/150] | Train iter [300/586] | acc1 = 88.672 | acc5 = 98.828 | loss = 0.51031 | lr = 0.01000
Epoch [47/150] | Train iter [350/586] | acc1 = 84.766 | acc5 = 98.438 | loss = 0.51083 | lr = 0.01000
Epoch [47/150] | Train iter [400/586] | acc1 = 86.328 | acc5 = 98.047 | loss = 0.51125 | lr = 0.01000
Epoch [47/150] | Train iter [450/586] | acc1 = 85.547 | acc5 = 98.047 | loss = 0.51407 | lr = 0.01000
Epoch [47/150] | Train iter [500/586] | acc1 = 85.547 | acc5 = 97.656 | loss = 0.51669 | lr = 0.01000
Epoch [47/150] | Train iter [550/586] | acc1 = 81.250 | acc5 = 98.828 | loss = 0.51906 | lr = 0.01000
Epoch [47/150] | Train iter [586/586] | acc1 = 80.833 | acc5 = 96.250 | loss = 0.51914 | lr = 0.01000
Epoch [47/150] | Validation | acc1 = 80.918 | acc5 = 98.291 | loss = 0.66476
========================================
Epoch [48/150] | Train iter [50/586] | acc1 = 87.109 | acc5 = 98.828 | loss = 0.48601 | lr = 0.01000
Epoch [48/150] | Train iter [100/586] | acc1 = 86.328 | acc5 = 96.484 | loss = 0.49369 | lr = 0.01000
Epoch [48/150] | Train iter [150/586] | acc1 = 85.156 | acc5 = 97.266 | loss = 0.50564 | lr = 0.01000
Epoch [48/150] | Train iter [200/586] | acc1 = 79.688 | acc5 = 98.438 | loss = 0.51095 | lr = 0.01000
Epoch [48/150] | Train iter [250/586] | acc1 = 83.984 | acc5 = 97.656 | loss = 0.51167 | lr = 0.01000
Epoch [48/150] | Train iter [300/586] | acc1 = 80.469 | acc5 = 97.266 | loss = 0.51318 | lr = 0.01000
Epoch [48/150] | Train iter [350/586] | acc1 = 83.594 | acc5 = 98.438 | loss = 0.51201 | lr = 0.01000
Epoch [48/150] | Train iter [400/586] | acc1 = 83.203 | acc5 = 96.875 | loss = 0.51515 | lr = 0.01000
Epoch [48/150] | Train iter [450/586] | acc1 = 80.469 | acc5 = 95.703 | loss = 0.51469 | lr = 0.01000
Epoch [48/150] | Train iter [500/586] | acc1 = 81.641 | acc5 = 97.656 | loss = 0.51378 | lr = 0.01000
Epoch [48/150] | Train iter [550/586] | acc1 = 83.984 | acc5 = 97.266 | loss = 0.51500 | lr = 0.01000
Epoch [48/150] | Train iter [586/586] | acc1 = 83.333 | acc5 = 95.417 | loss = 0.51750 | lr = 0.01000
Epoch [48/150] | Validation | acc1 = 81.650 | acc5 = 97.412 | loss = 0.61994
========================================
Epoch [49/150] | Train iter [50/586] | acc1 = 85.156 | acc5 = 98.438 | loss = 0.50673 | lr = 0.01000
Epoch [49/150] | Train iter [100/586] | acc1 = 88.281 | acc5 = 98.047 | loss = 0.50062 | lr = 0.01000
Epoch [49/150] | Train iter [150/586] | acc1 = 83.984 | acc5 = 96.875 | loss = 0.49883 | lr = 0.01000
Epoch [49/150] | Train iter [200/586] | acc1 = 84.766 | acc5 = 98.828 | loss = 0.50193 | lr = 0.01000
Epoch [49/150] | Train iter [250/586] | acc1 = 85.156 | acc5 = 99.219 | loss = 0.50439 | lr = 0.01000
Epoch [49/150] | Train iter [300/586] | acc1 = 80.469 | acc5 = 96.875 | loss = 0.50640 | lr = 0.01000
Epoch [49/150] | Train iter [350/586] | acc1 = 82.422 | acc5 = 97.656 | loss = 0.50977 | lr = 0.01000
Epoch [49/150] | Train iter [400/586] | acc1 = 81.250 | acc5 = 98.047 | loss = 0.51050 | lr = 0.01000
Epoch [49/150] | Train iter [450/586] | acc1 = 83.203 | acc5 = 96.484 | loss = 0.51306 | lr = 0.01000
Epoch [49/150] | Train iter [500/586] | acc1 = 84.375 | acc5 = 98.438 | loss = 0.51697 | lr = 0.01000
Epoch [49/150] | Train iter [550/586] | acc1 = 82.812 | acc5 = 97.656 | loss = 0.51718 | lr = 0.01000
Epoch [49/150] | Train iter [586/586] | acc1 = 85.000 | acc5 = 98.333 | loss = 0.51725 | lr = 0.01000
Epoch [49/150] | Validation | acc1 = 82.012 | acc5 = 98.115 | loss = 0.61074
========================================
Epoch [50/150] | Train iter [50/586] | acc1 = 87.891 | acc5 = 98.828 | loss = 0.47893 | lr = 0.01000
Epoch [50/150] | Train iter [100/586] | acc1 = 78.516 | acc5 = 96.875 | loss = 0.49183 | lr = 0.01000
Epoch [50/150] | Train iter [150/586] | acc1 = 84.375 | acc5 = 98.438 | loss = 0.49547 | lr = 0.01000
Epoch [50/150] | Train iter [200/586] | acc1 = 82.422 | acc5 = 98.438 | loss = 0.50015 | lr = 0.01000
Epoch [50/150] | Train iter [250/586] | acc1 = 83.594 | acc5 = 98.828 | loss = 0.50341 | lr = 0.01000
Epoch [50/150] | Train iter [300/586] | acc1 = 80.469 | acc5 = 96.484 | loss = 0.50803 | lr = 0.01000
Epoch [50/150] | Train iter [350/586] | acc1 = 80.469 | acc5 = 96.875 | loss = 0.51164 | lr = 0.01000
Epoch [50/150] | Train iter [400/586] | acc1 = 83.203 | acc5 = 98.828 | loss = 0.51291 | lr = 0.01000
Epoch [50/150] | Train iter [450/586] | acc1 = 79.297 | acc5 = 95.703 | loss = 0.51462 | lr = 0.01000
Epoch [50/150] | Train iter [500/586] | acc1 = 80.078 | acc5 = 93.750 | loss = 0.51582 | lr = 0.01000
Epoch [50/150] | Train iter [550/586] | acc1 = 83.984 | acc5 = 96.875 | loss = 0.51791 | lr = 0.01000
Epoch [50/150] | Train iter [586/586] | acc1 = 85.417 | acc5 = 97.500 | loss = 0.51820 | lr = 0.01000
Epoch [50/150] | Validation | acc1 = 84.326 | acc5 = 98.730 | loss = 0.53858
========================================
Epoch [51/150] | Train iter [50/586] | acc1 = 85.547 | acc5 = 98.438 | loss = 0.47672 | lr = 0.01000
Epoch [51/150] | Train iter [100/586] | acc1 = 84.766 | acc5 = 99.219 | loss = 0.48990 | lr = 0.01000
Epoch [51/150] | Train iter [150/586] | acc1 = 87.109 | acc5 = 97.266 | loss = 0.50008 | lr = 0.01000
Epoch [51/150] | Train iter [200/586] | acc1 = 82.422 | acc5 = 96.875 | loss = 0.50378 | lr = 0.01000
Epoch [51/150] | Train iter [250/586] | acc1 = 82.812 | acc5 = 99.219 | loss = 0.50908 | lr = 0.01000
Epoch [51/150] | Train iter [300/586] | acc1 = 82.031 | acc5 = 98.047 | loss = 0.51172 | lr = 0.01000
Epoch [51/150] | Train iter [350/586] | acc1 = 82.422 | acc5 = 97.656 | loss = 0.51310 | lr = 0.01000
Epoch [51/150] | Train iter [400/586] | acc1 = 82.422 | acc5 = 98.438 | loss = 0.51495 | lr = 0.01000
Epoch [51/150] | Train iter [450/586] | acc1 = 81.641 | acc5 = 98.828 | loss = 0.51720 | lr = 0.01000
Epoch [51/150] | Train iter [500/586] | acc1 = 85.547 | acc5 = 98.047 | loss = 0.51712 | lr = 0.01000
Epoch [51/150] | Train iter [550/586] | acc1 = 78.906 | acc5 = 97.656 | loss = 0.51793 | lr = 0.01000
Epoch [51/150] | Train iter [586/586] | acc1 = 86.250 | acc5 = 97.917 | loss = 0.51986 | lr = 0.01000
Epoch [51/150] | Validation | acc1 = 83.457 | acc5 = 98.330 | loss = 0.55860
========================================
Epoch [52/150] | Train iter [50/586] | acc1 = 83.984 | acc5 = 95.703 | loss = 0.49903 | lr = 0.01000
Epoch [52/150] | Train iter [100/586] | acc1 = 87.500 | acc5 = 97.266 | loss = 0.50648 | lr = 0.01000
Epoch [52/150] | Train iter [150/586] | acc1 = 82.031 | acc5 = 98.828 | loss = 0.51444 | lr = 0.01000
Epoch [52/150] | Train iter [200/586] | acc1 = 85.547 | acc5 = 99.609 | loss = 0.51560 | lr = 0.01000
Epoch [52/150] | Train iter [250/586] | acc1 = 83.594 | acc5 = 98.047 | loss = 0.51697 | lr = 0.01000
Epoch [52/150] | Train iter [300/586] | acc1 = 85.938 | acc5 = 98.828 | loss = 0.51383 | lr = 0.01000
Epoch [52/150] | Train iter [350/586] | acc1 = 89.844 | acc5 = 98.828 | loss = 0.51469 | lr = 0.01000
Epoch [52/150] | Train iter [400/586] | acc1 = 83.594 | acc5 = 97.266 | loss = 0.51395 | lr = 0.01000
Epoch [52/150] | Train iter [450/586] | acc1 = 84.375 | acc5 = 98.047 | loss = 0.51381 | lr = 0.01000
Epoch [52/150] | Train iter [500/586] | acc1 = 79.688 | acc5 = 96.875 | loss = 0.51631 | lr = 0.01000
Epoch [52/150] | Train iter [550/586] | acc1 = 87.109 | acc5 = 98.047 | loss = 0.51834 | lr = 0.01000
Epoch [52/150] | Train iter [586/586] | acc1 = 82.917 | acc5 = 97.917 | loss = 0.51833 | lr = 0.01000
Epoch [52/150] | Validation | acc1 = 78.594 | acc5 = 97.217 | loss = 0.79575
========================================
Epoch [53/150] | Train iter [50/586] | acc1 = 83.594 | acc5 = 96.484 | loss = 0.49833 | lr = 0.01000
Epoch [53/150] | Train iter [100/586] | acc1 = 84.766 | acc5 = 97.656 | loss = 0.50014 | lr = 0.01000
Epoch [53/150] | Train iter [150/586] | acc1 = 89.062 | acc5 = 98.047 | loss = 0.50150 | lr = 0.01000
Epoch [53/150] | Train iter [200/586] | acc1 = 87.891 | acc5 = 96.875 | loss = 0.50257 | lr = 0.01000
Epoch [53/150] | Train iter [250/586] | acc1 = 83.594 | acc5 = 98.828 | loss = 0.50829 | lr = 0.01000
Epoch [53/150] | Train iter [300/586] | acc1 = 83.984 | acc5 = 99.219 | loss = 0.50693 | lr = 0.01000
Epoch [53/150] | Train iter [350/586] | acc1 = 83.594 | acc5 = 97.266 | loss = 0.51085 | lr = 0.01000
Epoch [53/150] | Train iter [400/586] | acc1 = 81.250 | acc5 = 97.656 | loss = 0.51178 | lr = 0.01000
Epoch [53/150] | Train iter [450/586] | acc1 = 83.594 | acc5 = 98.047 | loss = 0.51399 | lr = 0.01000
Epoch [53/150] | Train iter [500/586] | acc1 = 89.062 | acc5 = 98.047 | loss = 0.51250 | lr = 0.01000
Epoch [53/150] | Train iter [550/586] | acc1 = 81.641 | acc5 = 98.047 | loss = 0.51408 | lr = 0.01000
Epoch [53/150] | Train iter [586/586] | acc1 = 84.167 | acc5 = 98.750 | loss = 0.51341 | lr = 0.01000
Epoch [53/150] | Validation | acc1 = 86.143 | acc5 = 98.828 | loss = 0.47666
========================================
Epoch [54/150] | Train iter [50/586] | acc1 = 81.641 | acc5 = 98.047 | loss = 0.52125 | lr = 0.01000
Epoch [54/150] | Train iter [100/586] | acc1 = 81.641 | acc5 = 96.484 | loss = 0.51276 | lr = 0.01000
Epoch [54/150] | Train iter [150/586] | acc1 = 81.250 | acc5 = 96.094 | loss = 0.51055 | lr = 0.01000
Epoch [54/150] | Train iter [200/586] | acc1 = 84.375 | acc5 = 96.484 | loss = 0.50750 | lr = 0.01000
Epoch [54/150] | Train iter [250/586] | acc1 = 87.891 | acc5 = 98.828 | loss = 0.50894 | lr = 0.01000
Epoch [54/150] | Train iter [300/586] | acc1 = 87.500 | acc5 = 97.656 | loss = 0.51013 | lr = 0.01000
Epoch [54/150] | Train iter [350/586] | acc1 = 85.938 | acc5 = 97.266 | loss = 0.51092 | lr = 0.01000
Epoch [54/150] | Train iter [400/586] | acc1 = 86.719 | acc5 = 97.266 | loss = 0.51325 | lr = 0.01000
Epoch [54/150] | Train iter [450/586] | acc1 = 80.859 | acc5 = 96.484 | loss = 0.51369 | lr = 0.01000
Epoch [54/150] | Train iter [500/586] | acc1 = 78.125 | acc5 = 97.266 | loss = 0.51682 | lr = 0.01000
Epoch [54/150] | Train iter [550/586] | acc1 = 80.469 | acc5 = 95.703 | loss = 0.51795 | lr = 0.01000
Epoch [54/150] | Train iter [586/586] | acc1 = 80.000 | acc5 = 97.500 | loss = 0.51938 | lr = 0.01000
Epoch [54/150] | Validation | acc1 = 85.537 | acc5 = 98.711 | loss = 0.48205
========================================
Epoch [55/150] | Train iter [50/586] | acc1 = 80.078 | acc5 = 98.828 | loss = 0.49396 | lr = 0.01000
Epoch [55/150] | Train iter [100/586] | acc1 = 82.031 | acc5 = 97.656 | loss = 0.49620 | lr = 0.01000
Epoch [55/150] | Train iter [150/586] | acc1 = 83.594 | acc5 = 98.438 | loss = 0.49592 | lr = 0.01000
Epoch [55/150] | Train iter [200/586] | acc1 = 86.328 | acc5 = 98.047 | loss = 0.50454 | lr = 0.01000
Epoch [55/150] | Train iter [250/586] | acc1 = 80.469 | acc5 = 98.047 | loss = 0.50748 | lr = 0.01000
Epoch [55/150] | Train iter [300/586] | acc1 = 85.156 | acc5 = 99.219 | loss = 0.50936 | lr = 0.01000
Epoch [55/150] | Train iter [350/586] | acc1 = 80.469 | acc5 = 97.266 | loss = 0.51044 | lr = 0.01000
Epoch [55/150] | Train iter [400/586] | acc1 = 85.156 | acc5 = 97.656 | loss = 0.50886 | lr = 0.01000
Epoch [55/150] | Train iter [450/586] | acc1 = 83.594 | acc5 = 96.875 | loss = 0.51202 | lr = 0.01000
Epoch [55/150] | Train iter [500/586] | acc1 = 82.422 | acc5 = 99.219 | loss = 0.51459 | lr = 0.01000
Epoch [55/150] | Train iter [550/586] | acc1 = 82.422 | acc5 = 97.266 | loss = 0.51353 | lr = 0.01000
Epoch [55/150] | Train iter [586/586] | acc1 = 79.167 | acc5 = 98.333 | loss = 0.51492 | lr = 0.01000
Epoch [55/150] | Validation | acc1 = 81.943 | acc5 = 97.471 | loss = 0.60061
========================================
Epoch [56/150] | Train iter [50/586] | acc1 = 81.641 | acc5 = 98.047 | loss = 0.50476 | lr = 0.01000
Epoch [56/150] | Train iter [100/586] | acc1 = 81.250 | acc5 = 97.266 | loss = 0.51249 | lr = 0.01000
Epoch [56/150] | Train iter [150/586] | acc1 = 81.250 | acc5 = 98.047 | loss = 0.50343 | lr = 0.01000
Epoch [56/150] | Train iter [200/586] | acc1 = 86.719 | acc5 = 98.047 | loss = 0.50599 | lr = 0.01000
Epoch [56/150] | Train iter [250/586] | acc1 = 80.078 | acc5 = 96.484 | loss = 0.51132 | lr = 0.01000
Epoch [56/150] | Train iter [300/586] | acc1 = 83.594 | acc5 = 98.047 | loss = 0.51354 | lr = 0.01000
Epoch [56/150] | Train iter [350/586] | acc1 = 83.984 | acc5 = 98.047 | loss = 0.51612 | lr = 0.01000
Epoch [56/150] | Train iter [400/586] | acc1 = 82.812 | acc5 = 97.266 | loss = 0.51738 | lr = 0.01000
Epoch [56/150] | Train iter [450/586] | acc1 = 80.469 | acc5 = 96.484 | loss = 0.51614 | lr = 0.01000
Epoch [56/150] | Train iter [500/586] | acc1 = 84.375 | acc5 = 98.828 | loss = 0.51658 | lr = 0.01000
Epoch [56/150] | Train iter [550/586] | acc1 = 86.719 | acc5 = 98.828 | loss = 0.51684 | lr = 0.01000
Epoch [56/150] | Train iter [586/586] | acc1 = 86.250 | acc5 = 97.500 | loss = 0.51758 | lr = 0.01000
Epoch [56/150] | Validation | acc1 = 85.752 | acc5 = 98.711 | loss = 0.47053
========================================
Epoch [57/150] | Train iter [50/586] | acc1 = 80.859 | acc5 = 97.266 | loss = 0.48058 | lr = 0.01000
Epoch [57/150] | Train iter [100/586] | acc1 = 84.375 | acc5 = 98.047 | loss = 0.49084 | lr = 0.01000
Epoch [57/150] | Train iter [150/586] | acc1 = 85.547 | acc5 = 98.047 | loss = 0.49531 | lr = 0.01000
Epoch [57/150] | Train iter [200/586] | acc1 = 80.859 | acc5 = 98.438 | loss = 0.50167 | lr = 0.01000
Epoch [57/150] | Train iter [250/586] | acc1 = 83.203 | acc5 = 97.266 | loss = 0.50234 | lr = 0.01000
Epoch [57/150] | Train iter [300/586] | acc1 = 81.641 | acc5 = 96.875 | loss = 0.50699 | lr = 0.01000
Epoch [57/150] | Train iter [350/586] | acc1 = 80.469 | acc5 = 97.656 | loss = 0.50703 | lr = 0.01000
Epoch [57/150] | Train iter [400/586] | acc1 = 83.984 | acc5 = 96.484 | loss = 0.50737 | lr = 0.01000
Epoch [57/150] | Train iter [450/586] | acc1 = 83.203 | acc5 = 96.484 | loss = 0.51039 | lr = 0.01000
Epoch [57/150] | Train iter [500/586] | acc1 = 82.812 | acc5 = 98.047 | loss = 0.51006 | lr = 0.01000
Epoch [57/150] | Train iter [550/586] | acc1 = 80.469 | acc5 = 97.266 | loss = 0.51143 | lr = 0.01000
Epoch [57/150] | Train iter [586/586] | acc1 = 82.500 | acc5 = 96.667 | loss = 0.51352 | lr = 0.01000
Epoch [57/150] | Validation | acc1 = 84.844 | acc5 = 98.203 | loss = 0.49827
========================================