CUDA available: True
Num training images: 49
Epoch: 0 | Iteration: 0 | Classification loss: 1.15717 | Regression loss: 1.02555 | Running loss: 2.18272
Epoch: 0 | Iteration: 1 | Classification loss: 1.18938 | Regression loss: 1.06953 | Running loss: 2.22081
Epoch: 0 | Iteration: 2 | Classification loss: 1.21099 | Regression loss: 1.05596 | Running loss: 2.23619
Epoch: 0 | Iteration: 3 | Classification loss: 1.17451 | Regression loss: 1.03986 | Running loss: 2.23073
Epoch: 0 | Iteration: 4 | Classification loss: 1.21018 | Regression loss: 1.15078 | Running loss: 2.25678
Epoch: 0 | Iteration: 5 | Classification loss: 1.17310 | Regression loss: 1.06271 | Running loss: 2.25328
Epoch: 0 | Iteration: 6 | Classification loss: 1.29184 | Regression loss: 1.22806 | Running loss: 2.29137
Epoch: 0 | Iteration: 7 | Classification loss: 1.23927 | Regression loss: 1.06368 | Running loss: 2.29282
Epoch: 0 | Iteration: 8 | Classification loss: 1.17078 | Regression loss: 1.08701 | Running loss: 2.28893
Epoch: 0 | Iteration: 9 | Classification loss: 1.16980 | Regression loss: 1.02959 | Running loss: 2.27997
Epoch: 0 | Iteration: 10 | Classification loss: 1.14136 | Regression loss: 1.03665 | Running loss: 2.27070
Epoch: 0 | Iteration: 11 | Classification loss: 1.18279 | Regression loss: 1.02904 | Running loss: 2.26580
Epoch: 0 | Iteration: 12 | Classification loss: 1.15179 | Regression loss: 1.05635 | Running loss: 2.26136
Epoch: 0 | Iteration: 13 | Classification loss: 1.08397 | Regression loss: 1.02763 | Running loss: 2.25066
Epoch: 0 | Iteration: 14 | Classification loss: 1.17279 | Regression loss: 1.01088 | Running loss: 2.24620
Epoch: 0 | Iteration: 15 | Classification loss: 1.14043 | Regression loss: 1.04205 | Running loss: 2.24222
Epoch: 0 | Iteration: 16 | Classification loss: 1.18793 | Regression loss: 1.03026 | Running loss: 2.24080
Epoch: 0 | Iteration: 17 | Classification loss: 1.20704 | Regression loss: 1.03542 | Running loss: 2.24090
Epoch: 0 | Iteration: 18 | Classification loss: 1.09298 | Regression loss: 1.05931 | Running loss: 2.23623
Epoch: 0 | Iteration: 19 | Classification loss: 1.17185 | Regression loss: 1.06065 | Running loss: 2.23605
Epoch: 0 | Iteration: 20 | Classification loss: 1.15081 | Regression loss: 1.07837 | Running loss: 2.23572
Epoch: 0 | Iteration: 21 | Classification loss: 1.17758 | Regression loss: 1.01141 | Running loss: 2.23359
Epoch: 0 | Iteration: 22 | Classification loss: 1.13545 | Regression loss: 0.99739 | Running loss: 2.22921
Epoch: 0 | Iteration: 23 | Classification loss: 1.15342 | Regression loss: 1.03607 | Running loss: 2.22756
Epoch: 0 | Iteration: 24 | Classification loss: 1.14240 | Regression loss: 1.05062 | Running loss: 2.22618
Epoch: 1 | Iteration: 0 | Classification loss: 1.12465 | Regression loss: 1.00963 | Running loss: 2.22264
Epoch: 1 | Iteration: 1 | Classification loss: 1.12502 | Regression loss: 1.04493 | Running loss: 2.22069
Epoch: 1 | Iteration: 2 | Classification loss: 1.08626 | Regression loss: 0.99860 | Running loss: 2.21584
Epoch: 1 | Iteration: 3 | Classification loss: 1.13294 | Regression loss: 0.96818 | Running loss: 2.21188
Epoch: 1 | Iteration: 4 | Classification loss: 1.11283 | Regression loss: 1.01668 | Running loss: 2.20914
Epoch: 1 | Iteration: 5 | Classification loss: 0.98570 | Regression loss: 1.03385 | Running loss: 2.20302
Epoch: 1 | Iteration: 6 | Classification loss: 1.15518 | Regression loss: 1.03022 | Running loss: 2.20247
Epoch: 1 | Iteration: 7 | Classification loss: 1.12008 | Regression loss: 1.01442 | Running loss: 2.20041
Epoch: 1 | Iteration: 8 | Classification loss: 1.20642 | Regression loss: 1.24842 | Running loss: 2.20790
Epoch: 1 | Iteration: 9 | Classification loss: 1.04473 | Regression loss: 1.11142 | Running loss: 2.20642
Epoch: 1 | Iteration: 10 | Classification loss: 0.97516 | Regression loss: 1.05675 | Running loss: 2.20157
Epoch: 1 | Iteration: 11 | Classification loss: 0.87426 | Regression loss: 0.99547 | Running loss: 2.19260
Epoch: 1 | Iteration: 12 | Classification loss: 1.04336 | Regression loss: 0.96756 | Running loss: 2.18782
Epoch: 1 | Iteration: 13 | Classification loss: 0.98875 | Regression loss: 1.00454 | Running loss: 2.18283
Epoch: 1 | Iteration: 14 | Classification loss: 1.00869 | Regression loss: 1.07370 | Running loss: 2.18032
Epoch: 1 | Iteration: 15 | Classification loss: 1.01848 | Regression loss: 0.97584 | Running loss: 2.17578
Epoch: 1 | Iteration: 16 | Classification loss: 1.19305 | Regression loss: 1.05751 | Running loss: 2.17756
Epoch: 1 | Iteration: 17 | Classification loss: 1.06856 | Regression loss: 1.00376 | Running loss: 2.17512
Epoch: 1 | Iteration: 18 | Classification loss: 1.08232 | Regression loss: 1.02514 | Running loss: 2.17358
Epoch: 1 | Iteration: 19 | Classification loss: 1.06384 | Regression loss: 1.03402 | Running loss: 2.17190
Epoch: 1 | Iteration: 20 | Classification loss: 1.04267 | Regression loss: 1.05061 | Running loss: 2.17019
Epoch: 1 | Iteration: 21 | Classification loss: 0.95243 | Regression loss: 1.03172 | Running loss: 2.16623
Epoch: 1 | Iteration: 22 | Classification loss: 1.06779 | Regression loss: 0.96078 | Running loss: 2.16336
Epoch: 1 | Iteration: 23 | Classification loss: 1.03324 | Regression loss: 1.04194 | Running loss: 2.16156
Epoch: 1 | Iteration: 24 | Classification loss: 0.96801 | Regression loss: 1.00506 | Running loss: 2.15779
Model saved to: E:\test_onesis\1115-2\model_final.pth