Closed penguinwang96825 closed 10 months ago
train_epcoch.log files: epoch: 1 - train Avg_loss: 6.68, train Avg_acc: 15.58, train Lr_value: 4.00e-02 epoch: 2 - train Avg_loss: 2.07, train Avg_acc: 62.32, train Lr_value: 8.00e-02 epoch: 3 - train Avg_loss: 1.38, train Avg_acc: 75.12, train Lr_value: 1.20e-01 epoch: 4 - train Avg_loss: 1.19, train Avg_acc: 78.82, train Lr_value: 1.60e-01 epoch: 5 - train Avg_loss: 1.13, train Avg_acc: 80.03, train Lr_value: 2.00e-01 epoch: 6 - train Avg_loss: 1.07, train Avg_acc: 81.41, train Lr_value: 2.00e-01 epoch: 7 - train Avg_loss: 1.01, train Avg_acc: 82.61, train Lr_value: 2.00e-01 epoch: 8 - train Avg_loss: 9.65e-01, train Avg_acc: 83.32, train Lr_value: 1.99e-01 epoch: 9 - train Avg_loss: 9.32e-01, train Avg_acc: 83.95, train Lr_value: 1.98e-01 epoch: 10 - train Avg_loss: 9.09e-01, train Avg_acc: 84.43, train Lr_value: 1.97e-01 epoch: 11 - train Avg_loss: 8.86e-01, train Avg_acc: 84.84, train Lr_value: 1.96e-01 epoch: 12 - train Avg_loss: 8.79e-01, train Avg_acc: 84.96, train Lr_value: 1.94e-01 epoch: 13 - train Avg_loss: 8.61e-01, train Avg_acc: 85.27, train Lr_value: 1.93e-01 epoch: 14 - train Avg_loss: 8.48e-01, train Avg_acc: 85.57, train Lr_value: 1.91e-01 epoch: 15 - train Avg_loss: 8.36e-01, train Avg_acc: 85.86, train Lr_value: 1.89e-01 epoch: 16 - train Avg_loss: 8.23e-01, train Avg_acc: 86.08, train Lr_value: 1.86e-01 epoch: 17 - train Avg_loss: 8.13e-01, train Avg_acc: 86.17, train Lr_value: 1.84e-01 epoch: 18 - train Avg_loss: 8.01e-01, train Avg_acc: 86.48, train Lr_value: 1.81e-01 epoch: 19 - train Avg_loss: 7.94e-01, train Avg_acc: 86.55, train Lr_value: 1.78e-01 epoch: 20 - train Avg_loss: 7.81e-01, train Avg_acc: 86.78, train Lr_value: 1.75e-01 epoch: 21 - train Avg_loss: 1.06, train Avg_acc: 86.95, train Lr_value: 1.72e-01 epoch: 22 - train Avg_loss: 1.56, train Avg_acc: 88.16, train Lr_value: 1.68e-01 epoch: 23 - train Avg_loss: 2.04, train Avg_acc: 88.79, train Lr_value: 1.64e-01 epoch: 24 - train Avg_loss: 2.47, train Avg_acc: 89.23, train Lr_value: 1.61e-01 epoch: 25 - train Avg_loss: 2.87, train Avg_acc: 89.42, train Lr_value: 1.57e-01 epoch: 26 - train Avg_loss: 3.18, train Avg_acc: 89.68, train Lr_value: 1.53e-01 epoch: 27 - train Avg_loss: 3.43, train Avg_acc: 89.81, train Lr_value: 1.49e-01 epoch: 28 - train Avg_loss: 3.65, train Avg_acc: 89.99, train Lr_value: 1.44e-01 epoch: 29 - train Avg_loss: 3.83, train Avg_acc: 89.99, train Lr_value: 1.40e-01 epoch: 30 - train Avg_loss: 3.95, train Avg_acc: 90.09, train Lr_value: 1.35e-01 epoch: 31 - train Avg_loss: 4.02, train Avg_acc: 90.37, train Lr_value: 1.31e-01 epoch: 32 - train Avg_loss: 4.08, train Avg_acc: 90.52, train Lr_value: 1.26e-01 epoch: 33 - train Avg_loss: 4.10, train Avg_acc: 90.71, train Lr_value: 1.22e-01 epoch: 34 - train Avg_loss: 4.14, train Avg_acc: 90.73, train Lr_value: 1.17e-01 epoch: 35 - train Avg_loss: 4.13, train Avg_acc: 90.92, train Lr_value: 1.12e-01 epoch: 36 - train Avg_loss: 4.11, train Avg_acc: 91.15, train Lr_value: 1.07e-01 epoch: 37 - train Avg_loss: 4.10, train Avg_acc: 91.27, train Lr_value: 1.02e-01 epoch: 38 - train Avg_loss: 4.06, train Avg_acc: 91.43, train Lr_value: 9.76e-02 epoch: 39 - train Avg_loss: 4.05, train Avg_acc: 91.53, train Lr_value: 9.28e-02 epoch: 40 - train Avg_loss: 3.98, train Avg_acc: 91.76, train Lr_value: 8.80e-02 epoch: 41 - train Avg_loss: 3.95, train Avg_acc: 91.93, train Lr_value: 8.32e-02 epoch: 42 - train Avg_loss: 3.89, train Avg_acc: 92.10, train Lr_value: 7.85e-02 epoch: 43 - train Avg_loss: 3.85, train Avg_acc: 92.30, train Lr_value: 7.38e-02 epoch: 44 - train Avg_loss: 3.81, train Avg_acc: 92.33, train Lr_value: 6.91e-02 epoch: 45 - train Avg_loss: 3.73, train Avg_acc: 92.60, train Lr_value: 6.46e-02 epoch: 46 - train Avg_loss: 3.68, train Avg_acc: 92.72, train Lr_value: 6.01e-02 epoch: 47 - train Avg_loss: 3.61, train Avg_acc: 92.96, train Lr_value: 5.57e-02 epoch: 48 - train Avg_loss: 3.55, train Avg_acc: 93.08, train Lr_value: 5.14e-02 epoch: 49 - train Avg_loss: 3.49, train Avg_acc: 93.19, train Lr_value: 4.73e-02 epoch: 50 - train Avg_loss: 3.40, train Avg_acc: 93.51, train Lr_value: 4.32e-02 epoch: 51 - train Avg_loss: 3.33, train Avg_acc: 93.69, train Lr_value: 3.93e-02 epoch: 52 - train Avg_loss: 3.27, train Avg_acc: 93.80, train Lr_value: 3.56e-02 epoch: 53 - train Avg_loss: 3.21, train Avg_acc: 94.03, train Lr_value: 3.19e-02 epoch: 54 - train Avg_loss: 3.13, train Avg_acc: 94.12, train Lr_value: 2.85e-02 epoch: 55 - train Avg_loss: 3.06, train Avg_acc: 94.32, train Lr_value: 2.52e-02 epoch: 56 - train Avg_loss: 2.99, train Avg_acc: 94.48, train Lr_value: 2.21e-02 epoch: 57 - train Avg_loss: 2.90, train Avg_acc: 94.71, train Lr_value: 1.91e-02 epoch: 58 - train Avg_loss: 2.83, train Avg_acc: 94.90, train Lr_value: 1.64e-02 epoch: 59 - train Avg_loss: 2.75, train Avg_acc: 95.00, train Lr_value: 1.39e-02 epoch: 60 - train Avg_loss: 2.66, train Avg_acc: 95.16, train Lr_value: 1.15e-02 epoch: 61 - train Avg_loss: 2.61, train Avg_acc: 95.32, train Lr_value: 9.36e-03 epoch: 62 - train Avg_loss: 2.53, train Avg_acc: 95.52, train Lr_value: 7.43e-03 epoch: 63 - train Avg_loss: 2.43, train Avg_acc: 95.78, train Lr_value: 5.72e-03 epoch: 64 - train Avg_loss: 2.38, train Avg_acc: 95.80, train Lr_value: 4.22e-03 epoch: 65 - train Avg_loss: 2.32, train Avg_acc: 95.89, train Lr_value: 2.96e-03 epoch: 66 - train Avg_loss: 2.26, train Avg_acc: 96.04, train Lr_value: 1.91e-03 epoch: 67 - train Avg_loss: 2.22, train Avg_acc: 96.14, train Lr_value: 1.10e-03 epoch: 68 - train Avg_loss: 2.17, train Avg_acc: 96.24, train Lr_value: 5.17e-04 epoch: 69 - train Avg_loss: 2.15, train Avg_acc: 96.25, train Lr_value: 1.67e-04 epoch: 70 - train Avg_loss: 2.14, train Avg_acc: 96.24, train Lr_value: 5.00e-05
Thanks for the prompt reply!
@yfchenlucky I didn't change any configurations from the yaml file. At epoch 70, my training accuracy is 95.88%, I guess I just need to further optimise the model to get a similar result as yours.
I ran the exact same script for the EResNet experiment on VoxCeleb. The EER and minDCF I got is 1.0105 and 0.1146, which is not comparable to the paper. The only difference is that I trained the model on 4 A100 machines, but I doubt that is the reason behind. Can you please provide the
train.log
andtrain_epoch.log
files?I also notice that in prepare_data_csv.csv, the default segment duration is 4 seconds, but in conf/eres2net.yaml it's 3 seconds. May I ask why is that?