Datasets:
SunayBhat1
/
PureGen_Poisons

Dataset card Data Studio Files
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2020-01-31 20:20:47 Iteration 700 Training Loss: 6.648e-02 Loss in Target Net: 9.414e-03
2020-01-31 20:21:10 Iteration 750 Training Loss: 6.724e-02 Loss in Target Net: 8.768e-03
2020-01-31 20:21:33 Iteration 800 Training Loss: 6.995e-02 Loss in Target Net: 1.217e-02
2020-01-31 20:21:56 Iteration 850 Training Loss: 6.944e-02 Loss in Target Net: 8.728e-03
2020-01-31 20:22:19 Iteration 900 Training Loss: 6.818e-02 Loss in Target Net: 1.152e-02
2020-01-31 20:22:41 Iteration 950 Training Loss: 7.268e-02 Loss in Target Net: 9.525e-03
2020-01-31 20:23:03 Iteration 1000 Training Loss: 6.352e-02 Loss in Target Net: 1.280e-02
2020-01-31 20:23:24 Iteration 1050 Training Loss: 6.771e-02 Loss in Target Net: 1.165e-02
2020-01-31 20:23:45 Iteration 1100 Training Loss: 6.986e-02 Loss in Target Net: 1.200e-02
2020-01-31 20:24:05 Iteration 1150 Training Loss: 6.748e-02 Loss in Target Net: 1.214e-02
2020-01-31 20:24:26 Iteration 1200 Training Loss: 7.087e-02 Loss in Target Net: 9.921e-03
2020-01-31 20:24:46 Iteration 1250 Training Loss: 6.945e-02 Loss in Target Net: 1.028e-02
2020-01-31 20:25:07 Iteration 1300 Training Loss: 6.637e-02 Loss in Target Net: 9.684e-03
2020-01-31 20:25:27 Iteration 1350 Training Loss: 6.921e-02 Loss in Target Net: 1.191e-02
2020-01-31 20:25:48 Iteration 1400 Training Loss: 7.082e-02 Loss in Target Net: 9.283e-03
2020-01-31 20:26:09 Iteration 1450 Training Loss: 7.283e-02 Loss in Target Net: 1.054e-02
2020-01-31 20:26:30 Iteration 1500 Training Loss: 6.391e-02 Loss in Target Net: 8.260e-03
2020-01-31 20:26:51 Iteration 1550 Training Loss: 6.737e-02 Loss in Target Net: 1.033e-02
2020-01-31 20:27:13 Iteration 1600 Training Loss: 7.488e-02 Loss in Target Net: 1.033e-02
2020-01-31 20:27:33 Iteration 1650 Training Loss: 6.463e-02 Loss in Target Net: 1.051e-02
2020-01-31 20:27:54 Iteration 1700 Training Loss: 6.827e-02 Loss in Target Net: 1.200e-02
2020-01-31 20:28:16 Iteration 1750 Training Loss: 6.622e-02 Loss in Target Net: 7.134e-03
2020-01-31 20:28:37 Iteration 1800 Training Loss: 6.736e-02 Loss in Target Net: 7.432e-03
2020-01-31 20:28:58 Iteration 1850 Training Loss: 6.996e-02 Loss in Target Net: 1.030e-02
2020-01-31 20:29:20 Iteration 1900 Training Loss: 6.315e-02 Loss in Target Net: 1.152e-02
2020-01-31 20:29:40 Iteration 1950 Training Loss: 6.948e-02 Loss in Target Net: 9.045e-03
2020-01-31 20:30:00 Iteration 2000 Training Loss: 6.837e-02 Loss in Target Net: 8.269e-03
2020-01-31 20:30:21 Iteration 2050 Training Loss: 7.177e-02 Loss in Target Net: 7.196e-03
2020-01-31 20:30:43 Iteration 2100 Training Loss: 6.670e-02 Loss in Target Net: 9.880e-03
2020-01-31 20:31:04 Iteration 2150 Training Loss: 6.555e-02 Loss in Target Net: 1.135e-02
2020-01-31 20:31:24 Iteration 2200 Training Loss: 6.915e-02 Loss in Target Net: 1.123e-02
2020-01-31 20:31:45 Iteration 2250 Training Loss: 7.276e-02 Loss in Target Net: 8.342e-03
2020-01-31 20:32:05 Iteration 2300 Training Loss: 7.051e-02 Loss in Target Net: 1.278e-02
2020-01-31 20:32:26 Iteration 2350 Training Loss: 6.417e-02 Loss in Target Net: 8.454e-03
2020-01-31 20:32:47 Iteration 2400 Training Loss: 6.387e-02 Loss in Target Net: 1.001e-02
2020-01-31 20:33:09 Iteration 2450 Training Loss: 6.817e-02 Loss in Target Net: 6.902e-03
2020-01-31 20:33:30 Iteration 2500 Training Loss: 7.482e-02 Loss in Target Net: 1.028e-02
2020-01-31 20:33:51 Iteration 2550 Training Loss: 6.752e-02 Loss in Target Net: 8.341e-03
2020-01-31 20:34:12 Iteration 2600 Training Loss: 6.393e-02 Loss in Target Net: 1.195e-02
2020-01-31 20:34:33 Iteration 2650 Training Loss: 6.582e-02 Loss in Target Net: 1.214e-02
2020-01-31 20:34:54 Iteration 2700 Training Loss: 6.833e-02 Loss in Target Net: 9.801e-03
2020-01-31 20:35:14 Iteration 2750 Training Loss: 6.542e-02 Loss in Target Net: 1.232e-02
2020-01-31 20:35:35 Iteration 2800 Training Loss: 6.671e-02 Loss in Target Net: 1.085e-02
2020-01-31 20:35:55 Iteration 2850 Training Loss: 6.555e-02 Loss in Target Net: 9.809e-03
2020-01-31 20:36:16 Iteration 2900 Training Loss: 6.808e-02 Loss in Target Net: 8.030e-03
2020-01-31 20:36:37 Iteration 2950 Training Loss: 6.790e-02 Loss in Target Net: 8.943e-03
2020-01-31 20:36:58 Iteration 3000 Training Loss: 6.747e-02 Loss in Target Net: 6.345e-03
2020-01-31 20:37:19 Iteration 3050 Training Loss: 6.771e-02 Loss in Target Net: 1.045e-02
2020-01-31 20:37:39 Iteration 3100 Training Loss: 6.552e-02 Loss in Target Net: 8.849e-03
2020-01-31 20:38:00 Iteration 3150 Training Loss: 6.588e-02 Loss in Target Net: 8.093e-03
2020-01-31 20:38:20 Iteration 3200 Training Loss: 7.810e-02 Loss in Target Net: 7.629e-03
2020-01-31 20:38:41 Iteration 3250 Training Loss: 6.568e-02 Loss in Target Net: 1.054e-02
2020-01-31 20:39:02 Iteration 3300 Training Loss: 6.675e-02 Loss in Target Net: 9.089e-03
2020-01-31 20:39:23 Iteration 3350 Training Loss: 6.627e-02 Loss in Target Net: 1.143e-02
2020-01-31 20:39:43 Iteration 3400 Training Loss: 6.903e-02 Loss in Target Net: 9.473e-03
2020-01-31 20:40:04 Iteration 3450 Training Loss: 6.884e-02 Loss in Target Net: 1.060e-02
2020-01-31 20:40:25 Iteration 3500 Training Loss: 6.572e-02 Loss in Target Net: 8.985e-03
2020-01-31 20:40:46 Iteration 3550 Training Loss: 6.714e-02 Loss in Target Net: 7.494e-03
2020-01-31 20:41:09 Iteration 3600 Training Loss: 7.642e-02 Loss in Target Net: 9.305e-03
2020-01-31 20:41:32 Iteration 3650 Training Loss: 6.859e-02 Loss in Target Net: 8.240e-03
2020-01-31 20:41:54 Iteration 3700 Training Loss: 6.748e-02 Loss in Target Net: 1.069e-02
2020-01-31 20:42:17 Iteration 3750 Training Loss: 7.008e-02 Loss in Target Net: 9.603e-03
2020-01-31 20:42:40 Iteration 3800 Training Loss: 6.481e-02 Loss in Target Net: 5.140e-03
2020-01-31 20:43:03 Iteration 3850 Training Loss: 6.348e-02 Loss in Target Net: 7.105e-03
2020-01-31 20:43:26 Iteration 3900 Training Loss: 6.905e-02 Loss in Target Net: 9.952e-03
2020-01-31 20:43:48 Iteration 3950 Training Loss: 6.542e-02 Loss in Target Net: 8.002e-03
2020-01-31 20:44:10 Iteration 3999 Training Loss: 6.838e-02 Loss in Target Net: 1.189e-02
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-01-31 20:44:14, Epoch 0, Iteration 7, loss 1.812 (4.049), acc 84.615 (69.400)
2020-01-31 20:44:15, Epoch 30, Iteration 7, loss 0.103 (0.165), acc 98.077 (98.200)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[6.1065288, -2.5514705, -55.68069, 4.766194, -31.091572, 2.7249024, 31.680014, -66.87765, 33.795795, -85.394775], Poisons' Predictions:[8, 8, 8, 8, 6]
2020-01-31 20:44:19 Epoch 59, Val iteration 0, acc 92.000 (92.000)
2020-01-31 20:44:26 Epoch 59, Val iteration 19, acc 92.800 (92.270)
* Prec: 92.2700008392334
--------
SENet18
Using Adam for retraining
Files already downloaded and verified
2020-01-31 20:44:28, Epoch 0, Iteration 7, loss 0.583 (0.501), acc 84.615 (87.600)
2020-01-31 20:44:29, Epoch 30, Iteration 7, loss 0.013 (0.334), acc 100.000 (95.200)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-5.782268, 2.723313, -11.032023, -2.4208288, 9.030633, -9.6758995, 17.04169, -12.544495, 20.682116, -12.8171215], Poisons' Predictions:[6, 6, 8, 6, 8]
2020-01-31 20:44:30 Epoch 59, Val iteration 0, acc 93.000 (93.000)
2020-01-31 20:44:32 Epoch 59, Val iteration 19, acc 92.200 (91.580)
* Prec: 91.58000144958496
--------
ResNet50
Using Adam for retraining
Files already downloaded and verified
2020-01-31 20:44:34, Epoch 0, Iteration 7, loss 0.011 (0.662), acc 100.000 (90.000)
2020-01-31 20:44:35, Epoch 30, Iteration 7, loss 0.000 (0.033), acc 100.000 (99.200)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-24.76003, -36.720352, -37.782963, -17.124737, -44.533653, -37.868286, 23.44795, -28.513283, 22.714247, -8.657951], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-01-31 20:44:36 Epoch 59, Val iteration 0, acc 94.600 (94.600)
2020-01-31 20:44:40 Epoch 59, Val iteration 19, acc 94.400 (93.270)
* Prec: 93.27000160217285
--------
ResNeXt29_2x64d
Using Adam for retraining