Datasets:
SunayBhat1
/
PureGen_Poisons

Dataset card Data Studio Files
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2020-02-04 21:39:21 Iteration 700 Training Loss: 7.149e-02 Loss in Target Net: 1.298e-02
2020-02-04 21:40:43 Iteration 750 Training Loss: 6.637e-02 Loss in Target Net: 7.672e-03
2020-02-04 21:42:10 Iteration 800 Training Loss: 6.602e-02 Loss in Target Net: 1.224e-02
2020-02-04 21:43:38 Iteration 850 Training Loss: 7.247e-02 Loss in Target Net: 9.982e-03
2020-02-04 21:45:06 Iteration 900 Training Loss: 6.394e-02 Loss in Target Net: 8.804e-03
2020-02-04 21:46:33 Iteration 950 Training Loss: 6.331e-02 Loss in Target Net: 9.294e-03
2020-02-04 21:48:04 Iteration 1000 Training Loss: 6.828e-02 Loss in Target Net: 1.298e-02
2020-02-04 21:49:30 Iteration 1050 Training Loss: 6.432e-02 Loss in Target Net: 1.014e-02
2020-02-04 21:50:55 Iteration 1100 Training Loss: 6.291e-02 Loss in Target Net: 1.061e-02
2020-02-04 21:52:18 Iteration 1150 Training Loss: 6.841e-02 Loss in Target Net: 1.189e-02
2020-02-04 21:53:42 Iteration 1200 Training Loss: 6.379e-02 Loss in Target Net: 8.908e-03
2020-02-04 21:55:06 Iteration 1250 Training Loss: 6.053e-02 Loss in Target Net: 7.442e-03
2020-02-04 21:56:31 Iteration 1300 Training Loss: 6.623e-02 Loss in Target Net: 8.227e-03
2020-02-04 21:57:54 Iteration 1350 Training Loss: 6.466e-02 Loss in Target Net: 1.329e-02
2020-02-04 21:59:16 Iteration 1400 Training Loss: 7.137e-02 Loss in Target Net: 8.736e-03
2020-02-04 22:00:41 Iteration 1450 Training Loss: 6.596e-02 Loss in Target Net: 1.256e-02
2020-02-04 22:02:02 Iteration 1500 Training Loss: 6.610e-02 Loss in Target Net: 9.522e-03
2020-02-04 22:03:25 Iteration 1550 Training Loss: 6.431e-02 Loss in Target Net: 1.192e-02
2020-02-04 22:04:50 Iteration 1600 Training Loss: 6.092e-02 Loss in Target Net: 1.068e-02
2020-02-04 22:06:15 Iteration 1650 Training Loss: 6.619e-02 Loss in Target Net: 1.055e-02
2020-02-04 22:07:37 Iteration 1700 Training Loss: 6.428e-02 Loss in Target Net: 7.234e-03
2020-02-04 22:09:03 Iteration 1750 Training Loss: 6.283e-02 Loss in Target Net: 9.736e-03
2020-02-04 22:10:28 Iteration 1800 Training Loss: 6.659e-02 Loss in Target Net: 8.304e-03
2020-02-04 22:11:48 Iteration 1850 Training Loss: 6.094e-02 Loss in Target Net: 1.019e-02
2020-02-04 22:13:12 Iteration 1900 Training Loss: 6.420e-02 Loss in Target Net: 9.021e-03
2020-02-04 22:14:33 Iteration 1950 Training Loss: 6.250e-02 Loss in Target Net: 8.364e-03
2020-02-04 22:15:58 Iteration 2000 Training Loss: 6.708e-02 Loss in Target Net: 7.900e-03
2020-02-04 22:17:23 Iteration 2050 Training Loss: 6.365e-02 Loss in Target Net: 1.004e-02
2020-02-04 22:18:50 Iteration 2100 Training Loss: 6.440e-02 Loss in Target Net: 7.724e-03
2020-02-04 22:20:19 Iteration 2150 Training Loss: 6.362e-02 Loss in Target Net: 8.733e-03
2020-02-04 22:21:48 Iteration 2200 Training Loss: 6.418e-02 Loss in Target Net: 1.283e-02
2020-02-04 22:23:16 Iteration 2250 Training Loss: 6.432e-02 Loss in Target Net: 9.777e-03
2020-02-04 22:24:43 Iteration 2300 Training Loss: 5.839e-02 Loss in Target Net: 7.243e-03
2020-02-04 22:26:09 Iteration 2350 Training Loss: 6.872e-02 Loss in Target Net: 1.057e-02
2020-02-04 22:27:34 Iteration 2400 Training Loss: 6.368e-02 Loss in Target Net: 9.966e-03
2020-02-04 22:29:00 Iteration 2450 Training Loss: 6.567e-02 Loss in Target Net: 7.464e-03
2020-02-04 22:30:26 Iteration 2500 Training Loss: 6.743e-02 Loss in Target Net: 8.010e-03
2020-02-04 22:31:51 Iteration 2550 Training Loss: 6.745e-02 Loss in Target Net: 9.086e-03
2020-02-04 22:33:18 Iteration 2600 Training Loss: 6.656e-02 Loss in Target Net: 7.117e-03
2020-02-04 22:34:45 Iteration 2650 Training Loss: 6.434e-02 Loss in Target Net: 6.814e-03
2020-02-04 22:36:13 Iteration 2700 Training Loss: 6.393e-02 Loss in Target Net: 1.125e-02
2020-02-04 22:37:40 Iteration 2750 Training Loss: 6.702e-02 Loss in Target Net: 9.286e-03
2020-02-04 22:39:07 Iteration 2800 Training Loss: 6.450e-02 Loss in Target Net: 9.281e-03
2020-02-04 22:40:36 Iteration 2850 Training Loss: 6.669e-02 Loss in Target Net: 1.094e-02
2020-02-04 22:42:00 Iteration 2900 Training Loss: 6.237e-02 Loss in Target Net: 8.297e-03
2020-02-04 22:43:25 Iteration 2950 Training Loss: 6.972e-02 Loss in Target Net: 9.150e-03
2020-02-04 22:44:49 Iteration 3000 Training Loss: 6.757e-02 Loss in Target Net: 8.574e-03
2020-02-04 22:46:11 Iteration 3050 Training Loss: 6.385e-02 Loss in Target Net: 9.615e-03
2020-02-04 22:47:32 Iteration 3100 Training Loss: 6.586e-02 Loss in Target Net: 8.351e-03
2020-02-04 22:48:57 Iteration 3150 Training Loss: 6.565e-02 Loss in Target Net: 7.459e-03
2020-02-04 22:50:21 Iteration 3200 Training Loss: 6.140e-02 Loss in Target Net: 6.849e-03
2020-02-04 22:51:45 Iteration 3250 Training Loss: 6.383e-02 Loss in Target Net: 9.674e-03
2020-02-04 22:53:11 Iteration 3300 Training Loss: 6.349e-02 Loss in Target Net: 8.868e-03
2020-02-04 22:54:37 Iteration 3350 Training Loss: 6.154e-02 Loss in Target Net: 7.188e-03
2020-02-04 22:56:04 Iteration 3400 Training Loss: 6.091e-02 Loss in Target Net: 7.815e-03
2020-02-04 22:57:32 Iteration 3450 Training Loss: 6.080e-02 Loss in Target Net: 5.000e-03
2020-02-04 22:58:58 Iteration 3500 Training Loss: 6.409e-02 Loss in Target Net: 7.700e-03
2020-02-04 23:00:24 Iteration 3550 Training Loss: 6.662e-02 Loss in Target Net: 4.896e-03
2020-02-04 23:01:50 Iteration 3600 Training Loss: 6.776e-02 Loss in Target Net: 9.086e-03
2020-02-04 23:03:15 Iteration 3650 Training Loss: 6.485e-02 Loss in Target Net: 6.186e-03
2020-02-04 23:04:40 Iteration 3700 Training Loss: 6.357e-02 Loss in Target Net: 6.093e-03
2020-02-04 23:06:03 Iteration 3750 Training Loss: 6.346e-02 Loss in Target Net: 7.522e-03
2020-02-04 23:07:26 Iteration 3800 Training Loss: 7.049e-02 Loss in Target Net: 7.288e-03
2020-02-04 23:08:50 Iteration 3850 Training Loss: 6.294e-02 Loss in Target Net: 8.395e-03
2020-02-04 23:10:15 Iteration 3900 Training Loss: 6.261e-02 Loss in Target Net: 9.361e-03
2020-02-04 23:11:39 Iteration 3950 Training Loss: 6.923e-02 Loss in Target Net: 7.345e-03
2020-02-04 23:12:59 Iteration 3999 Training Loss: 6.094e-02 Loss in Target Net: 6.707e-03
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-02-04 23:13:17, Epoch 0, Iteration 7, loss 5.659 (6.704), acc 75.000 (53.600)
2020-02-04 23:13:18, Epoch 30, Iteration 7, loss 0.213 (0.081), acc 96.154 (98.200)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[22.203016, -7.0067816, -39.148438, 10.737192, -23.160873, -3.2698743, 35.186546, -45.02813, 36.67109, -131.0977], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-02-04 23:13:45 Epoch 59, Val iteration 0, acc 90.800 (90.800)
2020-02-04 23:14:31 Epoch 59, Val iteration 19, acc 91.600 (92.040)
* Prec: 92.04000129699708
--------
SENet18
Using Adam for retraining
Files already downloaded and verified
2020-02-04 23:14:35, Epoch 0, Iteration 7, loss 0.998 (0.901), acc 92.308 (88.600)
2020-02-04 23:14:36, Epoch 30, Iteration 7, loss 0.540 (0.155), acc 90.385 (96.600)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-2.7718794, -2.22433, -16.034311, -7.3027053, 9.320602, -10.789871, 25.304953, -19.775488, 15.862936, -16.028679], Poisons' Predictions:[8, 6, 6, 8, 8]
2020-02-04 23:14:40 Epoch 59, Val iteration 0, acc 91.600 (91.600)
2020-02-04 23:14:47 Epoch 59, Val iteration 19, acc 92.200 (91.130)
* Prec: 91.13000221252442
--------
ResNet50
Using Adam for retraining
Files already downloaded and verified
2020-02-04 23:14:54, Epoch 0, Iteration 7, loss 0.003 (1.625), acc 100.000 (83.200)
2020-02-04 23:14:54, Epoch 30, Iteration 7, loss 0.000 (0.009), acc 100.000 (99.600)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-38.456234, -6.8165436, -42.21165, -20.35215, -16.315521, -38.97589, 15.209051, -42.088387, 21.007547, -24.090862], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-02-04 23:15:01 Epoch 59, Val iteration 0, acc 93.400 (93.400)
2020-02-04 23:15:21 Epoch 59, Val iteration 19, acc 95.000 (93.760)
* Prec: 93.76000137329102
--------
ResNeXt29_2x64d
Using Adam for retraining