SunayBhat1/PureGen_Poisons · Datasets at Hugging Face

text stringlengths 5 1.13k
Path: chk-black-end2end/mean-3Repeat/1500/48
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-04 05:56:50 Iteration 0 Training Loss: 9.871e-01 Loss in Target Net: 1.354e+00
2020-02-04 05:59:48 Iteration 50 Training Loss: 2.717e-01 Loss in Target Net: 3.771e-01
2020-02-04 06:02:40 Iteration 100 Training Loss: 2.389e-01 Loss in Target Net: 3.872e-01
2020-02-04 06:05:30 Iteration 150 Training Loss: 2.183e-01 Loss in Target Net: 3.960e-01
2020-02-04 06:08:18 Iteration 200 Training Loss: 2.184e-01 Loss in Target Net: 4.450e-01
2020-02-04 06:11:13 Iteration 250 Training Loss: 2.080e-01 Loss in Target Net: 3.112e-01
2020-02-04 06:14:22 Iteration 300 Training Loss: 2.039e-01 Loss in Target Net: 3.363e-01
2020-02-04 06:17:48 Iteration 350 Training Loss: 2.057e-01 Loss in Target Net: 3.315e-01
2020-02-04 06:20:45 Iteration 400 Training Loss: 2.077e-01 Loss in Target Net: 3.738e-01
2020-02-04 06:23:31 Iteration 450 Training Loss: 2.043e-01 Loss in Target Net: 3.706e-01
2020-02-04 06:26:51 Iteration 500 Training Loss: 1.919e-01 Loss in Target Net: 3.479e-01
2020-02-04 06:30:19 Iteration 550 Training Loss: 1.979e-01 Loss in Target Net: 3.117e-01
2020-02-04 06:33:40 Iteration 600 Training Loss: 1.966e-01 Loss in Target Net: 3.608e-01
2020-02-04 06:36:41 Iteration 650 Training Loss: 1.959e-01 Loss in Target Net: 3.204e-01
2020-02-04 06:39:49 Iteration 700 Training Loss: 1.878e-01 Loss in Target Net: 3.189e-01
2020-02-04 06:42:52 Iteration 750 Training Loss: 2.042e-01 Loss in Target Net: 3.241e-01
2020-02-04 06:45:48 Iteration 800 Training Loss: 1.912e-01 Loss in Target Net: 2.964e-01
2020-02-04 06:48:40 Iteration 850 Training Loss: 1.907e-01 Loss in Target Net: 2.796e-01
2020-02-04 06:51:48 Iteration 900 Training Loss: 1.970e-01 Loss in Target Net: 2.689e-01
2020-02-04 06:54:56 Iteration 950 Training Loss: 1.952e-01 Loss in Target Net: 2.851e-01
2020-02-04 06:58:04 Iteration 1000 Training Loss: 1.901e-01 Loss in Target Net: 2.711e-01
2020-02-04 07:01:04 Iteration 1050 Training Loss: 1.891e-01 Loss in Target Net: 2.881e-01
2020-02-04 07:04:21 Iteration 1100 Training Loss: 1.976e-01 Loss in Target Net: 2.784e-01
2020-02-04 07:07:47 Iteration 1150 Training Loss: 1.899e-01 Loss in Target Net: 2.733e-01
2020-02-04 07:11:07 Iteration 1200 Training Loss: 1.894e-01 Loss in Target Net: 2.975e-01
2020-02-04 07:14:25 Iteration 1250 Training Loss: 1.872e-01 Loss in Target Net: 2.648e-01
2020-02-04 07:17:30 Iteration 1300 Training Loss: 1.865e-01 Loss in Target Net: 2.970e-01
2020-02-04 07:20:37 Iteration 1350 Training Loss: 1.888e-01 Loss in Target Net: 2.682e-01
2020-02-04 07:23:32 Iteration 1400 Training Loss: 1.845e-01 Loss in Target Net: 2.735e-01
2020-02-04 07:26:45 Iteration 1450 Training Loss: 1.879e-01 Loss in Target Net: 2.770e-01
2020-02-04 07:30:01 Iteration 1499 Training Loss: 1.878e-01 Loss in Target Net: 3.017e-01
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-02-04 07:30:53, Epoch 0, Iteration 7, loss 0.593 (0.438), acc 80.769 (89.800)
2020-02-04 07:35:44, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-0.48019427, -1.2721854, -1.4871289, -0.8818588, -3.1599402, -0.72431755, 1.7482462, -2.5770376, 7.822167, 1.4577525], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-02-04 07:40:51 Epoch 59, Val iteration 0, acc 93.600 (93.600)
2020-02-04 07:41:42 Epoch 59, Val iteration 19, acc 94.400 (93.280)
* Prec: 93.28000106811524
--------
------SUMMARY------
TIME ELAPSED (mins): 93
TARGET INDEX: 48
DPN92 1
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='1', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=1500, poison_label=8, poison_lr=0.04, poison_momentum=0.9, poison_num=5, poison_opt='adam', resume_poison_ite=0, retrain_bsize=64, retrain_epochs=60, retrain_lr=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, subs_chk_name=['ckpt-%s-4800-dp0.200-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.250-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.300-droplayer0.000.t7'], subs_dp=[0.2, 0.25, 0.3], subset_group=0, substitute_nets=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d'], target_index=49, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
Path: chk-black-end2end/mean-3Repeat/1500/49
Selected base image indices: [213, 225, 227, 247, 249]
2020-02-04 06:00:13 Iteration 0 Training Loss: 9.707e-01 Loss in Target Net: 1.228e+00
2020-02-04 06:02:57 Iteration 50 Training Loss: 2.483e-01 Loss in Target Net: 1.791e-01
2020-02-04 06:05:39 Iteration 100 Training Loss: 2.202e-01 Loss in Target Net: 1.222e-01
2020-02-04 06:08:20 Iteration 150 Training Loss: 2.100e-01 Loss in Target Net: 1.205e-01
2020-02-04 06:11:09 Iteration 200 Training Loss: 2.073e-01 Loss in Target Net: 1.310e-01
2020-02-04 06:14:11 Iteration 250 Training Loss: 1.989e-01 Loss in Target Net: 1.290e-01
2020-02-04 06:17:37 Iteration 300 Training Loss: 1.951e-01 Loss in Target Net: 1.277e-01
2020-02-04 06:20:29 Iteration 350 Training Loss: 1.892e-01 Loss in Target Net: 1.377e-01
2020-02-04 06:23:07 Iteration 400 Training Loss: 1.920e-01 Loss in Target Net: 1.529e-01
2020-02-04 06:26:16 Iteration 450 Training Loss: 1.920e-01 Loss in Target Net: 1.479e-01
2020-02-04 06:29:41 Iteration 500 Training Loss: 1.879e-01 Loss in Target Net: 1.345e-01
2020-02-04 06:33:00 Iteration 550 Training Loss: 1.930e-01 Loss in Target Net: 1.437e-01
2020-02-04 06:35:59 Iteration 600 Training Loss: 1.878e-01 Loss in Target Net: 1.303e-01
2020-02-04 06:39:01 Iteration 650 Training Loss: 1.866e-01 Loss in Target Net: 1.318e-01
2020-02-04 06:42:04 Iteration 700 Training Loss: 1.853e-01 Loss in Target Net: 1.309e-01
2020-02-04 06:44:52 Iteration 750 Training Loss: 1.849e-01 Loss in Target Net: 1.421e-01
2020-02-04 06:47:37 Iteration 800 Training Loss: 1.837e-01 Loss in Target Net: 1.169e-01
2020-02-04 06:50:31 Iteration 850 Training Loss: 1.845e-01 Loss in Target Net: 1.326e-01
2020-02-04 06:53:43 Iteration 900 Training Loss: 1.801e-01 Loss in Target Net: 1.512e-01
2020-02-04 06:56:40 Iteration 950 Training Loss: 1.851e-01 Loss in Target Net: 1.440e-01
2020-02-04 06:59:36 Iteration 1000 Training Loss: 1.786e-01 Loss in Target Net: 1.468e-01
2020-02-04 07:02:31 Iteration 1050 Training Loss: 1.809e-01 Loss in Target Net: 1.341e-01
2020-02-04 07:05:55 Iteration 1100 Training Loss: 1.835e-01 Loss in Target Net: 1.424e-01
2020-02-04 07:09:14 Iteration 1150 Training Loss: 1.792e-01 Loss in Target Net: 1.387e-01
2020-02-04 07:12:32 Iteration 1200 Training Loss: 1.813e-01 Loss in Target Net: 1.365e-01
2020-02-04 07:15:44 Iteration 1250 Training Loss: 1.820e-01 Loss in Target Net: 1.465e-01
2020-02-04 07:18:40 Iteration 1300 Training Loss: 1.771e-01 Loss in Target Net: 1.397e-01
2020-02-04 07:21:35 Iteration 1350 Training Loss: 1.834e-01 Loss in Target Net: 1.489e-01
2020-02-04 07:24:25 Iteration 1400 Training Loss: 1.811e-01 Loss in Target Net: 1.578e-01
2020-02-04 07:27:39 Iteration 1450 Training Loss: 1.828e-01 Loss in Target Net: 1.569e-01
2020-02-04 07:30:51 Iteration 1499 Training Loss: 1.798e-01 Loss in Target Net: 1.518e-01
Evaluating against victims networks
DPN92
Using Adam for retraining
Files already downloaded and verified
2020-02-04 07:31:41, Epoch 0, Iteration 7, loss 0.426 (0.437), acc 88.462 (89.200)
2020-02-04 07:36:34, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-3.636374, -1.5425495, -3.7675586, 0.731222, -2.0005157, 1.8318896, 8.16703, -2.5203903, 4.700195, -1.5962037], Poisons' Predictions:[8, 8, 8, 8, 8]
2020-02-04 07:41:42 Epoch 59, Val iteration 0, acc 92.800 (92.800)
2020-02-04 07:42:30 Epoch 59, Val iteration 19, acc 93.000 (92.790)
* Prec: 92.79000282287598
--------
------SUMMARY------
TIME ELAPSED (mins): 91
TARGET INDEX: 49
DPN92 0
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='5', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=1500, poison_label=8, poison_lr=0.04, poison_momentum=0.9, poison_num=5, poison_opt='adam', resume_poison_ite=0, retrain_bsize=64, retrain_epochs=60, retrain_lr=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, subs_chk_name=['ckpt-%s-4800-dp0.200-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.250-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.300-droplayer0.000.t7'], subs_dp=[0.2, 0.25, 0.3], subset_group=0, substitute_nets=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d'], target_index=5, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
Path: chk-black-end2end/mean-3Repeat/1500/5
Selected base image indices: [213, 225, 227, 247, 249]

Path: chk-black-end2end/mean-3Repeat/1500/48

Selected base image indices: [213, 225, 227, 247, 249]

2020-02-04 05:56:50 Iteration 0 Training Loss: 9.871e-01 Loss in Target Net: 1.354e+00

2020-02-04 05:59:48 Iteration 50 Training Loss: 2.717e-01 Loss in Target Net: 3.771e-01

2020-02-04 06:02:40 Iteration 100 Training Loss: 2.389e-01 Loss in Target Net: 3.872e-01

2020-02-04 06:05:30 Iteration 150 Training Loss: 2.183e-01 Loss in Target Net: 3.960e-01

2020-02-04 06:08:18 Iteration 200 Training Loss: 2.184e-01 Loss in Target Net: 4.450e-01

2020-02-04 06:11:13 Iteration 250 Training Loss: 2.080e-01 Loss in Target Net: 3.112e-01

2020-02-04 06:14:22 Iteration 300 Training Loss: 2.039e-01 Loss in Target Net: 3.363e-01

2020-02-04 06:17:48 Iteration 350 Training Loss: 2.057e-01 Loss in Target Net: 3.315e-01

2020-02-04 06:20:45 Iteration 400 Training Loss: 2.077e-01 Loss in Target Net: 3.738e-01

2020-02-04 06:23:31 Iteration 450 Training Loss: 2.043e-01 Loss in Target Net: 3.706e-01

2020-02-04 06:26:51 Iteration 500 Training Loss: 1.919e-01 Loss in Target Net: 3.479e-01

2020-02-04 06:30:19 Iteration 550 Training Loss: 1.979e-01 Loss in Target Net: 3.117e-01

2020-02-04 06:33:40 Iteration 600 Training Loss: 1.966e-01 Loss in Target Net: 3.608e-01

2020-02-04 06:36:41 Iteration 650 Training Loss: 1.959e-01 Loss in Target Net: 3.204e-01

2020-02-04 06:39:49 Iteration 700 Training Loss: 1.878e-01 Loss in Target Net: 3.189e-01

2020-02-04 06:42:52 Iteration 750 Training Loss: 2.042e-01 Loss in Target Net: 3.241e-01

2020-02-04 06:45:48 Iteration 800 Training Loss: 1.912e-01 Loss in Target Net: 2.964e-01

2020-02-04 06:48:40 Iteration 850 Training Loss: 1.907e-01 Loss in Target Net: 2.796e-01

2020-02-04 06:51:48 Iteration 900 Training Loss: 1.970e-01 Loss in Target Net: 2.689e-01

2020-02-04 06:54:56 Iteration 950 Training Loss: 1.952e-01 Loss in Target Net: 2.851e-01

2020-02-04 06:58:04 Iteration 1000 Training Loss: 1.901e-01 Loss in Target Net: 2.711e-01

2020-02-04 07:01:04 Iteration 1050 Training Loss: 1.891e-01 Loss in Target Net: 2.881e-01

2020-02-04 07:04:21 Iteration 1100 Training Loss: 1.976e-01 Loss in Target Net: 2.784e-01

2020-02-04 07:07:47 Iteration 1150 Training Loss: 1.899e-01 Loss in Target Net: 2.733e-01

2020-02-04 07:11:07 Iteration 1200 Training Loss: 1.894e-01 Loss in Target Net: 2.975e-01

2020-02-04 07:14:25 Iteration 1250 Training Loss: 1.872e-01 Loss in Target Net: 2.648e-01

2020-02-04 07:17:30 Iteration 1300 Training Loss: 1.865e-01 Loss in Target Net: 2.970e-01

2020-02-04 07:20:37 Iteration 1350 Training Loss: 1.888e-01 Loss in Target Net: 2.682e-01

2020-02-04 07:23:32 Iteration 1400 Training Loss: 1.845e-01 Loss in Target Net: 2.735e-01

2020-02-04 07:26:45 Iteration 1450 Training Loss: 1.879e-01 Loss in Target Net: 2.770e-01

2020-02-04 07:30:01 Iteration 1499 Training Loss: 1.878e-01 Loss in Target Net: 3.017e-01

Evaluating against victims networks

DPN92

Using Adam for retraining

Files already downloaded and verified

2020-02-04 07:30:53, Epoch 0, Iteration 7, loss 0.593 (0.438), acc 80.769 (89.800)

2020-02-04 07:35:44, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)

Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-0.48019427, -1.2721854, -1.4871289, -0.8818588, -3.1599402, -0.72431755, 1.7482462, -2.5770376, 7.822167, 1.4577525], Poisons' Predictions:[8, 8, 8, 8, 8]

2020-02-04 07:40:51 Epoch 59, Val iteration 0, acc 93.600 (93.600)

2020-02-04 07:41:42 Epoch 59, Val iteration 19, acc 94.400 (93.280)

* Prec: 93.28000106811524

--------

------SUMMARY------

TIME ELAPSED (mins): 93

TARGET INDEX: 48

DPN92 1

Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='1', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=1500, poison_label=8, poison_lr=0.04, poison_momentum=0.9, poison_num=5, poison_opt='adam', resume_poison_ite=0, retrain_bsize=64, retrain_epochs=60, retrain_lr=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, subs_chk_name=['ckpt-%s-4800-dp0.200-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.250-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.300-droplayer0.000.t7'], subs_dp=[0.2, 0.25, 0.3], subset_group=0, substitute_nets=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d'], target_index=49, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')

Path: chk-black-end2end/mean-3Repeat/1500/49

Selected base image indices: [213, 225, 227, 247, 249]

2020-02-04 06:00:13 Iteration 0 Training Loss: 9.707e-01 Loss in Target Net: 1.228e+00

2020-02-04 06:02:57 Iteration 50 Training Loss: 2.483e-01 Loss in Target Net: 1.791e-01

2020-02-04 06:05:39 Iteration 100 Training Loss: 2.202e-01 Loss in Target Net: 1.222e-01

2020-02-04 06:08:20 Iteration 150 Training Loss: 2.100e-01 Loss in Target Net: 1.205e-01

2020-02-04 06:11:09 Iteration 200 Training Loss: 2.073e-01 Loss in Target Net: 1.310e-01

2020-02-04 06:14:11 Iteration 250 Training Loss: 1.989e-01 Loss in Target Net: 1.290e-01

2020-02-04 06:17:37 Iteration 300 Training Loss: 1.951e-01 Loss in Target Net: 1.277e-01

2020-02-04 06:20:29 Iteration 350 Training Loss: 1.892e-01 Loss in Target Net: 1.377e-01

2020-02-04 06:23:07 Iteration 400 Training Loss: 1.920e-01 Loss in Target Net: 1.529e-01

2020-02-04 06:26:16 Iteration 450 Training Loss: 1.920e-01 Loss in Target Net: 1.479e-01

2020-02-04 06:29:41 Iteration 500 Training Loss: 1.879e-01 Loss in Target Net: 1.345e-01

2020-02-04 06:33:00 Iteration 550 Training Loss: 1.930e-01 Loss in Target Net: 1.437e-01

2020-02-04 06:35:59 Iteration 600 Training Loss: 1.878e-01 Loss in Target Net: 1.303e-01

2020-02-04 06:39:01 Iteration 650 Training Loss: 1.866e-01 Loss in Target Net: 1.318e-01

2020-02-04 06:42:04 Iteration 700 Training Loss: 1.853e-01 Loss in Target Net: 1.309e-01

2020-02-04 06:44:52 Iteration 750 Training Loss: 1.849e-01 Loss in Target Net: 1.421e-01

2020-02-04 06:47:37 Iteration 800 Training Loss: 1.837e-01 Loss in Target Net: 1.169e-01

2020-02-04 06:50:31 Iteration 850 Training Loss: 1.845e-01 Loss in Target Net: 1.326e-01

2020-02-04 06:53:43 Iteration 900 Training Loss: 1.801e-01 Loss in Target Net: 1.512e-01

2020-02-04 06:56:40 Iteration 950 Training Loss: 1.851e-01 Loss in Target Net: 1.440e-01

2020-02-04 06:59:36 Iteration 1000 Training Loss: 1.786e-01 Loss in Target Net: 1.468e-01

2020-02-04 07:02:31 Iteration 1050 Training Loss: 1.809e-01 Loss in Target Net: 1.341e-01

2020-02-04 07:05:55 Iteration 1100 Training Loss: 1.835e-01 Loss in Target Net: 1.424e-01

2020-02-04 07:09:14 Iteration 1150 Training Loss: 1.792e-01 Loss in Target Net: 1.387e-01

2020-02-04 07:12:32 Iteration 1200 Training Loss: 1.813e-01 Loss in Target Net: 1.365e-01

2020-02-04 07:15:44 Iteration 1250 Training Loss: 1.820e-01 Loss in Target Net: 1.465e-01

2020-02-04 07:18:40 Iteration 1300 Training Loss: 1.771e-01 Loss in Target Net: 1.397e-01

2020-02-04 07:21:35 Iteration 1350 Training Loss: 1.834e-01 Loss in Target Net: 1.489e-01

2020-02-04 07:24:25 Iteration 1400 Training Loss: 1.811e-01 Loss in Target Net: 1.578e-01

2020-02-04 07:27:39 Iteration 1450 Training Loss: 1.828e-01 Loss in Target Net: 1.569e-01

2020-02-04 07:30:51 Iteration 1499 Training Loss: 1.798e-01 Loss in Target Net: 1.518e-01

Evaluating against victims networks

DPN92

Using Adam for retraining

Files already downloaded and verified

2020-02-04 07:31:41, Epoch 0, Iteration 7, loss 0.426 (0.437), acc 88.462 (89.200)

2020-02-04 07:36:34, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)

Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-3.636374, -1.5425495, -3.7675586, 0.731222, -2.0005157, 1.8318896, 8.16703, -2.5203903, 4.700195, -1.5962037], Poisons' Predictions:[8, 8, 8, 8, 8]

2020-02-04 07:41:42 Epoch 59, Val iteration 0, acc 92.800 (92.800)

2020-02-04 07:42:30 Epoch 59, Val iteration 19, acc 93.000 (92.790)

* Prec: 92.79000282287598

--------

------SUMMARY------

TIME ELAPSED (mins): 91

TARGET INDEX: 49

DPN92 0

Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='5', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=1500, poison_label=8, poison_lr=0.04, poison_momentum=0.9, poison_num=5, poison_opt='adam', resume_poison_ite=0, retrain_bsize=64, retrain_epochs=60, retrain_lr=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, subs_chk_name=['ckpt-%s-4800-dp0.200-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.250-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.300-droplayer0.000.t7'], subs_dp=[0.2, 0.25, 0.3], subset_group=0, substitute_nets=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d'], target_index=5, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')

Path: chk-black-end2end/mean-3Repeat/1500/5

Selected base image indices: [213, 225, 227, 247, 249]