diff --git a/examples/pytorch/cifar10/.dvc/tmp/rwlock b/examples/pytorch/cifar10/.dvc/tmp/rwlock
deleted file mode 100644
index 0967ef42..00000000
--- a/examples/pytorch/cifar10/.dvc/tmp/rwlock
+++ /dev/null
@@ -1 +0,0 @@
-{}
diff --git a/examples/pytorch/conf/cifar.yaml b/examples/pytorch/conf/cifar.yaml
index 1c960bb6..72de8749 100644
--- a/examples/pytorch/conf/cifar.yaml
+++ b/examples/pytorch/conf/cifar.yaml
@@ -1,55 +1,3 @@
-defaults:
-    - _self_
-    - data: torch_cifar
-    - model: torch_cifar
-    - attack: default
-    - files: cifar
-    - scorers: default
-    - override hydra/sweeper : optuna
-    - override hydra/sweeper/sampler : grid
-    - override hydra/launcher : joblib
-def_name : control
-atk_name : hsj
-dataset : cifar
-model_name : ResNet18
-device_id : gpu
-stage : '???'
-direction : 
-  -  "maximize"
-_target_ : deckard.base.experiment.Experiment
-optimizers : 
-  - accuracy
-hydra:
-  run:
-    dir: ${files.directory}/logs/${stage}/
-  sweep:
-    dir: ${files.directory}/logs/${stage}/${model_name}
-    subdir : ${def_name}/${atk_name}/${hydra.job.num}
-  sweeper:
-    sampler:
-      _target_: optuna.samplers.GridSampler
-    direction: ${direction}
-    study_name: ${model_name}_${def_name}_${atk_name}
-    storage: sqlite:///${dataset}.db
-    n_jobs: ${oc.env:HYDRA_SWEEPER_N_JOBS, 8}
-    n_trials: ${oc.env:HYDRA_SWEEPER_N_TRIALS, 128}
-    max_failure_rate: 1.0
-    params:
-      ++model.art.initialize.optimizer.lr: choice( 0.1, 0.01, 0.001, .0001, .00001, 0.000001)
-      ++model.trainer.nb_epoch: choice(1, 10, 30, 50, 100)
-    _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
-  launcher:
-    _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher
-    n_jobs: ${oc.env:HYDRA_SWEEPER_N_JOBS, 8}
-    prefer : threads
-    verbose: 10
-    timeout: null
-    pre_dispatch: n_jobs
-    batch_size: auto
-    temp_folder: /tmp/deckard
-    max_nbytes: 100000
-    mmap_mode: r
-
 defaults:
     - _self_
     - data: torch_cifar
@@ -81,8 +29,8 @@ hydra:
     sampler:
       _target_: optuna.samplers.GridSampler
     direction: ${direction}
-    study_name: ${model_name}_${def_name}_${atk_name}
-    storage: sqlite:///${dataset}.db
+    study_name: ${dataset}_${model_name}_${def_name}_${atk_name}
+    storage: sqlite:///optuna.db
     n_jobs: ${oc.env:HYDRA_SWEEPER_N_JOBS, 32}
     n_trials: ${oc.env:HYDRA_SWEEPER_N_TRIALS, 128}
     max_failure_rate: 1.0
diff --git a/examples/pytorch/conf/cifar100.yaml b/examples/pytorch/conf/cifar100.yaml
index a595d9e7..dadd4d07 100644
--- a/examples/pytorch/conf/cifar100.yaml
+++ b/examples/pytorch/conf/cifar100.yaml
@@ -23,14 +23,14 @@ hydra:
   run:
     dir: ${files.directory}/logs/${stage}/
   sweep:
-    dir: ${files.directory}/logs/${stage}/${model_name}/${model.trainer.nb_epochs}
-    subdir : ${def_name}/${atk_name}/${hydra.job.num}
+    dir: ${files.directory}/logs/${stage}/${model_name}/${atk_name}/${model.trainer.nb_epochs}/
+    subdir : ${def_name}/${hydra.job.num}
   sweeper:
     sampler:
       _target_: optuna.samplers.GridSampler
     direction: ${direction}
-    study_name: ${model_name}_${def_name}_${atk_name}
-    storage: sqlite:///${dataset}.db
+    study_name: ${dataset}_${model_name}_${def_name}_${atk_name}
+    storage: sqlite:///optuna.db
     n_jobs: ${oc.env:HYDRA_SWEEPER_N_JOBS, 16}
     n_trials: ${oc.env:HYDRA_SWEEPER_N_TRIALS, 128}
     max_failure_rate: 1.0
diff --git a/examples/pytorch/conf/mnist.yaml b/examples/pytorch/conf/mnist.yaml
index ab0ebc7e..1ab43006 100644
--- a/examples/pytorch/conf/mnist.yaml
+++ b/examples/pytorch/conf/mnist.yaml
@@ -23,14 +23,14 @@ hydra:
   run:
     dir: ${files.directory}/logs/${stage}/
   sweep:
-    dir: ${files.directory}/logs/${stage}/${model_name}/${model.trainer.nb_epochs}
-    subdir : ${def_name}/${atk_name}/${hydra.job.num}
+    dir: ${files.directory}/logs/${stage}/${model_name}/${atk_name}/${model.trainer.nb_epochs}/
+    subdir : ${def_name}/${hydra.job.num}
   sweeper:
     sampler:
       _target_: optuna.samplers.GridSampler
     direction: ${direction}
-    study_name: ${model_name}_${def_name}_${atk_name}
-    storage: sqlite:///${dataset}.db
+    study_name: ${dataset}_${model_name}_${def_name}_${atk_name}
+    storage: sqlite:///optuna.db
     n_jobs: ${oc.env:HYDRA_SWEEPER_N_JOBS, 32}
     n_trials: ${oc.env:HYDRA_SWEEPER_N_TRIALS, 128}
     max_failure_rate: 1.0
diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock
index 8bbbfc23..95222e30 100644
--- a/examples/pytorch/dvc.lock
+++ b/examples/pytorch/dvc.lock
@@ -624,8 +624,8 @@ stages:
       nfiles: 3
     - path: conf/mnist.yaml
       hash: md5
-      md5: 73ed848330388ef5d98a54d03e3cdcf9
-      size: 1671
+      md5: 95cb45a0526e83ffb7777edebe4fa9ff
+      size: 1680
     - path: conf/model
       hash: md5
       md5: eb0d17ad4826334ac8488c1683106580.dir
@@ -651,8 +651,8 @@ stages:
       nfiles: 1
     - path: conf/cifar.yaml
       hash: md5
-      md5: 5171900fd679f57392fa59f160301911
-      size: 1681
+      md5: 8088e4f61386676df9560a65c6a2697f
+      size: 1688
     - path: conf/data
       hash: md5
       md5: eafea1e119e2f54e9788ee46101afc79.dir
@@ -689,8 +689,8 @@ stages:
       nfiles: 1
     - path: conf/cifar100.yaml
       hash: md5
-      md5: 38d9e68b647acbca29a89ef6a699b4bf
-      size: 1684
+      md5: 11aa3a14e9d39196a1f46f46ee434493
+      size: 1692
     - path: conf/data
       hash: md5
       md5: eafea1e119e2f54e9788ee46101afc79.dir
@@ -1920,3 +1920,87 @@ stages:
       hash: md5
       md5: e50857f937b52c64d262302b4aa9740e
       size: 53174519
+  attacks@FGM-mnist-ResNet18-1:
+    cmd: bash scripts/FGM.sh  ++model.init.name=torch_example.ResNet18  stage=attack
+      model_name=ResNet18 attack.attack_size=100 model.trainer.nb_epochs=1 data=torch_mnist
+      model=torch_mnist +direction="[maximize,maximize,minimize]" ++optimizers="[accuracy,adv_accuracy,adv_success]"
+      --config-name mnist.yaml
+    deps:
+    - path: mnist/reports/attack/default/score_dict.json
+      hash: md5
+      md5: d38a998a2b08eff46f3bab06e7c77d64
+      size: 851
+    - path: mnist/reports/train/default/score_dict.json
+      hash: md5
+      md5: 1f33102e4833438d6a2151c8eb49d4e7
+      size: 525
+    - path: scripts/FGM.sh
+      hash: md5
+      md5: 8605a0b6717cb85022a60ee362adfd37
+      size: 217
+    - path: scripts/models.sh
+      hash: md5
+      md5: 295e4d857ee4514c8014e95bf87012a4
+      size: 1364
+    outs:
+    - path: mnist/logs/attack/ResNet18/FGM/1/
+      hash: md5
+      md5: 32c0f1cdc0dbb1912343d66ec1486140.dir
+      size: 11926866
+      nfiles: 2561
+  attacks@FGM-mnist-ResNet18-10:
+    cmd: bash scripts/FGM.sh  ++model.init.name=torch_example.ResNet18  stage=attack
+      model_name=ResNet18 attack.attack_size=100 model.trainer.nb_epochs=10 data=torch_mnist
+      model=torch_mnist +direction="[maximize,maximize,minimize]" ++optimizers="[accuracy,adv_accuracy,adv_success]"
+      --config-name mnist.yaml
+    deps:
+    - path: mnist/reports/attack/default/score_dict.json
+      hash: md5
+      md5: d38a998a2b08eff46f3bab06e7c77d64
+      size: 851
+    - path: mnist/reports/train/default/score_dict.json
+      hash: md5
+      md5: 1f33102e4833438d6a2151c8eb49d4e7
+      size: 525
+    - path: scripts/FGM.sh
+      hash: md5
+      md5: 8605a0b6717cb85022a60ee362adfd37
+      size: 217
+    - path: scripts/models.sh
+      hash: md5
+      md5: 295e4d857ee4514c8014e95bf87012a4
+      size: 1364
+    outs:
+    - path: mnist/logs/attack/ResNet18/FGM/10/
+      hash: md5
+      md5: 92e511213bf942f55e7f924657dde7d4.dir
+      size: 9303027
+      nfiles: 2561
+  attacks@FGM-mnist-ResNet18-20:
+    cmd: bash scripts/FGM.sh  ++model.init.name=torch_example.ResNet18  stage=attack
+      model_name=ResNet18 attack.attack_size=100 model.trainer.nb_epochs=20 data=torch_mnist
+      model=torch_mnist +direction="[maximize,maximize,minimize]" ++optimizers="[accuracy,adv_accuracy,adv_success]"
+      --config-name mnist.yaml
+    deps:
+    - path: mnist/reports/attack/default/score_dict.json
+      hash: md5
+      md5: d38a998a2b08eff46f3bab06e7c77d64
+      size: 851
+    - path: mnist/reports/train/default/score_dict.json
+      hash: md5
+      md5: 1f33102e4833438d6a2151c8eb49d4e7
+      size: 525
+    - path: scripts/FGM.sh
+      hash: md5
+      md5: 8605a0b6717cb85022a60ee362adfd37
+      size: 217
+    - path: scripts/models.sh
+      hash: md5
+      md5: 295e4d857ee4514c8014e95bf87012a4
+      size: 1364
+    outs:
+    - path: mnist/logs/attack/ResNet18/FGM/20/
+      hash: md5
+      md5: c3294e1c13e61cb2d3094f565edab466.dir
+      size: 8189626
+      nfiles: 2561
diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml
index f249f80b..41043e5f 100644
--- a/examples/pytorch/dvc.yaml
+++ b/examples/pytorch/dvc.yaml
@@ -86,11 +86,12 @@ stages:
   #     - ${item.dataset}/${files.reports}/train/${item.model}/
   attacks:
     matrix:
+      atk_name : [FGM, PGD, Deep, Pixel, Thresh, HSJ]
       dataset : [mnist, cifar, cifar100]
       model : [ResNet18, ResNet34, ResNet50, ResNet101, ResNet152]
-      epochs : [1] #10, 20, 30, 50, 100
+      epochs : [1, 10, 20, 30, 50, 100] #
     cmd: >- 
-      bash attacks.sh 
+      bash scripts/${item.atk_name}.sh 
       ++model.init.name=torch_example.${item.model} 
       stage=attack
       model_name=${item.model}
@@ -104,9 +105,10 @@ stages:
     deps:
       - ${item.dataset}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage
       - ${item.dataset}/${files.reports}/train/${files.name}/${files.score_dict_file}
-      - attacks.sh
+      - scripts/${item.atk_name}.sh
+      - scripts/models.sh
     outs:
-      - ${item.dataset}/logs/attack/${item.model}/${item.epochs}/:
+      - ${item.dataset}/logs/attack/${item.model}/${item.atk_name}/${item.epochs}/:
           cache: True
           persist: True
   compile:
diff --git a/examples/pytorch/main.sh b/examples/pytorch/main.sh
deleted file mode 100644
index 34e724ac..00000000
--- a/examples/pytorch/main.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/bin/bash
-paper_dir=~/ml_afr/
-# set downstream to 2 or nothing
-for d in */ ; do
-    cd $d
-    # run command and write to log file
-    dvc repro --downstream clean -f  >| dvc_repro.log
-    # dvc push
-    cd -
-done
-# change to paper directory
-cd $paper_dir
-# run dvc repro and dvc push
-dvc repro
-dvc push
-# change back to original directory
-cd -
diff --git a/examples/pytorch/old_cifar100.yaml b/examples/pytorch/old_cifar100.yaml
deleted file mode 100644
index 471c78a7..00000000
--- a/examples/pytorch/old_cifar100.yaml
+++ /dev/null
@@ -1,226 +0,0 @@
-_target_: deckard.base.experiment.Experiment
-atk_name: hsj
-attack:
-  _target_: deckard.base.attack.Attack
-  attack_size: 100
-  data:
-    _target_: deckard.base.data.Data
-    generate:
-      name: torch_cifar100
-      path: original_data/
-    sample:
-      random_state: 0
-      stratify: true
-      test_size: 12000
-      train_size: 48000
-  init:
-    _target_: deckard.base.attack.AttackInitializer
-    batch_size: 128
-    model:
-      _target_: deckard.base.model.Model
-      art:
-        _target_: deckard.base.model.art_pipeline.ArtPipeline
-        clip_values:
-        - 0
-        - 255
-        criterion:
-          name: torch.nn.CrossEntropyLoss
-        data:
-          _target_: deckard.base.data.Data
-          generate:
-            name: torch_cifar100
-            path: original_data/
-          sample:
-            random_state: 0
-            stratify: true
-            test_size: 12000
-            train_size: 48000
-        initialize:
-          clip_values:
-          - 0
-          - 255
-          criterion:
-            name: torch.nn.CrossEntropyLoss
-          optimizer:
-            lr: 0.01
-            momentum: 0.9
-            name: torch.optim.SGD
-        library: pytorch
-        optimizer:
-          lr: 0.01
-          momentum: 0.9
-          name: torch.optim.SGD
-      data:
-        _target_: deckard.base.data.Data
-        generate:
-          name: torch_cifar100
-          path: original_data/
-        sample:
-          random_state: 0
-          stratify: true
-          test_size: 12000
-          train_size: 48000
-      init:
-        _target_: deckard.base.model.ModelInitializer
-        name: torch_example.ResNet18
-        num_channels: 3
-        num_classes: 100
-      library: pytorch
-      trainer:
-        batch_size: 128
-        nb_epochs: 1
-        verbose: true
-    name: art.attacks.evasion.HopSkipJump
-  method: evasion
-  model:
-    _target_: deckard.base.model.Model
-    art:
-      _target_: deckard.base.model.art_pipeline.ArtPipeline
-      clip_values:
-      - 0
-      - 255
-      criterion:
-        name: torch.nn.CrossEntropyLoss
-      data:
-        _target_: deckard.base.data.Data
-        generate:
-          name: torch_cifar100
-          path: original_data/
-        sample:
-          random_state: 0
-          stratify: true
-          test_size: 12000
-          train_size: 48000
-      initialize:
-        clip_values:
-        - 0
-        - 255
-        criterion:
-          name: torch.nn.CrossEntropyLoss
-        optimizer:
-          lr: 0.01
-          momentum: 0.9
-          name: torch.optim.SGD
-      library: pytorch
-      optimizer:
-        lr: 0.01
-        momentum: 0.9
-        name: torch.optim.SGD
-    data:
-      _target_: deckard.base.data.Data
-      generate:
-        name: torch_cifar100
-        path: original_data/
-      sample:
-        random_state: 0
-        stratify: true
-        test_size: 12000
-        train_size: 48000
-    init:
-      _target_: deckard.base.model.ModelInitializer
-      name: torch_example.ResNet18
-      num_channels: 3
-      num_classes: 100
-    library: pytorch
-    trainer:
-      batch_size: 128
-      nb_epochs: 1
-      verbose: true
-data:
-  _target_: deckard.base.data.Data
-  generate:
-    name: torch_cifar100
-    path: original_data/
-  sample:
-    random_state: 0
-    stratify: true
-    test_size: 12000
-    train_size: 48000
-dataset: cifar100
-def_name: control
-device_id: gpu
-direction:
-- maximize
-files:
-  _target_: deckard.base.files.FileConfig
-  adv_predictions_file: adv_predictions.json
-  attack_dir: attacks
-  attack_file: attack
-  attack_type: .pkl
-  directory: cifar100
-  model_dir: models
-  model_file: model
-  model_type: .pt
-  name: default
-  params_file: params.yaml
-  predictions_file: predictions.json
-  reports: reports
-  score_dict_file: score_dict.json
-model:
-  _target_: deckard.base.model.Model
-  art:
-    _target_: deckard.base.model.art_pipeline.ArtPipeline
-    clip_values:
-    - 0
-    - 255
-    criterion:
-      name: torch.nn.CrossEntropyLoss
-    data:
-      _target_: deckard.base.data.Data
-      generate:
-        name: torch_cifar100
-        path: original_data/
-      sample:
-        random_state: 0
-        stratify: true
-        test_size: 12000
-        train_size: 48000
-    initialize:
-      clip_values:
-      - 0
-      - 255
-      criterion:
-        name: torch.nn.CrossEntropyLoss
-      optimizer:
-        lr: 0.01
-        momentum: 0.9
-        name: torch.optim.SGD
-    library: pytorch
-    optimizer:
-      lr: 0.01
-      momentum: 0.9
-      name: torch.optim.SGD
-  data:
-    _target_: deckard.base.data.Data
-    generate:
-      name: torch_cifar100
-      path: original_data/
-    sample:
-      random_state: 0
-      stratify: true
-      test_size: 12000
-      train_size: 48000
-  init:
-    _target_: deckard.base.model.ModelInitializer
-    name: torch_example.ResNet18
-    num_channels: 3
-    num_classes: 100
-  library: pytorch
-  trainer:
-    batch_size: 128
-    nb_epochs: 1
-    verbose: true
-model_name: ResNet18
-optimizers:
-- accuracy
-scorers:
-  _target_: deckard.base.scorer.ScorerDict
-  accuracy:
-    _target_: deckard.base.scorer.ScorerConfig
-    direction: maximize
-    name: sklearn.metrics.accuracy_score
-  log_loss:
-    _target_: deckard.base.scorer.ScorerConfig
-    direction: minimize
-    name: sklearn.metrics.log_loss
-stage: ???
diff --git a/examples/pytorch/plots/dvc.lock b/examples/pytorch/plots/dvc.lock
deleted file mode 100644
index 792bf29b..00000000
--- a/examples/pytorch/plots/dvc.lock
+++ /dev/null
@@ -1,707 +0,0 @@
-schema: '2.0'
-stages:
-  merge:
-    cmd: python -m deckard.layers.merge --smaller_file data/cifar.csv data/cifar100.csv
-      data/mnist.csv  --output_folder data --output_file merged.csv
-    deps:
-    - path: data/cifar.csv
-      hash: md5
-      md5: 4c6e158a421999f4eae6c238395f63e4
-      size: 20578565
-    - path: data/cifar100.csv
-      hash: md5
-      md5: c7bbce2ed692f4697b9ab4cbbe36f9c9
-      size: 30226661
-    - path: data/mnist.csv
-      hash: md5
-      md5: 083e65989c7b8a5f103d407eb675aaf8
-      size: 70077338
-    outs:
-    - path: data/merged.csv
-      hash: md5
-      md5: f98b901fa3ee817a55a36f78d0c95233
-      size: 124219079
-  plot:
-    cmd: python -m deckard.layers.plots --path plots/ --file data/merged_afr.csv -c
-      plots.yaml
-    deps:
-    - path: data/merged_afr.csv
-      hash: md5
-      md5: e8c06cf834297c5ee44950ea4d640cc8
-      size: 125347398
-    - path: plots.yaml
-      hash: md5
-      md5: 0f349596bdb4c2cee3c462325dffe4ba
-      size: 5070
-    params:
-      plots.yaml:
-        cat_plot:
-        - file: adv_accuracy_vs_defence_type.pdf
-          hue: model_name
-          kind: boxen
-          set:
-            yscale: linear
-          x: def_gen
-          xlabels: Defence Type
-          y: adv_accuracy
-          ylabels: Adv. Accuracy
-          rotation: 90
-          hue_order:
-          - ResNet18
-          - ResNet34
-          - ResNet50
-          - ResNet101
-          - ResNet152
-          legend_title: Model
-        - file: ben_accuracy_vs_defence_type.pdf
-          hue: model_name
-          kind: boxen
-          x: def_gen
-          xlabels: Defence Type
-          y: accuracy
-          ylabels: Ben. Accuracy
-          rotation: 90
-          hue_order:
-          - ResNet18
-          - ResNet34
-          - ResNet50
-          - ResNet101
-          - ResNet152
-          legend_title: Model
-        - file: ben_failures_per_train_time_vs_defence_type.pdf
-          hue: model_name
-          kind: boxen
-          set:
-            yscale: log
-          x: def_gen
-          xlabels: Defence Type
-          y: c_ben
-          ylabels: $\bar{C}_{ben.}$
-          rotation: 90
-          hue_order:
-          - ResNet18
-          - ResNet34
-          - ResNet50
-          - ResNet101
-          - ResNet152
-          legend_title: Model
-        - file: adv_failures_per_train_time_vs_defence_type.pdf
-          hue: model_name
-          kind: boxen
-          set:
-            yscale: log
-          x: def_gen
-          xlabels: Defence Type
-          y: c_adv
-          ylabels: $\bar{C}_{adv.}$
-          rotation: 90
-          hue_order:
-          - ResNet18
-          - ResNet34
-          - ResNet50
-          - ResNet101
-          - ResNet152
-          legend_title: Model
-        - file: adv_failures_per_train_time_vs_attack_type.pdf
-          hue: model_name
-          kind: boxen
-          set:
-            yscale: log
-          x: atk_gen
-          xlabels: Attack Type
-          y: c_adv
-          ylabels: $\bar{C}_{adv.}$
-          rotation: 90
-          hue_order:
-          - ResNet18
-          - ResNet34
-          - ResNet50
-          - ResNet101
-          - ResNet152
-          legend_title: Model
-        - file: adv_failures_per_test_time_vs_defence_type.pdf
-          hue: model_name
-          kind: boxen
-          x: def_gen
-          xlabels: Defence Type
-          y: adv_failure_rate
-          ylabels: $f_{adv.}$
-          rotation: 90
-          hue_order:
-          - ResNet18
-          - ResNet34
-          - ResNet50
-          - ResNet101
-          - ResNet152
-          legend_title: Model
-        - file: adv_accuracy_vs_attack_type.pdf
-          hue: model_name
-          kind: boxen
-          x: atk_gen
-          xlabels: Attack Type
-          y: adv_accuracy
-          ylabels: Adv. Accuracy
-          rotation: 90
-          hue_order:
-          - ResNet18
-          - ResNet34
-          - ResNet50
-          - ResNet101
-          - ResNet152
-          legend_title: Model
-        - file: ben_failure_rate_vs_defence_type.pdf
-          hue: model_name
-          kind: boxen
-          set:
-            yscale: log
-          x: def_gen
-          xlabels: Defence Type
-          y: failure_rate
-          ylabels: $f_{ben}(t; \theta)$
-          rotation: 90
-          hue_order:
-          - ResNet18
-          - ResNet34
-          - ResNet50
-          - ResNet101
-          - ResNet152
-          legend_title: Model
-        line_plot:
-        - file: def_param_vs_accuracy.pdf
-          hue: def_gen
-          legend:
-            bbox_to_anchor:
-            - 1.05
-            - 1
-            title: Defence
-          title: Ben. Accuracy vs Defence Strength
-          x: def_value
-          x_scale: linear
-          xlabel: Defence Control Parameter
-          y: accuracy
-          y_scale:
-          ylabel: Ben. Accuracy
-          hue_order:
-          - Control
-          - Conf
-          - Gauss-in
-          - Gauss-out
-          - FSQ
-          errorbar: se
-          err_style: bars
-        - file: def_param_vs_adv_accuracy.pdf
-          hue: def_gen
-          legend:
-            bbox_to_anchor:
-            - 1.05
-            - 1
-            title: Defence
-          title: Adv. Accuracy vs Defence Strength
-          x: def_value
-          x_scale: linear
-          xlabel: Defence Control Parameter
-          y: adv_accuracy
-          y_scale:
-          ylabel: Adv. Accuracy
-          hue_order:
-          - Control
-          - Conf
-          - Gauss-in
-          - Gauss-out
-          - FSQ
-          errorbar: se
-          err_style: bars
-        - file: def_param_vs_adv_failure_rate.pdf
-          hue: def_gen
-          legend:
-            bbox_to_anchor:
-            - 1.05
-            - 1
-            title: Defence
-          title: $f_{adv}$ vs Defence Strength
-          x: def_value
-          x_scale: linear
-          xlabel: Defence Control Parameter
-          y: adv_failure_rate
-          y_scale: linear
-          ylabel: $f_{adv.}$
-          hue_order:
-          - Control
-          - Conf
-          - Gauss-in
-          - Gauss-out
-          - FSQ
-          errorbar: se
-          err_style: bars
-        - file: atk_param_vs_accuracy.pdf
-          hue: atk_gen
-          legend:
-            bbox_to_anchor:
-            - 1.05
-            - 1
-          title: Adv. Accuracy vs Attack Strength
-          x: atk_value
-          x_scale: linear
-          xlabel: Attack Control Parameter
-          y: adv_accuracy
-          y_scale:
-          ylabel: Adv. Accuracy
-          hue_order:
-          - FGM
-          - PGD
-          - Deep
-          - HSJ
-          - Pixel
-          - Thresh
-          errorbar: se
-          err_style: bars
-        scatter_plot:
-        - x: train_time_per_sample
-          y: adv_failure_rate
-          hue: model_name
-          xlabel: $t_{train}$
-          ylabel: $f_{adv}$
-          title: $f_{adv}$ vs $t_{train}$
-          file: adv_failure_rate_vs_train_time.pdf
-          x_scale: log
-          legend:
-            title: Model Name
-            bbox_to_anchor:
-            - 1.05
-            - 1
-          hue_order:
-          - ResNet18
-          - ResNet34
-          - ResNet50
-          - ResNet101
-          - ResNet152
-    outs:
-    - path: plots/adv_accuracy_vs_attack_type.pdf
-      hash: md5
-      md5: 589df119d033ae26afd171bb532b70b0
-      size: 36004
-    - path: plots/adv_accuracy_vs_defence_type.pdf
-      hash: md5
-      md5: 234b264b494a6e396dbaff15ba1c06b7
-      size: 33276
-    - path: plots/adv_failure_rate_vs_train_time.pdf
-      hash: md5
-      md5: 409d1d5afc7f53a0bbd6235ea40f3670
-      size: 276434
-    - path: plots/adv_failures_per_test_time_vs_defence_type.pdf
-      hash: md5
-      md5: bc209f40e4dc19e9b64aa120907f6029
-      size: 41070
-    - path: plots/adv_failures_per_train_time_vs_attack_type.pdf
-      hash: md5
-      md5: 18eb7fb28635190e81bb9bd6e670a43f
-      size: 45248
-    - path: plots/adv_failures_per_train_time_vs_defence_type.pdf
-      hash: md5
-      md5: 164dd701541215cd35a31a5506a851b9
-      size: 41334
-    - path: plots/atk_param_vs_accuracy.pdf
-      hash: md5
-      md5: 2f65cc39685356bb1173b3c84a930d65
-      size: 21456
-    - path: plots/ben_accuracy_vs_defence_type.pdf
-      hash: md5
-      md5: b582dadd56d56ff587411b17a1435fa2
-      size: 34713
-    - path: plots/ben_failure_rate_vs_defence_type.pdf
-      hash: md5
-      md5: 1c800cde38bdaa41d2758d75e977d35c
-      size: 43727
-    - path: plots/ben_failures_per_train_time_vs_defence_type.pdf
-      hash: md5
-      md5: 77c8a489ec159b88afeac48ee0aeeb18
-      size: 41312
-    - path: plots/def_param_vs_accuracy.pdf
-      hash: md5
-      md5: 8247cc225c005509e2fe9ece9d4c88af
-      size: 19260
-    - path: plots/def_param_vs_adv_accuracy.pdf
-      hash: md5
-      md5: 3146258daf3dc7d7cea85f957a77557d
-      size: 18821
-    - path: plots/def_param_vs_adv_failure_rate.pdf
-      hash: md5
-      md5: 600022c5a83acf5532e84f7f0daa635c
-      size: 23053
-  afr:
-    cmd: python -m deckard.layers.afr  --data_file data/merged.csv  --target adv_failures
-      --duration_col adv_fit_time  --config_file afr.yaml --plots_folder plots/
-    deps:
-    - path: data/merged.csv
-      hash: md5
-      md5: f98b901fa3ee817a55a36f78d0c95233
-      size: 124219079
-    params:
-      afr.yaml:
-        covariates:
-        - adv_fit_time
-        - accuracy
-        - train_time
-        - atk_value
-        - def_value
-        - data.sample.random_state
-        - Epochs
-        - model_layers
-        - id
-        - atk_gen
-        - def_gen
-        - adv_failures
-        - adv_accuracy
-        - predict_time
-        cox:
-          plot:
-            file: cox_aft.pdf
-            title: Cox Model
-            title: Cox Model
-            qq_title: Cox QQ Plot
-          t0: 0.3
-          model:
-            penalizer: 0.2
-          labels:
-            data.sample.random_state: Random State
-            atk_value: Attack Strength
-            train_time: $t_{train}$
-            predict_proba_time: $t_{predict}$
-            adv_accuracy: Adv. Accuracy
-            accuracy: Ben. Accuracy
-            adv_fit_time: $t_{attack}$
-            adv_failure_rate: $f_{adv.}(t;\theta)$
-            failure_rate: $f_{ben.}(t;\theta)$
-            Epochs: No. of Epochs
-            model.trainer.batch_size: Batch Size
-            def_gen: Defence
-        exponential:
-          plot:
-            file: exponential_aft.pdf
-            title: Exponential Model
-            qq_title: Exponential QQ Plot
-          t0: 0.1
-          model:
-            breakpoints:
-            - 0.1
-          labels:
-            'Intercept: rho_': $\rho$
-            'Intercept: lambda_': $\lambda$
-            'data.sample.random_state: lambda_': Random State
-            'atk_value: lambda_': Attack Strength
-            'train_time: lambda_': $t_{train}$
-            'predict_proba_time: lambda_': $t_{predict}$
-            'adv_accuracy: lambda_': Adv. Accuracy
-            'accuracy: lambda_': Ben. Accuracy
-            'adv_fit_time: lambda_': $t_{attack}$
-            'adv_failure_rate: lambda_': $f_{adv.}(t;\theta)$
-            'failure_rate: lambda_': $f_{ben.}(t;\theta)$
-            'Epochs: lambda_': No. of Epochs
-            'model.trainer.batch_size: lambda_': Batch Size
-            def_gen: Defence
-            ': lambda_': ''
-        gamma:
-          plot:
-            file: gamma_aft.pdf
-            title: Generalized Gamma Model
-            qq_title: Gamma QQ Plot
-          t0: 0.3
-          model:
-            penalizer: 0.3
-          labels:
-            'Intercept: alpha_': $\alpha$
-            'Intercept: beta_': $\beta$
-            'data.sample.random_state: beta_': Random State
-            'atk_value: beta_': Attack Strength
-            'train_time: beta_': $t_{train}$
-            'predict_proba_time: beta_': $t_{predict}$
-            'adv_accuracy: beta_': Adv. Accuracy
-            'accuracy: beta_': Ben. Accuracy
-            'adv_fit_time: beta_': $t_{attack}$
-            'adv_failure_rate: beta_': $h_{adv.}(t;\theta)$
-            'failure_rate: beta_': $h_{ben.}(t;\theta)$
-            'Epochs: beta_': No. of Epochs
-            'model.trainer.batch_size: beta_': Batch Size
-            def_gen: Defence
-            'attack.init.eps: beta_': $\varepsilon$
-        log_logistic:
-          plot:
-            file: log_logistic_aft.pdf
-            title: Log logistic AFR Model
-            qq_title: Log Logistic QQ Plot
-          t0: 0.1
-          model:
-            penalizer: 0.1
-          labels:
-            'Intercept: beta_': $\beta$
-            'Intercept: alpha_': $\alpha$
-            'data.sample.random_state: alpha_': Random State
-            'atk_value: alpha_': Attack Strength
-            'train_time: alpha_': $t_{train}$
-            'predict_proba_time: alpha_': $t_{predict}$
-            'adv_accuracy: alpha_': Adv. Accuracy
-            'accuracy: alpha_': Ben. Accuracy
-            'adv_fit_time: alpha_': $t_{attack}$
-            'adv_failure_rate: alpha_': $h_{adv.}(t;\theta)$
-            'failure_rate: alpha_': $h_{ben.}(t;\theta)$
-            'Epochs: alpha_': No. of Epochs
-            'model.trainer.batch_size: alpha_': Batch Size
-            def_gen: Defence
-            'attack.init.eps: alpha_': $\varepsilon$
-        log_normal:
-          plot:
-            file: log_normal_aft.pdf
-            title: Log Normal AFR Model
-            qq_title: Log Normal QQ Plot
-          t0: 2
-          t0: 2
-          model:
-            penalizer: 0.5
-          labels:
-            'Intercept: sigma_': $\sigma$
-            'Intercept: mu_': $\mu$
-            'atk_value: mu_': Attack Strength
-            'train_time: mu_': $t_{train}$
-            'predict_proba_time: mu_': $t_{predict}$
-            'adv_accuracy: mu_': Adv. Accuracy
-            'accuracy: mu_': Ben. Accuracy
-            'adv_fit_time: mu_': $t_{attack}$
-            'adv_failure_rate: mu_': $h_{adv.}(t;\theta)$
-            'failure_rate: mu_': $h_{ben.}(t;\theta)$
-            'Epochs: mu_': No. of Epochs
-            'model.trainer.batch_size: mu_': Batch Size
-            def_gen: Defence
-            'attack.init.eps: mu_': $\varepsilon$
-            'data.sample.random_state: mu_': Random State
-        weibull:
-          plot:
-            file: weibull_aft.pdf
-            title: Weibull AFR Model
-            qq_title: Weibull QQ Plot
-          t0: 1
-          model:
-            penalizer: 0.1
-          labels:
-            'Intercept: rho_': $\rho$
-            'Intercept: lambda_': $\lambda$
-            'data.sample.random_state: lambda_': Random State
-            'atk_value: lambda_': Attack Strength
-            'train_time: lambda_': $t_{train}$
-            'predict_proba_time: lambda_': $t_{predict}$
-            'adv_accuracy: lambda_': Adv. Accuracy
-            'accuracy: lambda_': Ben. Accuracy
-            'adv_fit_time: lambda_': $t_{attack}$
-            'adv_failure_rate: lambda_': $f_{adv.}(t;\theta)$
-            'failure_rate: lambda_': $f_{ben.}(t;\theta)$
-            'Epochs: lambda_': No. of Epochs
-            'model.trainer.batch_size: lambda_': Batch Size
-            def_gen: Defence
-            'model_layers: lambda_': Layers
-            'def_value: lambda_': Defence Strength
-            'predict_time: lambda_': $t_{predict}$
-            ': lambda_': ''
-    outs:
-    - path: plots/aft_comparison.csv
-      hash: md5
-      md5: c2baa8f717cdc1da5c66c45b6fbe15ed
-      size: 484
-    - path: plots/aft_comparison.tex
-      hash: md5
-      md5: 36326edf164e3e50eb1fa9cdfe779883
-      size: 771
-    - path: plots/cox_aft.pdf
-      hash: md5
-      md5: 49cc31dfa8deb7a973efa79aa5661023
-      size: 29060
-    - path: plots/cox_aft_dummies.pdf
-      hash: md5
-      md5: 2886a507102de701086572d45da2dfa8
-      size: 29294
-    - path: plots/cox_qq.pdf
-      hash: md5
-      md5: 1871952097e3a7ced7597800a08864a1
-      size: 19783
-    - path: plots/cox_summary.csv
-      hash: md5
-      md5: 5c2afaed99bd7a6459ffd1417b085386
-      size: 4704
-    - path: plots/exponential_aft.pdf
-      hash: md5
-      md5: bc07de53e57a4b8ac93621e31afce321
-      size: 31719
-    - path: plots/exponential_aft_dummies.pdf
-      hash: md5
-      md5: 1590788252530487ef7c12a2412533c5
-      size: 31689
-    - path: plots/exponential_qq.pdf
-      hash: md5
-      md5: 5eb9206940c0556752308da1e30db16e
-      size: 20360
-    - path: plots/exponential_summary.csv
-      hash: md5
-      md5: 14692228f8862cdcf958ca6519f3eeb8
-      size: 6115
-    - path: plots/gamma_aft.pdf
-      hash: md5
-      md5: c14f76b8a748a81eb652df11c4168430
-      size: 28179
-    - path: plots/gamma_aft_dummies.pdf
-      hash: md5
-      md5: 0a64f85cf0c0dbf042c2af6ac3f1e514
-      size: 34487
-    - path: plots/gamma_qq.pdf
-      hash: md5
-      md5: 86b107aed5da777f5e11c7148cece4f6
-      size: 19114
-    - path: plots/gamma_summary.csv
-      hash: md5
-      md5: eae68c47d2098fd5dcb6a4456e50909d
-      size: 14596
-    - path: plots/log_logistic_aft.pdf
-      hash: md5
-      md5: c7927f6d138223b3d0814e08ae70cf50
-      size: 29805
-    - path: plots/log_logistic_aft_dummies.pdf
-      hash: md5
-      md5: 3451396aab89eda953b67b7013043df5
-      size: 30815
-    - path: plots/log_logistic_qq.pdf
-      hash: md5
-      md5: 6db594338475a53d925e72d9a5be69e6
-      size: 21187
-    - path: plots/log_logistic_summary.csv
-      hash: md5
-      md5: bc339874fa9cb8aa52471140047bab73
-      size: 5167
-    - path: plots/log_normal_aft.pdf
-      hash: md5
-      md5: 60147688508ead164e45910baace6fd9
-      size: 30291
-    - path: plots/log_normal_aft_dummies.pdf
-      hash: md5
-      md5: 267b2a890ffd2a034d9825cefec12a45
-      size: 31496
-    - path: plots/log_normal_qq.pdf
-      hash: md5
-      md5: ff928a6fbd9d7277df3f88c99d22c096
-      size: 22085
-    - path: plots/log_normal_summary.csv
-      hash: md5
-      md5: d48ff310a60a8ad40313cf3b69ab5e58
-      size: 5149
-    - path: plots/weibull_aft.pdf
-      hash: md5
-      md5: 799bddf708e9c4a48784783c629e722a
-      size: 31698
-    - path: plots/weibull_aft_dummies.pdf
-      hash: md5
-      md5: 99fef4eb8ec5dbf7844ba6e76c904b73
-      size: 31043
-    - path: plots/weibull_qq.pdf
-      hash: md5
-      md5: 258bebd08669196cf738183ecd05dc14
-      size: 19374
-    - path: plots/weibull_summary.csv
-      hash: md5
-      md5: 8ee7e7d66bcf24eb8424460826ab7421
-      size: 5217
-  copy_results:
-    cmd: mkdir -p ~/ml_afr/plots &&  cp -r plots/* ~/ml_afr/plots/
-    deps:
-    - path: plots/
-      hash: md5
-      md5: 5858e5f1ee079e8c99ac53c7834601da.dir
-      size: 5938101
-      nfiles: 43
-  clean:
-    cmd: python -m deckard.layers.clean_data -i data/merged.csv -o data/clean.csv
-      -c clean.yaml
-    deps:
-    - path: data/merged.csv
-      hash: md5
-      md5: 1b4802747f91a7001c43401ad21d997a
-      size: 50761113
-    params:
-      clean.yaml:
-        fillna:
-          Epochs: 10
-    outs:
-    - path: data/clean.csv
-      hash: md5
-      md5: 2913117ef7997065dafc860c9d3080f5
-      size: 54972304
-  clean@mnist:
-    cmd: python -m deckard.layers.clean_data -i data/attack_mnist.csv  -o data/mnist.csv  -c
-      ../conf/clean_mnist.yaml --drop_if_empty adv_fit_time accuracy train_time predict_time
-      adv_accuracy Epochs
-    deps:
-    - path: ../conf/clean_mnist.yaml
-      hash: md5
-      md5: bb112947b87ca42a244135a52cc5e7d5
-      size: 1003
-    - path: data/attack_mnist.csv
-      hash: md5
-      md5: c4db49ae4fc1a0e6fa4c3d52b03d650a
-      size: 93006845
-    outs:
-    - path: data/mnist.csv
-      hash: md5
-      md5: 083e65989c7b8a5f103d407eb675aaf8
-      size: 70077338
-  clean@cifar:
-    cmd: python -m deckard.layers.clean_data -i data/attack_cifar.csv  -o data/cifar.csv  -c
-      ../conf/clean_cifar.yaml --drop_if_empty adv_fit_time accuracy train_time predict_time
-      adv_accuracy Epochs
-    deps:
-    - path: ../conf/clean_cifar.yaml
-      hash: md5
-      md5: 4e2abc093db66b77b424854549b80497
-      size: 961
-    - path: data/attack_cifar.csv
-      hash: md5
-      md5: 5a430aa13b88dff6a8fdf0277c9cd53d
-      size: 23267442
-    outs:
-    - path: data/cifar.csv
-      hash: md5
-      md5: 4c6e158a421999f4eae6c238395f63e4
-      size: 20578565
-  clean@cifar100:
-    cmd: python -m deckard.layers.clean_data -i data/attack_cifar100.csv  -o data/cifar100.csv  -c
-      ../conf/clean_cifar100.yaml --drop_if_empty adv_fit_time accuracy train_time
-      predict_time adv_accuracy Epochs
-    deps:
-    - path: ../conf/clean_cifar100.yaml
-      hash: md5
-      md5: 5a8cc7e71f7036e2f590bb882acd34fb
-      size: 897
-    - path: data/attack_cifar100.csv
-      hash: md5
-      md5: ea55355b5b530f5751aea19d13067099
-      size: 36035453
-    outs:
-    - path: data/cifar100.csv
-      hash: md5
-      md5: c7bbce2ed692f4697b9ab4cbbe36f9c9
-      size: 30226661
-  predict_survival_time:
-    cmd: python predict_with_best.py  --data data/merged.csv  --config_file afr.yaml  --model
-      weibull  --target adv_failures  --duration_col adv_fit_time  --output data/merged_afr.csv
-    deps:
-    - path: afr.yaml
-      hash: md5
-      md5: 82c7a6430d38320263db502d57cbd5c6
-      size: 5728
-    - path: data/merged.csv
-      hash: md5
-      md5: f98b901fa3ee817a55a36f78d0c95233
-      size: 124219079
-    - path: plots/aft_comparison.tex
-      hash: md5
-      md5: 36326edf164e3e50eb1fa9cdfe779883
-      size: 771
-    outs:
-    - path: data/merged_afr.csv
-      hash: md5
-      md5: e8c06cf834297c5ee44950ea4d640cc8
-      size: 125347398
diff --git a/examples/pytorch/scripts/Deep.sh b/examples/pytorch/scripts/Deep.sh
new file mode 100644
index 00000000..0e7293e3
--- /dev/null
+++ b/examples/pytorch/scripts/Deep.sh
@@ -0,0 +1,8 @@
+bash scripts/models.sh \
+    stage=attack \
+    attack=default \
+    ++attack.init.name=art.attacks.evasion.DeepFool \
+    ++attack.init.max_iter=10 \
+    ++attack.init.batch_size=4096 \
+    ++attack.init.nb_grads=1,3,5,8,10 \
+    atk_name=Deep $@
\ No newline at end of file
diff --git a/examples/pytorch/scripts/FGM.sh b/examples/pytorch/scripts/FGM.sh
new file mode 100644
index 00000000..7b66169f
--- /dev/null
+++ b/examples/pytorch/scripts/FGM.sh
@@ -0,0 +1,7 @@
+bash scripts/models.sh \
+    stage=attack \
+    attack=default \
+    ++attack.init.name=art.attacks.evasion.FastGradientMethod \
+    ++attack.init.eps=.001,.01,.1,.5,1 \
+    ++attack.init.norm=2 \
+    atk_name=FGM  $@
\ No newline at end of file
diff --git a/examples/pytorch/scripts/HSJ.sh b/examples/pytorch/scripts/HSJ.sh
new file mode 100644
index 00000000..fffa757e
--- /dev/null
+++ b/examples/pytorch/scripts/HSJ.sh
@@ -0,0 +1,9 @@
+bash scripts/models.sh \
+    stage=attack \
+    attack=default \
+    ++attack.init.name=art.attacks.evasion.HopSkipJump \
+    ++attack.init.max_iter=1,3,5,10,15 \
+    ++attack.init.init_eval=3 \
+    ++attack.init.max_eval=10 \
+    ++attack.init.norm=2 \
+    atk_name=HSJ $@
\ No newline at end of file
diff --git a/examples/pytorch/scripts/PGD.sh b/examples/pytorch/scripts/PGD.sh
new file mode 100644
index 00000000..8c45315d
--- /dev/null
+++ b/examples/pytorch/scripts/PGD.sh
@@ -0,0 +1,9 @@
+bash scripts/models.sh \
+    stage=attack \
+    attack=default \
+    ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent \
+    ++attack.init.eps=.001,.01,.1,.5,1 \
+    ++attack.init.norm=2 \
+    ++attack.init.eps_step=.001,.003,.01 \
+    atk_name=PGD \
+    ++attack.init.max_iter=1,5,10,50,100 $@
\ No newline at end of file
diff --git a/examples/pytorch/scripts/Pixel.sh b/examples/pytorch/scripts/Pixel.sh
new file mode 100644
index 00000000..f684b572
--- /dev/null
+++ b/examples/pytorch/scripts/Pixel.sh
@@ -0,0 +1,7 @@
+bash scripts/models.sh \
+    stage=attack \
+    attack=default  \
+    ++attack.init.name=art.attacks.evasion.PixelAttack \
+    ~attack.init.batch_size \
+    ++attack.init.th=1,4,16,64,256 \
+    atk_name=Pixel $@
\ No newline at end of file
diff --git a/examples/pytorch/scripts/Thresh.sh b/examples/pytorch/scripts/Thresh.sh
new file mode 100644
index 00000000..7f2e774c
--- /dev/null
+++ b/examples/pytorch/scripts/Thresh.sh
@@ -0,0 +1,7 @@
+bash scripts/models.sh \
+    stage=attack \
+    attack=default \
+    ++attack.init.name=art.attacks.evasion.ThresholdAttack \
+    ~attack.init.batch_size \
+    ++attack.init.th=1,4,16,64,256 \
+    atk_name=Thresh $@
\ No newline at end of file
diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/scripts/attacks.sh
similarity index 84%
rename from examples/pytorch/attacks.sh
rename to examples/pytorch/scripts/attacks.sh
index bba66e7c..0ed0d0da 100644
--- a/examples/pytorch/attacks.sh
+++ b/examples/pytorch/scripts/attacks.sh
@@ -10,7 +10,7 @@
 #     ++attack.init.eps=.001,.01,.1,.5,1 \
 #     ++attack.init.norm=2 \
 #     atk_name=FGM  $@
-# # #####################################################
+# #####################################################
 # # Projected Gradient Descent
 # bash models.sh \
 #     stage=attack \
@@ -43,23 +43,23 @@
 #     ++attack.init.norm=2 \
 #     atk_name=HSJ $@
 # #####################################################
-# PixelAttack
-bash models.sh \
-    stage=attack \
-    attack=default  \
-    ++attack.init.name=art.attacks.evasion.PixelAttack \
-    ~attack.init.batch_size \
-    ++attack.init.th=1,4,16,64,256 \
-    atk_name=Pixel $@
-# #####################################################
-# ThresholdAttack
-bash models.sh \
-    stage=attack \
-    attack=default \
-    ++attack.init.name=art.attacks.evasion.ThresholdAttack \
-    ~attack.init.batch_size \
-    ++attack.init.th=1,4,16,64,256 \
-    atk_name=Thresh $@
+# # # PixelAttack
+# bash models.sh \
+#     stage=attack \
+#     attack=default  \
+#     ++attack.init.name=art.attacks.evasion.PixelAttack \
+#     ~attack.init.batch_size \
+#     ++attack.init.th=1,4,16,64,256 \
+#     atk_name=Pixel $@
+# # #####################################################
+# # ThresholdAttack
+# bash models.sh \
+#     stage=attack \
+#     attack=default \
+#     ++attack.init.name=art.attacks.evasion.ThresholdAttack \
+#     ~attack.init.batch_size \
+#     ++attack.init.th=1,4,16,64,256 \
+#     atk_name=Thresh $@
 # #####################################################
 # # ZooAttack
 # bash models.sh \
diff --git a/examples/pytorch/models.sh b/examples/pytorch/scripts/models.sh
similarity index 84%
rename from examples/pytorch/models.sh
rename to examples/pytorch/scripts/models.sh
index 2a54e307..8a3864b8 100644
--- a/examples/pytorch/models.sh
+++ b/examples/pytorch/scripts/models.sh
@@ -14,9 +14,6 @@ python -m deckard.layers.optimise \
   def_name=FSQ \
   $@ --multirun
 
-rm -rf mnist/models
-rm -rf cifar/models
-rm -rf cifar100/models
 
 # Gaussian Augmentation (Input)
 python -m deckard.layers.optimise \
@@ -27,9 +24,6 @@ python -m deckard.layers.optimise \
   def_name=Gauss-In \
   $@ --multirun
 
-rm -rf mnist/models
-rm -rf cifar/models
-rm -rf cifar100/model
 
 # Gaussian Noise (Output)
 python -m deckard.layers.optimise \
@@ -39,9 +33,6 @@ python -m deckard.layers.optimise \
   def_name=Gauss-Out \
   $@ --multirun
 
-rm -rf mnist/models
-rm -rf cifar/models
-rm -rf cifar100/models
 
 # High Confidence
 python -m deckard.layers.optimise \
@@ -50,6 +41,3 @@ python -m deckard.layers.optimise \
     def_name=Conf \
     $@ --multirun
 
-rm -rf mnist/models
-rm -rf cifar/models
-rm -rf cifar100/models
diff --git a/examples/pytorch/wait.sh b/examples/pytorch/scripts/wait.sh
similarity index 100%
rename from examples/pytorch/wait.sh
rename to examples/pytorch/scripts/wait.sh