diff --git a/tutorials/usage_testing.py b/tutorials/usage_testing.py
index 47662217..c1fc60df 100644
--- a/tutorials/usage_testing.py
+++ b/tutorials/usage_testing.py
@@ -7,7 +7,7 @@
 import requests
 import torch
 import torchvision
-
+import lightning as L
 # from torch import nn
 # from torch import optim
 from torch.utils.data import DataLoader
@@ -16,19 +16,19 @@
 from torchvision.utils import make_grid
 from tqdm import tqdm
 
-from quanda.explainers.wrappers.captum_influence import (
+from quanda.explainers.wrappers import (
     CaptumSimilarity,
     captum_similarity_explain,
 )
-from quanda.metrics.localization.class_detection import ClassDetectionMetric
-from quanda.metrics.randomization.model_randomization import (
+from quanda.metrics.localization import ClassDetectionMetric
+from quanda.metrics.randomization import (
     ModelRandomizationMetric,
 )
-from quanda.metrics.unnamed.dataset_cleaning import DatasetCleaningMetric
-from quanda.metrics.unnamed.top_k_overlap import TopKOverlapMetric
-from quanda.toy_benchmarks.subclass_detection import SubclassDetection
-from quanda.utils.training.base_pl_module import BasicLightningModule
-from quanda.utils.training.trainer import Trainer
+from quanda.metrics.unnamed import DatasetCleaningMetric
+from quanda.metrics.unnamed import TopKOverlapMetric
+from quanda.toy_benchmarks.localization import SubclassDetection
+from quanda.utils.training import BasicLightningModule
+from quanda.utils.training import Trainer
 
 DEVICE = "cuda:0"  # "cuda" if torch.cuda.is_available() else "cpu"
 torch.set_float32_matmul_precision("medium")
@@ -72,6 +72,7 @@ def main():
 
     # load model with pre-trained weights
     model = resnet18(weights=None, num_classes=10)
+    init_model = resnet18(weights=None, num_classes=10)
     model.load_state_dict(weights_pretrained)
     model.to(DEVICE)
     model.eval()
@@ -131,20 +132,42 @@ def accuracy(net, loader):
     top_k = TopKOverlapMetric(model=model, train_dataset=train_set, top_k=1, device=DEVICE)
 
     # dataset cleaning
+    max_epochs = 1
+    criterion = torch.nn.CrossEntropyLoss()
+    optimizer = torch.optim.SGD
+    lr = 0.1
+    optimizer_kwargs = {"momentum": 0.9, "weight_decay": 5e-4}
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR
+    scheduler_kwargs = {"T_max": max_epochs}
+
     pl_module = BasicLightningModule(
-        model=copy.deepcopy(model),
-        optimizer=torch.optim.SGD,
-        lr=0.1,
-        criterion=torch.nn.CrossEntropyLoss(),
+        model=model,
+        optimizer=optimizer,
+        optimizer_kwargs=optimizer_kwargs,
+        scheduler=scheduler,
+        scheduler_kwargs=scheduler_kwargs,
+        lr=lr,
+        criterion=criterion,
     )
-    trainer = Trainer.from_lightning_module(model, pl_module)
+
+    init_pl_module = BasicLightningModule(
+        model=init_model,
+        optimizer=optimizer,
+        optimizer_kwargs=optimizer_kwargs,
+        scheduler=scheduler,
+        scheduler_kwargs=scheduler_kwargs,
+        lr=lr,
+        criterion=criterion,
+    )
+
+    trainer = L.Trainer(max_epochs=max_epochs)
 
     data_clean = DatasetCleaningMetric(
-        model=model,
+        model=pl_module,
+        init_model=copy.deepcopy(init_pl_module),
         train_dataset=train_set,
         global_method="sum_abs",
         trainer=trainer,
-        trainer_fit_kwargs={"max_epochs": 3},
         top_k=50,
         device=DEVICE,
     )
@@ -179,33 +202,16 @@ def accuracy(net, loader):
     # Subclass Detection Benchmark Generation and Evaluation
     # ++++++++++++++++++++++++++++++++++++++++++
 
-    max_epochs = 1
-    criterion = torch.nn.CrossEntropyLoss()
-    optimizer = torch.optim.SGD
-    lr = 0.1
-    optimizer_kwargs = {"momentum": 0.9, "weight_decay": 5e-4}
-    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR
-    scheduler_kwargs = {"T_max": max_epochs}
-
-    trainer = BasicLightningModule(
-        model=model,
-        optimizer=optimizer,
-        optimizer_kwargs=optimizer_kwargs,
-        scheduler=scheduler,
-        scheduler_kwargs=scheduler_kwargs,
-        lr=lr,
-        criterion=criterion,
-    )
+    trainer = L.Trainer(max_epochs=max_epochs)
 
     bench = SubclassDetection.generate(
-        model=model,
+        model=copy.deepcopy(init_pl_module),
         train_dataset=train_set,
         trainer=trainer,
         val_dataset=val_set,
         n_classes=10,
         n_groups=2,
         class_to_group="random",
-        trainer_fit_kwargs={"max_epochs": max_epochs},
         seed=42,
         batch_size=100,
         device=DEVICE,
@@ -214,7 +220,7 @@ def accuracy(net, loader):
     score = bench.evaluate(
         expl_dataset=test_set,
         explainer_cls=CaptumSimilarity,
-        expl_kwargs={"layers": "avgpool", "batch_size": 100},
+        expl_kwargs={"layers": "model.avgpool", "batch_size": 100},
         cache_dir="./cache",
         model_id="default_model_id",
     )