diff --git a/README.md b/README.md
index ef17c0e..5bf6a16 100644
--- a/README.md
+++ b/README.md
@@ -21,15 +21,19 @@ bash run-docker.sh
 cd Thesis
 ```
 
-## Step 4: Pretrain LeNet5 on MNIST
+## Step 4: Pretrain LeNet5 on MNIST / ResNet18 on CIFAR10
 ```
 python pretrain.py --model-name LeNet5 --dataset MNIST --training-epochs 10
+python pretrain.py --model-name resnet18 --dataset CIFAR10 --training-epochs 30
 ```
 
 ## Step 5: Train agent on LeNet5
 ```
 mkdir LeNet5
 python train.py --model-name LeNet5 --dataset MNIST --model-path <path to model (should be inside checkpoints folder ending in _best.tar)> --freq 300 --target-fps 6000 --board U250 --num-episodes 30
+
+mkdir resnet18
+python train.py --model-name resnet18 --dataset CIFAR10 --model-path <path to model (should be inside checkpoints folder ending in _best.tar)> --freq 200 --target-fps 400 --board U250 --num-episodes 200
 ```
 ## Step 6: Test agent on LeNet5
 ```
diff --git a/exporter/Exporter.py b/exporter/Exporter.py
index b26f2fd..17b53ce 100644
--- a/exporter/Exporter.py
+++ b/exporter/Exporter.py
@@ -228,29 +228,6 @@ def streamline_lenet(model: ModelWrapper, cfg: build.DataflowBuildConfig):
 
 	return model
 
-def streamline_simple(model: ModelWrapper, cfg: build.DataflowBuildConfig):
-	model = model.transform(ConvertSubToAdd())
-	model = model.transform(ConvertDivToMul())
-
-	model = model.transform(collapse.CollapseRepeatedMul())
-	model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
-	model = model.transform(absorb.AbsorbSignBiasIntoMultiThreshold())
-	model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
-
-	model = model.transform(reorder.MoveScalarMulPastConv())
-	model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
-	model = model.transform(collapse.CollapseRepeatedMul())
-	model = model.transform(reorder.MoveMulPastMaxPool())
-	model = model.transform(reorder.MoveScalarLinearPastInvariants())
-	model = model.transform(reorder.MoveScalarMulPastMatMul())
-	model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
-	model = model.transform(absorb.AbsorbScalarMulAddIntoTopK())
-
-	if VerificationStepType.STREAMLINED_PYTHON in cfg._resolve_verification_steps():
-		verify_step(model, cfg, "streamlined_python", need_parent=False)
-
-
-	return model
 
 def streamline_resnet(model: ModelWrapper, cfg: build.DataflowBuildConfig):
 	model = model.transform(ConvertSubToAdd())
@@ -330,34 +307,6 @@ def convert_to_hw_resnet(model: ModelWrapper, cfg: build.DataflowBuildConfig):
 
 	return model
 
-def convert_to_hw_simple(model: ModelWrapper, cfg: build.DataflowBuildConfig):
-	model = model.transform(InferDataLayouts())
-	model = model.transform(convert.InferPool())
-
-	model = model.transform(LowerConvsToMatMul())
-	model = model.transform(convert.InferConvInpGen())
-	model = model.transform(convert.InferVectorVectorActivation())
-	model = model.transform(convert.InferBinaryMatrixVectorActivation())
-	model = model.transform(convert.InferQuantizedMatrixVectorActivation())
-
-	model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
-	model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
-	model = model.transform(absorb.AbsorbConsecutiveTransposes())
-
-	model = model.transform(InferDataLayouts())
-	model = model.transform(convert.InferThresholdingLayer())
-
-	model = model.transform(InferDataLayouts())
-	model = model.transform(convert.InferLabelSelectLayer())
-
-	model = model.transform(InferDataLayouts())
-	model = model.transform(RemoveCNVtoFCFlatten())
-
-	model = model.transform(RoundAndClipThresholds())
-	model = tidy_up(model)
-
-	return model
-
 def convert_to_hw_lenet(model: ModelWrapper, cfg: build.DataflowBuildConfig):
 	model = model.transform(InferDataLayouts())
 	model = model.transform(convert.InferPool())
diff --git a/train/exporter/Exporter.py b/train/exporter/Exporter.py
index f46f8cd..276f94b 100644
--- a/train/exporter/Exporter.py
+++ b/train/exporter/Exporter.py
@@ -230,26 +230,6 @@ def streamline_lenet(model):
 
 	return model
 
-def streamline_simple(model):
-	model = model.transform(ConvertSubToAdd())
-	model = model.transform(ConvertDivToMul())
-
-	model = model.transform(collapse.CollapseRepeatedMul())
-	model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
-	model = model.transform(absorb.AbsorbSignBiasIntoMultiThreshold())
-	model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
-
-	model = model.transform(reorder.MoveScalarMulPastConv())
-	model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
-	model = model.transform(collapse.CollapseRepeatedMul())
-	model = model.transform(reorder.MoveMulPastMaxPool())
-	model = model.transform(reorder.MoveScalarLinearPastInvariants())
-	model = model.transform(reorder.MoveScalarMulPastMatMul())
-	model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
-	model = model.transform(absorb.AbsorbScalarMulAddIntoTopK())
-
-	return model
-
 def streamline_resnet(model):
 	model = model.transform(ConvertSubToAdd())
 	model = model.transform(ConvertDivToMul())
@@ -347,35 +327,7 @@ def convert_to_hw_lenet(model):
 	model = tidy_up(model)
 
 	return model
-
-def convert_to_hw_simple(model):
-	model = model.transform(InferDataLayouts())
-	model = model.transform(convert.InferPool())
-
-	model = model.transform(LowerConvsToMatMul())
-	model = model.transform(convert.InferConvInpGen())
-	model = model.transform(convert.InferVectorVectorActivation())
-	model = model.transform(convert.InferBinaryMatrixVectorActivation())
-	model = model.transform(convert.InferQuantizedMatrixVectorActivation())
-
-	model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
-	model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
-	model = model.transform(absorb.AbsorbConsecutiveTransposes())
-
-	model = model.transform(InferDataLayouts())
-	model = model.transform(convert.InferThresholdingLayer())
-
-	model = model.transform(InferDataLayouts())
-	model = model.transform(convert.InferLabelSelectLayer())
-
-	model = model.transform(InferDataLayouts())
-	model = model.transform(RemoveCNVtoFCFlatten())
-
-	model = model.transform(RoundAndClipThresholds())
-	model = tidy_up(model)
-
-	return model
-
+	
 def name_nodes(model):
 	model = model.transform(GiveUniqueNodeNames())
 	model = model.transform(GiveReadableTensorNames())
diff --git a/train/finetune/Finetuner.py b/train/finetune/Finetuner.py
index 6dc6a7f..6769729 100644
--- a/train/finetune/Finetuner.py
+++ b/train/finetune/Finetuner.py
@@ -14,15 +14,13 @@
 from .calibrate import calibrate
 from pretrain.models.LeNet5 import LeNet5
 from pretrain.models.resnet import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
-from pretrain.models.Simple import Simple
 
 networks = {'LeNet5' : LeNet5,
 			'resnet18' : ResNet18, 
 			'resnet34' : ResNet34,
 			'resnet50' : ResNet50,
 			'resnet101' : ResNet101,
-			'resnet152' : ResNet152,
-			'Simple' : Simple}
+			'resnet152' : ResNet152}
 
 class Finetuner(object):
 	def __init__(self, args, model_config):