diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index ac10ed2..4b3953a 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -14,6 +14,13 @@
     mpactbackend.py
 )
 
+declare_mlir_python_sources(MPACTPythonSources.SampleModels
+  ROOT_DIR "${MPACT_PYTHON_ROOT_DIR}"
+  ADD_TO_PARENT MPACTPythonSources
+  SOURCES
+    models/resnet.py
+)
+
 #-------------------------------------------------------------------------------
 # Python Modules
 #-------------------------------------------------------------------------------
diff --git a/python/mpact/models/resnet.py b/python/mpact/models/resnet.py
new file mode 100644
index 0000000..c3f5925
--- /dev/null
+++ b/python/mpact/models/resnet.py
@@ -0,0 +1,255 @@
+import torch
+import numpy as np
+
+
+def spike(input):
+    return (input >= 0).float()
+
+
+class Straight(torch.nn.Module):
+    def forward(self, input):
+        return input
+
+
+class tdLayer(torch.nn.Module):
+    def __init__(self, layer, bn=None):
+        super(tdLayer, self).__init__()
+        self.layer = layer
+        self.bn = bn if bn is not None else Straight()
+
+    def forward(self, X):
+        T = X.size(-1)
+        out = []
+        for t in range(T):
+            m = self.layer(X[..., t])
+            out.append(m)
+        out = torch.stack(out, dim=-1)
+        out = self.bn(out)
+        return out
+
+
+class LIF(torch.nn.Module):
+    def __init__(self):
+        super(LIF, self).__init__()
+        self.thresh = 1.0
+        self.decay = 0.5
+        self.act = spike
+        self.gama = 1.0
+
+    def forward(self, X, gama=1):
+        mem = 0
+        spike_pot = []
+        T = X.size(-1)
+        for t in range(T):
+            mem = mem * self.decay + X[..., t]
+            spike = self.act(mem - self.thresh)
+            mem = mem * (1.0 - spike)
+            spike_pot.append(spike)
+        spike_pot = torch.stack(spike_pot, dim=-1)
+        return spike_pot
+
+
+class tdBatchNorm(torch.nn.BatchNorm2d):
+    def __init__(
+        self,
+        num_features,
+        eps=1e-05,
+        momentum=0.1,
+        alpha=1,
+        affine=True,
+        track_running_stats=True,
+    ):
+        super(tdBatchNorm, self).__init__(
+            num_features, eps, momentum, affine, track_running_stats
+        )
+        self.alpha = alpha
+
+    def forward(self, input):
+        exponential_average_factor = 0.0
+        mean = self.running_mean
+        var = self.running_var
+        input = (
+            self.alpha
+            * (input - mean[None, :, None, None, None])
+            / (torch.sqrt(var[None, :, None, None, None] + self.eps))
+        )
+        if self.affine:
+            input = (
+                input * self.weight[None, :, None, None, None]
+                + self.bias[None, :, None, None, None]
+            )
+        return input
+
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    return torch.nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=dilation,
+        groups=groups,
+        bias=False,
+        dilation=dilation,
+    )
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    return torch.nn.Conv2d(
+        in_planes, out_planes, kernel_size=1, stride=stride, bias=False
+    )
+
+
+class BasicBlock(torch.nn.Module):
+    expansion = 1
+
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        stride=1,
+        downsample=None,
+        groups=1,
+        base_width=64,
+        dilation=1,
+        norm_layer=None,
+    ):
+        super(BasicBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = tdBatchNorm
+            # norm_layer = nn.BatchNorm2d
+        if groups != 1 or base_width != 64:
+            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
+        if dilation > 1:
+            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = norm_layer(planes)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+        self.conv1_s = tdLayer(self.conv1, self.bn1)
+        self.conv2_s = tdLayer(self.conv2, self.bn2)
+        self.spike1 = LIF()
+        self.spike2 = LIF()
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1_s(x)
+        out = self.spike1(out)
+        out = self.conv2_s(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.spike2(out)
+
+        return out
+
+
+class ResNety(torch.nn.Module):
+    def __init__(
+        self,
+        block,
+        layers,
+        num_classes=10,
+        zero_init_residual=False,
+        groups=1,
+        width_per_group=64,
+        replace_stride_with_dilation=None,
+        norm_layer=None,
+    ):
+        super(ResNety, self).__init__()
+        if norm_layer is None:
+            norm_layer = tdBatchNorm
+            # norm_layer = nn.BatchNorm2d
+        self._norm_layer = norm_layer
+        self.inplanes = 64
+        self.dilation = 1
+        self.groups = groups
+        self.base_width = width_per_group
+        self.pre = torch.nn.Sequential(
+            tdLayer(
+                layer=torch.nn.Conv2d(
+                    3, self.inplanes, kernel_size=(3, 3), stride=(1, 1)
+                ),
+                bn=self._norm_layer(self.inplanes),
+            ),
+            LIF(),
+        )
+        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.avgpool = tdLayer(torch.nn.AdaptiveAvgPool2d((1, 1)))
+        self.fc = tdLayer(torch.nn.Linear(256, num_classes))
+        self.T = 6
+        for m in self.modules():
+            if isinstance(m, torch.nn.Conv2d):
+                torch.nn.init.kaiming_normal_(
+                    m.weight, mode="fan_out", nonlinearity="relu"
+                )
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        norm_layer = self._norm_layer
+        downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = tdLayer(
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                norm_layer(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(
+            block(
+                self.inplanes,
+                planes,
+                stride,
+                downsample,
+                self.groups,
+                self.base_width,
+                previous_dilation,
+                norm_layer,
+            )
+        )
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(
+                block(
+                    self.inplanes,
+                    planes,
+                    groups=self.groups,
+                    base_width=self.base_width,
+                    dilation=self.dilation,
+                    norm_layer=norm_layer,
+                )
+            )
+
+        return torch.nn.Sequential(*layers)
+
+    def _forward_impl(self, input):
+        out = []
+        input = input.unsqueeze(-1).repeat(1, 1, 1, 1, self.T)
+        x = self.pre(input)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1, x.size(-1))
+        x = self.fc(x)
+        for t in range(self.T):
+            out.append(x[..., t])
+        return torch.stack(out, dim=1)
+
+    def forward(self, x):
+        return self._forward_impl(x)
+
+
+def resnet20():
+    return ResNety(block=BasicBlock, layers=[2, 2, 2], num_classes=10)
diff --git a/test/python/resnet.py b/test/python/resnet.py
index 850028a..b353e8d 100644
--- a/test/python/resnet.py
+++ b/test/python/resnet.py
@@ -5,257 +5,9 @@
 
 from mpact.mpactbackend import mpact_jit, mpact_jit_compile, mpact_jit_run
 
+from mpact.models.resnet import resnet20
 
-def spike(input):
-    return (input >= 0).float()
-
-
-class Straight(torch.nn.Module):
-    def forward(self, input):
-        return input
-
-
-class tdLayer(torch.nn.Module):
-    def __init__(self, layer, bn=None):
-        super(tdLayer, self).__init__()
-        self.layer = layer
-        self.bn = bn if bn is not None else Straight()
-
-    def forward(self, X):
-        T = X.size(-1)
-        out = []
-        for t in range(T):
-            m = self.layer(X[..., t])
-            out.append(m)
-        out = torch.stack(out, dim=-1)
-        out = self.bn(out)
-        return out
-
-
-class LIF(torch.nn.Module):
-    def __init__(self):
-        super(LIF, self).__init__()
-        self.thresh = 1.0
-        self.decay = 0.5
-        self.act = spike
-        self.gama = 1.0
-
-    def forward(self, X, gama=1):
-        mem = 0
-        spike_pot = []
-        T = X.size(-1)
-        for t in range(T):
-            mem = mem * self.decay + X[..., t]
-            spike = self.act(mem - self.thresh)
-            mem = mem * (1.0 - spike)
-            spike_pot.append(spike)
-        spike_pot = torch.stack(spike_pot, dim=-1)
-        return spike_pot
-
-
-class tdBatchNorm(torch.nn.BatchNorm2d):
-    def __init__(
-        self,
-        num_features,
-        eps=1e-05,
-        momentum=0.1,
-        alpha=1,
-        affine=True,
-        track_running_stats=True,
-    ):
-        super(tdBatchNorm, self).__init__(
-            num_features, eps, momentum, affine, track_running_stats
-        )
-        self.alpha = alpha
-
-    def forward(self, input):
-        exponential_average_factor = 0.0
-        mean = self.running_mean
-        var = self.running_var
-        input = (
-            self.alpha
-            * (input - mean[None, :, None, None, None])
-            / (torch.sqrt(var[None, :, None, None, None] + self.eps))
-        )
-        if self.affine:
-            input = (
-                input * self.weight[None, :, None, None, None]
-                + self.bias[None, :, None, None, None]
-            )
-        return input
-
-
-def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
-    return torch.nn.Conv2d(
-        in_planes,
-        out_planes,
-        kernel_size=3,
-        stride=stride,
-        padding=dilation,
-        groups=groups,
-        bias=False,
-        dilation=dilation,
-    )
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    return torch.nn.Conv2d(
-        in_planes, out_planes, kernel_size=1, stride=stride, bias=False
-    )
-
-
-class BasicBlock(torch.nn.Module):
-    expansion = 1
-
-    def __init__(
-        self,
-        inplanes,
-        planes,
-        stride=1,
-        downsample=None,
-        groups=1,
-        base_width=64,
-        dilation=1,
-        norm_layer=None,
-    ):
-        super(BasicBlock, self).__init__()
-        if norm_layer is None:
-            norm_layer = tdBatchNorm
-            # norm_layer = nn.BatchNorm2d
-        if groups != 1 or base_width != 64:
-            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
-        if dilation > 1:
-            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
-        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = norm_layer(planes)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = norm_layer(planes)
-        self.downsample = downsample
-        self.stride = stride
-        self.conv1_s = tdLayer(self.conv1, self.bn1)
-        self.conv2_s = tdLayer(self.conv2, self.bn2)
-        self.spike1 = LIF()
-        self.spike2 = LIF()
-
-    def forward(self, x):
-        identity = x
-
-        out = self.conv1_s(x)
-        out = self.spike1(out)
-        out = self.conv2_s(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.spike2(out)
-
-        return out
-
-
-class ResNety(torch.nn.Module):
-    def __init__(
-        self,
-        block,
-        layers,
-        num_classes=10,
-        zero_init_residual=False,
-        groups=1,
-        width_per_group=64,
-        replace_stride_with_dilation=None,
-        norm_layer=None,
-    ):
-        super(ResNety, self).__init__()
-        if norm_layer is None:
-            norm_layer = tdBatchNorm
-            # norm_layer = nn.BatchNorm2d
-        self._norm_layer = norm_layer
-        self.inplanes = 64
-        self.dilation = 1
-        self.groups = groups
-        self.base_width = width_per_group
-        self.pre = torch.nn.Sequential(
-            tdLayer(
-                layer=torch.nn.Conv2d(
-                    3, self.inplanes, kernel_size=(3, 3), stride=(1, 1)
-                ),
-                bn=self._norm_layer(self.inplanes),
-            ),
-            LIF(),
-        )
-        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-        self.avgpool = tdLayer(torch.nn.AdaptiveAvgPool2d((1, 1)))
-        self.fc = tdLayer(torch.nn.Linear(256, num_classes))
-        self.T = 6
-        for m in self.modules():
-            if isinstance(m, torch.nn.Conv2d):
-                torch.nn.init.kaiming_normal_(
-                    m.weight, mode="fan_out", nonlinearity="relu"
-                )
-
-    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
-        norm_layer = self._norm_layer
-        downsample = None
-        previous_dilation = self.dilation
-        if dilate:
-            self.dilation *= stride
-            stride = 1
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = tdLayer(
-                conv1x1(self.inplanes, planes * block.expansion, stride),
-                norm_layer(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(
-            block(
-                self.inplanes,
-                planes,
-                stride,
-                downsample,
-                self.groups,
-                self.base_width,
-                previous_dilation,
-                norm_layer,
-            )
-        )
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(
-                block(
-                    self.inplanes,
-                    planes,
-                    groups=self.groups,
-                    base_width=self.base_width,
-                    dilation=self.dilation,
-                    norm_layer=norm_layer,
-                )
-            )
-
-        return torch.nn.Sequential(*layers)
-
-    def _forward_impl(self, input):
-        out = []
-        input = input.unsqueeze(-1).repeat(1, 1, 1, 1, self.T)
-        x = self.pre(input)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.avgpool(x)
-        x = x.view(x.size(0), -1, x.size(-1))
-        x = self.fc(x)
-        for t in range(self.T):
-            out.append(x[..., t])
-        return torch.stack(out, dim=1)
-
-    def forward(self, x):
-        return self._forward_impl(x)
-
-
-resnet = ResNety(block=BasicBlock, layers=[2, 2, 2], num_classes=10)
+resnet = resnet20()
 resnet.train(False)  # switch to inference
 
 # Get a random input.