diff --git a/minerva/models/nets/__init__.py b/minerva/models/nets/__init__.py
index bbfdb31..7844e14 100644
--- a/minerva/models/nets/__init__.py
+++ b/minerva/models/nets/__init__.py
@@ -2,6 +2,7 @@
 from .image.deeplabv3 import DeepLabV3
 from .image.setr import SETR_PUP
 from .image.unet import UNet
+from .image.resnet import ResNet
 from .image.wisenet import WiseNet
 from .mlp import MLP
 
@@ -11,5 +12,6 @@
     "SETR_PUP",
     "UNet",
     "WiseNet",
-    "MLP"
+    "MLP",
+    "ResNet",
 ]
diff --git a/minerva/models/nets/image/resnet.py b/minerva/models/nets/image/resnet.py
new file mode 100644
index 0000000..9f29c19
--- /dev/null
+++ b/minerva/models/nets/image/resnet.py
@@ -0,0 +1,252 @@
+import torch
+import torch.nn as nn
+from typing import Any, Literal, Optional
+from minerva.models.nets.base import SimpleSupervisedModel
+
+
+class ResNetBlock(nn.Module):
+    """
+    Implementation of a single ResNet block.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        intermediate_channels: int,
+        identity_downsample: Optional[torch.nn.Module] = None,
+        stride: int = 1
+    ):
+        """
+        Parameters
+        ----------
+        in_channels : int
+            The number of input channels to the block.
+        intermediate_channels : int
+            The number of channels in the intermediate convolutional layers within the block.
+        identity_downsample : nn.Module, optional
+            A downsampling layer to match the dimensions of the input and output if they differ. 
+            If `None`, no downsampling is performed. Default is `None`.
+        stride : int, optional
+            The stride value for the first convolutional layer in the block. It determines the 
+            downsampling factor for the spatial dimensions. Default is `1`.
+        """
+
+        super().__init__()
+        self.expansion = 4
+        self.conv1 = nn.Conv2d(
+            in_channels,
+            intermediate_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=False,
+        )
+
+        self.bn1 = nn.BatchNorm2d(intermediate_channels)
+        self.conv2 = nn.Conv2d(
+            intermediate_channels,
+            intermediate_channels,
+            kernel_size=3,
+            stride=stride,
+            padding=1,
+            bias=False,
+        )
+
+        self.bn2 = nn.BatchNorm2d(intermediate_channels)
+        self.conv3 = nn.Conv2d(
+            intermediate_channels,
+            intermediate_channels * self.expansion,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=False,
+        )
+
+        self.bn3 = nn.BatchNorm2d(intermediate_channels * self.expansion)
+        self.relu = nn.ReLU()
+        
+        self.identity_downsample = identity_downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x.clone()
+
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x = self.relu(x)
+        x = self.conv3(x)
+        x = self.bn3(x)
+
+        if self.identity_downsample is not None:
+            identity = self.identity_downsample(identity)
+
+        x = torch.add(x, identity)
+        x = self.relu(x)
+        return x
+
+
+class _ResNet(torch.nn.Module):
+    """Implementation of ResNet model."""
+
+    def __init__(
+            self,
+            layer_sizes: list[int],
+            image_channels: int,
+            num_classes: int,
+        ):
+        """Implementation of ResNet model.
+
+        Parameters
+        ----------
+        layer_sizes : list of int
+            A list specifying the number of layers in each residual block stage. For example:
+            - ResNet-50: [3, 4, 6, 3]
+            - ResNet-101: [3, 4, 23, 3]
+            - ResNet-152: [3, 8, 36, 3]
+        image_channels : int
+            The number of channels in the input image, typically 3 for RGB images or 1 for grayscale.
+        num_classes : int
+            The number of output classes for the classification task.
+        """
+
+        super().__init__()
+        self.in_channels = 64
+        self.conv1 = nn.Conv2d(
+            image_channels, 64, kernel_size=7, stride=2, padding=3, bias=False
+        )
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        intermidiate_channels = [64, 128, 256, 512]
+        strides = [1, 2, 2, 2]
+
+        self.layers = nn.ModuleList([])
+
+        for i in range(len(layer_sizes)):
+            layer = self.make_layer(layer_sizes[i], intermidiate_channels[i], strides[i])
+            self.layers.append(layer)
+
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(intermidiate_channels[-1] * 4, num_classes)
+
+    def make_layer(self, num_residual_blocks, intermediate_channels, stride):
+        identity_downsample = None
+        layers = []
+        expansion = intermediate_channels * 4
+
+        if stride != 1 or self.in_channels != expansion:
+            identity_downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.in_channels,
+                    expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False,
+                ),
+                nn.BatchNorm2d(expansion),
+            )
+
+        layers.append(
+            ResNetBlock(self.in_channels, intermediate_channels, identity_downsample, stride)
+        )
+
+        self.in_channels = expansion
+
+        for _ in range(num_residual_blocks - 1):
+            layers.append(ResNetBlock(self.in_channels, intermediate_channels))
+
+        return nn.Sequential(*layers)
+    
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        for layer in self.layers:
+            x = layer(x)
+
+        x = self.avgpool(x)
+        x = x.reshape(x.shape[0], -1)
+        x = self.fc(x)
+
+        return x
+
+class ResNet(SimpleSupervisedModel):
+    """
+    This class is a simple implementation of the ResNet (Residual Network) model, 
+    which is widely used in image classification and other computer vision tasks. 
+    The ResNet architecture introduces residual connections, allowing deeper networks 
+    to be trained by mitigating the vanishing gradient problem. The model consists 
+    of repeated building blocks with skip connections that add the input of a 
+    layer to its output after applying transformations. ResNet was originally 
+    proposed by He et al. in 2015.
+
+    This implementation supports ResNet-50, ResNet-101, and ResNet-152, offering 
+    flexibility in network depth based on the specific use case. The model can 
+    handle arbitrary input sizes and supports both RGB and grayscale images.
+
+    References
+    ----------
+    He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings 
+    of the IEEE conference on computer vision and pattern recognition. 2016.
+
+    
+
+    Notes
+    -----
+    - The expected input size is (N, C, H, W), where:
+      - N is the batch size,
+      - C is the number of channels,
+      - H is the height of the input image, and
+      - W is the width of the input image.
+    - The output shape is (N, num_classes), where `num_classes` corresponds to 
+      the number of classes specified during initialization.
+    """
+
+    def __init__(
+        self,
+        type: Literal["50", "101", "152"] = "50",
+        img_channel: int = 3, 
+        num_classes: int = 1000,
+        learning_rate: float = 1e-3,
+        loss_fn: Optional[torch.nn.Module] = None,
+        **kwargs: dict[str, Any],
+    ):
+        """Wrapper implementation of the ResNet model.
+
+        Parameters
+        ----------
+        type : Literal["50", "101", "152"], optional
+            The type of ResNet architecture to use. Options are:
+            - "50": ResNet-50
+            - "101": ResNet-101
+            - "152": ResNet-152
+            Default is "50".
+        img_channel : int, optional
+            The number of channels in the input image, by default 3 (for RGB images).
+        num_classes : int, optional
+            The number of output classes for the classification task, by default 1000.
+        learning_rate : float, optional
+            The learning rate for the Adam optimizer, by default 1e-3.
+        loss_fn : torch.nn.Module, optional
+            The function used to compute the loss. If `None`, `MSELoss` will be used, 
+            by default None.
+        kwargs : dict
+            Additional arguments to be passed to the `SimpleSupervisedModel` class.
+        """
+        resnet_type = { "50": [3, 4, 23, 3], "101": [3, 4, 23, 3], "152": [3, 8, 36, 3] } 
+        backbone = _ResNet(layer_sizes=resnet_type[type], image_channels=img_channel, num_classes=num_classes)
+
+        super().__init__(
+            backbone=backbone,
+            fc=torch.nn.Identity(),
+            loss_fn=loss_fn or torch.nn.CrossEntropyLoss(),
+            learning_rate=learning_rate,
+            flatten=False,
+            **kwargs,
+        )
\ No newline at end of file
diff --git a/tests/models/nets/image/test_resnet.py b/tests/models/nets/image/test_resnet.py
new file mode 100644
index 0000000..7552e28
--- /dev/null
+++ b/tests/models/nets/image/test_resnet.py
@@ -0,0 +1,166 @@
+import lightning as L
+import torch
+import torchmetrics
+
+from minerva.models.nets import ResNet
+from minerva.utils.data import RandomDataModule
+
+
+def test_resnet50():
+    # Test the class instantiation
+    model = ResNet(type="50", img_channel=3, num_classes=1000)
+    assert model is not None
+
+    # Generate a random input tensor (B, C, H, W)
+    input_shape = (2, 3, 224, 224)
+    x = torch.rand(*input_shape)
+
+    # Test the forward method
+    output = model(x)
+    expected_output_shape = (2, 1000)  # For classification, output matches num_classes
+
+    print(output.shape == expected_output_shape, output.shape)
+    assert (
+        output.shape == expected_output_shape
+    ), f"Expected output shape {expected_output_shape}, but got {output.shape}"
+
+    # Test the training step
+    target = torch.rand(expected_output_shape)
+    loss = model.training_step((x, target), 0).item()
+    assert loss is not None
+    assert loss >= 0, f"Expected non-negative loss, but got {loss}"
+
+def test_resnet101():
+    # Test the class instantiation
+    model = ResNet(type="101", img_channel=3, num_classes=1000)
+    assert model is not None
+
+    # Generate a random input tensor (B, C, H, W)
+    input_shape = (2, 3, 224, 224)
+    x = torch.rand(*input_shape)
+
+    # Test the forward method
+    output = model(x)
+    expected_output_shape = (2, 1000)  # For classification, output matches num_classes
+
+    print(output.shape == expected_output_shape, output.shape)
+    assert (
+        output.shape == expected_output_shape
+    ), f"Expected output shape {expected_output_shape}, but got {output.shape}"
+
+    # Test the training step
+    target = torch.rand(expected_output_shape)
+    loss = model.training_step((x, target), 0).item()
+    assert loss is not None
+    assert loss >= 0, f"Expected non-negative loss, but got {loss}"
+
+def test_resnet152():
+    # Test the class instantiation
+    model = ResNet(type="101", img_channel=3, num_classes=1000)
+    assert model is not None
+
+    # Generate a random input tensor (B, C, H, W)
+    input_shape = (3, 3, 224, 224)
+    x = torch.rand(*input_shape)
+
+    # Test the forward method
+    output = model(x)
+    expected_output_shape = (3, 1000)  # For classification, output matches num_classes
+
+    print(output.shape == expected_output_shape, output.shape)
+    assert (
+        output.shape == expected_output_shape
+    ), f"Expected output shape {expected_output_shape}, but got {output.shape}"
+
+    # Test the training step
+    target = torch.rand(expected_output_shape)
+    loss = model.training_step((x, target), 0).item()
+    assert loss is not None
+    assert loss >= 0, f"Expected non-negative loss, but got {loss}"
+
+def test_resnet50_train_metrics():
+    metrics = {
+        "accuracy": torchmetrics.Accuracy(task="multiclass", num_classes=1000),
+        "top_k_accuracy": torchmetrics.Accuracy(task="multiclass", num_classes=1000, top_k=5),
+    }
+
+    data_module = RandomDataModule(
+        data_shape=(3, 224, 224),
+        label_shape=None,
+        num_classes=1000,
+        num_train_samples=10,
+        batch_size=2,
+    )
+
+    model = ResNet(
+        type="50",
+        img_channel=3,
+        num_classes=1000,
+        train_metrics=metrics,
+    )
+    trainer = L.Trainer(accelerator="cpu", max_epochs=1, devices=1)
+
+    # Run training
+    trainer.fit(model, data_module)
+
+    assert "train_accuracy" in trainer.logged_metrics
+    assert "train_top_k_accuracy" in trainer.logged_metrics
+    assert "train_loss" in trainer.logged_metrics
+
+def test_resnet101_train_metrics():
+    metrics = {
+        "accuracy": torchmetrics.Accuracy(task="multiclass", num_classes=1000),
+        "top_k_accuracy": torchmetrics.Accuracy(task="multiclass", num_classes=1000, top_k=5),
+    }
+
+    data_module = RandomDataModule(
+        data_shape=(3, 500, 500),
+        label_shape=None,
+        num_classes=1000,
+        num_train_samples=10,
+        batch_size=3,
+    )
+
+    model = ResNet(
+        type="101",
+        img_channel=3,
+        num_classes=1000,
+        train_metrics=metrics,
+    )
+    trainer = L.Trainer(accelerator="cpu", max_epochs=1, devices=1)
+
+    # Run training
+    trainer.fit(model, data_module)
+
+    assert "train_accuracy" in trainer.logged_metrics
+    assert "train_top_k_accuracy" in trainer.logged_metrics
+    assert "train_loss" in trainer.logged_metrics
+
+def test_resnet152_train_metrics():
+    metrics = {
+        "accuracy": torchmetrics.Accuracy(task="multiclass", num_classes=1000),
+        "top_k_accuracy": torchmetrics.Accuracy(task="multiclass", num_classes=1000, top_k=5),
+    }
+
+    data_module = RandomDataModule(
+        data_shape=(3, 768, 768),
+        label_shape=None,
+        num_classes=1000,
+        num_train_samples=10,
+        batch_size=2,
+    )
+
+    model = ResNet(
+        type="152",
+        img_channel=3,
+        num_classes=1000,
+        train_metrics=metrics,
+    )
+    trainer = L.Trainer(accelerator="cpu", max_epochs=1, devices=1)
+
+    # Run training
+    trainer.fit(model, data_module)
+
+    assert "train_accuracy" in trainer.logged_metrics
+    assert "train_top_k_accuracy" in trainer.logged_metrics
+    assert "train_loss" in trainer.logged_metrics