diff --git a/minerva/analysis/metrics/cropped_metric.py b/minerva/analysis/metrics/cropped_metric.py
deleted file mode 100644
index 8519fb9..0000000
--- a/minerva/analysis/metrics/cropped_metric.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import torch
-from torchmetrics import Metric
-
-
-class CroppedMetric(Metric):
-    def __init__(
-        self,
-        target_h_size: int,
-        target_w_size: int,
-        metric: Metric,
-        dist_sync_on_step: bool = False,
-    ):
-        """
-        Initializes a new instance of CroppedMetric.
-
-        Parameters
-        ----------
-            target_h_size: int
-                The target height size.
-            target_w_size: int
-                The target width size.
-            dist_sync_on_step: bool, optional
-                Whether to synchronize metric state across processes at each step.
-                Defaults to False.
-        """
-        super().__init__(dist_sync_on_step=dist_sync_on_step)
-        self.metric = metric
-        self.target_h_size = target_h_size
-        self.target_w_size = target_w_size
-
-    def update(self, preds: torch.Tensor, target: torch.Tensor):
-        """
-        Updates the metric state with the predictions and targets.
-
-        Parameters
-        ----------
-            preds: torch.Tensor
-                The predicted tensor.
-            target:
-                torch.Tensor The target tensor.
-        """
-
-        preds = self.crop(preds)
-        target = self.crop(target)
-        self.metric.update(preds, target)
-
-    def compute(self) -> float:
-        """
-        Computes the cropped metric.
-
-        Returns:
-            float: The cropped metric.
-        """
-        return self.metric.compute()
-
-    def crop(self, x: torch.Tensor) -> torch.Tensor:
-        """crops the input tensor to the target size.
-
-        Parameters
-        ----------
-        x : torch.Tensor
-            The input tensor.
-
-        Returns
-        -------
-        torch.Tensor
-            The cropped tensor.
-        """
-        h, w = x.shape[-2:]
-        start_h = (h - self.target_h_size) // 2
-        start_w = (w - self.target_w_size) // 2
-        end_h = start_h + self.target_h_size
-        end_w = start_w + self.target_w_size
-        return x[..., start_h:end_h, start_w:end_w]
diff --git a/minerva/analysis/metrics/transformed_metrics.py b/minerva/analysis/metrics/transformed_metrics.py
new file mode 100644
index 0000000..d6efa0f
--- /dev/null
+++ b/minerva/analysis/metrics/transformed_metrics.py
@@ -0,0 +1,181 @@
+import warnings
+from typing import Optional
+
+import torch
+from torchmetrics import Metric
+
+
+class CroppedMetric(Metric):
+    def __init__(
+        self,
+        target_h_size: int,
+        target_w_size: int,
+        metric: Metric,
+        dist_sync_on_step: bool = False,
+    ):
+        """
+        Initializes a new instance of CroppedMetric.
+
+        Parameters
+        ----------
+            target_h_size: int
+                The target height size.
+            target_w_size: int
+                The target width size.
+            dist_sync_on_step: bool, optional
+                Whether to synchronize metric state across processes at each step.
+                Defaults to False.
+        """
+        super().__init__(dist_sync_on_step=dist_sync_on_step)
+        self.metric = metric
+        self.target_h_size = target_h_size
+        self.target_w_size = target_w_size
+
+    def update(self, preds: torch.Tensor, target: torch.Tensor):
+        """
+        Updates the metric state with the predictions and targets.
+
+        Parameters
+        ----------
+            preds: torch.Tensor
+                The predicted tensor.
+            target:
+                torch.Tensor The target tensor.
+        """
+
+        preds = self.crop(preds)
+        target = self.crop(target)
+        self.metric.update(preds, target)
+
+    def compute(self) -> float:
+        """
+        Computes the cropped metric.
+
+        Returns:
+            float: The cropped metric.
+        """
+        return self.metric.compute()
+
+    def crop(self, x: torch.Tensor) -> torch.Tensor:
+        """crops the input tensor to the target size.
+
+        Parameters
+        ----------
+        x : torch.Tensor
+            The input tensor.
+
+        Returns
+        -------
+        torch.Tensor
+            The cropped tensor.
+        """
+        h, w = x.shape[-2:]
+        start_h = (h - self.target_h_size) // 2
+        start_w = (w - self.target_w_size) // 2
+        end_h = start_h + self.target_h_size
+        end_w = start_w + self.target_w_size
+
+        return x[..., start_h:end_h, start_w:end_w]
+
+
+class ResizeMetric(Metric):
+    def __init__(
+        self,
+        target_h_size: Optional[int],
+        target_w_size: Optional[int],
+        metric: Metric,
+        keep_aspect_ratio: bool = False,
+        dist_sync_on_step: bool = False,
+    ):
+        """
+        Initializes a new instance of ResizeMetric.
+
+        Parameters
+        ----------
+            target_h_size: int
+                The target height size.
+            target_w_size: int
+                The target width size.
+            dist_sync_on_step: bool, optional
+                Whether to synchronize metric state across processes at each step.
+                Defaults to False.
+        """
+        super().__init__(dist_sync_on_step=dist_sync_on_step)
+
+        if target_h_size is None and target_w_size is None:
+            raise ValueError(
+                "At least one of target_h_size or target_w_size must be provided."
+            )
+
+        if (
+            target_h_size is not None and target_w_size is None
+        ) and keep_aspect_ratio is False:
+            warnings.warn(
+                "A target_w_size is not provided, but keep_aspect_ratio is set to False. keep_aspect_ratio will be set to True. If you want to resize the image to a specific width, please provide a target_w_size."
+            )
+            keep_aspect_ratio = True
+
+        if (
+            target_w_size is not None and target_h_size is None
+        ) and keep_aspect_ratio is False:
+            warnings.warn(
+                "A target_h_size is not provided, but keep_aspect_ratio is set to False. keep_aspect_ratio will be set to True. If you want to resize the image to a specific height, please provide a target_h_size."
+            )
+            keep_aspect_ratio = True
+
+        self.metric = metric
+        self.target_h_size = target_h_size
+        self.target_w_size = target_w_size
+        self.keep_aspect_ratio = keep_aspect_ratio
+
+    def update(self, preds: torch.Tensor, target: torch.Tensor):
+        """
+        Updates the metric state with the predictions and targets.
+
+        Parameters
+        ----------
+            preds: torch.Tensor
+                The predicted tensor.
+            target:
+                torch.Tensor The target tensor.
+        """
+
+        preds = self.resize(preds)
+        target = self.resize(target)
+        self.metric.update(preds, target)
+
+    def compute(self) -> float:
+        """
+        Computes the resized metric.
+
+        Returns:
+            float: The resized metric.
+        """
+        return self.metric.compute()
+
+    def resize(self, x: torch.Tensor) -> torch.Tensor:
+        """Resizes the input tensor to the target size.
+
+        Parameters
+        ----------
+        x : torch.Tensor
+            The input tensor.
+
+        Returns
+        -------
+        torch.Tensor
+            The resized tensor.
+        """
+        h, w = x.shape[-2:]
+
+        target_h_size = self.target_h_size
+        target_w_size = self.target_w_size
+        if self.keep_aspect_ratio:
+            if self.target_h_size is None:
+                scale = target_w_size / w
+                target_h_size = int(h * scale)
+            elif self.target_w_size is None:
+                scale = target_h_size / h
+                target_w_size = int(w * scale)
+
+        return torch.nn.functional.interpolate(x, size=(target_h_size, target_w_size))