diff --git a/src/careamics/lvae_training/calibration.py b/src/careamics/lvae_training/calibration.py
new file mode 100644
index 00000000..48eba250
--- /dev/null
+++ b/src/careamics/lvae_training/calibration.py
@@ -0,0 +1,172 @@
+from typing import Union
+
+import numpy as np
+import torch
+from scipy import stats
+
+
+def get_last_index(bin_count, quantile):
+    cumsum = np.cumsum(bin_count)
+    normalized_cumsum = cumsum / cumsum[-1]
+    for i in range(1, len(normalized_cumsum)):
+        if normalized_cumsum[-i] < quantile:
+            return i - 1
+    return None
+
+
+def get_first_index(bin_count, quantile):
+    cumsum = np.cumsum(bin_count)
+    normalized_cumsum = cumsum / cumsum[-1]
+    for i in range(len(normalized_cumsum)):
+        if normalized_cumsum[i] > quantile:
+            return i
+    return None
+
+
+class Calibration:
+    def __init__(self, num_bins: int = 15):
+        self._bins = num_bins
+        self._bin_boundaries = None
+
+    def logvar_to_std(self, logvar: np.ndarray) -> np.ndarray:
+        return np.exp(logvar / 2)
+
+    def compute_bin_boundaries(self, predict_std: np.ndarray) -> np.ndarray:
+        """Compute the bin boundaries for `num_bins` bins and predicted std values."""
+        min_std = np.min(predict_std)
+        max_std = np.max(predict_std)
+        return np.linspace(min_std, max_std, self._bins + 1)
+
+    def compute_stats(
+        self, pred: np.ndarray, pred_std: np.ndarray, target: np.ndarray
+    ) -> dict[int, dict[str, Union[np.ndarray, list]]]:
+        """
+        It computes the bin-wise RMSE and RMV for each channel of the predicted image.
+
+        Recall that:
+            - RMSE = np.sqrt((pred - target)**2 / num_pixels)
+            - RMV = np.sqrt(np.mean(pred_std**2))
+
+        ALGORITHM
+        - For each channel:
+            - Given the bin boundaries, assign pixels of `std_ch` array to a specific bin index.
+            - For each bin index:
+                - Compute the RMSE, RMV, and number of pixels for that bin.
+
+        NOTE: each channel of the predicted image/logvar has its own stats.
+
+        Parameters
+        ----------
+        pred: np.ndarray
+            Predicted patches, shape (n, h, w, c).
+        pred_std: np.ndarray
+            Std computed over the predicted patches, shape (n, h, w, c).
+        target: np.ndarray
+            Target GT image, shape (n, h, w, c).
+        """
+        self._bin_boundaries = {}
+        stats_dict = {}
+        for ch_idx in range(pred.shape[-1]):
+            stats_dict[ch_idx] = {
+                'bin_count': [],
+                'rmv': [],
+                'rmse': [],
+                'bin_boundaries': None,
+                'bin_matrix': [],
+                'rmse_err': []
+            }
+            pred_ch = pred[..., ch_idx]
+            std_ch = pred_std[..., ch_idx]
+            target_ch = target[..., ch_idx]
+            boundaries = self.compute_bin_boundaries(std_ch)
+            stats_dict[ch_idx]['bin_boundaries'] = boundaries
+            bin_matrix = np.digitize(std_ch.reshape(-1), boundaries)
+            bin_matrix = bin_matrix.reshape(std_ch.shape)
+            stats_dict[ch_idx]['bin_matrix'] = bin_matrix
+            error = (pred_ch - target_ch)**2
+            for bin_idx in range(1, 1+self._bins):
+                bin_mask = bin_matrix == bin_idx
+                bin_error = error[bin_mask]
+                bin_size = np.sum(bin_mask)
+                bin_error = np.sqrt(np.sum(bin_error) / bin_size) if bin_size > 0 else None
+                stderr = np.std(error[bin_mask]) / np.sqrt(bin_size) if bin_size > 0 else None
+                rmse_stderr = np.sqrt(stderr) if stderr is not None else None
+
+                bin_var = np.mean((std_ch[bin_mask]**2))
+                stats_dict[ch_idx]['rmse'].append(bin_error)
+                stats_dict[ch_idx]['rmse_err'].append(rmse_stderr)
+                stats_dict[ch_idx]['rmv'].append(np.sqrt(bin_var))
+                stats_dict[ch_idx]['bin_count'].append(bin_size)
+        return stats_dict
+
+
+def get_calibrated_factor_for_stdev(
+    pred: Union[np.ndarray, torch.Tensor],
+    pred_std: Union[np.ndarray, torch.Tensor],
+    target: Union[np.ndarray, torch.Tensor],
+    q_s: float = 0.00001,
+    q_e: float = 0.99999,
+    num_bins: int = 30,
+) -> dict[str, float]:
+    """Calibrate the uncertainty by multiplying the predicted std with a scalar.
+    
+    Parameters
+    ----------
+    pred : Union[np.ndarray, torch.Tensor]
+        Predicted image, shape (n, h, w, c).
+    pred_std : Union[np.ndarray, torch.Tensor]
+        Predicted std, shape (n, h, w, c).
+    target : Union[np.ndarray, torch.Tensor]
+        Target image, shape (n, h, w, c).
+    q_s : float, optional
+        Start quantile, by default 0.00001.
+    q_e : float, optional
+        End quantile, by default 0.99999.
+    num_bins : int, optional
+        Number of bins to use for calibration, by default 30.
+        
+    Returns
+    -------
+    dict[str, float]
+        Calibrated factor for each channel (slope + intercept).
+    """
+    calib = Calibration(num_bins=num_bins)
+    stats_dict = calib.compute_stats(pred, pred_std, target)
+    outputs = {}
+    for ch_idx in stats_dict.keys():
+        y = stats_dict[ch_idx]['rmse']
+        x = stats_dict[ch_idx]['rmv']
+        count = stats_dict[ch_idx]['bin_count']
+
+        first_idx = get_first_index(count, q_s)
+        last_idx = get_last_index(count, q_e)
+        x = x[first_idx:-last_idx]
+        y = y[first_idx:-last_idx]
+        slope, intercept, *_ = stats.linregress(x,y)
+        output = {'scalar': slope, 'offset': intercept}
+        outputs[ch_idx] = output
+    return outputs
+
+
+def plot_calibration(ax, calibration_stats):
+    first_idx = get_first_index(calibration_stats[0]["bin_count"], 0.001)
+    last_idx = get_last_index(calibration_stats[0]["bin_count"], 0.999)
+    ax.plot(
+        calibration_stats[0]["rmv"][first_idx:-last_idx],
+        calibration_stats[0]["rmse"][first_idx:-last_idx],
+        "o",
+        label=r"$\hat{C}_0$: Ch1",
+    )
+
+    first_idx = get_first_index(calibration_stats[1]["bin_count"], 0.001)
+    last_idx = get_last_index(calibration_stats[1]["bin_count"], 0.999)
+    ax.plot(
+        calibration_stats[1]["rmv"][first_idx:-last_idx],
+        calibration_stats[1]["rmse"][first_idx:-last_idx],
+        "o",
+        label=r"$\hat{C}_1: : Ch2$",
+    )
+
+    ax.set_xlabel("RMV")
+    ax.set_ylabel("RMSE")
+    ax.legend()
diff --git a/src/careamics/lvae_training/eval_utils.py b/src/careamics/lvae_training/eval_utils.py
index 9bc5af1a..04b93faa 100644
--- a/src/careamics/lvae_training/eval_utils.py
+++ b/src/careamics/lvae_training/eval_utils.py
@@ -5,14 +5,13 @@
     - quantify the performance of the model
     - create plots to visualize the results.
 """
-
-import math
 import os
-from typing import Dict, List, Literal, Union
+from typing import List, Literal, Union
 
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
+from scipy import stats
 import torch
 from torch import nn
 from torch.utils.data import Dataset
@@ -859,181 +858,4 @@ def stitch_predictions_new(predictions, dset):
             else:
                 raise ValueError(f"Unsupported shape {output.shape}")
 
-    return output
-
-
-# ------------------------------------------------------------------------------------------
-
-
-# ------------------------------------------------------------------------------------------
-### Classes and Functions used for Calibration
-class Calibration:
-
-    def __init__(
-        self, num_bins: int = 15, mode: Literal["pixelwise", "patchwise"] = "pixelwise"
-    ):
-        self._bins = num_bins
-        self._bin_boundaries = None
-        self._mode = mode
-        assert mode in ["pixelwise", "patchwise"]
-        self._boundary_mode = "uniform"
-        assert self._boundary_mode in ["quantile", "uniform"]
-        # self._bin_boundaries = {}
-
-    def logvar_to_std(self, logvar: np.ndarray) -> np.ndarray:
-        return np.exp(logvar / 2)
-
-    def compute_bin_boundaries(self, predict_logvar: np.ndarray) -> np.ndarray:
-        """
-        Compute the bin boundaries for `num_bins` bins and the given logvar values.
-        """
-        if self._boundary_mode == "quantile":
-            boundaries = np.quantile(
-                self.logvar_to_std(predict_logvar), np.linspace(0, 1, self._bins + 1)
-            )
-            return boundaries
-        else:
-            min_logvar = np.min(predict_logvar)
-            max_logvar = np.max(predict_logvar)
-            min_std = self.logvar_to_std(min_logvar)
-            max_std = self.logvar_to_std(max_logvar)
-        return np.linspace(min_std, max_std, self._bins + 1)
-
-    def compute_stats(
-        self, pred: np.ndarray, pred_logvar: np.ndarray, target: np.ndarray
-    ) -> Dict[int, Dict[str, Union[np.ndarray, List]]]:
-        """
-        It computes the bin-wise RMSE and RMV for each channel of the predicted image.
-
-        Recall that:
-            - RMSE = np.sqrt((pred - target)**2 / num_pixels)
-            - RMV = np.sqrt(np.mean(pred_std**2))
-
-        ALGORITHM
-        - For each channel:
-            - Given the bin boundaries, assign pixels of `std_ch` array to a specific bin index.
-            - For each bin index:
-                - Compute the RMSE, RMV, and number of pixels for that bin.
-
-        NOTE: each channel of the predicted image/logvar has its own stats.
-
-        Args:
-            pred: np.ndarray, shape (n, h, w, c)
-            pred_logvar: np.ndarray, shape (n, h, w, c)
-            target: np.ndarray, shape (n, h, w, c)
-        """
-        self._bin_boundaries = {}
-        stats = {}
-        for ch_idx in range(pred.shape[-1]):
-            stats[ch_idx] = {
-                "bin_count": [],
-                "rmv": [],
-                "rmse": [],
-                "bin_boundaries": None,
-                "bin_matrix": [],
-            }
-            pred_ch = pred[..., ch_idx]
-            logvar_ch = pred_logvar[..., ch_idx]
-            std_ch = self.logvar_to_std(logvar_ch)
-            target_ch = target[..., ch_idx]
-            if self._mode == "pixelwise":
-                boundaries = self.compute_bin_boundaries(logvar_ch)
-                stats[ch_idx]["bin_boundaries"] = boundaries
-                bin_matrix = np.digitize(std_ch.reshape(-1), boundaries)
-                bin_matrix = bin_matrix.reshape(std_ch.shape)
-                stats[ch_idx]["bin_matrix"] = bin_matrix
-                error = (pred_ch - target_ch) ** 2
-                for bin_idx in range(self._bins):
-                    bin_mask = bin_matrix == bin_idx
-                    bin_error = error[bin_mask]
-                    bin_size = np.sum(bin_mask)
-                    bin_error = (
-                        np.sqrt(np.sum(bin_error) / bin_size) if bin_size > 0 else None
-                    )  # RMSE
-                    bin_var = np.sqrt(np.mean(std_ch[bin_mask] ** 2))  # RMV
-                    stats[ch_idx]["rmse"].append(bin_error)
-                    stats[ch_idx]["rmv"].append(bin_var)
-                    stats[ch_idx]["bin_count"].append(bin_size)
-            else:
-                raise NotImplementedError("Patchwise mode is not implemented yet.")
-        return stats
-
-
-def nll(x, mean, logvar):
-    """
-    Log of the probability density of the values x under the Normal
-    distribution with parameters mean and logvar.
-
-    :param x: tensor of points, with shape (batch, channels, dim1, dim2)
-    :param mean: tensor with mean of distribution, shape
-                 (batch, channels, dim1, dim2)
-    :param logvar: tensor with log-variance of distribution, shape has to be
-                   either scalar or broadcastable
-    """
-    var = torch.exp(logvar)
-    log_prob = -0.5 * (
-        ((x - mean) ** 2) / var + logvar + torch.tensor(2 * math.pi).log()
-    )
-    nll = -log_prob
-    return nll
-
-
-def get_calibrated_factor_for_stdev(
-    pred: Union[np.ndarray, torch.Tensor],
-    pred_logvar: Union[np.ndarray, torch.Tensor],
-    target: Union[np.ndarray, torch.Tensor],
-    batch_size: int = 32,
-    epochs: int = 500,
-    lr: float = 0.01,
-):
-    """
-    Here, we calibrate the uncertainty by multiplying the predicted std (mmse estimate or predicted logvar) with a scalar.
-    We return the calibrated scalar. This needs to be multiplied with the std.
-
-    NOTE: Why is the input logvar and not std? because the model typically predicts logvar and not std.
-    """
-    # create a learnable scalar
-    scalar = torch.nn.Parameter(torch.tensor(2.0))
-    optimizer = torch.optim.Adam([scalar], lr=lr)
-
-    bar = tqdm(range(epochs))
-    for _ in bar:
-        optimizer.zero_grad()
-        # Select a random batch of predictions
-        mask = np.random.randint(0, pred.shape[0], batch_size)
-        pred_batch = torch.Tensor(pred[mask]).cuda()
-        pred_logvar_batch = torch.Tensor(pred_logvar[mask]).cuda()
-        target_batch = torch.Tensor(target[mask]).cuda()
-
-        loss = torch.mean(
-            nll(target_batch, pred_batch, pred_logvar_batch + torch.log(scalar))
-        )
-        loss.backward()
-        optimizer.step()
-        bar.set_description(f"nll: {loss.item()} scalar: {scalar.item()}")
-
-    return np.sqrt(scalar.item())
-
-
-def plot_calibration(ax, calibration_stats):
-    first_idx = get_first_index(calibration_stats[0]["bin_count"], 0.001)
-    last_idx = get_last_index(calibration_stats[0]["bin_count"], 0.999)
-    ax.plot(
-        calibration_stats[0]["rmv"][first_idx:-last_idx],
-        calibration_stats[0]["rmse"][first_idx:-last_idx],
-        "o",
-        label=r"$\hat{C}_0$: Ch1",
-    )
-
-    first_idx = get_first_index(calibration_stats[1]["bin_count"], 0.001)
-    last_idx = get_last_index(calibration_stats[1]["bin_count"], 0.999)
-    ax.plot(
-        calibration_stats[1]["rmv"][first_idx:-last_idx],
-        calibration_stats[1]["rmse"][first_idx:-last_idx],
-        "o",
-        label=r"$\hat{C}_1: : Ch2$",
-    )
-
-    ax.set_xlabel("RMV")
-    ax.set_ylabel("RMSE")
-    ax.legend()
+    return output
\ No newline at end of file