diff --git a/helpers/metrics.py b/helpers/metrics.py
new file mode 100644
index 0000000..1ebaa1a
--- /dev/null
+++ b/helpers/metrics.py
@@ -0,0 +1,72 @@
+from typing import Dict, List, Union
+from sklearn.metrics import confusion_matrix
+import pandas as pd
+
+from matplotlib import pyplot as plt
+import seaborn as sn
+import torch
+import torchmetrics
+
+
+def calculate_f1_score(
+    preds: Union[List, torch.Tensor], target: Union[List, torch.Tensor], average: str = "micro"
+):
+    if type(preds) == list:
+        preds = torch.as_tensor(preds)
+    if type(target) == list:
+        target = torch.as_tensor(target)
+
+    number_of_classes = max(max(target).item(), max(preds).item()) + 1
+    score = torchmetrics.functional.f1_score(
+        preds=preds, target=target, average=average, num_classes=number_of_classes
+    ).item()
+    return score
+
+
+def get_cm_from_predictions(
+    y_true: List, y_pred: List, encoding: Dict, normalize: str = "true"
+) -> pd.DataFrame:
+    """
+    Set normalize to None to get absolute numbers.
+    """
+    reverse_encoding = {v: k for k, v in encoding.items()}
+
+    classes = [reverse_encoding[i] for i in set(y_pred).union(set(y_true))]
+
+    cf_matrix = confusion_matrix(y_true, y_pred, normalize=normalize)
+
+    df = pd.DataFrame(
+        cf_matrix,
+        index=classes,
+        columns=classes,
+    ).round(2)
+    return df
+
+
+def plot_and_save_dual_cm(df_A: pd.DataFrame, df_B: pd.DataFrame, save_to_file: str):
+    """
+    Visualizes two confusion matrices next to each other. Input format of df_A and df_B matches
+    output of 'get_cm_from_predictions'.
+    """
+
+    fig, axes = plt.subplots(1, 2, figsize=(45, 20), constrained_layout=True)
+    fig.suptitle("Confusion Matrices A & B", fontsize=30)
+
+    sn.heatmap(df_A, annot=True, ax=axes[0])
+    sn.heatmap(df_B, annot=True, ax=axes[1])
+    axes[0].set_title("A", fontsize=25)
+    axes[1].set_title("B", fontsize=25)
+    axes[0].set_ylabel("True A", fontsize=20)
+    axes[0].set_xlabel("Predicted A", fontsize=20)
+    axes[1].set_ylabel("True B", fontsize=20)
+    axes[1].set_xlabel("Predicted B", fontsize=20)
+    axes[0].set_aspect("equal")
+    axes[1].set_aspect("equal")
+
+    plt.setp(axes[0].yaxis.get_majorticklabels(), rotation="horizontal")
+    plt.setp(axes[1].yaxis.get_majorticklabels(), rotation="horizontal")
+    plt.setp(axes[0].xaxis.get_majorticklabels(), rotation="vertical")
+    plt.setp(axes[1].xaxis.get_majorticklabels(), rotation="vertical")
+
+    fig.savefig(save_to_file, format="png")
+    return fig