diff --git a/src/deepforest/main.py b/src/deepforest/main.py
index 44a08dce4..524e1da41 100644
--- a/src/deepforest/main.py
+++ b/src/deepforest/main.py
@@ -806,7 +806,10 @@ def on_validation_epoch_end(self):
             self.predictions_df = pd.concat(self.predictions)
 
         #Evaluate every n epochs
-        if self.current_epoch % self.config["validation"]["val_accuracy_interval"] == 0:
+        if (self.config["validation"]["val_accuracy_interval"]
+                <= self.config["train"]["epochs"] and
+                self.current_epoch % self.config["validation"]["val_accuracy_interval"]
+                == 0):
             #Create a geospatial column
             ground_df = utilities.read_file(self.config["validation"]["csv_file"])
             ground_df["label"] = ground_df.label.apply(lambda x: self.label_dict[x])
diff --git a/tests/test_main.py b/tests/test_main.py
index d466e3b2c..151648206 100644
--- a/tests/test_main.py
+++ b/tests/test_main.py
@@ -970,3 +970,14 @@ def test_set_labels_invalid_length(m): # Expect a ValueError when setting an inv
     invalid_mapping = {"Object": 0, "Extra": 1}
     with pytest.raises(ValueError):
         m.set_labels(invalid_mapping)
+    
+def test_validation_interval_greater_than_epochs(m):
+    # Set interval higher than max_epochs to disable evaluation
+    m.config["validation"]["val_accuracy_interval"] = 3
+    m.config["train"]["epochs"] = 2
+    m.create_trainer()
+    m.trainer.fit(m)
+    
+    assert "box_precision" not in m.trainer.logged_metrics
+    assert "box_recall" not in m.trainer.logged_metrics
+    assert "empty_frame_accuracy" not in m.trainer.logged_metrics