diff --git a/frigate/util/classification.py b/frigate/util/classification.py
index 55a204616..4ee5e1d54 100644
--- a/frigate/util/classification.py
+++ b/frigate/util/classification.py
@@ -15,20 +15,23 @@ LEARNING_RATE = 0.001
 
 
 @staticmethod
-def generate_representative_dataset(dataset_dir: str):
-    image_paths = []
-    for root, dirs, files in os.walk(dataset_dir):
-        for file in files:
-            if file.lower().endswith((".jpg", ".jpeg", ".png")):
-                image_paths.append(os.path.join(root, file))
+def generate_representative_dataset_factory(dataset_dir: str):
+    def generate_representative_dataset():
+        image_paths = []
+        for root, dirs, files in os.walk(dataset_dir):
+            for file in files:
+                if file.lower().endswith((".jpg", ".jpeg", ".png")):
+                    image_paths.append(os.path.join(root, file))
 
-    for path in image_paths[:300]:
-        img = cv2.imread(path)
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = cv2.resize(img, (224, 224))
-        img_array = np.array(img, dtype=np.float32) / 255.0
-        img_array = img_array[None, ...]
-        yield [img_array]
+        for path in image_paths[:300]:
+            img = cv2.imread(path)
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+            img = cv2.resize(img, (224, 224))
+            img_array = np.array(img, dtype=np.float32) / 255.0
+            img_array = img_array[None, ...]
+            yield [img_array]
+
+    return generate_representative_dataset
 
 
 @staticmethod
@@ -92,7 +95,9 @@ def train_classification_model(model_dir: str) -> bool:
     # convert model to tflite
     converter = tf.lite.TFLiteConverter.from_keras_model(model)
     converter.optimizations = [tf.lite.Optimize.DEFAULT]
-    converter.representative_dataset = generate_representative_dataset(dataset_dir)
+    converter.representative_dataset = generate_representative_dataset_factory(
+        dataset_dir
+    )
     converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
     converter.inference_input_type = tf.uint8
     converter.inference_output_type = tf.uint8