diff --git a/frigate/util/classification.py b/frigate/util/classification.py
index 228d60477..03229cc73 100644
--- a/frigate/util/classification.py
+++ b/frigate/util/classification.py
@@ -122,9 +122,9 @@ def get_dataset_image_count(model_name: str) -> int:
 
 class ClassificationTrainingProcess(FrigateProcess):
     def __init__(self, model_name: str) -> None:
-        self.BASE_WEIGHT_PATH = os.environ.get(
-            "TF_KERAS_MOBILENET_V2_ENDPOINT",
-            "https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/",
+        self.BASE_WEIGHT_URL = os.environ.get(
+            "TF_KERAS_MOBILENET_V2_WEIGHTS_URL",
+            "",
         )
         super().__init__(
             stop_event=None,
@@ -184,23 +184,24 @@ class ClassificationTrainingProcess(FrigateProcess):
                 )
                 return False
 
-            alpha = 0.35
+            weights_path = "imagenet"
             # Download MobileNetV2 weights if not present
-            weights_filename = (
-                f"mobilenet_v2_weights_tf_dim_ordering_tf_kernels_{alpha}_224_no_top.h5"
-            )
-            weights_path = os.path.join(MODEL_CACHE_DIR, "MobileNet", weights_filename)
-            if not os.path.exists(weights_path):
-                weights_url = self.BASE_WEIGHT_PATH + weights_filename
-                logger.info(f"Downloading MobileNet V2 weights file: {weights_url}")
-                ModelDownloader.download_from_url(weights_url, weights_path)
+            if self.BASE_WEIGHT_URL:
+                weights_path = os.path.join(
+                    MODEL_CACHE_DIR, "MobileNet", "mobilenet_v2_weights.h5"
+                )
+                if not os.path.exists(weights_path):
+                    logger.info("Downloading MobileNet V2 weights file")
+                    ModelDownloader.download_from_url(
+                        self.BASE_WEIGHT_URL, weights_path
+                    )
 
             # Start with imagenet base model with 35% of channels in each layer
             base_model = MobileNetV2(
                 input_shape=(224, 224, 3),
                 include_top=False,
                 weights=weights_path,
-                alpha=alpha,
+                alpha=0.35,
             )
             base_model.trainable = False  # Freeze pre-trained layers