diff --git a/deepface/models/facial_recognition/Buffalo_L.py b/deepface/models/facial_recognition/Buffalo_L.py
index c1a1bcb..9b6b8d2 100644
--- a/deepface/models/facial_recognition/Buffalo_L.py
+++ b/deepface/models/facial_recognition/Buffalo_L.py
@@ -10,7 +10,7 @@ try:
     from insightface.model_zoo import get_model
 except ModuleNotFoundError as err:
     raise ModuleNotFoundError(
-        "InsightFace is an optional dependency for the Buffalo_L model."
+        "InsightFace is an optional dependency for the Buffalo_L model. "
         "You can install it with: pip install insightface>=0.7.3"
     ) from err
 
@@ -18,21 +18,43 @@ class Buffalo_L(FacialRecognition):
     def __init__(self):
         self.model = None
         self.input_shape = (112, 112)  # Buffalo_L expects 112x112
-        self.output_shape = 512  # Embedding size
+        self.output_shape = 512        # Embedding size
         self.load_model()
 
     def load_model(self):
         """
         Load the InsightFace Buffalo_L recognition model.
         """
-        # Use DeepFace's utility to download weights if necessary
-        model_rel_path = os.path.join("insightface", "buffalo_l", "w600k_r50.onnx")
+        # Define the model filename and subdirectory
+        sub_dir = "buffalo_l"
+        model_file = "w600k_r50.onnx"
+        model_rel_path = os.path.join(sub_dir, model_file)
+
+        # Define the weights directory and ensure the buffalo_l subdirectory exists
+        weights_dir = os.path.join(os.path.expanduser("~"), ".deepface", "weights")
+        buffalo_l_dir = os.path.join(weights_dir, sub_dir)
+        if not os.path.exists(buffalo_l_dir):
+            os.makedirs(buffalo_l_dir, exist_ok=True)
+            print("Created directory:", buffalo_l_dir)
+
+        # Download the model weights if not already present
         weights_path = weight_utils.download_weights_if_necessary(
-            file_name="webface_r50.onnx",
+            file_name=model_rel_path,
             source_url="https://drive.google.com/uc?export=download&confirm=pbef&id=1N0GL-8ehw_bz2eZQWz2b0A5XBdXdxZhg"
         )
-        # Load model from weights folder
-        self.model = get_model("buffalo_l/w600k_r50.onnx", root=os.path.dirname(weights_path))
+        print("Downloaded model path:", weights_path)
+
+        # Verify that the model file exists at the expected location
+        expected_model_path = os.path.join(buffalo_l_dir, model_file)
+        if os.path.exists(expected_model_path):
+            print("Model file found at expected location:", expected_model_path)
+        else:
+            print("Model file NOT found at expected location:", expected_model_path)
+
+        # Use the full absolute path for loading the model
+        full_model_path = os.path.join(buffalo_l_dir, model_file)
+        print("Full model path:", full_model_path)
+        self.model = get_model(full_model_path)
         self.model.prepare(ctx_id=-1, input_size=self.input_shape)
 
     def preprocess(self, img: np.ndarray) -> np.ndarray:
@@ -47,7 +69,7 @@ class Buffalo_L(FacialRecognition):
             img = img[0]  # Remove batch dimension
         if img.max() <= 1.0:  # If normalized to [0, 1]
             img = (img * 255).astype(np.uint8)
-        # Always convert RGB to BGR (DeepFace outputs RGB, InsightFace expects BGR)
+        # Convert RGB to BGR (DeepFace outputs RGB, InsightFace expects BGR)
         img = img[:, :, ::-1]
         return img