diff --git a/README.md b/README.md
index 8edf306..7e50f15 100644
--- a/README.md
+++ b/README.md
@@ -153,7 +153,7 @@ Face recognition models are regular [convolutional neural networks](https://sefi
 Similarity could be calculated by different metrics such as [Cosine Similarity](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Angular Distance, Euclidean Distance or L2 normalized Euclidean. The default configuration uses cosine similarity. According to [experiments](https://github.com/serengil/deepface/tree/master/benchmarks), no distance metric is overperforming than other.
 
 ```python
-metrics = ["cosine", "euclidean", "euclidean_l2", 'angular']
+metrics = ["cosine", "euclidean", "euclidean_l2", "angular"]
 
 result = DeepFace.verify(
   img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1]
diff --git a/deepface/modules/streaming.py b/deepface/modules/streaming.py
index 75e543b..01da948 100644
--- a/deepface/modules/streaming.py
+++ b/deepface/modules/streaming.py
@@ -223,7 +223,7 @@ def search_identity(
             'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
             'centerface' or 'skip' (default is opencv).
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
-            'euclidean', 'euclidean_l2', angular, (default is cosine).
+            'euclidean', 'euclidean_l2', 'angular', (default is cosine).
     Returns:
         result (tuple): result consisting of following objects
             identified image path (str)
@@ -474,7 +474,7 @@ def perform_facial_recognition(
             'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s',
             'yolov11m', 'centerface' or 'skip' (default is opencv).
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
-            'euclidean', 'euclidean_l2', angular (default is cosine).
+            'euclidean', 'euclidean_l2', 'angular' (default is cosine).
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
             OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
     Returns:
diff --git a/deepface/modules/verification.py b/deepface/modules/verification.py
index c5d1d53..60395ec 100644
--- a/deepface/modules/verification.py
+++ b/deepface/modules/verification.py
@@ -51,7 +51,7 @@ def verify(
             'centerface' or 'skip' (default is opencv)
 
         distance_metric (string): Metric for measuring similarity. Options: 'cosine',
-            'euclidean', 'euclidean_l2', angular (default is cosine).
+            'euclidean', 'euclidean_l2', 'angular' (default is cosine).
 
         enforce_detection (boolean): If no face is detected in an image, raise an exception.
             Set to False to avoid the exception for low-resolution images (default is True).
@@ -454,17 +454,17 @@ def find_threshold(model_name: str, distance_metric: str) -> float:
             "cosine": 0.68,
             "euclidean": 1.17,
             "euclidean_l2": 1.17,
-            "angular": 0.43,
+            "angular": 0.39,
         },  # 4096d - tuned with LFW
-        "Facenet": {"cosine": 0.40, "euclidean": 10, "euclidean_l2": 0.80, "angular": 0.47},
-        "Facenet512": {"cosine": 0.30, "euclidean": 23.56, "euclidean_l2": 1.04, "angular": 0.49},
-        "ArcFace": {"cosine": 0.68, "euclidean": 4.15, "euclidean_l2": 1.13, "angular": 0.43},
-        "Dlib": {"cosine": 0.07, "euclidean": 0.6, "euclidean_l2": 0.4, "angular": 0.50},
-        "SFace": {"cosine": 0.593, "euclidean": 10.734, "euclidean_l2": 1.055, "angular": 0.445},
-        "OpenFace": {"cosine": 0.10, "euclidean": 0.55, "euclidean_l2": 0.55, "angular": 0.50},
-        "DeepFace": {"cosine": 0.23, "euclidean": 64, "euclidean_l2": 0.64, "angular": 0.49},
-        "DeepID": {"cosine": 0.015, "euclidean": 45, "euclidean_l2": 0.17, "angular": 0.50},
-        "GhostFaceNet": {"cosine": 0.65, "euclidean": 35.71, "euclidean_l2": 1.10, "angular": 0.43},
+        "Facenet": {"cosine": 0.40, "euclidean": 10, "euclidean_l2": 0.80, "angular": 0.33},
+        "Facenet512": {"cosine": 0.30, "euclidean": 23.56, "euclidean_l2": 1.04, "angular": 0.35},
+        "ArcFace": {"cosine": 0.68, "euclidean": 4.15, "euclidean_l2": 1.13, "angular": 0.39},
+        "Dlib": {"cosine": 0.07, "euclidean": 0.6, "euclidean_l2": 0.4, "angular": 0.12},
+        "SFace": {"cosine": 0.593, "euclidean": 10.734, "euclidean_l2": 1.055, "angular": 0.36},
+        "OpenFace": {"cosine": 0.10, "euclidean": 0.55, "euclidean_l2": 0.55, "angular": 0.11},
+        "DeepFace": {"cosine": 0.23, "euclidean": 64, "euclidean_l2": 0.64, "angular": 0.12},
+        "DeepID": {"cosine": 0.015, "euclidean": 45, "euclidean_l2": 0.17, "angular": 0.04},
+        "GhostFaceNet": {"cosine": 0.65, "euclidean": 35.71, "euclidean_l2": 1.10, "angular": 0.38},
         "Buffalo_L": {"cosine": 0.55, "euclidean": 0.6, "euclidean_l2": 1.1, "angular": 0.45},
     }