diff --git a/README.md b/README.md
index b136e6b..a4549c3 100644
--- a/README.md
+++ b/README.md
@@ -90,15 +90,15 @@ Face recognition models basically represent facial images as multi-dimensional v
 embedding_objs = DeepFace.represent(img_path = "img.jpg")
 ```
 
-This function returns an array as embedding. The size of the embedding array would be different based on the model name. For instance, VGG-Face is the default model and it represents facial images as 2622 dimensional vectors.
+This function returns an array as embedding. The size of the embedding array would be different based on the model name. For instance, VGG-Face is the default model and it represents facial images as 4096 dimensional vectors.
 
 ```python
 embedding = embedding_objs[0]["embedding"]
 assert isinstance(embedding, list)
-assert model_name = "VGG-Face" and len(embedding) == 2622
+assert model_name = "VGG-Face" and len(embedding) == 4096
 ```
 
-Here, embedding is also [plotted](https://sefiks.com/2020/05/01/a-gentle-introduction-to-face-recognition-in-deep-learning/) with 2622 slots horizontally. Each slot is corresponding to a dimension value in the embedding vector and dimension value is explained in the colorbar on the right. Similar to 2D barcodes, vertical dimension stores no information in the illustration.
+Here, embedding is also [plotted](https://sefiks.com/2020/05/01/a-gentle-introduction-to-face-recognition-in-deep-learning/) with 4096 slots horizontally. Each slot is corresponding to a dimension value in the embedding vector and dimension value is explained in the colorbar on the right. Similar to 2D barcodes, vertical dimension stores no information in the illustration.
 
 <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/embedding.jpg" width="95%" height="95%"></p>
 
diff --git a/tests/test_enforce_detection.py b/tests/test_enforce_detection.py
index 7fa281d..74c4704 100644
--- a/tests/test_enforce_detection.py
+++ b/tests/test_enforce_detection.py
@@ -33,7 +33,7 @@ def test_disabled_enforce_detection_for_non_facial_input_on_represent():
     assert "w" in objs[0]["facial_area"].keys()
     assert "h" in objs[0]["facial_area"].keys()
     assert isinstance(objs[0]["embedding"], list)
-    assert len(objs[0]["embedding"]) == 2622  # embedding of VGG-Face
+    assert len(objs[0]["embedding"]) == 4096  # embedding of VGG-Face
 
     logger.info("✅ disabled enforce detection with non facial input test for represent tests done")
 
diff --git a/tests/test_represent.py b/tests/test_represent.py
index 2dd68ea..4b45594 100644
--- a/tests/test_represent.py
+++ b/tests/test_represent.py
@@ -10,7 +10,7 @@ def test_standard_represent():
     for embedding_obj in embedding_objs:
         embedding = embedding_obj["embedding"]
         logger.debug(f"Function returned {len(embedding)} dimensional vector")
-        assert len(embedding) == 2622
+        assert len(embedding) == 4096
     logger.info("✅ test standard represent function done")