diff --git a/README.md b/README.md
index d4408a6..8bca3d7 100644
--- a/README.md
+++ b/README.md
@@ -24,6 +24,8 @@ facenet_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "Facenet")
 openface_result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "OpenFace")
 ```
 
+VGG-Face has the highest accuracy score but it is not convenient for real time studies because of its complex structure. Facenet is a complex model as well. On the other hand, OpenFace has a close accuracy score but it performs the fastest. That's why, OpenFace is much more convenient for real time studies.
+
 ## Similarity
 
 These models actually find the vector embeddings of faces. Decision of verification is based on the distance between vectors. Distance could be found by different metrics such as [`Cosine Similarity`](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below.
@@ -34,8 +36,6 @@ result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distan
 result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = "euclidean_l2")
 ```
 
-VGG-Face has the highest accuracy score but it is not convenient for real time studies because of its complex structure. Facenet is a complex model as well. On the other hand, OpenFace has a close accuracy score but it performs the fastest. That's why, OpenFace is much more convenient for real time studies.
-
 ## Verification
 
 Verification function returns a tuple including boolean verification result, distance between two faces and max threshold to identify. 
@@ -67,8 +67,8 @@ Deepface also offers facial attribute analysis including [`age`](https://sefiks.
 
 ```python
 from deepface import DeepFace
-demography = DeepFace.analyze("img.zip") #passing nothing as 2nd argument will find everything
-#demography = DeepFace.analyze("img.zip", ['age', 'gender', 'race', 'emotion']) #identical to above line
+demography = DeepFace.analyze("img.jpg") #passing nothing as 2nd argument will find everything
+#demography = DeepFace.analyze("img.jpg", ['age', 'gender', 'race', 'emotion']) #identical to above line
 ```
 
 Analysis function returns a json object.
@@ -147,4 +147,4 @@ There are many ways to support a project - starring⭐️ the GitHub repos is ju
 
 # Licence
 
-Chefboost is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details.
+Deepface is licensed under the MIT License - see [`LICENSE`](https://github.com/serengil/deepface/blob/master/LICENSE) for more details.