diff --git a/README.md b/README.md
index f616009..94265e2 100644
--- a/README.md
+++ b/README.md
@@ -60,6 +60,16 @@ The complexity of each face recognition model is different. Mean ± std. dev. of
 | Building     | 2.35 s ± 46.9 ms | 6.37 s ± 1.28 s  | 25.7 s ± 7.93 s  | 23.9 s ± 2.52 s   |
 | Verification | 897 ms ± 38.3 ms | 616 ms ± 12.1 ms | 684 ms ± 7.69 ms | 605 ms ± 13.2 ms  |
 
+## Passing pre-built models
+
+You can build a face recognition model once and pass this to verify function as well. This might be logical if you need to call verify function several times.
+
+```python
+from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
+model = VGGFace.loadModel()
+DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", model = model)
+```
+
 ## 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.
@@ -95,6 +105,28 @@ Model building and prediction times are different for those facial analysis mode
 | Building   | 243 ms ± 15.2 ms | 2.25 s ± 34.9    | 2.25 s ± 90.9 ms | 2.23 s ± 68.6 ms |
 | Prediction | 389 ms ± 11.4 ms | 524 ms ± 16.1 ms | 516 ms ± 10.8 ms | 493 ms ± 20.3 ms |
 
+## Passing pre-built models
+
+You can build facial attribute analysis models once and pass these to analyze function as well. This might be logical if you need to call analyze function several times.
+
+```python
+import json
+from deepface.extendedmodels import Age, Gender, Race, Emotion
+
+emotion_model = Emotion.loadModel()
+age_model = Age.loadModel()
+gender_model = Gender.loadModel()
+race_model = Race.loadModel()
+
+models = {}
+models["emotion"] = emotion_model
+models["age"] = age_model
+models["gender"] = gender_model
+models["race"] = race_model
+
+DeepFace.analyze("img1.jpg", models=facial_attribute_models)
+```
+
 # Streaming and Real Time Analysis
 
 You can run deepface for real time videos as well. Calling stream function under the DeepFace interface will access your webcam and apply both face recognition and facial attribute analysis. Stream function expects a database folder including face images. VGG-Face is the default face recognition model and cosine similarity is the default distance metric similar to verify function. The function starts to analyze if it can focus a face sequantially 5 frames. Then, it shows results 5 seconds.