diff --git a/README.md b/README.md
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@@ -53,6 +53,8 @@ df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")
#dfs = DeepFace.find(img_path = ["img1.jpg", "img2.jpg"], db_path = "C:/workspace/my_db")
```
+
+
**Supported face recognition models**
Face recognition can be handled by different models. Currently, [`VGG-Face`](https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/) , [`Google FaceNet`](https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/), [`OpenFace`](https://sefiks.com/2019/07/21/face-recognition-with-openface-in-keras/) and [`Facebook DeepFace`](https://sefiks.com/2020/02/17/face-recognition-with-facebook-deepface-in-keras/) models are supported in deepface. The default configuration verifies faces with **VGG-Face** model. You can set the base model while verification as illustared below. Accuracy and speed show difference based on the performing model.
@@ -96,7 +98,7 @@ for metric in metrics:
A face recognition task can be handled by several models and similarity metrics. We can [combine](https://sefiks.com/2020/06/03/mastering-face-recognition-with-ensemble-learning/) the precictions of all of those models and metrics to improve the accuracy of a face recognition task. This offers a huge improvement on accuracy, precision and recall but it runs much slower than single models.
-
+
```python
resp_obj = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "Ensemble")
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