diff --git a/README.md b/README.md
index a0bb6df..42a7b0d 100644
--- a/README.md
+++ b/README.md
@@ -134,9 +134,11 @@ models = [
   "ArcFace", 
   "Dlib", 
   "SFace",
-  "GhostFaceNet"
+  "GhostFaceNet",
+  "Buffalo_L" (InsightFace-based, requires additional dependencies; see Installation)
 ]
 
+
 #face verification
 result = DeepFace.verify(
   img1_path = "img1.jpg",
@@ -157,11 +159,13 @@ embedding_objs = DeepFace.represent(
   model_name = models[2],
 )
 ```
+**Note:** The `Buffalo_L` model uses InsightFace’s `webface_r50.onnx`. If automated download fails, manually download it from [here](https://drive.google.com/file/d/1N0GL-8ehw_bz2eZQWz2b0A5XBdXdxZhg/view?usp=sharing) and place it in `~/.deepface/weights/buffalo_l/`.
 
 <p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/model-portfolio-20240316.jpg" width="95%" height="95%"></p>
 
 FaceNet, VGG-Face, ArcFace and Dlib are overperforming ones based on experiments - see [`BENCHMARKS`](https://github.com/serengil/deepface/tree/master/benchmarks) for more details. You can find the measured scores of various models in DeepFace and the reported scores from their original studies in the following table.
 
+
 | Model          | Measured Score | Declared Score     |
 | -------------- | -------------- | ------------------ |
 | Facenet512     | 98.4%          | 99.6%              |
@@ -202,6 +206,9 @@ dfs = DeepFace.find(
 )
 ```
 
+**Note:** The `Buffalo_L` model works best with cosine similarity. you can play around with the thresholds as it pleases you.
+
+
 **Facial Attribute Analysis** - [`Demo`](https://youtu.be/GT2UeN85BdA)
 
 DeepFace also comes with a strong facial attribute analysis module including [`age`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`gender`](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/), [`facial expression`](https://sefiks.com/2018/01/01/facial-expression-recognition-with-keras/) (including angry, fear, neutral, sad, disgust, happy and surprise) and [`race`](https://sefiks.com/2019/11/11/race-and-ethnicity-prediction-in-keras/) (including asian, white, middle eastern, indian, latino and black) predictions. Result is going to be the size of faces appearing in the source image.