From 571326890a93c595e071710bf3128debe90cd731 Mon Sep 17 00:00:00 2001
From: Sefik Ilkin Serengil <serengil@gmail.com>
Date: Sun, 11 Apr 2021 14:40:27 +0300
Subject: [PATCH] represent

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index b9ac700..48ab424 100644
--- a/README.md
+++ b/README.md
@@ -83,14 +83,14 @@ Euclidean L2 form [seems](https://youtu.be/i_MOwvhbLdI) to be more stable than c
 
 **Tech Stack** - [`Vlog`](https://youtu.be/R8fHsL7u3eE)
 
-<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/tech-stack.png" width="90%" height="90%"></p>
-
 The question is that where to store facial representations. You can find vector embeddings of facial images with the represent function.
 
 ```python
 embedding = DeepFace.represent("img.jpg", model_name = 'Facenet')
 ```
 
+<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/tech-stack.png" width="90%" height="90%"></p>
+
 Recommended tech stack for face verification is mainly based on [relational databases and regular SQL](https://sefiks.com/2021/02/06/deep-face-recognition-with-sql/) or key-value stores such as [Redis](https://sefiks.com/2021/03/02/deep-face-recognition-with-redis/) or [Cassandra](https://sefiks.com/2021/01/24/deep-face-recognition-with-cassandra/). Herein, key-value stores overperform than regular relational databases.
 
 Face verification is a subset of face recognition. In other words, you can run any face verification tool for face recognition as well. However, face verification has O(1) and face recognition has O(n) time complexity. That's why, face recognition becomes problematic with regular face verification tools on millions/billions level data and limited hardware.