diff --git a/README.md b/README.md
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@@ -4,6 +4,7 @@
 
 [![Downloads](https://static.pepy.tech/personalized-badge/deepface?period=total&units=international_system&left_color=grey&right_color=blue&left_text=downloads)](https://pepy.tech/project/deepface)
 [![Stars](https://img.shields.io/github/stars/serengil/deepface?color=yellow&style=flat&label=%E2%AD%90%20stars)](https://github.com/serengil/deepface/stargazers)
+[![Docker Pulls](https://img.shields.io/docker/pulls/serengil/deepface?logo=docker)](https://hub.docker.com/r/serengil/deepface)
 [![License](http://img.shields.io/:license-MIT-green.svg?style=flat)](https://github.com/serengil/deepface/blob/master/LICENSE)
 [![Tests](https://github.com/serengil/deepface/actions/workflows/tests.yml/badge.svg)](https://github.com/serengil/deepface/actions/workflows/tests.yml)
 [![DOI](http://img.shields.io/:DOI-10.17671/gazibtd.1399077-blue.svg?style=flat)](https://doi.org/10.17671/gazibtd.1399077)
@@ -363,34 +364,6 @@ cd scripts
 
 Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Default service endpoints will be `http://localhost:5005/verify` for face recognition, `http://localhost:5005/analyze` for facial attribute analysis, and `http://localhost:5005/represent` for vector representation. The API accepts images as file uploads (via form data), or as exact image paths, URLs, or base64-encoded strings (via either JSON or form data), providing versatile options for different client requirements. [Here](https://github.com/serengil/deepface/tree/master/deepface/api/postman), you can find a postman project to find out how these methods should be called.
 
-**Dockerized Service** - [`Demo`](https://youtu.be/9Tk9lRQareA)
-
-[![Docker Pulls](https://img.shields.io/docker/pulls/serengil/deepface?logo=docker)](https://hub.docker.com/r/serengil/deepface)
-
-The following command set will serve deepface on `localhost:5005` via docker. Then, you will be able to consume deepface services such as verify, analyze and represent. Also, if you want to build the image by your own instead of pre-built image from docker hub, [Dockerfile](https://github.com/serengil/deepface/blob/master/Dockerfile) is available in the root folder of the project.
-
-```shell
-# docker build -t serengil/deepface . # build docker image from Dockerfile
-docker pull serengil/deepface # use pre-built docker image from docker hub
-docker run -p 5005:5000 serengil/deepface
-```
-
-<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-dockerized-v2.jpg" width="50%" height="50%"></p>
-
-**Command Line Interface** - [`Demo`](https://youtu.be/PKKTAr3ts2s)
-
-DeepFace comes with a command line interface as well. You are able to access its functions in command line as shown below. The command deepface expects the function name as 1st argument and function arguments thereafter.
-
-```shell
-#face verification
-$ deepface verify -img1_path tests/dataset/img1.jpg -img2_path tests/dataset/img2.jpg
-
-#facial analysis
-$ deepface analyze -img_path tests/dataset/img1.jpg
-```
-
-You can also run these commands if you are running deepface with docker. Please follow the instructions in the [shell script](https://github.com/serengil/deepface/blob/master/scripts/dockerize.sh#L17).
-
 **Large Scale Facial Recognition** - [`Playlist`](https://www.youtube.com/playlist?list=PLsS_1RYmYQQGSJu_Z3OVhXhGmZ86_zuIm)
 
 If your task requires facial recognition on large datasets, you should combine DeepFace with a vector index or vector database. This setup will perform [approximate nearest neighbor](https://youtu.be/c10w0Ptn_CU) searches instead of exact ones, allowing you to identify a face in a database containing billions of entries within milliseconds. Common vector index solutions include [Annoy](https://youtu.be/Jpxm914o2xk), [Faiss](https://youtu.be/6AmEvDTKT-k), [Voyager](https://youtu.be/2ZYTV9HlFdU), [NMSLIB](https://youtu.be/EVBhO8rbKbg), [ElasticSearch](https://youtu.be/i4GvuOmzKzo). For vector databases, popular options are [Postgres with its pgvector extension](https://youtu.be/Xfv4hCWvkp0) and [RediSearch](https://youtu.be/yrXlS0d6t4w).