github/deepface
1
1
Fork 0
mirror of https://github.com/serengil/deepface.git synced 2025-06-06 19:45:21 +00:00
Code Issues Packages Projects Releases Wiki Activity

docker

Browse Source
This commit is contained in:
Sefik Ilkin Serengil 2023-01-08 17:27:00 +00:00 committed by GitHub
parent a522d4d278
commit 52af2fa851
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 3 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
README.md
View File

@ -269,7 +269,7 @@ Face recognition, facial attribute analysis and vector representation functions
**Dockerized Service**
You can deploy the deepface api on a kubernetes cluster with docker. The following shell script will serve deepface on `localhost:5000`. You need to re-configure the Dockerfile if you want to change the port. Then, even if you do not have a development environment, you will be able to consume deepface services. You can also access the inside of the docker image to run deepface related commands. Please follow the instructions in the shell script.
You can deploy the deepface api on a kubernetes cluster with docker. The following [shell script](https://github.com/serengil/deepface/blob/master/scripts/dockerize.sh) will serve deepface on `localhost:5000`. You need to re-configure the [Dockerfile](https://github.com/serengil/deepface/blob/master/Dockerfile) if you want to change the port. Then, even if you do not have a development environment, you will be able to consume deepface services such as verify and analyze. You can also access the inside of the docker image to run deepface related commands. Please follow the instructions in the [shell script](https://github.com/serengil/deepface/blob/master/scripts/dockerize.sh).
```shell
cd scripts
@ -290,6 +290,8 @@ $ deepface verify -img1_path tests/dataset/img1.jpg -img2_path tests/dataset/img
$ deepface analyze -img_path tests/dataset/img1.jpg
```
You can also run these command 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).
**Tech Stack** - [`Vlog`](https://youtu.be/R8fHsL7u3eE), [`Tutorial`](https://sefiks.com/2021/03/31/tech-stack-recommendations-for-face-recognition/)
Face recognition models represent facial images as vector embeddings. The idea behind facial recognition is that vectors should be more similar for same person than different persons. The question is that where and how to store facial embeddings in a large scale system. Tech stack is vast to store vector embeddings. To determine the right tool, you should consider your task such as face verification or face recognition, priority such as speed or confidence, and also data size.