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A better tensorflow implementation of deepinsight, aiming at smoothly production ready for cross-platforms. Currently only with inference, training code later.

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AIInAi/tf-insightface

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Introduction

  • InsightFace inference example (production ready architecture)

  • Face recognition demo with insightface (visualization missing, add later)

  • InsightFace training pipeline

What does this do if I know nothing about face recognitions?

This is a server, wrapping up with a frozen model, accepting a photo of face, then output a vector of 512 dimension to describe it.

It means:

  • You pbbly need another pipeline before this to detect a face bounding box first

  • Then you can run this project to describe this face

  • Later on, it's up to your purpose, if your purpose is face alignment/detection/distinguish, you need another classifier after this to do the job

An example of this is as following, borrowed from openface:

arch

Demo Facial Recognition App

Yes Yes Yes, I know your are lazy. So I made a demo app for you with following architecture:

sys - page 1

How to run it

  • Install Depenencies: pip install -r requirements.txt

  • Download pre-trained frozen model and put it under pretrained folder

  • Run example: python apps/example.py

  • You shall be able to see terminal output a 512 element array representing face feature embedded

  • Run demo: python apps/demo.py

  • You sahll be able to see it output the architecture described above logs

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A better tensorflow implementation of deepinsight, aiming at smoothly production ready for cross-platforms. Currently only with inference, training code later.

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