Series: One step to SOTA

From 1 image to edge face recognition.

Imagine you have limited resources, you want a realtime solution to serve face recognition model for specific person. This pipeline will have you to achieve that goal with speed and accuracy. With just only 1 image for input, you will have a model with realtime running and ready to be served in edge devices (cameras, mobile phone,...)

1. git clone https://github.com/transybao1393/face-recognition-pipeline .
2. Follow Before doing anything part
3. Running steps

1. Download and create models to mtcnn/models folder from URL https://drive.google.com/file/d/1TTdqNEqYjWTTMYGxm8IT6t41mRef-5Yu/view?usp=sharing
2. Create folder mtcnn/training_data/processed and mtcnn/training_data/raw
3. Create folder /preview
4. Create folder /video

Please follow instruction in Makefile

• Multi processing and even better multithreading implementation for better memory and CPU usage.
• Python generator pipeline to optimize memory managment.
• Cython / Rust migration
• Improve speed and accuracy when multiple model recognition
• Image Ingestion layer to improve caching and data serving

This project is licensed under the BSD-3-Clause License - see the LICENSE.md file for details