PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation

Overview

PocketNet

This is the official repository of the paper:

PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation

Paper on arxiv: arxiv

evaluation

Face recognition model training

Download MS1MV2 dataset from insightface on strictly follow the licence distribution

Extract the dataset and place it in the data folder

Rename the config/config_xxxxxx.py to config/config.py

  1. Train PocketNet with ArcFace loss
    • ./train.sh
  2. Train PocketNet with template knowledge distillation
    • ./train_kd.sh
  3. Train PocketNet with multi-step template knowledge distillation
    • ./train_kd.sh
Model Parameters (M) configuration log pretrained model
PocketNetS-128 0.92 Config log Pretrained-model
PocketNetS-256 0.99 Config log Pretrained-model
PocketNetM-128 1.68 Config log Pretrained-model
PocketNetM-256 1.75 Config log Pretrained-model

Differentiable architecture search training

To-do

  • Add pretrained model
  • Training configuration
  • Add NAS code
  • Add evaluation results
  • Add requirements

If you use any of the provided code in this repository, please cite the following paper:

@misc{boutros2021pocketnet,
      title={PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation}, 
      author={Fadi Boutros and Patrick Siebke and Marcel Klemt and Naser Damer and Florian Kirchbuchner and Arjan Kuijper},
      year={2021},
      eprint={2108.10710},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
Owner
Fadi Boutros
Fadi Boutros
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