Paper on arxiv: arxiv
Accepted at IEEE ACCESS Journal
Model training: In the paper, we employ MS1MV2 as the training dataset which can be downloaded from InsightFace (MS1M-ArcFace in DataZoo) Download MS1MV2 dataset from insightface on strictly follow the licence distribution
Unzip the dataset and place it in the data folder
Rename the config/config_xxxxxx.py to config/config.py
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 |
All code has been trained and tested using Pytorch 1.7.1
The code of NAS is available under NAS
If you use any of the provided code in this repository, please cite the following paper:
@article{boutros2021pocketnet,
author = {Fadi Boutros and
Patrick Siebke and
Marcel Klemt and
Naser Damer and
Florian Kirchbuchner and
Arjan Kuijper},
title = {PocketNet: Extreme Lightweight Face Recognition Network Using Neural
Architecture Search and Multistep Knowledge Distillation},
journal = {{IEEE} Access},
volume = {10},
pages = {46823--46833},
year = {2022},
url = {https://doi.org/10.1109/ACCESS.2022.3170561},
doi = {10.1109/ACCESS.2022.3170561},
}
This project is licensed under the terms of the Attribution-NonCommercial-ShareAlike 4.0
International (CC BY-NC-SA 4.0) license.
Copyright (c) 2021 Fraunhofer Institute for Computer Graphics Research IGD Darmstadt