Update 10/31/2019 We upload the loss.py
as our implementation of the original ArcFace
and the proposed ArcNegFace
. Now you can use them to train the model and compare the results. If you use the ms1m-v3 provided by the LFR challenge, you would reproduce the reported results as listed in our paper.
This is the model and code for the paper 'Towards Flops-constrained Face Recognition', which win the 1st place in the ICCV19 Lightweight Face Recognition Challenge, large video track. This repo only covers the two 30Gflops tracks (deepglint-large and iQiyi-large) that we took participate in. For more details such as network design, training strategy and bag of tricks, please refer to our workshop paper.
- Using this link to download the EfficientPolyFace and QAN++ models.
- Clone this repo and override the files with the same name.
- Use this example to generate the representation (feature vector) for a single image:
python test_tp.py --test_img 00000f9f87210c8eb9f5fb488b1171d7.jpg --save_path ./
- Use this example to generate the representation and the quality for a single image. This will produce two files, which store the representation and quality respectively:
python test_quality.py --test_img 000005_99.jpg
Note that to generate quality for a set/sequence/video, you need to generate representations and qualities for all frames in this step by a loop.
- Use
generate_aggregation_feat.py
to generate presentations for a video.
Please cite our paper if this project helps your research.
@inproceedings{liu2019towards,
title={Towards Flops-constrained Face Recognition},
author={Liu, Yu and Song, Guanglu and Zhang, Manyuan and Liu, Jihao and Zhou, Yucong and Yan, Junjie},
booktitle={Proceedings of the ICCV Workshop},
year={2019}
}