ElasticFace-main.zip

## This is the official repository of the paper: #### ElasticFace: Elastic Margin Loss for Deep Face Recognition Paper on arxiv: [arxiv](https://arxiv.org/pdf/2109.09416.pdf) #### *** Accepted CVPR workshops 2022 ***  | Model | Log file| Pretrained model| checkpoint | | ------------- | ------------- |------------- | ------------- | | ElasticFace-Arc |[log file](https://drive.google.com/file/d/1jGm6rHh-jJ40c34u5eXBgAhR3u4KHblH/view?usp=sharing) |[pretrained-mode](https://drive.google.com/drive/folders/1q3ws_BQLmgXyiy2msvHummXq4pRqc1rx?usp=sharing) | 295672backbone.pth | | ElasticFace-Cos |[log file](https://drive.google.com/file/d/1XgfEQgEabinH--VhIusWQ8Js43vz1vK0/view?usp=sharing) |[pretrained-mode](https://drive.google.com/drive/folders/1ZiLLZXQ1jMzFwMGhYjtMwcdHmuedQb-2?usp=sharing) | 295672backbone.pth | | ElasticFace-Arc+ |[log file](https://drive.google.com/file/d/1cWphaOqgCtmJ8zgVfnMXh0mVl6EqQZNd/view?usp=sharing) |[pretrained-mode](https://drive.google.com/drive/folders/1sf-fNV5CeSpWuFj6Hkwp7Js8SBXjbPo_?usp=sharing) | 295672backbone.pth | | ElasticFace-Cos+ |[log file](https://drive.google.com/file/d/1aqCN5yfzgGijJLg2hcrsW3fvwHeHNu6W/view?usp=sharing) |[pretrained-mode](https://drive.google.com/drive/folders/19LXrjVNt60JBZP7JqsvOSWMwGLGrcJl5?usp=sharing) | 295672backbone.pth | Evaluation result: See: [Paper with code](https://paperswithcode.com/paper/elasticface-elastic-margin-loss-for-deep-face) ### Face recognition model training 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](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_) on strictly follow the licence distribution Unzip the dataset and place it in the data folder Set the config.output and config.loss in the config/config.py All code has been trained and tested using Pytorch 1.7.1 ## Face recognition evaluation ##### evaluation on LFW, AgeDb-30, CPLFW, CALFW and CFP-FP: 1. download the data from their offical webpages. 2. alternative: The evaluation datasets are available in the training dataset package as bin file 3. set the config.rec to dataset folder e.g. data/faces_emore 4. set the config.val_targets for list of the evaluation dataset 5. download the pretrained model from link the previous table 6. set the config.output to path to pretrained model weights 7. run eval/evaluation.py 8. the output is test.log contains the evaluation results over all epochs ### To-do - [x] Add evaluation script If you use any of the code provided in this repository, please cite the following paper: ## Citation ``` @InProceedings{Boutros_2022_CVPR, author = {Boutros, Fadi and Damer, Naser and Kirchbuchner, Florian and Kuijper, Arjan}, title = {ElasticFace: Elastic Margin Loss for Deep Face Recognition}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {1578-1587} } ``` ## License ``` 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 ```