Implementation-ArcFace-in-MATLAB

Versión 1.1.1 (88,1 MB) por Nobuaki Fukuchi

This is implementatoin of ArcFace(https://arxiv.org/abs/1801.07698) in MATLAB. In this demo, train and test pet images by using ArcFace.

https://github.com/fukuchi0105/Implementation-ArcFace-in-MATLAB

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ArcFace_MATLAB

This is implementatoin of ArcFace(https://arxiv.org/abs/1801.07698) in MATLAB. In this demo, train and test pet images by using ArcFace. This network can

calculate the similarity between your pet images and each breed of dogs and cats.
calculate the similarity between your pet images and the other pet images.

Products used in development

MATLAB R2021b
Image Processing Toolbox
Deep Learning Toolbox
add on: Deep Learning Toolbox Model for ResNet-50 Network
Parallel Computing Toolbox (if you want to use GPU in training)

Setup

dataset: Datasets is automatically downloaded from Oxford-IIIT Pet Datset(https://www.robots.ox.ac.uk/~vgg/data/pets/).

To Run:
・Train and Test:(GPU is recommanded)
　1．Open arcface_train.mlx
　2. Change directory to "arcface_demo"
　3. Execute Run
・Test:
　1．Open arcface_train.mlx
　2. Change directory to "arcface_demo"
　3. Go to "Test Model" Section
　　 If you want to use train model by run, remove commnet of "load dlnet_end.mat"
　4．Run section of "Test Model"

Getting Started

Please open arcface_train.mlx　and read instructions

Examples

To learn how to use this in testing workflows, see arface_train.mlx.

License

The license for Implementation-ArcFace-in-MATLAB is available in the license.txt file in this GitHub repository.

Include any other License information here, including third-party content using separate license agreements

Community Support

MATLAB Central

Citar como

Nobuaki Fukuchi (2025). Implementation-ArcFace-in-MATLAB (https://github.com/fukuchi0105/Implementation-ArcFace-in-MATLAB/releases/tag/v1.1.1), GitHub. Recuperado 28 octubre, 2025.

Deng, Jiankang, et al. “ArcFace: Additive Angular Margin Loss for Deep Face Recognition.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers (IEEE), 2021, pp. 1–1, doi:10.1109/tpami.2021.3087709.

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Compatibilidad con la versión de MATLAB

Se creó con R2021b

Compatible con cualquier versión

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ArcFaceLayer.m

Versión	Publicado	Notas de la versión
1.1.1	3 mar 2022	See release notes for this release on GitHub: https://github.com/fukuchi0105/Implementation-ArcFace-in-MATLAB/releases/tag/v1.1.1	Descargar
1.1.0	3 mar 2022		Descargar
1.0.0.0	28 sep 2022	See release notes for this release on GitHub: https://github.com/fukuchi0105/Implementation-ArcFace-in-MATLAB/releases/tag/v1.0.0	Descargar

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.

MLA	Deng, Jiankang, et al. “ArcFace: Additive Angular Margin Loss for Deep Face Recognition.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers (IEEE), 2021, pp. 1–1, doi:10.1109/tpami.2021.3087709.
APA	Deng, J., Guo, J., Yang, J., Xue, N., Cotsia, I., & Zafeiriou, S. P. (2021). ArcFace: Additive Angular Margin Loss for Deep Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. Institute of Electrical and Electronics Engineers (IEEE). Retrieved from https://doi.org/10.1109%2Ftpami.2021.3087709
BibTeX	@article{Deng_2021, doi = {10.1109/tpami.2021.3087709}, url = {https://doi.org/10.1109%2Ftpami.2021.3087709}, year = 2021, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, pages = {1--1}, author = {Jiankang Deng and Jia Guo and Jing Yang and Niannan Xue and Irene Cotsia and Stefanos P Zafeiriou}, title = {{ArcFace}: Additive Angular Margin Loss for Deep Face Recognition}, journal = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence} }