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FaceX-Zoo: A PyTorch Toolbox for Face Recognition
29th ACM International Conference on Multimedia, MM 2021 ; : 3779-3782, 2021.
Article in English | Scopus | ID: covidwho-1533097
ABSTRACT
Due to the remarkable progress in recent years, deep face recognition is in great need of public support for practical model production and further exploration. The demands are in three folds, including 1) modular training scheme, 2) standard and automatic evaluation, and 3) groundwork of deployment. To meet these demands, we present a novel open-source project, named FaceX-Zoo, which is constructed with modular and scalable design, and oriented to the academic and industrial community of face-related analysis. FaceX-Zoo provides 1) the training module with various choices of backbone and supervisory head;2) the evaluation module that enables standard and automatic test on most popular benchmarks;3) the module of simple yet fully functional face SDK for the validation and primary application of end-to-end face recognition;4) the additional module that integrates a group of useful tools. Based on these easy-to-use modules, FaceX-Zoo can help the community to easily build stateof-the-art solutions for deep face recognition and, such like the newly-emerged challenge of masked face recognition caused by the worldwide COVID-19 pandemic. Besides, FaceX-Zoo can be easily upgraded and scaled up along with further exploration in face related fields. The source codes and models have been released and received over 900 stars at https//github.com/JDAI-CV/FaceX-Zoo. © 2021 ACM.

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 29th ACM International Conference on Multimedia, MM 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 29th ACM International Conference on Multimedia, MM 2021 Year: 2021 Document Type: Article