[Project] Port of the tensorflow facenet pretrained models to pytorch
Hi all, this project contains pytorch pretrained inception resnets ported from the davidsandberg/facenet github repo. Models are implemented and used according to the standard pytorch/torchvision methodology (inheritable model modules, torchvision style model zoo for downloaded/cached pretrained state dictionaries etc.). Currently, the project covers face detection using MTCNN and face recognition. MTCNN is implemented as a single stand-alone pytorch module that wraps the p-, r-, and o-net modules as well as the post-processing, making it easy to chain MTCNN and recognition resnets together in a face recognition pipeline.
The motivation for the project was the lack of a clean implementation in pytorch that provides the performance of the davidsandberg/facenet github repo. My aim was to build a project that could be easily used to add value existing pytorch projects without a great deal of effort.
Performance wise, I see similar or better inference speed on my local machine when compared to the original repo, but that one data point doesn’t say a hell of a lot. Any extra testing or feedback much appreciated.