I want to use the MS Celeb dataset (100,000 classes) to label images of various celebrities. I was thinking of 2 ways to do this.

1. Train a new face recognition model from scratch on this dataset with 100,000 classes as output.
2. Use a pretrained model like face_recognition library to get the encodings of a face and compare it (L2 distance) with the encodings of all the 100,000 classes. The class whose encodings are closest to this face, will be the face’s class.

I don’t know if this ‘comparing’ method will work well with 100,000 classes (the encodings are of 128 dimensions). What do you guys think will be more feasible?