How to train and return 128 embedding? about facenet-pytorch HOT 2 OPEN

What do you need 128-dim embeddings for?

It will probably be easier to either:

1. Add a single linear layer at the end of the model to do the mapping down to 128, freeze all the existing layers, and finetune, or
2. Use PCA to reduce the dimension

Training from scratch will be extremely involved just to get a smaller output dimension. I'd recommend just using PCA.

from facenet-pytorch.

What do you need 128-dim embeddings for?

It will probably be easier to either:

1. Add a single linear layer at the end of the model to do the mapping down to 128, freeze all the existing layers, and finetune, or
2. Use PCA to reduce the dimension

Training from scratch will be extremely involved just to get a smaller output dimension. I'd recommend just using PCA.

Thanks for the reply, I was planning to retrain with other demographic face dataset, the davidsandberg one is trained mainly on Caucasian, when come to Asian face, the performance is poor, so I was asking whether this pytorch version of facenet able to retrain from the scratch since davidsandberg one is limited to tensorflow version 1 and not compatible with latest nvidia cards.

from facenet-pytorch.