FACEID: learning face recognition/identification with neural network Structure of the code [faceid]: key components for the learning task train.py: invoke training and evaluation infer.py: invoke inference of trained models config.py: this is where all specifiable parameters are defined [script]: shell scripts that runs defined tasks [dataset_info]: optionally, one could put dataset information here in .json format Train Option1 : After acquiring of the datasets, one could simply invoke train.py to train a model. Option2 : Invoke training through bash command: pls refer to script/93-train.sh. Highlights competitive accuray/TPR/FPR with large training datasets (large number of identities, i.e. millions) distributed data parallel training, enables switching among non-distributed / DP / DDP training effortlessly ==distributed FC layer training: enables training multiple datasets simultaneously==