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tf_face's Introduction

tf_face: tensoflow codes for face recognition

Dependencies

tensorflow 1.1

Core codes

file	description	execution scripts
train_model.py	CNN training using whole dataset	run_train.sh
train_model_subset.py	CNN training using a randomly sampled dataset	run_train_subset.sh
train_fusion.py	training with multi-patch fusion	run_train_fusion.sh
test_ensemble.py	testing with model ensembling	run_lfwTest.sh

Helper codes

file	usage	execution scripts
save_weights.py	saving trainable parameters as .npy file	run_saveWeights.sh
train_utils.py	helper code for training	--
test_utils.py	helper code for evaluation	--
test_lfw.py	helper code for lfw test with pretrained model	run_lfwTest.sh
extract_feature.py	helper code for feature extraction given a image path list	run_extract.sh

Why hyper-parameters tunning is important?

Some observations of hyper-parameters tunning

1. Learning rate -- dropping lr too quickly may cause early stopping

2. Optimizer -- RMSProp vs. Adam

3. Dropout -- increase dropout may improve generalization.

4. Regularity -- center loss boosts performance

5. Dataset -- trained with whole dataset vs. trained with randomly sampled subset

(Maybe modifying randomly sampled method to boostrapping can bring some benefit)

Properly tunned model can push the performance to the limit

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