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Extract a complete process from insightface_tf to do face recognition and verification.

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README.md
 
 
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dataset.py
 
 
eval_veri.py
eval_veri.py
 
 
eval_veri.sh
eval_veri.sh
 
 
loss.py
loss.py
 
 
mx2tfrecords.py
mx2tfrecords.py
 
 
requirement.txt
requirement.txt
 
 
resnet101.py
resnet101.py
 
 
resnet50.py
resnet50.py
 
 
train.py
train.py
 
 
train.sh
train.sh
 
 
verification.py
verification.py
 
 

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Tiny-Face-Recognition

Extract an complete process from insightface_tf to do face recognition and verification using pure TensorFlow.

Dataset

Training and Testing Dataset Download Website: Baidu

Contains:

  • train.rec/train.idx : Main training data
  • *.bin : varification data

Examples

Make TFRecords File:

$ python3 mx2tfrecords.py --bin_path '/Users/finup/Desktop/faces_emore/train.rec' --idx_path '/Users/finup/Desktop/faces_emore/train.idx' --tfrecords_file_path '/Users/finup/Desktop/faces_emore/tfrecords'

Train:

python3 train.py --tfrecords '/home/sunruina/face_recognition/data_set/ms_celeb_arcpaper_tfrecords/tran.tfrecords' --batch_size 64 --num_classes 85742 --ckpt_save_dir '/home/sunruina/face_recognition/data_set/ms_celeb_arcpaper_tfrecords/face_real403_ckpt' --epoch 10000

Test:

$ python3 eval_veri.py --datasets '/Users/finup/Desktop/faces_emore/cfp_fp.bin' --dataset_name 'cfp_fp' --num_classes 85742 --ckpt_restore_dir '/Users/finup/Desktop/faces_emore/Face_vox_iter_78900.ckpt'

Results

Datasets backbone loss steps batch_size acc
lfw resnet50 ArcFace 78900 64 0.9903
cfp_ff resnet50 ArcFace 78900 64 0.9847
cfp_fp resnet50 ArcFace 78900 64 0.8797
agedb_30 resnet50 ArcFace 78900 64 0.8991

Limited by the training time, so I just release the half-epoch training results temporarily. The model will be optimized later.

References

  1. InsightFace_TF
  2. InsightFace_MxNet
@inproceedings{deng2018arcface,
title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition},
author={Deng, Jiankang and Guo, Jia and Niannan, Xue and Zafeiriou, Stefanos},
booktitle={CVPR},
year={2019}
}

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<tf.Tensor 'gradients/conv4_3c_3x3/Conv2D_grad/tuple/control_dependency_1:0' shape=(3, 3, 256, 256) dtype=float32>, <tf.Variable 'conv4_3c_3x3/kernel:0' shape=(3, 3, 256, 256) dtype=float32_ref>

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<tf.Tensor 'gradients/bn4_3c_3x3/FusedBatchNorm_grad/tuple/control_dependency_2:0' shape=(256,) dtype=float32>, <tf.Variable 'bn4_3c_3x3/beta:0' shape=(256,) dtype=float32_ref>

<tf.Tensor 'gradients/conv4_3c_1x1_increase/Conv2D_grad/tuple/control_dependency_1:0' shape=(1, 1, 256, 1024) dtype=float32>, <tf.Variable 'conv4_3c_1x1_increase/kernel:0' shape=(1, 1, 256, 1024) dtype=float32_ref>)

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Extract a complete process from insightface_tf to do face recognition and verification.

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