Training a face Recognizer using ResNet50 + ArcFace in TensorFlow 2.0

The aim of this project is to train a state of art face recognizer using TensorFlow 2.0. The architecture chosen is a modified version of ResNet50 and the loss function used is ArcFace, both originally developed by deepinsight in mxnet.

The dataset used for training is the MS1M-ArcFace dataset used in insightface, and it is available their dataset zoo. The images are aligned using mtcnn and cropped to 112x112.

The results of the training are evaluated with lfw, cfp_ff, cfp_fp and age_db30, using the same metrics as deepinsight.

The full training and evaluation code is provided, as well as some trained weights.

A Dockerfile is also provided with all prerequisites installed.

Prerequisites

If you are not using the provided Dockerfile, you will need to install the following packages:

pip3 install tensorflow-gpu==2.0.0b1 pillow mxnet matplotlib==3.0.3 opencv-python==3.4.1.15 scikit-learn

Preparing the dataset

Download the MS1M-ArcFace dataset from insightface model zoo and unzip it to the dataset folder.

Convert the dataset to the tensorflow format:

cd dataset
mkdir converted_dataset
python3 convert_dataset.py

Training the model

For training using 1 GPU:

python3 train.py

For training using multiple GPU:

python3 train_multigpu.py

The training process can be followed loading the generated log file (in output/logs) with tensorboard.

Evaluating the model

The model can be evaluated using the lfw, cfp_ff, cfp_fp and age_db30 databases. The metrics are the same used in insightface.

Before launching the test, you may change the checkpoint path in the evaluation.py script.

python3 evaluation.py

Trained models

model A

model name	train db	normalization layer	reg loss	batch size	gpus	total_steps	download
model A	casia	batch normalization	uncontrolled	16*8	1	150k	model a

dbname	accuracy
lfw	0.9772
cfp_ff	0.9793
cfp_fp	0.8786
age_db30	0.8752

model B

model name	train db	normalization layer	reg loss	batch size	gpus	total_steps	download
model B	ms1m	batch renormalization	uncontrolled	16*8	1	768k	model b

dbname	accuracy
lfw	0.9962
cfp_ff	0.9964
cfp_fp	0.9329
age_db30	0.9547

model C (multigpu)

model name	train db	normalization layer	reg loss	batch size	gpus	download
model C	ms1m	batch renormalization	uncontrolled	384	3	model c

dbname	accuracy
lfw	0.9967
cfp_ff	0.9970
cfp_fp	0.9403
age_db30	0.9652

DeepDream2045/FaceRecognition_TF2