🔥 ArcFace (Additive Angular Margin Loss for Deep Face Recognition, published in CVPR 2019) implemented in Tensorflow 2.0+. This is an unofficial implementation. 🔥

Additive Angular Margin Loss(ArcFace) has a clear geometric interpretation due to the exact correspondence to the geodesic distance on the hypersphere, and consistently outperforms the state-of-the-art and can be easily implemented with negligible computational overhead.

Original Paper:   Arxiv   CVPR2019

Offical Implementation:   MXNet

📑

• Installation
• Data Preparing
• Training and Testing
• Benchmark and Models
• References

🍕

Create a new python virtual environment by Anaconda or just use pip in your python environment and then clone this repository as following.

git clone https://github.com/peteryuX/arcface-tf2.git
cd arcface-tf2

conda env create -f environment.yml
conda activate arcface-tf2

pip install -r requirements.txt

🍺

All datasets used in this repository can be found from face.evoLVe.PyTorch's Data-Zoo.

Note:

• Both training and testing dataset are "Align_112x112" version.

Download MS-Celeb-1M datasets, then extract and convert them to tfrecord as traning data as following.

# Binary Image: convert really slow, but loading faster when traning.
python data/convert_train_binary_tfrecord.py --dataset_path="/path/to/ms1m_align_112/imgs" --output_path="./data/ms1m_bin.tfrecord"

# Online Image Loading: convert really fast, but loading slower when training.
python data/convert_train_tfrecord.py --dataset_path="/path/to/ms1m_align_112/imgs" --output_path="./data/ms1m.tfrecord"

Note:

• You can run python ./dataset_checker.py to check if the dataloader work.

Download LFW, Aged30 and CFP-FP datasets, then extract them to /your/path/to/test_dataset. These testing data are already binary files, so it's not necessary to do any preprocessing. The directory structure should be like bellow.

/your/path/to/test_dataset/
    -> lfw_align_112/lfw
        -> data/
        -> meta/
        -> ...
    -> agedb_align_112/agedb_30
        -> ...
    -> cfp_align_112/cfp_fp
        -> ...

🍭

You can modify your own dataset path or other settings of model in ./configs/*.yaml for training and testing, which like below.

# general (shared both in training and testing)
batch_size: 128
input_size: 112
embd_shape: 512
sub_name: 'arc_res50'
backbone_type: 'ResNet50' # or 'MobileNetV2'
head_type: ArcHead # or 'NormHead': FC to targets.
is_ccrop: False # central-cropping or not

# train
train_dataset: './data/ms1m_bin.tfrecord' # or './data/ms1m.tfrecord'
binary_img: True # False if dataset is online decoding
num_classes: 85742
num_samples: 5822653
epochs: 5
base_lr: 0.01
w_decay: !!float 5e-4
save_steps: 1000

# test
test_dataset: '/your/path/to/test_dataset'

Note:

• The sub_name is the name of outputs directory used in checkpoints and logs folder. (make sure of setting it unique to other models)
• The head_type is used to choose ArcFace head or normal fully connected layer head for classification in training. (see more detail in ./modules/models.py)
• The is_ccrop means doing central-cropping on both trainging and testing data or not.
• The binary_img is used to choose the type of training data, which should be according to the data type you created in the Data-Preparing.

Here have two modes for training your model, which should be perform the same results at the end.

# traning with tf.GradientTape(), great for debugging.
python train.py --mode="eager_tf" --cfg_path="./configs/arc_res50.yaml"

# training with model.fit().
python train.py --mode="fit" --cfg_path="./configs/arc_res50.yaml"

You can download my trained models for testing from Benchmark and Models without training it yourself. And, evaluate the models you got with the corresponding cfg file on the testing dataset. The testing code in ./modules/evaluations.py were modified from face.evoLVe.

python test.py --cfg_path="./configs/arc_res50.yaml"

You can also encode image into latent vector by model. For example, encode the image from ./data/BruceLee.jpg and save to ./output_embeds.npy as following.

python test.py --cfg_path="./configs/arc_res50.yaml" --img_path="./data/BruceLee.jpg"

☕

Verification results (%) of different backbone, head tpye, data augmentation and loss function.

Note:

• The 'CCrop' tag above means doing central-cropping on both trainging and testing data, which could eliminate the redundant boundary of intput face data (especially for AgeDB-30).
• All training settings of the models can be found in the corresponding ./configs/*.yaml files.
• Based on the property of the training dataset, all the pre-trained models can only be used for non-commercial applications.

🍔

Thanks for these source codes porviding me with knowledges to complete this repository.

• https://github.com/deepinsight/insightface/tree/master/recognition (Official)
  • Face Analysis Project on MXNet http://insightface.ai
• https://github.com/zzh8829/yolov3-tf2
  • YoloV3 Implemented in TensorFlow 2.0
• https://github.com/ZhaoJ9014/face.evoLVe.PyTorch
  • face.evoLVe: High-Performance Face Recognition Library based on PyTorch
• https://github.com/luckycallor/InsightFace-tensorflow
  • Tensoflow implementation of InsightFace (ArcFace: Additive Angular Margin Loss for Deep Face Recognition).
• https://github.com/dmonterom/face_recognition_TF2
  • Training a face Recognizer using ResNet50 + ArcFace in TensorFlow 2.0