🔥 RetinaFace (RetinaFace: Single-stage Dense Face Localisation in the Wild, published in 2019) implemented (ResNet50, MobileNetV2 trained on single GPU) in Tensorflow 2.0+. This is an unofficial implementation. 🔥

RetinaFace presents a robust single-stage face detector, which performs pixel-wise face localisation on various scales of faces by taking advantages of joint extra-supervised and self-supervised multi-task learning(manually annotate five facial landmarks on the WIDER FACE). On the WIDER FACE hard test set, RetinaFace outperforms the state of the art average precision (AP) by 1.1% (achieving AP equal to 91.4%).

Original Paper:   Arxiv

Offical Implementation:   MXNet

:: Results from this reporepository. ::

📑

• Installation
• Data Preparing
• Training and Testing
• Benchmark
• 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/retinaface-tf2.git
cd retinaface-tf2

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

pip install -r requirements.txt

🍺

Step 1: Download the WIDER FACE dataset images from the download links bellow.

Step 2: Download the Retinaface official annotations (face bounding boxes & five facial landmarks) from the download links bellow.

Step 3: Extract downloaded files into ./data/widerface/. The directory structure should be like bellow.

./data/widerface/
    train/
        images/
        label.txt
    val/
        images/
        label.txt

Step 4: Convert the training images and annotations to tfrecord file with the the script bellow.

# Binary Image (recommend): need additional space
python data/convert_train_tfrecord.py --output_path="./data/widerface_train_bin.tfrecord" --is_binary=True
# Online Image Loading:
python data/convert_train_tfrecord.py --output_path="./data/widerface_train.tfrecord" --is_binary=False

Note:

• You can run python ./dataset_checker.py to check if the dataloader work. Please modify the inside setting (in ./dataset_checker.py) for different situations.

🍭

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

# general setting
batch_size: 8
input_size: 640
backbone_type: 'ResNet50'  # 'ResNet50', 'MobileNetV2'
sub_name: 'retinaface_res50'

# training dataset
dataset_path: './data/widerface_train_bin.tfrecord'
dataset_len: 12880  # number of training samples
using_bin: True
using_flip: True
using_distort: True

# testing dataset
testing_dataset_path: './data/widerface/val'

# network
out_channel: 256

# anchor setting
min_sizes: [[16, 32], [64, 128], [256, 512]]
steps: [8, 16, 32]
match_thresh: 0.45
ignore_thresh: 0.3
variances: [0.1, 0.2]
clip: False

# training setting
epoch: 100
init_lr: !!float 1e-2
lr_decay_epoch: [50, 68]
lr_rate: 0.1
warmup_epoch: 5
min_lr: !!float 1e-3

weights_decay: !!float 5e-4
momentum: 0.9

pretrain: True

save_steps: 2000

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 using_bin is used to choose the type of training data, which should be according to the data type you created in the Data-Preparing.
• The save_steps is the number interval steps of saving checkpoint file.

Train the Retinaface model by yourself, or dowload it from BenchmarkModels.

# train ResNet50 backbone model
python train.py --cfg_path="./configs/retinaface_res50.yaml" --gpu=0
# or train MobileNetV2 backbone model
python train.py --cfg_path="./configs/retinaface_mbv2.yaml" --gpu=0

Note:

• The --gpu is used to choose the id of your avaliable GPU devices with CUDA_VISIBLE_DEVICES system varaible.
• You can visualize the learning rate scheduling by running "python ./modules/lr_scheduler.py".

You can download my trained models for testing from Models without training it yourself. And, evaluate the models you got with the corresponding cfg file on the testing dataset.

Step 1: Produce txt results and visualizations from model.

# Test ResNet50 backbone model
python test_widerface.py --cfg_path="./configs/retinaface_res50.yaml" --gpu=0
# or
# Test ResNet50 backbone model
python test_widerface.py --cfg_path="./configs/retinaface_mbv2.yaml" --gpu=0

Note:

• The visualizations results would be saved into ./results/.

Step 2: Evaluate txt results. (Codes come from Here)

cd ./widerface_evaluate
python setup.py build_ext --inplace
python evaluation.py

You can detect on your image by the model. For example, detect on the image from ./data/0_Parade_marchingband_1_149.jpg as following.

python test.py --cfg_path="./configs/retinaface_res50.yaml" --img_path="./data/0_Parade_marchingband_1_149.jpg" --down_scale_factor=1.0
# or
python test.py --cfg_path="./configs/retinaface_mbv2.yaml" --img_path="./data/0_Parade_marchingband_1_149.jpg" --down_scale_factor=1.0

Note:

• You can down scale your input by the --down_scale_factor.

Demo face detection on your webcam.

python test.py --cfg_path="./configs/retinaface_res50.yaml" --webcam=True --down_scale_factor=1.0
# or
python test.py --cfg_path="./configs/retinaface_mbv2.yaml" --webcam=True --down_scale_factor=1.0

☕

Note:

• Tensorflow2 version (ResNet50/MobileNetV2) in this repository only trained with batch size (8/16) on single GPU.
• All models evaluate on original image scale.

🍩

Note:

• After dowloading these models, extract them into ./checkpoints for restoring.
• All training settings of the models can be found in the corresponding ./configs/*.yaml files.
• You can see the tensorboard traning logs in here.
• 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/detection/retinaface (Official)
  • Face Analysis Project on MXNet http://insightface.ai
• https://github.com/biubug6/Pytorch_Retinaface
  • Retinaface get 80.99% in widerface hard val using mobilenet0.25.
• https://github.com/wondervictor/WiderFace-Evaluation
  • Python Evaluation Code for Wider Face Dataset
• https://github.com/zzh8829/yolov3-tf2
  • YoloV3 Implemented in TensorFlow 2.0