MTCNN_pytorch

The difference from the paper is adding batch normalization after convolution.

prerequisites

Python 3.6+
PyTorch 1.3+
Torchvision 0.4.0+ (need high version because Torchvision support nms now.)
requirements.txt

Datasets

CeleA. CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including

10,177 number of identities,
202,599 number of face images, and
5 landmark locations, 40 binary attributes annotations per image.

The dataset can be employed as the training and test sets for the following computer vision tasks: face attribute recognition, face detection, landmark (or facial part) localization, and face editing & synthesis.

Generate data

python gen_data.py 
# just change the dataset(CeleA) directory to your own in code then run it

Training

python train_pnet.py
python train_rnet.py
python train_onet.py

Webcam Demo:

python webcam_demo.py

Demo:

python demo.py

Output: