🚧This repo is no longer maintained, it can be seamlessly replaced with the new repo batch-face with the exact same usage.🚧
The batch-face provides more utility for face detection, face alignment and face reconstruction.
Fast and reliable face detection with RetinaFace.
This repo provides the out-of-box RetinaFace detector.
- Python 3.5+ (it may work with other versions too)
- Linux, Windows or macOS
- PyTorch (>=1.0)
While not required, for optimal performance it is highly recommended to run the code using a CUDA enabled GPU.
The easiest way to install it is using pip:
pip install git+https://github.com/elliottzheng/face-detection.git@masterfrom skimage import io
from face_detection import RetinaFace
detector = RetinaFace()
img= io.imread('examples/obama.jpg')
faces = detector(img)
box, landmarks, score = faces[0]In order to specify the device (GPU or CPU) on which the code will run one can explicitly pass the device id.
from face_detection import RetinaFace
# 0 means using GPU with id 0 for inference
# default -1: means using cpu for inference
detector = RetinaFace(gpu_id=0) | GPU(GTX 1080TI,batch size=1) | GPU(GTX 1080TI，batch size=750) | CPU(Intel(R) Core(TM) i7-7800X CPU @ 3.50GHz) | |
|---|---|---|---|
| FPS | 44.02405810720893 | 96.64058005582535 | 15.452635835550483 |
| SPF | 0.022714852809906007 | 0.010347620010375976 | 0.0647138786315918 |
All the input images must of the same size.
Detector with CUDA process batch input faster than the same amount of single input.
from skimage import io
from face_detection import RetinaFace
detector = RetinaFace()
img= io.imread('examples/obama.jpg')
all_faces = detector([img,img]) # return faces list of all images
box, landmarks, score = all_faces[0][0]
- Network and pretrained model are from biubug6/Pytorch_Retinaface
@inproceedings{deng2019retinaface,
title={RetinaFace: Single-stage Dense Face Localisation in the Wild},
author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos},
booktitle={arxiv},
year={2019}