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https://github.com/elliottzheng/face-detection
Fast and reliable face detection with RetinaFace.PyTorch
https://github.com/elliottzheng/face-detection
face-detection python pytorch retinaface
Last synced: 4 months ago
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Fast and reliable face detection with RetinaFace.PyTorch
- Host: GitHub
- URL: https://github.com/elliottzheng/face-detection
- Owner: elliottzheng
- License: mit
- Created: 2020-01-14T05:23:02.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-12-16T13:08:34.000Z (about 1 year ago)
- Last Synced: 2024-10-14T14:49:06.662Z (4 months ago)
- Topics: face-detection, python, pytorch, retinaface
- Language: Python
- Homepage:
- Size: 1.72 MB
- Stars: 114
- Watchers: 6
- Forks: 24
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## 🚧This repo is no longer maintained, it can be seamlessly replaced with the new repo [batch-face](https://github.com/elliottzheng/batch-face) with the exact same usage.🚧
**The [batch-face](https://github.com/elliottzheng/batch-face) provides more utility for face detection, face alignment and face reconstruction.**
# Face Detection
Fast and reliable face detection with [RetinaFace](https://arxiv.org/abs/1905.00641).
This repo provides the out-of-box RetinaFace detector.
### Requirements
- 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.
## Install
The easiest way to install it is using pip:
```bash
pip install git+https://github.com/elliottzheng/face-detection.git@master
```
## Usage
##### Detect face and five landmarks on single image
```python
from skimage import io
from face_detection import RetinaFace
detector = RetinaFace()
img= io.imread('examples/obama.jpg')
faces = detector(img)
box, landmarks, score = faces[0]
```
##### Running on CPU/GPU
In order to specify the device (GPU or CPU) on which the code will run one can explicitly pass the device id.
```python
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 |
##### Batch input for faster detection
All the input images must of the same size.
**Detector with CUDA process batch input faster than the same amount of single input.**
```python
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]
```
## Reference
- Network and pretrained model are from [biubug6/Pytorch_Retinaface](https://github.com/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}
```