Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
This repo contains the code, data and trained models for the paper Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks. Try out the Gradio Web Demo:
MTCNN is a popular algorithm for face detection that uses multiple neural networks to detect faces in images. It is capable of detecting faces under various lighting and pose conditions and can detect multiple faces in an image.
We have implemented MTCNN using the pytorch framework. Pytorch is a popular deep learning framework that provides tools for building and training neural networks.
Description of file
How to Install
conda create -n env python=3.8 -y
conda activate env
pip install -r requirements.txt
- download WIDER_FACE face detection data then store it into ./data_set/face_detection
- download CNN_FacePoint face detection and landmark data then store it into ./data_set/face_landmark
How to Run
The checkpoints will be saved in a subfolder of
Finetuning from an existing checkpoint
model path should be a subdirectory in the
./model_store/ directory, e.g.
Use the sh file to test in order
To detect a single image
To detect a video stream from a camera
The result of "--net=pnet"
The result of "--net=rnet"
The result of "--net=onet"