Face Detection in Realtime

This repository is the implementation of face detection in real time using YOLOv3 framework with keras(tensorflow backend). For using in embeded devices, so I choose a computation-efficient CNN architecture named ShuffleNet version 2 and train it from scratch(about 50 epoches) on FDDB Datasets.

For some reasons,I just public the pre_trained weights, inference code and network architecture, if you want to know more,please feel free to drop a comment or contact me.

1.Requirements

• tensorflow
• keras
• cv2
• dlib(optional)
• basic packages, e.g. numpy, matplotlib,etc.

2. Usage

• there is only one parameter should be noticed, i.e. pre-trained model path, run python detect_realtime.py -m path_to_pretrained_model (default './weights/shufflenetv2.h5' for this repo), another parameter is video,the video path. script for video.
• or follow detect_realtime_instruction.ipynb for more detail, notebook for picture.

3.References

• (repo) YOLOv3
• (repo) ShuffleNetV2
• (paper) YOLOv3: An Incremental Improvement
• (paper) ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

4. Appendix

• shufflenetV2 for face detection architecture