This repo is updated frequently, keeping up with the latest code is highly recommended.
2019.10.14
The official PyTorch version of LFFD is first online. Now the repo is only preview version. Face detection code for v2 version is released nightly.2019.10.16
Now the face detection code for v2 version can train normally. The code of other tasks will be updated soon.This repo is the official PyTorch source code of paper "LFFD: A Light and Fast Face Detector for Edge Devices". Our paper presents a light and fast face detector (LFFD) for edge devices. LFFD considerably balances both accuracy and latency, resulting in small model size, fast inference speed while achieving excellent accuracy. Understanding the essence of receptive field makes detection networks interpretable.
In practical, we have deployed it in cloud and edge devices (like NVIDIA Jetson series and ARM-based embedding system). The comprehensive performance of LFFD is robust enough to support our applications.
In fact, our method is a general detection framework that applicable to one class detection, such as face detection, pedestrian detection, head detection, vehicle detection and so on. In general, an object class, whose average ratio of the longer side and the shorter side is less than 5, is appropriate to apply our framework for detection.
Several practical advantages:
on the way
We re-implement the proposed method using PyTorch. The MXNet Version is here
Tips:
- use PyTorch with cudnn.
- build numpy from source with OpenBLAS. This will improve the training efficiency.
- make sure cv2 links to libjpeg-turbo, not libjpeg. This will improve the jpeg decode efficiency.
git clone https://github.com/becauseofAI/lffd-pytorch.git
If you benefit from our work in your research and product, please kindly cite the paper
@inproceedings{LFFD,
title={LFFD: A Light and Fast Face Detector for Edge Devices},
author={He, Yonghao and Xu, Dezhong and Wu, Lifang and Jian, Meng and Xiang, Shiming and Pan, Chunhong},
booktitle={arXiv:1904.10633},
year={2019}
}
becauseofAI[1], Yonghao He[2]
[1]E-mails: helloai777@gmail.com
[2]E-mails: yonghao.he@ia.ac.cn / yonghao.he@aliyun.com
If you are interested in this work, any innovative contributions are welcome!!!
Internship is open at NLPR, CASIA all the time. Send me your resumes!