📢 This is cloned repository!

This repository is cloned from Tencent/FaceDetection-DSFD and modified for research, compatibility, functionality and convenience.

Update

• 2019.04: Release pytorch-version DSFD inference code.
• 2019.03: DSFD is accepted by CVPR2019.
• 2018.10: Our DSFD ranks No.1 on WIDER FACE and FDDB

Introduction

In this repo, we propose a novel face detection network, named DSFD, with superior performance over the state-of-the-art face detectors. You can use the code to evaluate our DSFD for face detection.

For more details, please refer to our paper DSFD: Dual Shot Face Detector! or poster slide!

Our DSFD face detector achieves state-of-the-art performance on WIDER FACE and FDDB benchmark.

WIDER FACE

Backbone	Easy	Medium	Hard	E2E latency (s)	Download
ResNet-152	0.967	0.952	0.905	6.26	here

Easy, Medium and Hard denote AP on WIDER FACE validation set Easy, Medium and Hard, respectively.

E2E latency denotes an end-to-end latency (= preprocess + network + TTA + postprocess).

Latency is measured with batch size 1 on RTX 2080Ti GPU and Threadripper 2950X CPU.

Confidence thresholds were set to 0.01 for both AP and latency benchmark.

FDDB

Requirements

• cudatoolkit==10.2
• cudnn==7.6
• python==3.6
• torch==1.4.0
• torchvision==0.5.0

Development Environments

Conda

• conda-py36torch14.yml is appropriate if you'd like to use this repository on conda environment
• Please refer to Managing environments — conda documentation for more details

Docker

• nvcr.io/nvidia/pytorch:19.11-py3 is appropriate if you'd like to use this repository on docker container
• Please refer to PyTorch | NVIDIA NGC for more details

Getting Started

Installation

• Clone this repository

> git clone https://github.com/swoook/dsfd.git
> cd ${DSFD_DIR}/dsfd

Demo

• Refer to demo.md for detailed instructions

WIDER FACE Validation

1. Data Preparation : Refer to data-preparation.md for detailed instructions
2. Evaluation: Refer to validation.md for detailed instructions

Qualitative Results

### Citation If you find DSFD useful in your research, please consider citing: ``` @inproceedings{li2018dsfd, title={DSFD: Dual Shot Face Detector}, author={Li, Jian and Wang, Yabiao and Wang, Changan and Tai, Ying and Qian, Jianjun and Yang, Jian and Wang, Chengjie and Li, Jilin and Huang, Feiyue}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, year={2019} } ``` ## Contact For any question, please file an issue or contact ``` Jian Li: swordli@tencent.com ```