This repository is cloned from Tencent/FaceDetection-DSFD and modified for research, compatibility, functionality and convenience.
- 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
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.
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.
cudatoolkit==10.2
cudnn==7.6
python==3.6
torch==1.4.0
torchvision==0.5.0
- 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
- 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
- Clone this repository
> git clone https://github.com/swoook/dsfd.git
> cd ${DSFD_DIR}/dsfd
- Refer to demo.md for detailed instructions
- Data Preparation : Refer to data-preparation.md for detailed instructions
- Evaluation: Refer to validation.md for detailed instructions