EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection, a real-time CPU face detector.
Joonhyun Jeong1,2, Beomyoung Kim1,2, Joonsang Yu1,3, YoungJoon Yoo1
1 NAVER Cloud, ImageVision
2 KAIST
3 NAVER AI Lab
- Comparison with other CPU-real time & GPU face detectors on WiderFace.
| Easy MAP | Medium MAP | hard MAP | Latency (480x640) | |
|---|---|---|---|---|
| MTCNN | 0.851 | 0.820 | 0.607 | 4.0ms |
| FaceBoxes | 0.885 | 0.862 | 0.773 | 35.7 ms |
| RetinaFace (MobileNetV1) | 0.886 | 0.870 | 0.809 | 58.5ms |
| EResFD-1x | 0.890 | 0.879 | 0.804 | 37.7 ms |
- download WIDER face dataset
python3 prepare_wider_data.py
according to yourself dataset path,modify data/config.py On widerface,
python3 test_wider.py --wider_root [widerface_directory] --model ./weights/eresfd_16.pth
you can test yourself image
python3 demo.py --model ./weights/eresfd_16.pth
If you find that this project helps your research, please consider citing as below:
@article{jeong2022eresfd,
title={EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection},
author={Jeong, Joonhyun and Kim, Beomyoung and Yu, Joonsang and Yoo, Youngjoon},
journal={arXiv preprint arXiv:2204.01209},
year={2022}
}
EResFD
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