Face-Detector with landmark
Features
- Retinaface-mobile0.25 model converted into ncnn python/ opencv onnx/ pytorch python
- Face-Detector-1MB slim
- 5 key points of face detection
- Support onnx export
- Network parameter and flop calculation
lightweight face detector with keypoint detection
Provides a series of face detectors suitable for mobile deployment including key face detectors: Modified the anchor size of Retinaface-mobile0.25 to make it more suitable for edge computing; Reimplemented Face-Detector-1MB and added key point detection and ncnn C++ The deployment function, in most cases, the accuracy is better than the original version.
Requirments
- Ubuntu18.04
- Python3.7
- opencv
- numpy
Accuracy
Widerface test
- Accuracy in wider face val (single-scale input resolution: 320*240）
| method | Easy | Medium | Hard |
|---|---|---|---|
| libfacedetection v1（caffe） | 0.65 | 0.5 | 0.233 |
| libfacedetection v2（caffe） | 0.714 | 0.585 | 0.306 |
| version-slim(original) | 0.765 | 0.662 | 0.385 |
| version-RFB(original) | 0.784 | 0.688 | 0.418 |
| version-slim(our) | 0.795 | 0.683 | 0.34.5 |
| version-RFB(our) | 0.814 | 0.710 | 0.363 |
| Retinaface-Mobilenet-0.25(our) | 0.811 | 0.697 | 0.376 |
- Accuracy in wider face val (single-scale input resolution:：640*480）
| method | Easy | Medium | Hard |
|---|---|---|---|
| libfacedetection v1（caffe） | 0.741 | 0.683 | 0.421 |
| libfacedetection v2（caffe） | 0.773 | 0.718 | 0.485 |
| version-slim(original) | 0.757 | 0.721 | 0.511 |
| version-RFB(original) | 0.851 | 0.81 | 0.541 |
| version-slim(our) | 0.850 | 0.808 | 0.595 |
| version-RFB(our) | 0.865 | 0.828 | 0.622 |
| Retinaface-Mobilenet-0.25(our) | 0.873 | 0.836 | 0.638 |
ps: When testing, the long side is 320 or 640, and the image is scaled in equal proportions.
Parameter and flop
| method | parameter(M) | flop(M) |
|---|---|---|
| version-slim(our) | 0.343 | 98.793 |
| version-RFB(our) | 0.359 | 118.435 |
| Retinaface-Mobilenet-0.25(our) | 0.426 | 193.921 |
ps: 320*240 as input
Python inference
coming soon
References
@inproceedings{deng2019retinaface,
title={RetinaFace: Single-stage Dense Face Localisation in the Wild},
author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos},
booktitle={arxiv},
year={2019}