2019.07.25
This repos is first online. Face detection code and trained models are released.2019.08.15
This repos is formally released. Any advice and error reports are sincerely welcome.2019.08.22
face_detection: latency evaluation on TX2 is added.2019.08.25
face_detection: RetinaFace-MobileNet-0.25 is added for comparison (both accuracy and latency).2019.09.09
LFFD is ported to NCNN (link) and MNN (link) by SyGoing, great thanks to SyGoing.2019.09.10
face_detection: important bug fix: vibration offset should be subtracted by shift in data iterator. This bug may result in lower accuracy, inaccurate bbox prediction and bbox vibration in test phase.
We will upgrade v1 and v2 as soon as possible (should have higher accuracy and more stable).2019.09.17
face_detection: model v2 is upgraded! After fixing the bug, we have fine-tuned the old v2 model. The accuracy on
WIDER FACE is improved significantly! Please try new v2.2019.09.18
pedestrian_detection: preview version of model v1 for Caltech Pedestrian Dataset is released.2019.09.23
head_detection: model v1 for brainwash dataset is released.2019.10.02
license_plate_detection: model v1 for CCPD dataset is released. (The accuracy is very high and the latency is very short! Have a try.)2019.10.02
Currently, we have provided some application-oriented detectors. Subsequently, we will put most energy to
next generation framework for single-class detection. Any feedback is welcome.2019.10.16
face_detection: the preview of PyTorch version is ready (link). Any feedback is welcome.2019.10.16
Tips: data preparation is important, irrational values of (x,y,w,h) may introduce nan in training; we
trained models with convs followed by BNs. But we found that the convergence is not stable, and can not reach a good point.2019.11.08
face_detection: caffe version of LFFD is provided by vicwer (great thanks). Guys who are familiar with caffe can navigate to /face_detection/caffemodel
for details.2020.03.27
license_plate_detection: model v1_small for CCPD dataset is released. v1_small has much less parameters than v1, hence it is much faster.
The AP of v1_small is 0.982 (vs v1-0.989). Please check README.md. Besides, a commercial-ready license plate recognition repo which adopted LFFD as the detector is hightly recommended!This repo releases the 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:
We train LFFD on train set of WIDER FACE benchmark. All methods are evaluated on val/test sets under the SIO schema (please refer to the paper for details).
Method | Easy Set | Medium Set | Hard Set |
---|---|---|---|
DSFD | 0.949(0.966) | 0.936(0.957) | 0.850(0.904) |
PyramidBox | 0.937(0.961) | 0.927(0.950) | 0.867(0.889) |
S3FD | 0.923(0.937) | 0.907(0.924) | 0.822(0.852) |
SSH | 0.921(0.931) | 0.907(0.921) | 0.702(0.845) |
FaceBoxes | 0.840 | 0.766 | 0.395 |
FaceBoxes3.2× | 0.798 | 0.802 | 0.715 |
LFFD | 0.910 | 0.881 | 0.780 |
Method | Easy Set | Medium Set | Hard Set |
---|---|---|---|
DSFD | 0.947(0.960) | 0.934(0.953) | 0.845(0.900) |
PyramidBox | 0.926(0.956) | 0.920(0.946) | 0.862(0.887) |
S3FD | 0.917(0.928) | 0.904(0.913) | 0.821(0.840) |
SSH | 0.919(0.927) | 0.903(0.915) | 0.705(0.844) |
FaceBoxes | 0.839 | 0.763 | 0.396 |
FaceBoxes3.2× | 0.791 | 0.794 | 0.715 |
LFFD | 0.896 | 0.865 | 0.770 |
Method | Disc ROC curves score |
---|---|
DFSD | 0.984 |
PyramidBox | 0.982 |
S3FD | 0.981 |
SSH | 0.977 |
FaceBoxes3.2× | 0.905 |
FaceBoxes | 0.960 |
LFFD | 0.973 |
In the paper, three hardware platforms are used for latency evaluation: NVIDIA GTX TITAN Xp, NVIDIA TX2 and Rasberry Pi 3 Model B+ (ARM A53).
We report the latency of inference only (for NVIDIA hardwares, data transfer is included), excluding pre-processing and post-processing. The batchsize is set to 1 for all evaluations.
Resolution-> | 640×480 | 1280×720 | 1920×1080 | 3840×2160 |
---|---|---|---|---|
DSFD | 78.08ms(12.81 FPS) | 187.78ms(5.33 FPS) | 392.82ms(2.55 FPS) | 1562.50ms(0.64 FPS) |
PyramidBox | 50.51ms(19.08 FPS) | 143.34ms(6.98 FPS) | 331.93ms(3.01 FPS) | 1344.07ms(0.74 FPS) |
S3FD | 21.75ms(45.95 FPS) | 55.73ms(17.94 FPS) | 119.53ms(8.37 FPS) | 471.31ms(2.21 FPS) |
SSH | 22.44ms(44.47 FPS) | 55.29ms(18.09 FPS) | 118.43ms(8.44 FPS) | 463.10ms(2.16 FPS) |
FaceBoxes3.2× | 6.80ms(147.00 FPS) | 12.96ms(77.19 FPS) | 25.37ms(39.41 FPS) | 111.98ms(8.93 FPS) |
LFFD | 7.60ms(131.40 FPS) | 16.37ms(61.07 FPS) | 31.27ms(31.98 FPS) | 87.79ms(11.39 FPS) |
Resolution-> | 160×120 | 320×240 | 640×480 |
---|---|---|---|
FaceBoxes3.2× | 11.20ms(89.29 FPS) | 19.62ms(50.97 FPS) | 72.74ms(13.75 FPS) |
LFFD | 7.30ms(136.99 FPS) | 19.64ms(50.92 FPS) | 64.70ms(15.46 FPS) |
Resolution-> | 160×120 | 320×240 | 640×480 |
---|---|---|---|
FaceBoxes3.2× | 167.20ms(5.98 FPS) | 686.19ms(1.46 FPS) | 3232.26ms(0.31 FPS) |
LFFD | 118.45ms(8.44 FPS) | 409.19ms(2.44 FPS) | 4114.15ms(0.24 FPS) |
On NVIDIA platform, TensorRT is the best choice for inference. So we conduct additional latency evaluations using TensorRT (the latency is dramatically decreased!!!). As for ARM based platform, we plan to use MNN and Tengine for latency evaluation. Details can be found in the sub-project face_detection.
We implement the proposed method using MXNet Module API.
Tips:
- use MXNet 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/YonghaoHe/A-Light-and-Fast-Face-Detector-for-Edge-Devices.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}
}
Yonghao He
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!