Sample and Computation Redistribution for Efficient Face Detection

Overview

Introduction

SCRFD is an efficient high accuracy face detection approach which initially described in Arxiv.

prcurve

Performance

Precision, flops and infer time are all evaluated on VGA resolution.

ResNet family

Method Backbone Easy Medium Hard #Params(M) #Flops(G) Infer(ms)
DSFD (CVPR19) ResNet152 94.29 91.47 71.39 120.06 259.55 55.6
RetinaFace (CVPR20) ResNet50 94.92 91.90 64.17 29.50 37.59 21.7
HAMBox (CVPR20) ResNet50 95.27 93.76 76.75 30.24 43.28 25.9
TinaFace (Arxiv20) ResNet50 95.61 94.25 81.43 37.98 172.95 38.9
- - - - - - - -
ResNet-34GF ResNet50 95.64 94.22 84.02 24.81 34.16 11.8
SCRFD-34GF Bottleneck Res 96.06 94.92 85.29 9.80 34.13 11.7
ResNet-10GF ResNet34x0.5 94.69 92.90 80.42 6.85 10.18 6.3
SCRFD-10GF Basic Res 95.16 93.87 83.05 3.86 9.98 4.9
ResNet-2.5GF ResNet34x0.25 93.21 91.11 74.47 1.62 2.57 5.4
SCRFD-2.5GF Basic Res 93.78 92.16 77.87 0.67 2.53 4.2

Mobile family

Method Backbone Easy Medium Hard #Params(M) #Flops(G) Infer(ms)
RetinaFace (CVPR20) MobileNet0.25 87.78 81.16 47.32 0.44 0.802 7.9
FaceBoxes (IJCB17) - 76.17 57.17 24.18 1.01 0.275 2.5
- - - - - - - -
MobileNet-0.5GF MobileNetx0.25 90.38 87.05 66.68 0.37 0.507 3.7
SCRFD-0.5GF Depth-wise Conv 90.57 88.12 68.51 0.57 0.508 3.6

X64 CPU Performance of SCRFD-0.5GF:

Test-Input-Size CPU Single-Thread Easy Medium Hard
Original-Size(scale1.0) - 90.91 89.49 82.03
640x480 28.3ms 90.57 88.12 68.51
320x240 11.4ms - - -

precision and infer time are evaluated on AMD Ryzen 9 3950X, using the simple PyTorch CPU inference by setting OMP_NUM_THREADS=1 (no mkldnn).

Installation

Please refer to mmdetection for installation.

  1. Install mmcv. (mmcv-full==1.2.6 and 1.3.3 was tested)
  2. Install build requirements and then install mmdet.
    pip install -r requirements/build.txt
    pip install -v -e .  # or "python setup.py develop"
    

Pretrained-Models

Name Easy Medium Hard FLOPs Params(M) Infer(ms) Link
SCRFD_500M 90.57 88.12 68.51 500M 0.57 3.6 download
SCRFD_1G 92.38 90.57 74.80 1G 0.64 4.1 download
SCRFD_2.5G 93.78 92.16 77.87 2.5G 0.67 4.2 download
SCRFD_10G 95.16 93.87 83.05 10G 3.86 4.9 download
SCRFD_34G 96.06 94.92 85.29 34G 9.80 11.7 download
SCRFD_500M_KPS 90.97 88.44 69.49 500M 0.57 3.6 download
SCRFD_2.5G_KPS 93.80 92.02 77.13 2.5G 0.82 4.3 download
SCRFD_10G_KPS 95.40 94.01 82.80 10G 4.23 5.0 download

mAP, FLOPs and inference latency are all evaluated on VGA resolution. _KPS means the model includes 5 keypoints prediction.

Convert to ONNX

Please refer to tools/scrfd2onnx.py

Generated onnx model can accept dynamic input as default.

You can also set specific input shape by pass --shape 640 640, then output onnx model can be optimized by onnx-simplifier.

Inference

Put your input images or videos in ./input directory. The output will be saved in ./output directory. In root directory of project, run the following command for image:

python inference_image.py --input "./input/test.jpg"

and for video:

python inference_video.py --input "./input/obama.mp4"

Use -sh for show results during code running or not

Note that you can pass some other arguments. Take a look at inference_video.py file.

Owner
Sajjad Aemmi
AI MSc Student at Ferdowsi University of Mashhad - Teacher - Machine Learning Engineer - WebDeveloper - Graphist
Sajjad Aemmi
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