Sample and Computation Redistribution for Efficient Face Detection

Introduction

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

Performance

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

ResNet family

Mobile family

X64 CPU Performance of SCRFD-0.5GF:

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

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.