Do Face Detection on Coolpi with FastDeploy

zhengbicheng

Brief Introduction

With the improvement of AI computing power, Face Detection algorithms have gradually shifted from machine learning to deep learning. Through this tutorial, you will learn how to use FastDeploy to quickly implement face detection on Coolpi.

What is SCRFD

SCRFD is an efficient high accuracy face detection approach which initially described in Arxiv, and accepted by ICLR-2022. In order to facilitate you to quickly understand the parameters of the model, we only give the parameters of common models. If you need to view the detailed data of the model, please go to InsightFace's official Github.

Name	Easy	Medium	Hard	FLOPs	Params(M)
SCRFD_500M_KPS	90.97	88.44	69.49	500M	0.57
SCRFD_2.5G_KPS	93.80	92.02	77.13	2.5G	0.82
SCRFD_10G_KPS	95.40	94.01	82.80	10G	4.23

Workflow

# Download CoolPI-AI
git clone https://github.com/yanyitech/coolpi-ai.git

# Go to face detection demo
cd example/face_detection

# Build and make
mkdir build
cd build
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../third_party/fastdeploy-develop
make -j8

# Download model and picture
wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/scrfd_500m_bnkps_shape640x640_rknpu2.zip
unzip scrfd_500m_bnkps_shape640x640_rknpu2.zip
wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg

# Run
./infer_with_scrfd scrfd_500m_bnkps_shape640x640_rknpu2/scrfd_500m_bnkps_shape640x640_rk3588_quantized.rknn \
              test_lite_face_detector_3.jpg \
              1

How to transform the model

If you need to convert your ONNX model to RKNN model, FastDeploy also provides the corresponding method. You only need to modify the configuration file of the model in the tools/rknpu2/config to achieve rapid model conversion

git clone https://github.com/PaddlePaddle/FastDeploy.git
wget  https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/scrfd_500m_bnkps_shape640x640.zip
unzip scrfd_500m_bnkps_shape640x640.zip
python  /Path/To/FastDeploy/tools/rknpu2/export.py \
        --config_path tools/rknpu2/config/scrfd_quantized.yaml \
        --target_platform rk3588

Result

Input Data

Output Data