PFLD - Ultralight - 400FPS(CPU)

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!!2022年结合MobileOne的性能更加优异的人脸关键点检测算法已开源->PFLD_GhostOne

实现功能

• PFLD的训练/测试/评估/ncnn C++推理
• PFLD_Ulitralight的训练/测试/评估/ncnn C++推理
• 人脸98个关键点检测
• 支持onnx导出

基于PFLD进行优化的超轻量级人脸关键点检测器

提供了一系列适合移动端部署的人脸关键点检测器： 对PFLD网络的基础结构进行优化，并调整网络结构，使其更适合边缘计算，而且在绝大部分情况下精度均好于原始PFLD网络，在CPU下运行最快可达到400fps。

运行环境

• Ubuntu 16.04
• Python 3.7
• Pytorch 1.4
• CUDA 10.1

如果想省去环境搭建工作，可以使用以下Docker环境: Docker_PFLD

精度

WFLW测试结果

输入大小为112x112

Model	Width	NME	Inference Time(NCNN 1xCore)	Model Size
PFLD	0.25 1	0.06159 0.05837	5.5ms 39.7ms	0.45M 5M
PFLD_Ultralight	0.25 1	0.06101 (↓0.94%) 0.05749 (↓1.51%)	3.6ms (↓34.5%) 18.6ms (↓53.1%)	0.41M (↓8.89%) 3.40M (↓32.0%)
PFLD_Ultralight_Slim	0.25 1	0.06258 (↑1.61%) 0.05627 (↓3.60%)	3.3ms (↓40.0%) 16.0ms (↓59.7%)	0.38M (↓15.6%) 3.10M (↓38.0%)

输入大小为96x96

Model	Width	NME	Inference Time(NCNN 1xCore)	Model Size
PFLD	0.25 1	0.06136 0.05818	4.1ms 29.2ms	0.43M 4.8M
PFLD_Ultralight	0.25 1	0.06321 (↑3.0%) 0.05745 (↓1.25%)	2.7ms (↓34.1%) 14.0ms (↓52.1%)	0.39M (↓9.30%) 3.20M (↓33.3%)
PFLD_Ultralight_Slim	0.25 1	0.06402 (↑4.33%) 0.05683 (↓2.32%)	2.5ms (↓40.0%) 12.0ms (↓59.7%)	0.37M (↓14.0%) 2.90M (↓39.6%)

Installation

Clone and install:

• git clone https://github.com/AnthonyF333/PFLD_UltraLight.git
• cd ./PFLD_UltraLight
• pip install -r requirement.txt
• Pytorch version 1.4.0 are needed.
• Codes are based on Python 3.7

Data:

• Download WFLW dataset:
  • WFLW.zip
• Move the WFLW.zip to ./data/ directory and unzip the WFLW.zip
• Run SetPreparation.py to generate the training and test data. 　　
• By default, it repeats 10 times for every image for augmentation, and save results in ./data/test_data/ and ./data/train_data/ directory. 　　

Training

Before training, check or modify network configuration (e.g. batch_size, epoch and steps etc..) in config.py.

• MODEL_TYPE: you can choose PFLD, PFLD_Ultralight or PFLD_Ultralight_Slim for different network.
• WIDTH_FACTOR: you can choose 1 for original network or 0.25 for narrower network.
• TRAIN_DATA_PATH: the path of training data, by default it is ./data/train_data/list.txt which is generate by SetPreparation.py.
• VAL_DATA_PATH: the path of validation data, by default it is ./data/test_data/list.txt which is generate by SetPreparation.py.

After modify the configuration, run train.py to start training.

Test

Before test, modify the configuration in test.py, include the model_path, test_dataset, model_type, width_factor etc. Then run test.py to align the images in the test dataset, and save results in ./test_result directory.

C++ inference ncnn

1. Generate the onnx file: Modify the model_type, width_factor, model_path in pytorch2onnx.py, and then run pytorch2onnx.py to generate the xxx.onnx file.
2. Simplify the onnx file: First run "pip3 install onnx-simplifier" to install the tool, then run "python3 -m onnxsim xxx.onnx xxx_sim.onnx" to simpify the onnx file.
3. Transform onnx to ncnn format: Run "./onnx2ncnn xxx_sim.onnx xxx_sim.param xxx_sim.bin" to generate the ncnn files.

References

• PFLD
• GhostNet

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