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pfld_106_face_landmarks

106点人脸关键点检测的PFLD算法实现

  • 😊ncnn cpp推理代码

  • 转换后的ONNX模型

  • 预训练权重

  • 性能测试

  • update GhostNet

  • update MobileNetV3

    Backbone param MACC nme Link ONNX
    MobileNetV2 1.26M 393M 4.96% v2 v2.onnx
    MobileNetV3 1.44M 201.8M 4.40% v3 v3.onnx
    MobileNetV3_Small 0.22M 13M 6.22% lite lite.onnx

测试电脑MacBook 2017 13-Inch CPU i5-3.1GHz (single core)

backbone FPS(onnxruntime cpu) Time(single face)
v2.onnx 60.9 16ms
V3.onnx 62.7 15.9ms
lite.onnx 255 3.9ms
  • Requirements
    torch=1.2.0
    torchvision
    opencv-python
    tqdm
    onnxruntime==1.2.2
    numpy
  • 数据集准备

    # 下载数据集到data/imgs下
    cd data
    python prepare.py
    # data 文件夹结构
    data/
      imgs/
      train_data/
        imgs/
        list.txt
      test_data/
        imgs/
        list.txt
  • 训练

CUDA_VISIBLE_DEVICES=0 python train.py --backbone=v3
# 可选backbone为v2 v3 lite
  • 结果 (MobileNetV2)

  • Thanks

https://github.com/polarisZhao/PFLD-pytorch

https://github.com/microsoft/onnxruntime

https://github.com/kuan-wang/pytorch-mobilenet-v3

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106点人脸关键点检测的PFLD算法实现

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