pfld_106_face_landmarks:106点人脸关键点检测的PFLD算法实现

# pfld_106_face_landmarks 106点人脸关键点检测的PFLD算法实现 - [x] 😊[ncnn cpp推理代码 ](https://github.com/Hsintao/pfld-ncnn) - [x] 转换后的ONNX模型 - [x] 预训练权重 - [x] 性能测试 - [x] update GhostNet - [x] update MobileNetV3 | Backbone | param | MACC | nme | Link | ONNX | | :---------------: | :---: | :----: | :---: | :----------------------------: | :--: | | MobileNetV2 | 1.26M | 393M | 4.96% | [v2](checkpoint/v2/v2.pth) | [v2.onnx](output/v2.onnx) | | MobileNetV3 | 1.44M | 201.8M | 4.40% | [v3](checkpoint/v3/v3.pth) | [v3.onnx](output/v3.onnx) | | MobileNetV3_Small | 0.22M | 13M | 6.22% | [lite](checkpoint/lite/lite.pth) | [lite.onnx](output/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 ```python torch=1.2.0 torchvision opencv-python tqdm onnxruntime==1.2.2 numpy ``` - 数据集准备 ```bash # 下载数据集到data/imgs下 cd data python prepare.py ``` ```bash # data 文件夹结构 data/ imgs/ train_data/ imgs/ list.txt test_data/ imgs/ list.txt ``` - 训练 ```bash 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