PFLD_GhostOne: PFLD+GhostNet+MobileOne
An ultralight face landmark detector based on PFLD, GhostNet and MobileOne
This project supplies a better face landmark detector that is suitable for embedding devices: PFLD_GhostOne which is more suitable for edge computing. In most cases, its accuracy is better than the original PFLD model, and its speed is around 55% faster than the original PFLD model.
The proposed GhostOne module is show below:
More details about GhostOne can be found on this blog
Implementation
- Support training, testing and ONNX inference of PFLD_GhostOne model
- Support detecting 98 facial landmarks
- Support exporting to ONNX format
Dependencies
- Ubuntu 18.04
- Python 3.9
- Pytorch 1.11.0
- CUDA 11.3
WFLW Test Result
Model input size is 112x112
| Model | NME | OpenVino Latency(ms) | NCNN Latency(ms) | ONNX Model Size(MB) |
|---|---|---|---|---|
| PFLD | 0.05438 | 1.65(CPU) 2.78(GPU) | 5.4(CPU) 5.1(GPU) | 4.66 |
| PFLD-GhostNet | 0.05347 | 1.79(CPU) 2.55(GPU) | 2.9(CPU) 5.3(GPU) | 3.09 |
| PFLD-GhostNet-Slim | 0.05410 | 2.11(CPU) 2.54(GPU) | 2.7(CPU) 5.2(GPU) | 2.83 |
| PFLD-GhostOne | 0.05207 | 1.79(CPU) 2.18(GPU) | 2.4(CPU) 5.0(GPU) | 2.71 |
The latency is the average time of running 1000 times on 11th Gen Intel(R) Core(TM) i5-11500
Model Zoo
| Model | Pretrained Model |
|---|---|
| PFLD | pfld_best.pth |
| PFLD-GhostNet | pfld_ghostnet_best.pth |
| PFLD-GhostNet-Slim | pfld_ghostnet_slim_best.pth |
| PFLD-GhostOne | pfld_ghostone_best.pth |
Installation
Clone and install:
- git clone https://github.com/AnthonyF333/PFLD_GhostOne.git
- cd ./PFLD_GhostOne
- pip install -r requirement.txt
- Pytorch version 1.11.0 are needed.
- Codes are based on Python 3.9
Data:
- Download WFLW dataset:
- 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 80 times for every image for augmentation, and save images in ./data/test_data_repeat80/ and ./data/train_data_repeat80/ 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_GhostNet or PFLD_GhostNet_Slim or PFLD_GhostOne for different network.
- TRAIN_DATA_PATH: the path of training data, by default it is ./data/train_data_repeat80/list.txt which is generate by SetPreparation.py.
- VAL_DATA_PATH: the path of validation data, by default it is ./data/test_data_repeat80/list.txt which is generate by SetPreparation.py.
After modify the configuration, run train.py to start training.
Export to ONNX
Modify the model_type and model_path in pytorch2onnx.py, and then run pytorch2onnx.py to generate the xxx.onnx file. It will optimize and simplify the ONNX file.
ONNX inference
Before test the model, modify the configuration in onnx_inference.py, include the pfld_onnx_model, test_folder, save_result_folder etc. Then run onnx_inference.py to detect and align the images in the test_folder, and save results in save_result_folder directory.