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Facial Landmark Detection

1. Quick start

(1)Clone the project

git clone https://github.com/HRNet/HRNet-Facial-Landmark-Detection.git

(2)Install dependencies

pip3 install -r requirements.txt

(3) download pre-trained model and test

python3 camera.py--cfg <CONFIG-FILE> --model-file <MODEL WEIGHT> 
# example:
python3 camera.py  --cfg experiments/face_landmark_detection_wflw_shufflenet_large.yaml  --model-file pretrained/shufflenet_plus.pth

2. Datasets

Your data directory should look like this:

.
└──data
    └── wflw
        ├── face_landmarks_wflw_test_blur.csv
        ├── face_landmarks_wflw_test.csv
        ├── face_landmarks_wflw_test_expression.csv
        ├── face_landmarks_wflw_test_illumination.csv
        ├── face_landmarks_wflw_test_largepose.csv
        ├── face_landmarks_wflw_test_makeup.csv
        ├── face_landmarks_wflw_test_occlusion.csv
        ├── face_landmarks_wflw_train.csv
        └── images

2 directories, 8 files

3. Training

python train.py --cfg <CONFIG-FILE>
# example:
python3 train.py --cfg experiments/face_alignment_wflw_hrnet_w18.yaml

4. benchmark

WFLW
NME model_size test pose illumination occlution blur makeup expression
shufflenet_plus 13.8M 4.79 8.56 4.73 5.80 5.47 4.77 5.15
HRNet 39.2M 4.60 7.86 4.57 5.42 5.36 4.26 4.78

5. project structure

.
├── data
│   └── wflw
│       ├── face_landmarks_wflw_test_blur.csv
│       ├── ...
│       ├── face_landmarks_wflw_train.csv
│       └── images
├── experiments
│   └── face_landmark_detection_wflw_shufflenet_large.yaml
├── output
│   ├── log
│   │   └── WFLW
│   └── WFLW
│       └── face_landmark_detection_wflw_shufflenet_large
├── README.md
├── requirements.txt
├── src
│   ├── datasets.py
│   ├── __init__.py
│   ├── loss.py
│   ├── models
│   │   ├── hrnet.py
│   │   ├── __init__.py
│   │   ├── shufflenet_bak.py
│   │   ├── shufflenet.py
│   │   └── utils.py
│   ├── transforms.py
│   └── utils.py
├── test.py
└── train.py  

13 directories, 28 files

6. TBD

  • face pose weighted
  • heatmap
  • graph network
  • model、dataset、loss
  • deployment
  • video stable

7. Reference

https://github.com/HRNet/HRNet-Facial-Landmark-Detection

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