Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild

Note

• The default dataset is 300W
• Check convert function in utils/util.py for processing original dataset

Installation

conda create -n PyTorch python=3.8
conda activate PyTorch
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch-lts
pip install opencv-python==4.5.5.64
pip install PyYAML
pip install tqdm

Train

• Configure your dataset path in main.py for training
• Download pretrained weights, see Pretrained weights
• Run bash main.sh $ --train for training, $ is number of GPUs

Test

• Configure your dataset path in main.py for testing
• Run python main.py --test for testing

Demo

• Configure your video path in main.py for visualizing the demo
• Run python main.py --demo for demo

Results

Backbone	Epochs	Test NME	Pretrained weights
IRNet18	120	3.27	model
IRNet50	120	3.11	model
IRNet100	120	3.08	model

Reference

• https://github.com/jhb86253817/PIPNet