Awesome
6D Rotation Representation for Unconstrained Head Pose Estimation
Note
- The default train dataset is
300W-LP - The default test dataset is
AFLW2000
Installation
conda create -n PyTorch python=3.11
conda activate PyTorch
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
pip install opencv-python==4.5.5.64
pip install scipy
pip install tqdm
pip install timm
Train
- Configure your dataset path in
main.pyfor training - Download IMAGENET pretrained weights
- Run
python main.py --trainfor Single-GPU training - Run
bash main.sh $ --trainfor Multi-GPU training,$is number of GPUs
Test
- Configure your dataset path in
main.pyfor testing - Run
python main.py --testfor testing
Demo
- Configure your video path in
main.pyfor visualizing the demo - Run
python main.py --demofor demo
Results
| Backbone | Epochs | Pitch | Yaw | Roll | MAE | Parameters(M) | FLOPS (B) | Throughput (images/s) |
|---|---|---|---|---|---|---|---|---|
| RepNet-A2 | 90 | 4.78 | 3.68 | 3.25 | 3.90 | 25.49 | 5.1 | 1322 |
| RepNet-B1G2* | 30 | 4.91 | 3.63 | 3.37 | 3.97 | 41.36 | 8.8 | 792 |
* means that the results are from original repo, see reference
