/pytorch_mpiigaze

A PyTorch implementation of MPIIGaze

Primary LanguagePythonMIT LicenseMIT

PyTorch implementation of MPIIGaze

Requirements

Download the dataset and preprocess it

$ wget http://datasets.d2.mpi-inf.mpg.de/MPIIGaze/MPIIGaze.tar.gz
$ tar xzvf MPIIGaze.tar.gz

$ python preprocess_data.py --dataset MPIIGaze --outdir data

Usage

$ python -u main.py --arch lenet --dataset data --test_id 0 --outdir results/00

Results

Model Mean Test Angle Error [degree] Training Time
LeNet 6.43 3m40s
ResNet-preact-8 5.78 7m27s 
$ python -u main.py --arch lenet --dataset data --test_id 0 --outdir results/lenet/00 --batch_size 32 --base_lr 0.01 --momentum 0.9 --nesterov True --weight_decay 1e-4 --epochs 40 --milestones '[30, 35]' --lr_decay 0.1


$ python -u main.py --arch resnet_preact --dataset data --test_id 0 --outdir results/resnet_preact/00 --batch_size 32 --base_lr 0.1 --momentum 0.9 --nesterov True --weight_decay 1e-4 --epochs 40 --milestones '[30, 35]' --lr_decay 0.1

References

  • Xucong Zhang and Yusuke Sugano and Mario Fritz and Bulling, Andreas, "Appearance-based Gaze Estimation in the Wild," Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 arXiv:1504.02863, Project Page
  • Xucong Zhang and Yusuke Sugano and Mario Fritz and Bulling, Andreas, "MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation," arXiv:1711.09017