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ETH-XGaze baseline

Official implementation of ETH-XGaze dataset baseline.

ETH-XGaze dataset

ETH-XGaze dataset is a gaze estimation dataset consisting of over one million high-resolution images of varying gaze under extreme head poses. We established a simple baseline test on our ETH-XGaze dataset and other datasets. This repository includes the code and pre-trained model. Please find more details about the dataset on our project page. Please note this repository is not responding to the dataset download, and I will not respond to any dataset download request in this repository. Thank you for your understanding.

License

The code is under the license of CC BY-NC-SA 4.0 license

Requirement

  • Python 3.5
  • Pytorch 1.1.0, torchvision
  • opencv-python

For model training

  • h5py to load the training data
  • configparser

For testing

  • dlib for face and facial landmark detection.

Training

  • You need to download the ETH-XGaze dataset for training. After downloading the data, make sure it is the version of pre-processed 224*224 pixels face patch. Put the data under '\data\xgaze'
  • Run the python main.py to train the model
  • The model will be saved under 'ckpt' folder.

Test

The demo.py files show how to perform the gaze estimation from input image. The example image is already in 'example/input' folder.

  • First, you need to download the pre-trained model, and put it under "ckpt" folder.
  • And then, run the 'python demo.py' for test.

Data normalization

The 'normalization_example.py' gives the example of data normalization from the raw dataset to the normalized data.

Citation