Cite as:

@article{zemblys2018gazeNet,
  title={gazeNet: End-to-end eye-movement event detection with deep neural networks},
  author={Zemblys, Raimondas and Niehorster, Diederick C and Holmqvist, Kenneth},
  journal={Behavior research methods},
  year={2018},
}

gazeNet was developed on linux using Python 2.7 and PyTorch 0.2.0_4. Other required packages: numpy, pandas, tensorboard, Levenshtein, scikit-learn

To train a new gazeNet model you will need your own coded eye-movement data or alternatively you can use Lund2013 dataset.

If you use your own dataset, convert it to ETData format and copy training and validation sets to separate folders in ./logdir/MODEL_DIR/data. See Config file below for further setup. Also check ./utils_lib/data_prep/tt_split.py for an example how to convert your data to ETData format.

In case you want to use Lund2013 dataset:

• navigate to ./etdata and run git clone https://github.com/richardandersson/EyeMovementDetectorEvaluation.git. This will download manually coded eye-movement data together with the software used to code that data.

• run ./utils_lib/data_prep/tt_split.py. Script will convert data from matlab to ETData format and save it to ./etdata/lund2013_npy. In addition it will split dataset into training, validation and testing sets and save it as pickled lists to ./etdata/gazeNet_data.

• from ./etdata/gazeNet_data copy data.unpaired_clean.pkl and data.val_clean.pkl to ./logdir/MODEL_DIR/data.

• from download data.gen.pkl and place it to ./logdir/MODEL_DIR/data

To train a new model run train.py. Script takes the following arguments:

  --model_dir MODEL_DIR
                        Directory in which to store the logging
  --num_workers NUM_WORKERS
                        Number of workers used in dataloading
  --num_epochs NUM_EPOCHS
                        Number of epochs to train
  --seed SEED           seed

For example run: python train.py --model_dir MODEL_DIR --num_epochs 20

This will train a model for 20 epochs and save it to ./logdir/MODEL_DIR/models. MODEL_DIR directory needs to contain config.json file and data directory that stores training and validation data.

Config file is a json file that describes model architecture, training parameters, and datasets. For an example file see ./logdir/model_final/config.json.

The following variables in config.json define training and validatation datasets. It can be a pickled lists of ETData arrays or folders with numpy files in ETData format (the later is experimental and was not extensively tested). NOTE that current version of the code uses 2 validation sets!

"data_train": ["data.gen.pkl"], 
"data_val": ["data.val_clean.pkl"], 
"data_train_gen": ["data.unpaired_clean.pkl"]

Training data that was used in the paper can be downloaded from , while validation datasets can be generated using ./utils_lib/data_prep/tt_split.py script (see Preparing Lund2013 dataset above ).

TODO: decribe other parameters

To run gazeNet use run_gazeNet.py. Script takes the following parameters:

positional arguments:
  root                  Root for datasets
  dataset               Dataset

optional arguments:
  -h, --help            show this help message and exit
  --model_dir MODEL_DIR
                        Directory in which to store the logging
  --model_name MODEL_NAME
                        Model
  --num_workers NUM_WORKERS
                        Number of workers used in dataloading
  --save_pred           Whether to save predictions; not currently used

For example to parse lund2013 dataset run: python run_gazeNet.py etdata lund2013_npy

By default the script uses the pretrained model - logdir/model_final/models/gazeNET_0004_00003750.pth.tar - the model that was used in the paper. The output, together with scanpaths and time series plots, will be saved to ./etdata/lund2013_npy_gazeNet.

To use your own trained model run: python run_gazeNet.py etdata lund2013_npy --model_dir MODEL_DIR --model_name MODEL_NAME where MODEL_DIR and MODEL_NAME are the directory and the file name of your model.