SatoshiRobatoFujimoto / L2CS-Net

The official PyTorch implementation of L2CS-Net for gaze estimation and tracking

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Installation

• Set up a virtual environment:

python3 -m venv venv
source venv/bin/activate

• Install required packages:

pip install -r requirements.txt  

Demo

• Install the face detector:

pip install git+https://github.com/elliottzheng/face-detection.git@master

• Download the pre-trained models from here and Store it to models/.
• Run:

 python demo.py \
 --snapshot models/L2CSNet_gaze360.pkl \
 --gpu 0 \
 --cam 0 \

This means the demo will run using L2CSNet_gaze360.pkl pretrained model

MPIIGaze

We provide the code for train and test MPIIGaze dataset with leave-one-person-out evaluation.

Prepare datasets

• Download MPIIFaceGaze dataset from here.
• Apply data preprocessing from here.
• Store the dataset to datasets/MPIIFaceGaze.

Train

 python train.py \
 --dataset mpiigaze \
 --snapshot output/snapshots \
 --gpu 0 \
 --num_epochs 50 \
 --batch_size 16 \
 --lr 0.00001 \
 --alpha 1 \

This means the code will perform leave-one-person-out training automatically and store the models to output/snapshots.

Test

 python test.py \
 --dataset mpiigaze \
 --snapshot output/snapshots/snapshot_folder \
 --evalpath evaluation/L2CS-mpiigaze  \
 --gpu 0 \

This means the code will perform leave-one-person-out testing automatically and store the results to evaluation/L2CS-mpiigaze.

To get the average leave-one-person-out accuracy use:

 python leave_one_out_eval.py \
 --evalpath evaluation/L2CS-mpiigaze  \
 --respath evaluation/L2CS-mpiigaze  \

This means the code will take the evaluation path and outputs the leave-one-out gaze accuracy to the evaluation/L2CS-mpiigaze.

Gaze360

We provide the code for train and test Gaze360 dataset with train-val-test evaluation.

Prepare datasets

• Download Gaze360 dataset from here.

• Apply data preprocessing from here.

• Store the dataset to datasets/Gaze360.

Train

 python train.py \
 --dataset gaze360 \
 --snapshot output/snapshots \
 --gpu 0 \
 --num_epochs 50 \
 --batch_size 16 \
 --lr 0.00001 \
 --alpha 1 \

This means the code will perform training and store the models to output/snapshots.

Test

 python test.py \
 --dataset gaze360 \
 --snapshot output/snapshots/snapshot_folder \
 --evalpath evaluation/L2CS-gaze360  \
 --gpu 0 \

This means the code will perform testing on snapshot_folder and store the results to evaluation/L2CS-gaze360.