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Ev-Eye: Event-based Eye Tracking with Motion Consistency

AIS 2024 Event-based Eye Tracking (CVPR Workshop 2024) 2nd Solution.

Environment

  • Ubuntu22.04
  • Python3.9
  • Nvidia GPU1080Ti

Using the following command line for the installation of dependencies rquired to trn Ev-Eye.

git clone https://github.com/MrFisher97/Solution_For_Event_based_Eye_Tracking_Challenge.git
cd Solution_For_Event_based_Eye_Tracking_Challenge
conda create -n EvEye python==3.9
conda activate EvEye
conda install pytorch=1.11 torchvision cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt

Pretrained Model

We test the model from checkpooint model_best_ep769_val_loss_0.0388.pth. The result are shown below:

Eval Test
Loss 0.0388 -
P5 acc 0.918 -
P10 acc 0.976 0.9948/0.9958
P15 acc 0.985 -
Dist 2.50 -

Instruction

Configuration

The configuration for training, evaluation and test is set on the configs/task.yaml, modify it according to your need.

Training

  • You should change the data_dir setting in configs/taks.yaml to your dataset path, then execute the following command to start training:
python train.py --config_file task.yaml
  • This process will generate the metadata and cached_dataset directories which enable the fast data loading in training.
  • The training script has already contain the testing phase using the model trained under all epoches.

Testing

  • Running test.py scripy will get the tracking prediction on test split and store the result on submission.csv file
python test.py --config_file task.yaml --checkpoint your_model_path --log_dir store_result_path

Viualization

Using the following command to visualze the tracking result on eval/test split:

python visualize --config_file task.yaml --checkpoint your_model_path --split test

Other details

Acknowledgement

This code is based on the https://github.com/EETChallenge/challenge_demo_code