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

Google Drive: https://drive.google.com/drive/folders/1aDEr-jOR1JeqiV-N49DuED27sSKuCKwu?usp=drive_link

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

MrFisher97 / Solution_For_Event_based_Eye_Tracking_Challenge