L2CS-Net: Fine-Grained Gaze Estimation

Gaze Estimation

Structure

• nn.py: Defines the L2CS neural network architecture.
• util.py: Contains utility functions and classes.
• datasets.py: Handles data loading, preprocessing, and augmentation.
• main.py: The main executable script that sets up the model, performs training,testing, and inference.

Installation

conda create -n PyTorch python=3.9
conda activate PyTorch
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
pip install opencv-python==4.5.5.64
pip install scipy
pip install tqdm
pip install timm

Gaze360 Dataset Preparation

• Download Gaze360 dataset from here.

• Apply data preprocessing from here.

Train

• Configure your dataset path in main.py for training
• Run python main.py --train for Single-GPU training
• Run bash main.sh $ --train for Multi-GPU training, $ is number of GPUs

Test

• Configure your dataset path in main.py for testing
• Run python main.py --test for testing

Demo

• Configure your video path in main.py for visualizing the demo
• Run python main.py --demo for demo

Results

Backbone	Epochs	MAE	Model
ResNet18	180	10.6	weight
ResNet18*	180	11.2

* means that the results are from original repo, see reference

Reference

• https://github.com/Ahmednull/L2CS-Net