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Gaze Target Estimation (Gaze-LLE)

How to Run

  1. set up a virtual environment.

    conda create -n gazelle -y python=3.11
conda activate gazelle
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu129
pip install transformers
pip install onnxsim
pip install onnx
pip install onnxscript
pip install opencv-python
pip install ultralytics
pip install timm
pip install scikit-learn

  2. download pretrained checkpoints.

    mkdir -p checkpoints
wget https://github.com/YapaLab/yolo-face/releases/download/v0.0.0/yolov12n-face.pt -P checkpoints
wget https://github.com/fkryan/gazelle/releases/download/v1.0.0/gazelle_dinov2_vitb14.pt -P checkpoints
wget https://github.com/fkryan/gazelle/releases/download/v1.0.0/gazelle_dinov2_vitb14_inout.pt -P checkpoints
wget https://github.com/fkryan/gazelle/releases/download/v1.0.0/gazelle_dinov2_vitl14.pt -P checkpoints
wget https://github.com/fkryan/gazelle/releases/download/v1.0.0/gazelle_dinov2_vitl14_inout.pt -P checkpoints

  3. check pytorch model inference

    cd ..
python infer.py

How to Run (TensorRT)

  1. generate onnx file

    python onnx_export.py

  2. generate tensorrt model

    python onnx2trt.py