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cv2022final's Introduction

CV2022Final - Pupil Tracking

Getting started

# Copy dataset/ into this directory
# (Using python 3.8.13)
pip3 install -r requirements.txt

cd script/
# Use model.pth to perform prediction
python3 eval.py

# Train your own model
python3 main.py

Current results

  • model1.pth
    • Overall weighted IoU: 0.7649
    • Average true negative rate: 0.8996
    • Benchmark score: 0.8053
  • model4.pth
    • Overall weighted IoU: 0.7756
    • Average true negative rate: 0.8841
    • Benchmark score: 0.8082
  • model12.pth
    • Overall weighted IoU: 0.7745
    • Average true negative rate: 0.9966
    • Benchmark score: 0.8411
  • model14.pth
    • Overall weighted IoU: 0.9340
    • Average true negative rate: 0.9703
    • Benchmark score: 0.9449

TODO

  1. Let the model predict confidence score
  2. Deal with imbalance data (e.g. focal loss, dice loss, etc.)
  3. Add CV preprocessed mask into input
  4. Add image augmentation (albumentation library)
  5. Test-time augmentation might be useful too

Files

  • script/main.py
    • Training segmentation model
  • script/dataset.py
    • Wrapping customized PyTorch dataset
  • script/model.py
    • Get model from segmentation_models_pytorch
  • script/loss.py
    • Implement loss functions and evaluation metrics
  • script/predict.py
    • Deep learning predicting function for eval.py
  • script/predict_cv.py
    • Classical CV predicting function for eval.py
  • script/modelXYZ.pth
    • Model weights trained by data SX, SY, SZ

cv2022final's People

Contributors

joey880627 avatar

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