/lung-xray

Stanford CS 271 Project

Primary LanguageJupyter Notebook

lung-xray

Stanford CS 271 Project

TODO:

  1. Change data to PyTorch Dataset
    • [DONE] Fill in dataset.py MyDataset
  2. Visualize/Explore data
    • viz.py
  3. Add model
    • models/pnunet.py
  4. Change loss/optimization
    • train.py
  5. Train/Tune - Try different models.

How to Run

  1. Start tensorboard using the checkpoints/ folder with tensorboard --logdir=checkpoints/
  2. Start and stop training using python train.py --checkpoint=<checkpoint name>. The code should automatically resume training at the previous epoch and continue logging to the previous tensorboard.
  3. Run python test.py --checkpoint=<checkpoint name> to get final predictions.

Directory Structure

  • checkpoints/ (Only created once you run train.py)
  • data/
  • metrics/
  • models/
    • layers/
    • ...
  • visualizers/
  • args.py (Modify default hyperparameters here)
  • dataset.py (Create Dataset here)
  • metric_tracker.py
  • models.py (You may opt to keep all your models in one place instead)
  • preprocess.py (Do any preprocessing steps you want before loading the data)
  • test.py
  • train.py
  • util.py
  • viz.py (Create more visualizations if necessary)