Solution for RANZCR CLiP - Catheter and Line Position Challenge

Source code of 22nd place solution for RANZCR CLiP - Catheter and Line Position Challenge.

Solution

Key points:

• EfficientNet
• 1024x1024 image resolution
• Soft pseudo labels
• Some MLOps for training and making a submission

Experiments

The progress of the solution during the competition can be seen in the laboratory journal. It describes all the single models and ensembles and shows CV, Public/Private LB scores.

Link: https://docs.google.com/spreadsheets/d/112wrfuQjNXEFyqQLVhu79Vf0uOabnZ1MaayEts2Gvto/edit?usp=sharing

Experiments: Ensembles:

Quick setup and start

Requirements

• Nvidia drivers >= 460, CUDA >= 11.2
• Docker, nvidia-docker

The provided Dockerfile is supplied to build an image with CUDA support and cuDNN.

Preparations

• Clone the repo.

  git clone git@github.com:lRomul/ranzcr-clip.git
  cd ranzcr-clip

• Download and extract dataset to the data folder.

Run

Batch size tuned for RTX 3090.

• Train first stage models

  ./train.sh b7v3_001 2 0,1 all  # ./train.sh EXPERIMENT N_DEVICES DEVICES FOLDS
  ./train.sh b6v3_001  # default settings: ./train.sh EXPERIMENT all all all

• Make soft pseudo labels

  make COMMAND="python predict.py --experiment b7v3_001"
  make COMMAND="python predict_val.py --experiment b7v3_001"
  make COMMAND="python predict.py --experiment b6v3_001"
  make COMMAND="python predict_val.py --experiment b6v3_001"

• Train second stage models

  ./train.sh kdb3v3_b71_001
  ./train.sh kdb4v3_b61_002
  ./train.sh kdb4v3_b71_001

• Make submission

  cd data/ranzcr-deps/
  ./download.sh kdb3v3_b71_001,kdb4v3_b61_002,kdb4v3_b71_001 

• Upload the contents of the folder data/ranzcr-deps/ to Kaggle dataset with the name RANZCR CLiP Dataset.

• Connect competition data and RANZCR CLiP Dataset to Kaggle Code. Run script code from data/ranzcr-deps/kernel.py.