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8th place solution for SIIM-ACR Pneumothorax Segmentation competition on Kaggle
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README.md

Kaggle SIIM-ACR Pneumothorax Segmentation (#8/1475)

Hardware

  • Ubuntu 16.04 LTS
  • 64 GB RAM / 2 TB HDD
  • 1x NVIDIA Titan V100 32GB
  • 1x Titan V 12GB

Software

  • Python 3.7.4
  • CUDA 10.0
  • cuDNN 7.6
  • PyTorch 1.1

Model Checkpoints

Download from Google Drive:

pip install gdown
gdown https://drive.google.com/uc?id=16gi0uVYDgbN5k77TLDWX6s7zCpDZSSw-

Models should be unzipped into ./segment/checkpoints/ in order to run code as is. There should be 3 folders:

./segment/checkpoints/TRAIN_V100/
./segment/checkpoints/TRAIN_SEGMENT/
./segment/checkpoints/TRAIN_DEEPLABXY/

Instructions

See entry_points.md for reproducing results. Relative filepaths and directories are used, so the code should work as is.

Note that TRAIN_V100 and TRAIN_DEEPLABXY models require V100 32GB GPUs to train with the current configurations. If you wish to train these models on a lower capacity GPU, I suggest using the following flag options:

--grad-accum 8 --batch-size 2 or --grad-accum 16 --batch-size 1

Model performance is not guaranteed to be the same with these modifications.

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