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This repository contains the basic code of our 9th place submission.

For questions refere to or create an issue!


Basic requirements are

  • Scitkit-Learn
  • Scikit-Image
  • Numpy, Scipy
  • Torchsample
  • Pytorch
  • XGBoost

This list may not be exhaustive!

Training a network

Just run the nn_finetune-files.

Create predictions for a network

Choose the network in and run it. Predictions are then saved to /predictions.

Calculate thresholds

This step is only necessary because of the current implementation. Run for your model. The saved thresholds we be used in the next step to compare XGBoost to averaging.

Make a submission from a single 5-fold model

Specify the network in and run it. This will run hyper parameter optmization for XGBoost. The approach chosen in this file is probably not good at all, since this was the first time I used XGBoost and only had a week to the competition deadline. Please tell me if you can do better. Also if you can make the same basic approach work for model ensembling, tell me! :) Submission are saved to /submissions

Make a weighted submissions from different submission files

Just specify your submissions and weights in

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