Skip to content
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.gitignore
LICENSE
ModuleTrainer.py
README.md
callbacks.py
datasets.py
find_best_threshold.py
labels.py
losses.py
model_tta_hyperopt.py
modules.py
nn_finetune_densenet_121.py
nn_finetune_densenet_169.py
nn_finetune_densenet_201.py
nn_finetune_resnet_101.py
nn_finetune_resnet_34.py
nn_finetune_resnet_50.py
nn_semisupervised_densenet_121.py
nn_semisupervised_densenet_169.py
nn_semisupervised_resnet_101.py
nn_semisupervised_resnet_18.py
nn_semisupervised_resnet_34.py
nn_semisupervised_resnet_50.py
paths.py
predict.py
save_thresholds.py
submit_ensemble.py
submit_predictions.py
transforms.py
util.py

README.md

KagglePlanetPytorch

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

For questions refere to https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/discussion/36887 or create an issue!

Requirements

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 predict.py and run it. Predictions are then saved to /predictions.

Calculate thresholds

This step is only necessary because of the current implementation. Run save_thresholds.py 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 model_tta_hyperopt.py 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 submit_ensemble.py

You can’t perform that action at this time.