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Pytorch code for the Data Science Bowl 2018 Challenge.

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stefan-schroedl/pytorch_dsb2018

 
 

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pytorch_dsb2018

  • This is my Pytorch code used for the Data Science Bowl 2018 Challenge. However, the boilerplate code is fairly general and can be used as a template for deep networks on images.
  • Implemetation includes:
    • Setting up models, optimizers, and schedulers
    • Loading and saving of checkpoints
    • Logging of metrics
    • Run seamlessly on CPU or GPU
    • Multi-objective learning
    • Instance and class weights
    • A refined plateau learn rate scheduler
    • A custom img_aug augmentor for random 5-crops
    • An implementation of group-normalization
    • Plotting and comparing of learning curves
    • Miscellaneous image pre/postprocessing functions
    • IOU (intersection-over-union) calculations
    • Code for failure mode analysis
    • A statistics class to incrementally compute average, std deviations, min and max