- 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
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Pytorch code for the Data Science Bowl 2018 Challenge.
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