A Python library with a set of bells and whistles for PyTorch
A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming:
What’s inside
- Easy model building using flexible encoder-decoder architecture.
- Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more.
- GPU-friendly test-time augmentation TTA for segmentation and classification
- GPU-friendly inference on huge (5000×5000) images
- Every-day common routines (fix/restore random seed, filesystem utils, metrics)
- Losses: BinaryFocalLoss, Focal, ReducedFocal, Lovasz, Jaccard and Dice losses, Wing Loss and more.
- Extras for Catalyst library (Visualization of batch predictions, additional metrics)
Showcase: Catalyst, Albumentations, Pytorch Toolbelt example: Semantic Segmentation @ CamVid
Honest answer is “I needed a convenient way to re-use code for my Kaggle career”. During 2018 I achieved a Kaggle Master badge and this been a long path.