compound-loss-pytorch

Compound loss for pytorch

jaccard_ce_loss_smp.py

Jaccard CE Loss from Feature Pyramid Network for Multi-Class Land Segmentation using segmentation_models.pytorch

unified_focal_loss_pytorch.py

PyTorch implementation of Unified Focal loss

• Official tensorflow repository

Losses

• Focal loss (symmetric and asymmetric)
• Focal Tversky loss (symmetric and asymmetric)
• Unified Focal loss (symmetric and asymmetric)

Todo

• Fix [Issue #3] Incompatible pytorch tensor shape
  • SymmetricFocalLoss
  • AsymmetricFocalLoss
  • SymmetricFocalTverskyLoss
  • AsymmetricFocalTverskyLoss

References

@misc{https://doi.org/10.48550/arxiv.1806.03510,
  doi = {10.48550/ARXIV.1806.03510},
  url = {https://arxiv.org/abs/1806.03510},
  author = {Seferbekov, Selim S. and Iglovikov, Vladimir I. and Buslaev, Alexander V. and Shvets, Alexey A.},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Feature Pyramid Network for Multi-Class Land Segmentation},
  publisher = {arXiv},
  year = {2018},
  copyright = {Creative Commons Attribution 4.0 International}
}

@misc{https://doi.org/10.48550/arxiv.2102.04525,
  doi = {10.48550/ARXIV.2102.04525},
  url = {https://arxiv.org/abs/2102.04525},
  author = {Yeung, Michael and Sala, Evis and Schönlieb, Carola-Bibiane and Rundo, Leonardo},
  keywords = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences, I.4.6; J.3},
  title = {Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation},
  publisher = {arXiv},
  year = {2021},
  copyright = {arXiv.org perpetual, non-exclusive license}
}