bermanmaxim/LovaszSoftmax

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1410 stars · 266 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Standalone PyTorch and TensorFlow implementations of the Lovász-Softmax and Lovász Hinge loss functions for optimizing the Jaccard index (IoU) in neural networks. Published as a CVPR 2018 paper, it includes demo Jupyter notebooks for binary and multiclass segmentation training.

Use cases

• optimize intersection-over-union directly when training segmentation models
• train semantic segmentation networks with a differentiable IoU surrogate loss
• improve mIoU on imbalanced segmentation datasets like satellite imagery
• fine-tune a cross-entropy-trained model with the Lovász-Softmax loss
• use Lovász Hinge for binary segmentation tasks in PyTorch or TensorFlow

When to choose

• you need a differentiable loss that directly targets the Jaccard index/IoU metric
• you are doing semantic segmentation with PyTorch or TensorFlow and want a drop-in loss layer
• your segmentation dataset has class imbalance where cross-entropy underperforms on mIoU

When to avoid

• you need a maintained library with regular updates and broad framework support
• you want losses for tasks beyond binary/multiclass segmentation
• you need a full training framework rather than standalone loss functions

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning computer-vision image-processing python loss-functions image-segmentation pytorch tensorflow jaccard-index cvpr-2018 semantic-segmentation gpu

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