CoinCheung/pytorch-loss

Health v2 · maintenance only

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2252 stars · 372 forks observed · 2026-08-28

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

A PyTorch library providing a collection of loss functions (focal loss, triplet loss, AMSoftmax, label-smooth CE, dice loss, lovasz-softmax, partial-Fc, etc.) plus utility ops like one-hot encoding, EMA, and custom activations. Some operators ship as CUDA extensions for faster, more memory-efficient execution.

Use cases

• train image segmentation models with dice or lovasz loss
• apply focal loss for class imbalance in detection
• use triplet loss or AMSoftmax for face recognition metric learning
• add label smoothing to cross-entropy training
• speed up loss computation with fused CUDA kernels
• use EMA of model weights during training

When to choose

• you need a broad set of ready-made PyTorch loss functions in one package
• you want CUDA-accelerated versions of losses for speed and memory efficiency
• you train face recognition or segmentation models and need margin-based or dice losses

When to avoid

• you need losses for frameworks other than PyTorch
• you cannot compile CUDA extensions and only want pure-PyTorch ops (though V1/V2 versions exist)
• you want a maintained, extensively documented library with guarantees

Facets

library · maturity active

machine-learning deep-learning gpu-computing machine-learning deep-learning computer-vision image-processing python cross-platform pytorch loss-functions cuda-extension focal-loss triplet-loss amsoftmax label-smoothing dice-loss lovasz-softmax partial-fc activation-functions ema gpu linux

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