HobbitLong/SupContrast

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3449 stars · 553 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch reference implementation of the Supervised Contrastive Learning paper (SupCon loss) that also supports SimCLR when labels are omitted. It includes training scripts for CIFAR-10/100 and ImageNet experiments with ResNet architectures.

Use cases

• implement supervised contrastive learning in pytorch
• train a simclr model on cifar
• compute supcon loss for representation learning
• reproduce supervised contrastive learning paper results
• learn contrastive loss implementation details
• pretrain image encoders with contrastive objectives

When to choose

• you need a reference implementation of the SupCon loss in PyTorch
• you want to reproduce the paper's CIFAR or ImageNet results
• you want a simple loss module that falls back to SimCLR without labels

When to avoid

• you need a maintained production training framework
• you want a general-purpose self-supervised library with many methods
• you need support for frameworks other than PyTorch

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning computer-vision python pytorch contrastive-learning simclr supervised-contrastive-loss self-supervised-learning representation-learning cifar research-code gpu

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