Releases

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Releases#

Version 0.1#

  • Base trainer offering the basic functionalities of stable-SSL (logging, checkpointing, data loading etc).

  • Template trainers for supervised and self-supervised learning (general joint embedding, JEPA, and teacher student models).

  • Examples of self-supervised learning methods : SimCLR, Barlow Twins, VicReg, DINO, MoCo, SimSiam.

  • Classes to load templates neural networks (backbone, projector, etc).

  • LARS optimizer.

  • Linear warmup schedulers.

  • Loss functions: NTXEnt, Barlow Twins, Negative Cosine Similarity, VICReg.

  • Base classes for multi-view dataloaders.

  • Functionalities to read the loggings and easily export the results.

  • RankMe, LiDAR metrics to monitor training.

  • Examples of extracting run data from WandB and utilizing it to create figures.

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