Releases
v1.5.10
DenseCL, SSL model improvements
Adds the DenseCL: Dense Contrastive Learning for Self-Supervised Visual Pre-Training method. See the docs .
Add TiCoTransform, thanks @radiradev !
Improvements to the pre-commit hooks, thanks @SauravMaheshkar !
Fix memory bank issue when using gather_distributed=True
and training on a single GPU
Fix student head update in DINO benchmark
Various improvements to MaskedVisionTransformer
Renaming of Lightly SSL to LightlySSL
Models
AIM: Scalable Pre-training of Large Autoregressive Image Models
Barlow Twins: Self-Supervised Learning via Redundancy Reduction, 2021
Bootstrap your own latent: A new approach to self-supervised Learning, 2020
DCL: Decoupled Contrastive Learning, 2021
DenseCL: Dense Contrastive Learning for Self-Supervised Visual Pre-Training, 2021
DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU, 2022
I-JEPA: Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture, 2023
MAE: Masked Autoencoders Are Scalable Vision Learners, 2021
MSN: Masked Siamese Networks for Label-Efficient Learning, 2022
MoCo: Momentum Contrast for Unsupervised Visual Representation Learning, 2019
NNCLR: Nearest-Neighbor Contrastive Learning of Visual Representations, 2021
PMSN: Prior Matching for Siamese Networks, 2022
SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
SimMIM: A Simple Framework for Masked Image Modeling, 2021
SimSiam: Exploring Simple Siamese Representation Learning, 2020
SMoG: Unsupervised Visual Representation Learning by Synchronous Momentum Grouping, 2022
SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, M. Caron, 2020
TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning, 2022
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Bardes, A. et. al, 2022
VICRegL: VICRegL: Self-Supervised Learning of Local Visual Features, 2022
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