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Implement iBOT source code.tar.gz (3.3 MB)
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| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| Parent folder | |||
| New DINO ViT Benchmark and BTLoss source code.tar.gz | 2025-04-22 | 3.3 MB | |
| New DINO ViT Benchmark and BTLoss source code.zip | 2025-04-22 | 4.1 MB | |
| README.md | 2025-04-22 | 3.2 kB | |
| Totals: 3 Items | 7.4 MB | 0 | |
Changes
- Added DINO ViT Benchmark
- Fixed BTLoss: ensure invariance to affine transformations by @adosar in https://github.com/lightly-ai/lightly/pull/1806
- Tested BTLoss: use default values for
torch.allcloseby @adosar in https://github.com/lightly-ai/lightly/pull/1810 - Added more detailed docstring to knn predict by @maxprogrammer007 in https://github.com/lightly-ai/lightly/pull/1812
- Removed
verboseinCosineWarmUpScheduler - Add LightlyTrain Reference
- Renamed
lightly-traincommand tolightly-ssl-train
Many thanks to our contributors!
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