Lightly

Latest version: v1.5.4

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1.4.7

Changes
- SwAV ImageNet benchmark have been added (1231)
- VicRegL Vertical Flip Probability (1266) thanks to iamharsha1999
- The multiviewcollate is now replaced with default collate (1262)
- Docstrings have been updated
- We have removed deprecated code for active learning (1251) and recommend to use our more [powerful approach](https://docs.lightly.ai/docs/active-learning-scorers) to active learning as outlined in [one](https://docs.lightly.ai/docs/active-learning-yolov7) or [our tutorials](https://docs.lightly.ai/docs/active-learning-yolov8-video)
- We have removed deprecated lightly-upload code (1247)

Models
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction, 2021](https://arxiv.org/abs/2103.03230)
- [Bootstrap your own latent: A new approach to self-supervised Learning, 2020](https://arxiv.org/abs/2006.07733)
- [DCL: Decoupled Contrastive Learning, 2021](https://arxiv.org/abs/2110.06848)
- [DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021](https://arxiv.org/abs/2104.14294)
- [FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU, 2022](https://link.springer.com/chapter/10.1007/978-3-031-16788-1_4)
- [MAE: Masked Autoencoders Are Scalable Vision Learners, 2021](https://arxiv.org/abs/2111.06377)
- [MSN: Masked Siamese Networks for Label-Efficient Learning, 2022](https://arxiv.org/abs/2204.07141)
- [MoCo: Momentum Contrast for Unsupervised Visual Representation Learning, 2019](https://arxiv.org/abs/1911.05722)
- [NNCLR: Nearest-Neighbor Contrastive Learning of Visual Representations, 2021](https://arxiv.org/pdf/2104.14548.pdf)
- [PMSN: Prior Matching for Siamese Networks, 2022](https://arxiv.org/abs/2210.07277)
- [SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020](https://arxiv.org/abs/2002.05709)
- [SimMIM: A Simple Framework for Masked Image Modeling, 2021](https://arxiv.org/abs/2111.09886)
- [SimSiam: Exploring Simple Siamese Representation Learning, 2020](https://arxiv.org/abs/2011.10566)
- [SMoG: Unsupervised Visual Representation Learning by Synchronous Momentum Grouping, 2022](https://arxiv.org/pdf/2207.06167.pdf)
- [SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, M. Caron, 2020](https://arxiv.org/abs/2006.09882)
- [TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning, 2022](https://arxiv.org/pdf/2206.10698.pdf)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Bardes, A. et. al, 2022](https://arxiv.org/abs/2105.04906)
- [VICRegL: VICRegL: Self-Supervised Learning of Local Visual Features, 2022](https://arxiv.org/abs/2210.01571)

1.4.6

Changes
- Fix torch.distributed import in vicregl_loss.py (1239)
- API Client: remove default query_tag_name from ApiWorkflowClient.upload_scores (1243)

Models
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction, 2021](https://arxiv.org/abs/2103.03230)
- [Bootstrap your own latent: A new approach to self-supervised Learning, 2020](https://arxiv.org/abs/2006.07733)
- [DCL: Decoupled Contrastive Learning, 2021](https://arxiv.org/abs/2110.06848)
- [DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021](https://arxiv.org/abs/2104.14294)
- [FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU, 2022](https://link.springer.com/chapter/10.1007/978-3-031-16788-1_4)
- [MAE: Masked Autoencoders Are Scalable Vision Learners, 2021](https://arxiv.org/abs/2111.06377)
- [MSN: Masked Siamese Networks for Label-Efficient Learning, 2022](https://arxiv.org/abs/2204.07141)
- [MoCo: Momentum Contrast for Unsupervised Visual Representation Learning, 2019](https://arxiv.org/abs/1911.05722)
- [NNCLR: Nearest-Neighbor Contrastive Learning of Visual Representations, 2021](https://arxiv.org/pdf/2104.14548.pdf)
- [PMSN: Prior Matching for Siamese Networks, 2022](https://arxiv.org/abs/2210.07277)
- [SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020](https://arxiv.org/abs/2002.05709)
- [SimMIM: A Simple Framework for Masked Image Modeling, 2021](https://arxiv.org/abs/2111.09886)
- [SimSiam: Exploring Simple Siamese Representation Learning, 2020](https://arxiv.org/abs/2011.10566)
- [SMoG: Unsupervised Visual Representation Learning by Synchronous Momentum Grouping, 2022](https://arxiv.org/pdf/2207.06167.pdf)
- [SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, M. Caron, 2020](https://arxiv.org/abs/2006.09882)
- [TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning, 2022](https://arxiv.org/pdf/2206.10698.pdf)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Bardes, A. et. al, 2022](https://arxiv.org/abs/2105.04906)
- [VICRegL: VICRegL: Self-Supervised Learning of Local Visual Features, 2022](https://arxiv.org/abs/2210.01571)

1.4.5

Changes
- add benchmark for SimCLR ImageNet
- API client improvements: sample creation with write URLs, handling of relevant filenames, support for `num_processes` and `num_threads`
- documentation improvements: a cleanup of the README, new overview chart and better links

Models
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction, 2021](https://arxiv.org/abs/2103.03230)
- [Bootstrap your own latent: A new approach to self-supervised Learning, 2020](https://arxiv.org/abs/2006.07733)
- [DCL: Decoupled Contrastive Learning, 2021](https://arxiv.org/abs/2110.06848)
- [DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021](https://arxiv.org/abs/2104.14294)
- [FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU, 2022](https://link.springer.com/chapter/10.1007/978-3-031-16788-1_4)
- [MAE: Masked Autoencoders Are Scalable Vision Learners, 2021](https://arxiv.org/abs/2111.06377)
- [MSN: Masked Siamese Networks for Label-Efficient Learning, 2022](https://arxiv.org/abs/2204.07141)
- [MoCo: Momentum Contrast for Unsupervised Visual Representation Learning, 2019](https://arxiv.org/abs/1911.05722)
- [NNCLR: Nearest-Neighbor Contrastive Learning of Visual Representations, 2021](https://arxiv.org/pdf/2104.14548.pdf)
- [PMSN: Prior Matching for Siamese Networks, 2022](https://arxiv.org/abs/2210.07277)
- [SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020](https://arxiv.org/abs/2002.05709)
- [SimMIM: A Simple Framework for Masked Image Modeling, 2021](https://arxiv.org/abs/2111.09886)
- [SimSiam: Exploring Simple Siamese Representation Learning, 2020](https://arxiv.org/abs/2011.10566)
- [SMoG: Unsupervised Visual Representation Learning by Synchronous Momentum Grouping, 2022](https://arxiv.org/pdf/2207.06167.pdf)
- [SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, M. Caron, 2020](https://arxiv.org/abs/2006.09882)
- [TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning, 2022](https://arxiv.org/pdf/2206.10698.pdf)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Bardes, A. et. al, 2022](https://arxiv.org/abs/2105.04906)
- [VICRegL: VICRegL: Self-Supervised Learning of Local Visual Features, 2022](https://arxiv.org/abs/2210.01571)

1.4.4

Changes
- add support for MoCoV3 projection head. Thanks to adamjstewart
- better support for distributed training: Better error messages and bugfixes
- updated benchmark results on Cifar10

Models
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction, 2021](https://arxiv.org/abs/2103.03230)
- [Bootstrap your own latent: A new approach to self-supervised Learning, 2020](https://arxiv.org/abs/2006.07733)
- [DCL: Decoupled Contrastive Learning, 2021](https://arxiv.org/abs/2110.06848)
- [DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021](https://arxiv.org/abs/2104.14294)
- [FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU, 2022](https://link.springer.com/chapter/10.1007/978-3-031-16788-1_4)
- [MAE: Masked Autoencoders Are Scalable Vision Learners, 2021](https://arxiv.org/abs/2111.06377)
- [MSN: Masked Siamese Networks for Label-Efficient Learning, 2022](https://arxiv.org/abs/2204.07141)
- [MoCo: Momentum Contrast for Unsupervised Visual Representation Learning, 2019](https://arxiv.org/abs/1911.05722)
- [NNCLR: Nearest-Neighbor Contrastive Learning of Visual Representations, 2021](https://arxiv.org/pdf/2104.14548.pdf)
- [PMSN: Prior Matching for Siamese Networks, 2022](https://arxiv.org/abs/2210.07277)
- [SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020](https://arxiv.org/abs/2002.05709)
- [SimMIM: A Simple Framework for Masked Image Modeling, 2021](https://arxiv.org/abs/2111.09886)
- [SimSiam: Exploring Simple Siamese Representation Learning, 2020](https://arxiv.org/abs/2011.10566)
- [SMoG: Unsupervised Visual Representation Learning by Synchronous Momentum Grouping, 2022](https://arxiv.org/pdf/2207.06167.pdf)
- [SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, M. Caron, 2020](https://arxiv.org/abs/2006.09882)
- [TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning, 2022](https://arxiv.org/pdf/2206.10698.pdf)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Bardes, A. et. al, 2022](https://arxiv.org/abs/2105.04906)
- [VICRegL: VICRegL: Self-Supervised Learning of Local Visual Features, 2022](https://arxiv.org/abs/2210.01571)

1.4.3

Changes

- Add support for SimCLRV2 projection head. Thanks to adamjstewart!
- Add by default BatchNorm layers to SimCLR projection head. Thanks to adamjstewart!

Models
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction, 2021](https://arxiv.org/abs/2103.03230)
- [Bootstrap your own latent: A new approach to self-supervised Learning, 2020](https://arxiv.org/abs/2006.07733)
- [DCL: Decoupled Contrastive Learning, 2021](https://arxiv.org/abs/2110.06848)
- [DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021](https://arxiv.org/abs/2104.14294)
- [FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU, 2022](https://link.springer.com/chapter/10.1007/978-3-031-16788-1_4)
- [MAE: Masked Autoencoders Are Scalable Vision Learners, 2021](https://arxiv.org/abs/2111.06377)
- [MSN: Masked Siamese Networks for Label-Efficient Learning, 2022](https://arxiv.org/abs/2204.07141)
- [MoCo: Momentum Contrast for Unsupervised Visual Representation Learning, 2019](https://arxiv.org/abs/1911.05722)
- [NNCLR: Nearest-Neighbor Contrastive Learning of Visual Representations, 2021](https://arxiv.org/pdf/2104.14548.pdf)
- [PMSN: Prior Matching for Siamese Networks, 2022](https://arxiv.org/abs/2210.07277)
- [SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020](https://arxiv.org/abs/2002.05709)
- [SimMIM: A Simple Framework for Masked Image Modeling, 2021](https://arxiv.org/abs/2111.09886)
- [SimSiam: Exploring Simple Siamese Representation Learning, 2020](https://arxiv.org/abs/2011.10566)
- [SMoG: Unsupervised Visual Representation Learning by Synchronous Momentum Grouping, 2022](https://arxiv.org/pdf/2207.06167.pdf)
- [SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, M. Caron, 2020](https://arxiv.org/abs/2006.09882)
- [TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning, 2022](https://arxiv.org/pdf/2206.10698.pdf)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Bardes, A. et. al, 2022](https://arxiv.org/abs/2105.04906)
- [VICRegL: VICRegL: Self-Supervised Learning of Local Visual Features, 2022](https://arxiv.org/abs/2210.01571)

1.4.2

Changes
- Lightly is now compatible with PyTorch 2.0 (but Lightly itself does not use it, yet)
- It's now possible to install Lightly "lightly" by [only installing the parts necessary for API communication](https://docs.lightly.ai/docs/install-lightly#install-the-lightly-python-client)
- Support newer setuptools (Thanks adamjstewart ! )
- Added missing config options
- Improved docstrings and document potential API errors

Models
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction, 2021](https://arxiv.org/abs/2103.03230)
- [Bootstrap your own latent: A new approach to self-supervised Learning, 2020](https://arxiv.org/abs/2006.07733)
- [DCL: Decoupled Contrastive Learning, 2021](https://arxiv.org/abs/2110.06848)
- [DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021](https://arxiv.org/abs/2104.14294)
- [FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU, 2022](https://link.springer.com/chapter/10.1007/978-3-031-16788-1_4)
- [MAE: Masked Autoencoders Are Scalable Vision Learners, 2021](https://arxiv.org/abs/2111.06377)
- [MSN: Masked Siamese Networks for Label-Efficient Learning, 2022](https://arxiv.org/abs/2204.07141)
- [MoCo: Momentum Contrast for Unsupervised Visual Representation Learning, 2019](https://arxiv.org/abs/1911.05722)
- [NNCLR: Nearest-Neighbor Contrastive Learning of Visual Representations, 2021](https://arxiv.org/pdf/2104.14548.pdf)
- [PMSN: Prior Matching for Siamese Networks, 2022](https://arxiv.org/abs/2210.07277)
- [SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020](https://arxiv.org/abs/2002.05709)
- [SimMIM: A Simple Framework for Masked Image Modeling, 2021](https://arxiv.org/abs/2111.09886)
- [SimSiam: Exploring Simple Siamese Representation Learning, 2020](https://arxiv.org/abs/2011.10566)
- [SMoG: Unsupervised Visual Representation Learning by Synchronous Momentum Grouping, 2022](https://arxiv.org/pdf/2207.06167.pdf)
- [SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, M. Caron, 2020](https://arxiv.org/abs/2006.09882)
- [TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning, 2022](https://arxiv.org/pdf/2206.10698.pdf)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Bardes, A. et. al, 2022](https://arxiv.org/abs/2105.04906)
- [VICRegL: VICRegL: Self-Supervised Learning of Local Visual Features, 2022](https://arxiv.org/abs/2210.01571)

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