VICReg

Changes

• Added VICReg Model (Special thanks to Boris Albar @b-albar). (See VICReg example)
• The memory bank now works with distributed training.
• Speedup all code working with runs and datasets.
• Add helpers for predictions.

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
• DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
• 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
• SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
• 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
• VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Bardes, A. et. al, 2022

Imagenet100 benchmarks and more random rotations

Changes

• Added Imagenet100 benchmarks
• Allow more finegrained control over random rotations

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
• DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
• 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
• SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
• 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

Update API Client

Changes

• Update API client to use RunWorkerLabels
• Updated API client documentation for tag endpoints

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
• DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
• 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
• SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
• 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

Update API Client

Changes

• Update API client embedding upload

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
• DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
• 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
• SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
• 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

Update API Client & Lightly Worker Docs

Changes

• Update API client for Lightly Worker v2.3.7
• Add docs for artifacts uploaded from the Lightly Worker
• Add docs for advanced relevant filenames options for the Lightly Worker
• Update docs for diversity selection strategy input types

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
• DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
• 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
• SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
• 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

Update Lightly Worker Docs & API Client

SMog Example

A simple example demonstrating the usage of SMog was added. Otherwise no major changes have been made

Other Changes

• Documentation updates regarding datapool, using predictions and subdir information
• Allow self signed certificates if API is self-hosted
• Utilities to work with artifacts

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
• DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
• 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
• SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
• 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

Multi-prototype SWaV

Multi-prototype SWaV (thanks a lot @Atharva-Phatak 🙂)

Implements a multi-prototype head for SWaV as discussed in #944.

Other Changes

• Fixes typehints for export functions in the ApiWorkflowClient
• Documentation updates
• Add scores argument to ObjectDetectionOutput

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
• DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
• 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
• SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
• 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

SMoG

SMoG: Unsupervised Visual Representation Learning by Synchronous Momentum Grouping

Lightly 1.2.30 comes with the SMoG model introduced in Unsupervised Visual Representation Learning
by Synchronous Momentum Grouping. Documentation and benchmarks will be released soon.

Breaking Change

• in the ApiWorkflowClient, create_dataset now throws an error where a dataset of the same name already exists. To reuse an existing dataset users should switch to using set_dataset_id_by_name.

Other Changes

• OBS (object storage service) remote datasources now supported
• documentation improvements

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
• DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
• 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
• SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
• 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

Improved Active Learning Score computation

Improved Active Learning Score computation

We slightly refactored the Active Learning Score computation to make it better from a software development point of view.

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
• DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
• 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
• SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
• SimSiam: Exploring Simple Siamese Representation Learning, 2020
• SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, M. Caron, 2020

Masked Siamese Networks

MSN: Masked Siamese Networks for Label-Efficient Learning

Lightly 1.2.28 comes with the new MSN model introduced in Masked Autoencoders Are Scalable Vision Learners. Please head over to our docs to see how to use MSN with Lightly: https://docs.lightly.ai/examples/msn.html

Other Changes

• Lightly is now compatible with Pytorch Lightning v1.7

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
• DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
• 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
• SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
• SimSiam: Exploring Simple Siamese Representation Learning, 2020
• SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, M. Caron, 2020