Releases: lightly-ai/lightly
Releases · lightly-ai/lightly
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
Server Side Encryption + Documentation
Changes
- if server side encryption is enabled for s3 datasources, the proper headers are sent
- expose advanced selection configuration classes and enums for typed configurations
Documentation
- Clarify which permissions need to be set for GCS when running a datapool
- Add instructions on how to re-use a checkpoint
- Minor docs updates
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
- 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
Patch download speed and dependency issue
Changes
- Speed up
lightly-download
using multithreading - Documentation updates
- Hotfix: Fix compatability issues with
pytorch-lightning>=1.7
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
- 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