Releases: lightly-ai/lightly
Video Reader Backend
Video Reader Backend
Torchvision Video Reader Compatability
If available, the video loader can use the torchvision video reader backend to load frames quicker.
The new sequential video loader also allows much faster iteration through frames when they are processed in order.
Continous Testing
Integration of continuous unit testing with a badge in the README.
New SimCLR Tutorial
SimCLR with only a few lines of code - tutorial on a clothing dataset.
Models
Custom Backbones Made Easy
Custom Backbones Made Easy
Decoupling Self-Supervised Models from ResNet
The implementation of SimCLR and MoCo have been changed such that they can now be constructed from an arbitrary backbone network.
Furthermore, the backbone of the self-supervised models is now called backbone instead of features.
Big Documentation Update
The documentation has received a lot of love and improvements to make working with lightly easier.
There is also a new tutorial on how to train MoCo on Cifar-10.
Minor Changes
The LightlyDataset can now be passed a list of indices marking relevant samples. Non-relevant samples will be ignored during further processing of the data.
Models
Customizable Checkpoint Callbacks, Batch Shuffling and More
Customizable Checkpoint Callbacks, Batch Shuffling and More
Fixed download speed for image datasets.
lightly-magic can now be used with trainer.max_epochs=0.
Fixed the pytorch-lightning warning: "Passing a ModelCheckpoint instance to Trainer(checkpoint_callbacks=...) is deprecated since v1.1 and will no longer be supported in v1.3."
Customizable Checkpoint Callback
Checkpoint callbacks are now customizable (even from the command-line):
# save the 5 best models
lightly-train input_dir='data/' checkpoint_callback.save_top_k=5
# don't save the model of the last epoch
lightly-train input_dir='data/' checkpoint_callback.save_last=FalseBatch Shuffling
Added batch shuffling to MoCo and SplitBatchNorm to simulate multi-gpu behaviour.
Image Resizing
Images can be resized before uploading them to the web-app:
# no resizing (default)
lightly-upload input_dir='data/' dataset_id='XYZ' token='123' resize=-1
# resize such that shortest edge of the image is 128
lightly-upload input_dir='data/' dataset_id='XYZ' token='123' resize=128
# resize images to (128, 128)
lightly-upload input_dir='data/' dataset_id='XYZ' token='123' resize=[128,128]Models
Video File Support and Minor Changes
Video File Support and Minor Changes
Refactoring of lightly.api.upload.py and lightly.api.utils.py.
Moved the checkpoint loading from the CLI to lightly.models.simclrandlightly.models.moco` respectively.
Minor bug fixes.
Video File Support
Lightly can now directly work with video files! No need to extract the frames first. Check the docs to see how!
Models
Bug Fixes and On-Premise Documentation
Bug Fixes and On-Premise Documentation
Fixed the efficiency display during embedding.
Added trainer.precision key to config. Train at half-precision with:
lightly-train input_dir=my/input/dir trainer.precision=16New On-Premise Documentation
The documentation received a whole new part on how to use the Lightly on-premise docker solution.
Models
Bug Fixes and Improvements
Bug Fixes and Improvements
Refactoring of lightly.api to remove circular imports.
Rewriting of import statements to ensure compatability with Python 3.6.
Handled the warning from pytorch_lightning during training.
Models
MoCo and New Documentation
MoCo and New Documentation
New Model: MoCo
lightly.models.moco.ResNetMoCo implements the momentum encoder architecture for self-supervised visual representation learning.
lightly.loss.memory_bank.MemoryBankWrapper allows the training of self-supervised models with a queue of negative samples.
New Documentation
The URL for the documentation has changed to https://docs.lightly.ai.
New section on how lightly works.
New tutorials have been added, check out the Pizza Tutorial to learn how to train a pizza classifier.
Further Changes:
Refactoring of lightly.api.
Default collate functions which implement the SimCLR and MoCo (v1) transfomations.
Collate functions work with tuple as input_size.
New tests and tox environments.
Removed sklearn dependency for PCA.
Models:
First Release: Welcome Lightly
First Release: Welcome Lightly
A python package for self-supervised learning on vision data.
New Name
The package was previously named borisml and is now being rebranded as lightly.
New Documentation
The new documentation is hosted at https://lightly.readthedocs.org.