Lightly

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):
bash
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=False

Batch Shuffling
Added [batch shuffling to MoCo](https://github.com/facebookresearch/moco/blob/master/moco/builder.py) and `SplitBatchNorm` to simulate multi-gpu behaviour.

Image Resizing
Images can be resized before uploading them to the web-app:
bash
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
- MoCo: [Momentum Contrast for Unsupervised Visual Representation Learning, 2019](https://arxiv.org/abs/1911.05722)
- SimCLR: [A Simple Framework for Contrastive Learning of Visual Representations, 2020](https://arxiv.org/abs/2002.05709)

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.simclr` and `lightly.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](https://docs.lightly.ai/tutorials/structure_your_input.html) to see how!

Models
- MoCo: [Momentum Contrast for Unsupervised Visual Representation Learning, 2019](https://arxiv.org/abs/1911.05722)
- SimCLR: [A Simple Framework for Contrastive Learning of Visual Representations, 2020](https://arxiv.org/abs/2002.05709)

Bug Fixes and On-Premise Documentation
Fixed the efficiency display during embedding.
Added `trainer.precision` key to config. Train at half-precision with:
bash
lightly-train input_dir=my/input/dir trainer.precision=16


New On-Premise Documentation
The documentation received a whole new part on how to use the Lightly on-premise docker solution.

Models
- MoCo: [Momentum Contrast for Unsupervised Visual Representation Learning, 2019](https://arxiv.org/abs/1911.05722)
- SimCLR: [A Simple Framework for Contrastive Learning of Visual Representations, 2020](https://arxiv.org/abs/2002.05709)

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: [Momentum Contrast for Unsupervised Visual Representation Learning, 2019](https://arxiv.org/abs/1911.05722)
- SimCLR: [A Simple Framework for Contrastive Learning of Visual Representations, 2020](https://arxiv.org/abs/2002.05709)

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](https://docs.lightly.ai/getting_started/lightly_at_a_glance.html).
New tutorials have been added, check out the [Pizza Tutorial](https://docs.lightly.ai/tutorials/platform/plot_pizza_filter.html) 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:
- MoCo: [Momentum Contrast for Unsupervised Visual Representation Learning, 2019](https://arxiv.org/abs/1911.05722)
- SimCLR: [A Simple Framework for Contrastive Learning of Visual Representations, 2020](https://arxiv.org/abs/2002.05709)

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.

Model
- SimCLR: [A Simple Framework for Contrastive Learning of Visual Representations, 2020](https://arxiv.org/abs/2002.05709)