New augmentations

We added 10 new transforms, among them Solarize, Equalize, and Posterize that were used in AutoAugment and RandAugment papers.

Here is an example of some new transforms:

https://i.redd.it/wi8mcxkntqs31.png

Support for images and masks with more than 3 channels

There are cases when you need to work with images and masks that have more than 3 channels (for example, Geospatial Images may contain 8 or more channels). Now the library supports multispectral images.

Added a page that lists pre-prints and papers that cite albumentations

We are delighted that albumentations are helpful to the academic community. We extended documentation with a page that lists all papers and preprints that cite albumentations in their work. At this moment, this number is 24.

Added a page that lists competitions in which top teams used albumentations.

We are delighted that albumentations help people to get top results in machine learning competitions at Kaggle and other platforms. We added a “Hall of Fame” where people can share their achievements.

This page contains a list of competitions, usually with sample code or a link to a paper. We encourage people to add more information about their results with pull requests, following the contributing guide.

You can install the new version by running pip install -U albumentations.

Full release notes are available on GitHub.