Albumentation – fast & flexible image augmentations

Image Augmentations is a powerful technique to improve model robustness and performance. There are many image augmentations libraries on the market: torchvision, imgaug, DALI, Augmentor, SOLT, etc.
In all of them, authors focussed on variety at the cost of speed, or the speed at the cost of flexibility.

Requirements for augmentations:
* Variety: they want to have a large set of standard and exotic augmentation for image classification, segmentation, and detection in one place.
* Performance: transforms should be as fast as possible.
* Flexibility: it should be easy to add new transforms or new types of transforms.
* Conciseness: all complexity of implementation should be hidden behind the API.

Albumentations were born out of necessity. The authors were actively participating in various Deep Learning competitions. To get to the top they needed something better than what was already available. All of them, independently, started working on more powerful augmentation pipelines. Later they merged their efforts and released the code in the form of the library.

To date Albumentations has more than 70 transforms and supports image classification, #segmentation, object and keypoint detection tasks.

The library was adopted by academics, Kaggle, and other communities.

ODS: #tool_albumentations
Link: https://albumentations.ai/
Github: https://github.com/albumentations-team/albumentations
Paper: https://www.mdpi.com/2078-2489/11/2/125

P.S. Following trend setup by #Catalyst team, we provide extensive description of project with the help of its creators.

#guestpost #augmentation #CV #DL #imageprocessing #ods #objectdetection #imageclassification #tool