Albumentations: feedback

Warning: This text is dry, and its primary purpose is logging. It would be mainly helpful to those who already use the library.

I am one of the core contributors to the Open Source library Albumentations.

The library is used to augment images and is typically used to train neural networks.

To train neural networks, you need a lot of labeled data.

There are two ways to address this:

• Collect and label new data. It is expensive and slow but gives solid value.
• Augmentations. There is typically a value, but you do not know how much and how to choose them correctly. But you get them for free and right now.

In practice, you use both methods.

We have worked on the library for more than four years. Last year I wrote a long post on how the library was born and how we promoted it. [ The birth of Albumentations ]

Current traction:

• Five million downloads in total.
• 11k downloads per day
• 10k stars at GitHub (Do not forget to add yours if you did not already :) )
• Winners of all or almost all Computer Vision competitions at Kaggle use it in their solutions.
• The paper about the library has 700+ citations in the scientific literature.
• Cited in 10 books.

There are a few different reasons why the library took off. Functionality and good promotion are one thing, but the most important is performance.

From the authors of the Augly, developed at Meta at CVPR 2022.

We developed the library as a typical open source — the core team in different countries. When someone wanted to add a feature, he did this without planning, syncs, or OKRs.

After all these years of development, I decided to perform Customer Research and ask…