Albumentations information

Albumentations
Original author(s)	Alexander Buslaev; Alex Parinov; Vladimir I. Iglovikov; Evegene Khvedchenya; Mikhail Druzhinin;
Initial release	September 2016; 7 years ago
Repository	github.com/albumentations-team/albumentations
Written in	Python;
Operating system	Linux; macOS; Windows;
Available in	English
Type	Library for machine learning and deep learning
License	MIT
Website	albumentations.ai

Albumentations is a powerful open-source image augmentation library created in June 2018 by a group of researchers and engineers, including Alexander Buslaev, Vladimir Iglovikov, and Alex Parinov. The library was designed to provide a flexible and efficient framework for data augmentation in computer vision tasks.

Data augmentation is a technique that involves artificially expanding the size of a dataset by creating new images through various transformations such as rotation, scaling, flipping, and color adjustments. This process helps improve the performance of machine learning models by providing a more diverse set of training examples.

Built on top of OpenCV, a widely used computer vision library, Albumentations provides high-performance implementations of various image processing functions. It also offers a rich set of image transformation functions and a simple API for combining them, allowing users to create custom augmentation pipelines tailored to their specific needs.^[3]

^ "First Commit". GitHub. 5 June 2018.
^ "MIT License". GitHub.
^ Alexander Buslaev; Vladimir Iglovikov; Alex Parinov; Eugene Khvedchenya; Alexandr A Kalinin (2020). "Albumentations: Fast and Flexible Image Augmentations". Information. 11 (2). MDPI: 125. arXiv:1809.06839. doi:10.3390/info11020125.

[1] "First Commit". GitHub. 5 June 2018.

[2] "MIT License". GitHub.

[3] Alexander Buslaev; Vladimir Iglovikov; Alex Parinov; Eugene Khvedchenya; Alexandr A Kalinin (2020). "Albumentations: Fast and Flexible Image Augmentations". Information. 11 (2). MDPI: 125. arXiv:1809.06839. doi:10.3390/info11020125.

Original author(s)	Alexander Buslaev Alex Parinov Vladimir I. Iglovikov Evegene Khvedchenya Mikhail Druzhinin
Initial release	September 2016; 7 years ago (2016-09)^[1]
Repository	github.com/albumentations-team/albumentations
Written in	Python
Operating system	Linux macOS Windows
Available in	English
Type	Library for machine learning and deep learning
License	MIT^[2]
Website	albumentations.ai

Machine learning and data mining
Part of a series on
Paradigms Supervised learning Unsupervised learning Online learning Batch learning Meta-learning Semi-supervised learning Self-supervised learning Reinforcement learning Curriculum learning Rule-based learning Quantum machine learning
Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured prediction Feature engineering Feature learning Learning to rank Grammar induction Ontology learning Multimodal learning
Supervised learning (classification • regression) Apprenticeship learning Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH CURE Hierarchical k-means Fuzzy Expectation–maximization (EM) DBSCAN OPTICS Mean shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA PGD t-SNE SDL
Structured prediction Graphical models Bayes net Conditional random field Hidden Markov
Anomaly detection RANSAC k-NN Local outlier factor Isolation forest
Artificial neural network Autoencoder Cognitive computing Deep learning DeepDream Feedforward neural network Recurrent neural network LSTM GRU ESN reservoir computing Restricted Boltzmann machine GAN Diffusion model SOM Convolutional neural network U-Net Transformer Vision Mamba Spiking neural network Memtransistor Electrochemical RAM (ECRAM)
Reinforcement learning Q-learning SARSA Temporal difference (TD) Multi-agent Self-play
Learning with humans Active learning Crowdsourcing Human-in-the-loop RLHF
Model diagnostics Coefficient of determination Confusion matrix Learning curve ROC curve
Mathematical foundations Kernel machines Bias–variance tradeoff Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Machine-learning venues ECML PKDD NeurIPS ICML ICLR IJCAI ML JMLR
Related articles Glossary of artificial intelligence List of datasets for machine-learning research List of datasets in computer vision and image processing Outline of machine learning
v t e