Albumentations: Fast and Flexible Image Augmentations
Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve corresponding output labels. In computer vision, image augmentations have become a common implicit regularization technique to comb...
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| Veröffentlicht in: | Information (Basel) Jg. 11; H. 2; S. 125 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Basel
MDPI AG
01.02.2020
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| Schlagworte: | |
| ISSN: | 2078-2489, 2078-2489 |
| Online-Zugang: | Volltext |
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| Abstract | Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve corresponding output labels. In computer vision, image augmentations have become a common implicit regularization technique to combat overfitting in deep learning models and are ubiquitously used to improve performance. While most deep learning frameworks implement basic image transformations, the list is typically limited to some variations of flipping, rotating, scaling, and cropping. Moreover, image processing speed varies in existing image augmentation libraries. We present Albumentations, a fast and flexible open source library for image augmentation with many various image transform operations available that is also an easy-to-use wrapper around other augmentation libraries. We discuss the design principles that drove the implementation of Albumentations and give an overview of the key features and distinct capabilities. Finally, we provide examples of image augmentations for different computer vision tasks and demonstrate that Albumentations is faster than other commonly used image augmentation tools on most image transform operations. |
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| AbstractList | Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve corresponding output labels. In computer vision, image augmentations have become a common implicit regularization technique to combat overfitting in deep learning models and are ubiquitously used to improve performance. While most deep learning frameworks implement basic image transformations, the list is typically limited to some variations of flipping, rotating, scaling, and cropping. Moreover, image processing speed varies in existing image augmentation libraries. We present Albumentations, a fast and flexible open source library for image augmentation with many various image transform operations available that is also an easy-to-use wrapper around other augmentation libraries. We discuss the design principles that drove the implementation of Albumentations and give an overview of the key features and distinct capabilities. Finally, we provide examples of image augmentations for different computer vision tasks and demonstrate that Albumentations is faster than other commonly used image augmentation tools on most image transform operations. |
| Author | Buslaev, Alexander Khvedchenya, Eugene Druzhinin, Mikhail Kalinin, Alexandr A. Iglovikov, Vladimir I. Parinov, Alex |
| Author_xml | – sequence: 1 givenname: Alexander surname: Buslaev fullname: Buslaev, Alexander – sequence: 2 givenname: Vladimir I. orcidid: 0000-0003-2946-5525 surname: Iglovikov fullname: Iglovikov, Vladimir I. – sequence: 3 givenname: Eugene orcidid: 0000-0002-2363-3850 surname: Khvedchenya fullname: Khvedchenya, Eugene – sequence: 4 givenname: Alex surname: Parinov fullname: Parinov, Alex – sequence: 5 givenname: Mikhail orcidid: 0000-0002-4213-9954 surname: Druzhinin fullname: Druzhinin, Mikhail – sequence: 6 givenname: Alexandr A. orcidid: 0000-0003-4563-3226 surname: Kalinin fullname: Kalinin, Alexandr A. |
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