{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/albumentations-fast-and-flexible-image","title":"Albumentations: fast and flexible image augmentations","arxiv_id":"1809.06839","date":"2018-09-18","proceeding":null,"authors":["Alexander Buslaev","Alex Parinov","Eugene Khvedchenya","Vladimir I. Iglovikov","Alexandr A. Kalinin"],"abstract":"Data augmentation is a commonly used technique for increasing both the size\nand the diversity of labeled training sets by leveraging input transformations\nthat preserve output labels. In computer vision domain, image augmentations\nhave become a common implicit regularization technique to combat overfitting in\ndeep convolutional neural networks and are ubiquitously used to improve\nperformance. While most deep learning frameworks implement basic image\ntransformations, the list is typically limited to some variations and\ncombinations of flipping, rotating, scaling, and cropping. Moreover, the image\nprocessing speed varies in existing tools for image augmentation. We present\nAlbumentations, a fast and flexible library for image augmentations with many\nvarious image transform operations available, that is also an easy-to-use\nwrapper around other augmentation libraries. We provide examples of image\naugmentations for different computer vision tasks and show that Albumentations\nis faster than other commonly used image augmentation tools on the most of\ncommonly used image transformations. The source code for Albumentations is made\npublicly available online at https://github.com/albu/albumentations","url_abs":"http://arxiv.org/abs/1809.06839v1","url_pdf":"http://arxiv.org/pdf/1809.06839v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"albumentations-fast-and-flexible-image","repo_url":"https://github.com/albu/albumentations","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"albumentations-fast-and-flexible-image","repo_url":"https://github.com/hemanth346/mde_bs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"albumentations-fast-and-flexible-image","repo_url":"https://github.com/mv-lab/kuzushiji-recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"albumentations-fast-and-flexible-image","repo_url":"https://github.com/open-mmlab/mmpose","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"image-augmentation","task_name":"Image Augmentation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.06839","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.06839"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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