Industry

Lyft
SberDevices
X5 Retail Group
MedHub
Recursion
Everypixel
Neuromation
Ultralytics
borzo
VITECH Lab
Piñata Farms
ЦФТ
Sharper Shape
incode
Anadea
Openface
ID R&D
NewYorker
AriSaf Tech
Celsus

Add a company that uses Albumentations

Please fill an issue in our GitHub repository using the following form to add a company to this list.


Deep learning research

Albumentations is widely used in research areas related to computer vision and deep learning. If you find this library useful for your research, please consider citing Albumentations: Fast and Flexible Image Augmentations:
@Article{info11020125,
    AUTHOR = {Buslaev, Alexander and Iglovikov, Vladimir I. and Khvedchenya, Eugene and Parinov, Alex and Druzhinin, Mikhail and Kalinin, Alexandr A.},
    TITLE = {Albumentations: Fast and Flexible Image Augmentations},
    JOURNAL = {Information},
    VOLUME = {11},
    YEAR = {2020},
    NUMBER = {2},
    ARTICLE-NUMBER = {125},
    URL = {https://www.mdpi.com/2078-2489/11/2/125},
    ISSN = {2078-2489},
    DOI = {10.3390/info11020125}
}

List of papers that cite Albumentations

- NYQ Abderrahim, S Abderrahim
78. Benchmark for generic product detection: a strong baseline for dense object detection
- J Marti Asenjo, A Martinez-Larraz Solís
- 王丽芳, 张程程, 秦品乐, 蔺素珍, 高媛, 窦杰亮
- S Turko, L Burmak, I Malyshev, S Shtykov, M Popov
- J Arroyave Lopez, RA Echavarria Echeverri
- S Zhang, Y Zou, T Wang, Y Xiong
- D Diaz Valencia, S Jaramillo Gonzales
- JM Esteban, J van de Loosdrecht, M Aghaei
- 晏旭, 马帅, 曾凤娇, 郭正华, 伍俊龙, 杨平, 许冰
- 杨必胜, 宗泽亮, 陈驰, 孙文鹿, 米晓新, 吴唯同
- X Yibin
- S Sakib, MTA Abid, NS Tiana, WA Asha, SM Huq
- Y Pu, Z Feng, Z Wang, Z Yang
327. Deep learning for skin lesion classification: augment, train, and ensemble= Aprendizado profundo para classificação de lesões de pele: aumento, treino e …
- L Chang, W Zhuang, R Wu, S Feng, H Liu, J Yu
- 林成创, 单纯, 赵淦森
- HEB CASS
358. Deep Learning for Skin Lesion Classification: Augment, Train, and Ensemble
- D Qi, K Hu, W Tan, Q Yao, J Liu
387. Comparison of the MultiRes U-Net and the classical U-Net on the performance of kidney and kidney tumor segmentation
- L Mak
- L Kalinathan, PG Prabavathy Balasundaram
- M Genaev, E Skolotneva, E Gultyaeva, E Orlova
- СВ Ульянов, АВ Филипьев, КВ Кошелев
- S Nasrin, J Alavi, P Viswanathan
- MA Genaev, ES Skolotneva, EI Gultyaeva, EA Orlova

Machine learning competitions


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