{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T03:45:49Z","timestamp":1792813549555,"version":"4.5.0"},"reference-count":82,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,24]],"date-time":"2020-02-24T00:00:00Z","timestamp":1582502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/info11020125","type":"journal-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T04:21:26Z","timestamp":1582604486000},"page":"125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2401,"title":["Albumentations: Fast and Flexible Image Augmentations"],"prefix":"10.3390","volume":"11","author":[{"given":"Alexander","family":"Buslaev","sequence":"first","affiliation":[{"name":"Mapbox, Minsk 220030, Belarus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2946-5525","authenticated-orcid":false,"given":"Vladimir I.","family":"Iglovikov","sequence":"additional","affiliation":[{"name":"Lyft Level 5, Palo Alto, CA 94304, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2363-3850","authenticated-orcid":false,"given":"Eugene","family":"Khvedchenya","sequence":"additional","affiliation":[{"name":"ODS.ai, Odessa 65000, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Parinov","sequence":"additional","affiliation":[{"name":"X5 Retail Group, Moscow 119049, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4213-9954","authenticated-orcid":false,"given":"Mikhail","family":"Druzhinin","sequence":"additional","affiliation":[{"name":"Simicon, Saint Petersburg 195009, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4563-3226","authenticated-orcid":false,"given":"Alexandr A.","family":"Kalinin","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"Shenzhen Research Institute of Big Data, Shenzhen 518172, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1162\/neco.1992.4.4.473","article-title":"Simplifying neural networks by soft weight-sharing","volume":"4","author":"Nowlan","year":"1992","journal-title":"Neural Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1021\/ci0342472","article-title":"The problem of overfitting","volume":"44","author":"Hawkins","year":"2004","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_4","unstructured":"Kuka\u010dka, J., Golkov, V., and Cremers, D. (2017). Regularization for deep learning: A taxonomy. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk, A., and Grochowski, M. (2018, January 9\u201312). Data augmentation for improving deep learning in image classification problem. Proceedings of the 2018 International Interdisciplinary PhD Workshop (IIPhDW), Swinouj\u015bcie, Poland.","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_7","unstructured":"Liu, S., Papailiopoulos, D., and Achlioptas, D. (2019). Bad Global Minima Exist and SGD Can Reach Them. arXiv."},{"key":"ref_8","unstructured":"Bengio, Y., Bastien, F., Bergeron, A., Boulanger-Lewandowski, N., Breuel, T., Chherawala, Y., Cisse, M., C\u00f4t\u00e9, M., Erhan, D., and Eustache, J. (2011, January 11\u201313). Deep learners benefit more from out-of-distribution examples. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL, USA."},{"key":"ref_9","unstructured":"Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., and Lakshminarayanan, B. (2020, January 26\u201330). AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. Proceedings of the International Conference on Learning Representations (ICLR), Millennium Hall, Addis Ababa, Ethiopia."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez-Garc\u00eda, A., and K\u00f6nig, P. (2018, January 4\u20137). Further advantages of data augmentation on convolutional neural networks. Proceedings of the International Conference on Artificial Neural Networks, Rhodes, Greece.","DOI":"10.1007\/978-3-030-01418-6_10"},{"key":"ref_11","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_12","unstructured":"Chollet, F. (2020, February 21). Keras. Available online: https:\/\/keras.io."},{"key":"ref_13","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). PyTorch: An imperative style, high-performance deep learning library. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_14","unstructured":"Ratner, A.J., Ehrenberg, H., Hussain, Z., Dunnmon, J., and R\u00e9, C. (2017, January 4\u20139). Learning to compose domain-specific transformations for data augmentation. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5858","DOI":"10.1109\/ACCESS.2017.2696121","article-title":"Smart Augmentation Learning an Optimal Data Augmentation Strategy","volume":"5","author":"Lemley","year":"2017","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q.V. (2018). AutoAugment: Learning Augmentation Policies from Data. arXiv.","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref_17","unstructured":"Bradski, G. (2000). The OpenCV Library. Dr. Dobb\u2019s J. Softw. Tools, Available online: https:\/\/www.drdobbs.com\/open-source\/the-opencv-library\/184404319."},{"key":"ref_18","unstructured":"Clark, A. (2020, February 21). Pillow. Available online: https:\/\/python-pillow.org\/."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1038\/nature10836","article-title":"The case for open computer programs","volume":"482","author":"Ince","year":"2012","journal-title":"Nature"},{"key":"ref_20","unstructured":"Jung, A.B., Wada, K., Crall, J., Tanaka, S., Graving, J., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., and Borovec, J. (2019, December 31). Imgaug. Available online: https:\/\/github.com\/aleju\/imgaug."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4522","DOI":"10.1093\/bioinformatics\/btz259","article-title":"Biomedical image augmentation using Augmentor","volume":"35","author":"Bloice","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Casado-Garc\u00eda, \u00c1., Dom\u00ednguez, C., Garc\u00eda-Dom\u00ednguez, M., Heras, J., In\u00e9s, A., Mata, E., and Pascual, V. (2019). CLoDSA: A tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks. BMC Bioinform., 20.","DOI":"10.1186\/s12859-019-2931-1"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201326). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_24","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_26","unstructured":"Howard, A.G. (2013). Some improvements on deep convolutional neural network based image classification. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_28","unstructured":"Wu, R., Yan, S., Shan, Y., Dang, Q., and Sun, G. (2015). Deep image: Scaling up image recognition. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ching, T., Himmelstein, D.S., Beaulieu-Jones, B.K., Kalinin, A.A., Do, B.T., Way, G.P., Ferrero, E., Agapow, P.M., Zietz, M., and Hoffman, M.M. (2018). Opportunities And Obstacles For Deep Learning In Biology And Medicine. J. R. Soc. Interface, 15.","DOI":"10.1098\/rsif.2017.0387"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Campilho, A., Karray, F., and ter Haar Romeny, B. (2018). Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis. Image Analysis and Recognition, Springer International Publishing.","DOI":"10.1007\/978-3-319-93000-8"},{"key":"ref_32","unstructured":"DeVries, T., and Taylor, G.W. (2017). Improved regularization of convolutional neural networks with cutout. arXiv."},{"key":"ref_33","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (May, January 30). mixup: Beyond empirical risk minimization. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_34","unstructured":"Guo, H., Mao, Y., and Zhang, R. (February, January 27). Mixup as locally linear out-of-manifold regularization. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_35","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., and Yoo, Y. (November, January 27). Cutmix: Regularization strategy to train strong classifiers with localizable features. Proceedings of the International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_36","unstructured":"Graham, B. (2014). Fractional max-pooling. arXiv."},{"key":"ref_37","unstructured":"Lee, H., Hwang, S.J., and Shin, J. (2019). Rethinking Data Augmentation: Self-Supervision and Self-Distillation. arXiv."},{"key":"ref_38","unstructured":"He, Z., Xie, L., Chen, X., Zhang, Y., Wang, Y., and Tian, Q. (2019). Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data. arXiv."},{"key":"ref_39","unstructured":"Tran, T., Pham, T., Carneiro, G., Palmer, L., and Reid, I. (2017, January 4\u20139). A bayesian data augmentation approach for learning deep models. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_40","unstructured":"Lim, S., Kim, I., Kim, T., Kim, C., and Kim, S. (2019, January 8\u201314). Fast AutoAugment. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada."},{"key":"ref_41","unstructured":"Ho, D., Liang, E., Chen, X., Stoica, I., and Abbeel, P. (2019, January 16\u201319). Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules. Proceedings of the International Conference on Machine Learning, Boca Raton, FL, USA."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Taylor, L., and Nitschke, G. (2018, January 18\u201321). Improving deep learning with generic data augmentation. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.","DOI":"10.1109\/SSCI.2018.8628742"},{"key":"ref_43","unstructured":"Joaquin, A.G., Krzysztof \u0141\u0119cki, J.L.S.P.M.S.A.W., and Zientkiewicz, M. (2019, December 31). Fast AI Data Preprocessing with NVIDIA DALI. Available online: https:\/\/devblogs.nvidia.com\/fast-ai-data-preprocessing-with-nvidia-dali\/."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kalinin, A.A., Allyn-Feuer, A., Ade, A., Fon, G.V., Meixner, W., Dilworth, D., De Wet, J.R., Higgins, G.A., Zheng, G., and Creekmore, A. (2018, January 18\u201322). 3D Cell Nuclear Morphology: Microscopy Imaging Dataset and Voxel-Based Morphometry Classification Results. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00304"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Parpulov, D., Samorodov, A., Makhov, D., Slavnova, E., Volchenko, N., and Iglovikov, V. (2018, January 7\u20138). Convolutional neural network application for cells segmentation in immunocytochemical study. Proceedings of the 2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), Yekaterinburg, Russia.","DOI":"10.1109\/USBEREIT.2018.8384557"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1038\/s41592-019-0612-7","article-title":"Nucleus segmentation across imaging experiments: The 2018 Data Science Bowl","volume":"16","author":"Caicedo","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wani, M.A., Kantardzic, M., and Sayed Mouchaweh, M. (2020). Medical Image Segmentation using Deep Neural Networks with Pre-trained Encoders. Deep Learning Applications, Springer.","DOI":"10.1007\/978-981-15-1816-4"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Neuhold, G., Ollmann, T., Bul\u00f2, S.R., and Kontschieder, P. (2017, January 22\u201329). The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes. Proceedings of the International Conf. on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.534"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Iglovikov, V., Seferbekov, S., Buslaev, A., and Shvets, A. (2018, January 18\u201322). TernausNetV2: Fully Convolutional Network for Instance Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00042"},{"key":"ref_51","unstructured":"Szegedy, C., Toshev, A., and Erhan, D. (2013, January 5\u20138). Deep neural networks for object detection. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: A review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_54","unstructured":"Brito, J.J., Li, J., Moore, J.H., Greene, C.S., Nogoy, N.A., Garmire, L.X., and Mangul, S. (2020). Enhancing rigor and reproducibility by improving software availability, usability, and archival stability. arXiv."},{"key":"ref_55","unstructured":"(2019, December 31). Albumentations. Available online: https:\/\/github.com\/albumentations-team\/albumentations."},{"key":"ref_56","unstructured":"Tiulpin, A. (2020, February 21). SOLT: Streaming over Lightweight Transformations. Available online: https:\/\/zenodo.org\/record\/3351977#.XlMrnEoRXIU."},{"key":"ref_57","unstructured":"(2019, December 31). Automold. Available online: https:\/\/github.com\/UjjwalSaxena\/Automold\u2013Road-Augmentation-Library."},{"key":"ref_58","unstructured":"(2019, December 31). APTOS 2019 Blindness Detection. Available online: https:\/\/www.kaggle.com\/c\/aptos2019-blindness-detection."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The pascal visual object classes challenge: A retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P. (2017, January 23\u201328). Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Iglovikov, V.I., Rakhlin, A., Kalinin, A.A., and Shvets, A.A. (2018). Paediatric bone age assessment using deep convolutional neural networks. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1101\/234120"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCSE.2011.37","article-title":"The NumPy array: A structure for efficient numerical computation","volume":"13","author":"Colbert","year":"2011","journal-title":"Comput. Sci. Eng."},{"key":"ref_65","unstructured":"Jung, A. (2020, February 21). Imgaug. Available online: https:\/\/github.com\/aleju\/imgaug."},{"key":"ref_66","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017, January 4\u20139). Automatic differentiation in PyTorch. Proceedings of the NIPS-W, Long Beach, CA, USA."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 16\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_69","unstructured":"Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., and Han, J. (2019). On the variance of the adaptive learning rate and beyond. arXiv."},{"key":"ref_70","unstructured":"Kolesnikov, S. (2020, February 21). Accelerated DL & RL. Available online: https:\/\/github.com\/catalyst-team\/catalyst."},{"key":"ref_71","unstructured":"(2019, December 31). Albumentations Demo. Available online: https:\/\/albumentations-demo.herokuapp.com\/."},{"key":"ref_72","unstructured":"(2019, December 31). Albumentations Demo. Available online: https:\/\/albumentations.ml."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Shvets, A.A., Rakhlin, A., Kalinin, A.A., and Iglovikov, V.I. (2018, January 17\u201320). Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00100"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Shvets, A.A., Iglovikov, V.I., Rakhlin, A., and Kalinin, A.A. (2018, January 17\u201320). Angiodysplasia detection and localization using deep convolutional neural networks. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00098"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Rakhlin, A., Tiulpin, A., Shvets, A.A., Kalinin, A.A., Iglovikov, V.I., and Nikolenko, S. (2019, January 16\u201320). Breast tumor cellularity assessment using deep neural networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, Long Beach, CA, USA.","DOI":"10.1101\/628693"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Martyniuk, T., Wu, J., and Wang, Z. (2019, January 16\u201320). DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Long Beach, CA, USA.","DOI":"10.1109\/ICCV.2019.00897"},{"key":"ref_77","unstructured":"Ostyakov, P., and Nikolenko, S.I. (2019). Adapting Convolutional Neural Networks for Geographical Domain Shift. arXiv."},{"key":"ref_78","unstructured":"Hasan, S., and Linte, C.A. (2019). U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument. arXiv."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Kuzin, A., Fattakhov, A., Kibardin, I., Iglovikov, V.I., and Dautov, R. (2018, January 10\u201313). Camera Model Identification Using Convolutional Neural Networks. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622031"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"93315","DOI":"10.1109\/ACCESS.2019.2924188","article-title":"A Framework for Knowing Who is Doing What in Aerial Surveillance Videos","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_81","unstructured":"(2019, December 31). Pytorch Ecosystem. Available online: https:\/\/pytorch.org\/ecosystem\/."},{"key":"ref_82","unstructured":"(2019, December 31). Open Data Science (ODS.ai). Available online: https:\/\/ods.ai."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/2\/125\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:01:17Z","timestamp":1760173277000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/2\/125"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,24]]},"references-count":82,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["info11020125"],"URL":"https:\/\/doi.org\/10.3390\/info11020125","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,24]]}}}