Domain Adaptation for Object Detection

dc.contributor.advisorШвай, Надія
dc.contributor.authorШпіганович, Владислав
dc.date.accessioned2024-03-25T07:22:03Z
dc.date.available2024-03-25T07:22:03Z
dc.date.issued2023
dc.description.abstractThis work covers a topic of domain adaptation for object detection, reviews methods used for semi-supervised domain adaptation and researches method for domain adaptive object detection, which is based on one-stage YOLOv5, which is superior in inference time. During experiments, we evaluate possible good hyperparameter changing strategies and apply knowledge distillation based model compressing technique. The results show validity of discussed method and confirm, that knowledge transferring techniques may help in domain adaptation.uk_UA
dc.identifier.urihttps://ekmair.ukma.edu.ua/handle/123456789/28373
dc.language.isoenuk_UA
dc.statusfirst publisheduk_UA
dc.subjectрseudo-label based self-traininguk_UA
dc.subjectYOLO modeluk_UA
dc.subjectPascalVOCuk_UA
dc.subjectкурсова роботаuk_UA
dc.titleDomain Adaptation for Object Detectionuk_UA
dc.typeOtheruk_UA
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