Domain Adaptation for Object Detection
| dc.contributor.advisor | Швай, Надія | |
| dc.contributor.author | Шпіганович, Владислав | |
| dc.date.accessioned | 2024-03-25T07:22:03Z | |
| dc.date.available | 2024-03-25T07:22:03Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | This 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.uri | https://ekmair.ukma.edu.ua/handle/123456789/28373 | |
| dc.language.iso | en | uk_UA |
| dc.status | first published | uk_UA |
| dc.subject | рseudo-label based self-training | uk_UA |
| dc.subject | YOLO model | uk_UA |
| dc.subject | PascalVOC | uk_UA |
| dc.subject | курсова робота | uk_UA |
| dc.title | Domain Adaptation for Object Detection | uk_UA |
| dc.type | Other | uk_UA |