Object feature extraction for YOLO detectors
| dc.contributor.advisor | Швай, Надія | |
| dc.contributor.author | Абашкін, Олександр | |
| dc.date.accessioned | 2024-03-22T11:29:52Z | |
| dc.date.available | 2024-03-22T11:29:52Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | The main goal of the research: To create an architecture that can surpass in quality and speed the solutions of that time such as the deformable part models (DPM) that were using the sliding window approach where the classifier is used for each evenly spaced location, and a the R-CNN that were using a network for generation potential bounding boxes and as a second stage applies a classifier on this regions. | uk_UA |
| dc.identifier.uri | https://ekmair.ukma.edu.ua/handle/123456789/28354 | |
| dc.language.iso | uk | uk_UA |
| dc.relation.organisation | НаУКМА | uk_UA |
| dc.status | first published | uk_UA |
| dc.subject | YOLO architecture | uk_UA |
| dc.subject | Batch Normalization | uk_UA |
| dc.subject | High-Resolution Classifier | uk_UA |
| dc.subject | MobileNetV1 | uk_UA |
| dc.subject | магістерська робота | uk_UA |
| dc.title | Object feature extraction for YOLO detectors | uk_UA |
| dc.type | Other | uk_UA |
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