Faster R-CNN model learning on synthetic images
Machine learning requires a human description of the data. The manual dataset description is very time consuming. In this article was examined how the model learns from artificially created images, with the least human participation in describing the data. It was checked how the model learned on ar...
| Published in: | Journal of Computer Sciences Institute |
|---|---|
| Main Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Lublin University of Technology
2020-12-01
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| Subjects: | |
| Online Access: | https://ph.pollub.pl/index.php/jcsi/article/view/2285 |
| _version_ | 1848652156142354432 |
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| author | Błażej Łach Edyta Łukasik |
| author_facet | Błażej Łach Edyta Łukasik |
| author_sort | Błażej Łach |
| collection | DOAJ |
| container_title | Journal of Computer Sciences Institute |
| description |
Machine learning requires a human description of the data. The manual dataset description is very time consuming. In this article was examined how the model learns from artificially created images, with the least human participation in describing the data. It was checked how the model learned on artificially produced images with augmentations and progressive image size. The model has achieve up to 3.35 higher mean average precision on syntetic dataset in the training with increasing images resolution. Augmentations improved the quality of detection on real photos. The production of artificially generated training data has a great impact on the acceleration of prepare training, because it does not require as much human resources as normal learning process.
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| format | Article |
| id | doaj-d986e99fad5d4a1eb95442d0b8bdf722 |
| institution | Directory of Open Access Journals |
| issn | 2544-0764 |
| language | English |
| publishDate | 2020-12-01 |
| publisher | Lublin University of Technology |
| record_format | Article |
| spelling | doaj-d986e99fad5d4a1eb95442d0b8bdf7222025-11-02T23:22:05ZengLublin University of TechnologyJournal of Computer Sciences Institute2544-07642020-12-011710.35784/jcsi.2285Faster R-CNN model learning on synthetic imagesBłażej Łach0Edyta Łukasik1Politechnika LubelskaLublin University of Technology Machine learning requires a human description of the data. The manual dataset description is very time consuming. In this article was examined how the model learns from artificially created images, with the least human participation in describing the data. It was checked how the model learned on artificially produced images with augmentations and progressive image size. The model has achieve up to 3.35 higher mean average precision on syntetic dataset in the training with increasing images resolution. Augmentations improved the quality of detection on real photos. The production of artificially generated training data has a great impact on the acceleration of prepare training, because it does not require as much human resources as normal learning process. https://ph.pollub.pl/index.php/jcsi/article/view/2285computer visionsynthetic imagesFaster R-CNNdeep learning |
| spellingShingle | Błażej Łach Edyta Łukasik Faster R-CNN model learning on synthetic images computer vision synthetic images Faster R-CNN deep learning |
| title | Faster R-CNN model learning on synthetic images |
| title_full | Faster R-CNN model learning on synthetic images |
| title_fullStr | Faster R-CNN model learning on synthetic images |
| title_full_unstemmed | Faster R-CNN model learning on synthetic images |
| title_short | Faster R-CNN model learning on synthetic images |
| title_sort | faster r cnn model learning on synthetic images |
| topic | computer vision synthetic images Faster R-CNN deep learning |
| url | https://ph.pollub.pl/index.php/jcsi/article/view/2285 |
| work_keys_str_mv | AT błazejłach fasterrcnnmodellearningonsyntheticimages AT edytałukasik fasterrcnnmodellearningonsyntheticimages |